essay meaning in medicine

What is medicine? Why it’s so important to answer this question

Executive Dean, Faculty of Humanities and Director, African Centre for Epistemology and Philosophy of Science, University of Johannesburg

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Alex Broadbent receives funding from the National Research Foundation of South Africa.

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What is medicine? We recognise it in all societies past and present. But the nature of medicine differs so greatly from place to place and time to time that it’s difficult to offer a single answer. So what is it that we see in common between a traditional healer’s throwing of bones and the cardiologist’s incisions?

One of the answers that often seems to be implicit in what we say and think about medicine is a curative thesis : medicine’s goal is to cure the sick. Curing the sick is the core medical competence, whose exercise is medicine’s core business.

But if the curative thesis is true, then most medicine throughout history – as well as much contemporary medicine – isn’t medicine at all. Much medicine was and is ineffective, or at best partially effective. The curative thesis leads to a dismissive attitude towards the past efforts upon which any current medicine is built, as well as failing to promote profitable collaboration between traditions.

A second idea is an inquiry thesis about medicine: although the goal of medicine is to cure, its core business is something quite different. It’s this thesis I explore in my latest article .

That “something” has to do with inquiring into the nature and causes of health and disease. The idea is that we don’t necessarily expect someone to be able to cure us. We will accept that they are a medical expert if they can show an understanding of our ailment, often by issuing an accurate prognosis. Perhaps they won’t have a complete understanding, but they should somehow be engaged with the larger project of inquiry into the nature and causes of health and disease.

The inquiry thesis offers a way to understand the history of medicine that makes it more than a tale of quackery and gullibility. It also provides a way to understand medical traditions that practised outside the West, or in the West in defiance of the mainstream. They may offer or at least engage with a project of obtaining; a kind of understanding that Western medicine cannot.

The inquiry model of medicine lays the ground for fruitful and respectful discussions between medical traditions that doesn’t descend into an untenable relativism about what works.

Towards understanding

The curative thesis faces a difficulty that I believe it cannot overcome.

We do not define an activity by its goal alone, unless it has at least some success in that respect. A blacksmith cannot be defined as one who makes horseshoes if he simply throws lumps of hot metal onto his anvil and hammers them randomly – occasionally producing something horseshoe-like, but more often producing a mess.

Yet, taking a historical perspective, something of this kind has been true of medicine for much of its history, before it developed a serious curative arsenal. Historian of medicine Roy Porter has remarked that

the prominence of medicine has lain only in small measure in its ability to make the sick well. This was always true, and remains so today.

What, then, could be the business of medicine – the thing in which we recognise expertise, even when we accept that there is no cure to be had?

This is where the inquiry model enters the picture. I propose that the business of medicine is understanding the nature and causes of health and disease, for the purpose of cure.

The core of the argument is simple: what could medical persons be good at doing, that relates to the goal of cure without achieving it? The most likely candidate is understanding. Understanding is something that we can gain without corresponding curative success.

Tackling objections

As with the curative thesis, there are several objections to the inquiry model. First, it is obvious that many doctors either don’t (fully) understand what they treat or, if they do, don’t (successfully) communicate this understanding to the patient. Who, then, understands? In what sense is the doctor’s competence understanding?

The answer is that understanding isn’t a binary. You can partially understand something. You can be one the road to understanding it better, by inquiring into it. Hence the inquiry model of medicine. The idea is not that medicine is a sack full of answers, but rather that it is an ongoing effort to find answers.

Another objection is that so-called understanding is often bogus, and that medicine is as unsuccessful in this regard as in cure. This fails to account for the historical record, which – at least for Western medicine –- is precisely a case of understanding without curative success.

And, just as false scientific theories have contributed to developing scientific understanding , so false medical theories have provided a foundation for what we now accept.

Medicine is an ancient and complex social phenomenon, variously seen as art, science and witchcraft. These visions share the goal of curing disease. But it is too crude to think medicine as only the business of curing, since in that case, few doctors would be in business.

The distinctive feature of medicine is that it tries to cure by obtaining some understanding of the nature and causes of health and disease: by inquiry, in short. This understanding of medicine permits a much healthier dialogue between proponents of different traditions, and enables a non-defensive perspective on areas where we remain sadly lacking in curative ability.

This is an edited, shortened version of an article that first appeared in the Canadian Medical Association Journal, ‘The inquiry model of medicine’ , accompanied by a podcast available on the article’s page and also here .

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Philosophy of Medicine

Philosophy of medicine is a field that seeks to explore fundamental issues in theory, research, and practice within the health sciences, particularly metaphysical and epistemological topics. Its historic roots arguably date back to ancient times, to the Hippocratic corpus among other sources, and there have been extended scholarly discussions on key concepts in the philosophy of medicine since at least the 1800s. Debates have occurred in the past over whether there is a distinct field rightly termed “philosophy of medicine” (e.g., Caplan 1992) but as there are now dedicated journals and professional organizations, a relatively well-established canon of scholarly literature, and distinctive questions and problems, it is defensible to claim that philosophy of medicine has now established itself. Although ethics and values are part of many problems addressed within the philosophy of medicine, bioethics is generally considered to be a distinct field, and hence is not explored in this entry (but see the entry on theory and bioethics ). That being said, philosophy of medicine serves as a foundation for many debates within bioethics, given that it analyzes fundamental components of the practice of medicine that frequently arise in bioethics such as concepts of disease. The philosophy of medicine also has made important contributions to general philosophy of science, and particularly to understandings of explanation, causation, and experimentation as well as debates over applications of scientific knowledge. Finally, the philosophy of medicine has contributed to discussions on methods and goals within both research and practice in the medical and health sciences. This entry focuses primarily on philosophy of medicine in the Western tradition, although there are growing literatures on philosophy of non-Western and alternative medical practices. It emphasizes philosophical literature while utilizing relevant scholarly publications from other disciplinary perspectives.

1. Introduction: How Should We Define Health and Disease?

2. contested and controversial disease categories, 3. theories, causes, and explanations in medicine, 4. reductionism and holism in medicine, 5. randomized controlled trials and evidence-based medicine, 6. animal models, 7. observational studies and case reports, 8. diagnosis, 9. clinician judgement and the role of expertise, 10. how are collective expert judgments made in medicine, 11. values in medical research, 12. measuring medical outcomes, other internet resources, related entries.

One of the fundamental and most long-standing debates in the philosophy of medicine relates to the basic concepts of health and disease (see concepts of health and disease ). It may seem obvious what we mean by such statements: people seek treatment from medical professionals when they are feeling unwell, and clinicians treat patients in order to help them restore or maintain their health. But people seek advice and assistance from medical professionals for other reasons, such as pregnancy which cannot be construed as a disease state, and high blood pressure which is asymptomatic. Thus the dividing line between disease and health is notoriously vague, due in part to the wide range of variations present in the human population and to debates over whether many concepts of disease are socially constructed. One of the further complicating factors is that both the concepts of health and disease typically involve both descriptive and evaluatory aspects (Engelhardt 1975), both in common usage among lay persons and members of the medical profession.

Exploring these distinctions remains epistemologically and morally important as these definitions influence when and where people seek medical treatment, and whether society regards them as “ill”, including in some health systems whether they are permitted to receive treatment. As Tristram Engelhardt has argued,

the concept of disease acts not only to describe and explain, but also to enjoin to action. It indicates a state of affairs as undesirable and to be overcome. (1975: 127)

Hence how we define disease, health, and related concepts is not a matter of mere philosophical or theoretical interest, but critical for ethical reasons, particularly to make certain that medicine contributes to people’s well-being, and for social reasons, as one’s well-being is critically related to whether one can live a good life.

The terms “disease” and “illness” often are used interchangeably, particularly by the general public but also by medical professionals. “Disease” is generally held to refer to any condition that literally causes “dis-ease” or “lack of ease” in an area of the body or the body as a whole. Such a condition can be caused by internal dysfunctions such as autoimmune diseases, by external factors such as infectious or environmentally-induced diseases, or by a combination of these factors as is the case with many so-called “genetic” diseases (on the idea of genetic disease and associated problems, see for instance Hesslow 1984, Ankeny 2002, Juengst 2004). It has been argued that there is no philosophically or scientifically compelling distinction between diseases and other types of complaints that many would not consider to be diseases such as small stature, obesity, or migraine headaches (Reznek 1987). The notion of “disease” is common among most cultures, and may even be a universal concept (Fabrega 1979). It is a useful concept as it allows a clear focus on problems that afflict particular human beings and suggests that medicine can help to control or ameliorate such problems. In contrast, “illness” is usually used to describe the more non-objective features of a condition, such as subjective feelings of pain and discomfort. It often refers to behavioral changes which are judged as undesirable and unwanted within a particular culture, and hence lead members of that culture to seek help, often from professionals identified as health providers of some type within that culture (on some of the complexities relating to the triad of concepts “disease, illness, sickness”, see Hofmann 2002).

The term “sickness” emphasizes the more social aspects of ill health, and typically highlights the lack of value placed on a particular condition by society. Disease conditions are investigated not only to be understood scientifically, but in hopes of correcting, preventing, or caring for the states that are disvalued, or that make people sick. The classic work of the sociologist Talcott Parsons (1951) showed how the “sick role” relieves one of certain social responsibilities (for example, allows one to take time off work or to avoid family responsibilities) and also relieves blame for being ill (though not necessarily from having become ill in the first place). Although there are exceptions and counterexamples to this model (for example, some chronic diseases), it does fit our generally accepted societal notions of what it means to be sick (and healthy), and the moral duties and responsibilities that accompany the designation of someone as sick.

The dominant approach in much of the recent philosophical scholarship on the philosophy of medicine views disease concepts as involving empirical judgments about human physiology (Boorse 1975, 1977, 1997; Scadding 1990; Wachbroit 1994; Thagard 1999; Ereshefsky 2009). These so-called “naturalists” (sometimes called “objectivists”, for example see Kitcher 1997, or “descriptivists”) focus on what is biologically natural and normal functioning for all human beings (or more precisely human beings who are members of relevant classes such as those within a particular age group or of the same sex). They argue that medicine should aim to discover and describe the underlying biological criteria which allow us to define various diseases. Christopher Boorse’s revised account has been the most influential in the literature, claiming that health is the absence of disease, where a disease is an internal state which either impairs normal functional ability or else a limitation on functional ability caused by the environment (Boorse 1997). “Normal functioning” is defined in terms of a reference class which is a natural class of organisms of uniform functional design (i.e., within a specific age group and sex), so that when a process or a part (such as an organ) functions in a normal way, it makes a contribution that is statistically typical to the survival and reproduction of the individual whose body contains that process or part. His definition includes specific reference to the environment so as not to rule out environmentally-induced conditions which are so common as to be statistically normal such as dental caries.

Many have criticized these approaches (to name just a few, Goosens 1980; Reznek 1987; Wakefield 1992; Amundson 2000; Cooper 2002), as well as naturalistic accounts of disease more generally. As they have noted, naturalistic accounts do not reflect our typical usage of the terms “disease” and “health” because they neglect to take into account any values which shape judgments about whether or not someone is healthy. The usual counterexamples proposed to naturalism are masturbation, which was widely believed to be a serious disease entity in the 18 th and 19 th centuries (Engelhardt 1974), and homosexuality, which for most of the 20 th century was classified as a disease in the Diagnostic and Statistical Manual (DSM) of the American Psychiatric Association. These are counterexamples as their redefinitions as non-disease conditions were due not to new biological information about these states of being but changes in society’s moral values. Naturalists respond to such arguments by pointing out that homosexuality and masturbation were never diseases in the first place but erroneous classifications, and thus these examples do not affect the validity of the definition of disease favored by them when it is applied rigorously.

A more telling criticism of naturalism is that although its advocates claim to rely exclusively on biological science to generate their definitions of health and disease, these rely implicitly on an equation of statistical and theoretical normality (or the “natural state” of the organism), at least in Boorse’s formulation (Ereshefsky 2009). But biology does not give us these norms directly, nor is there anything absolutely standard in “species design” (as many philosophers of biology have argued) despite Boorse’s claims. No particular genes are the “natural” ones for a given population, even if we take a subgroup according to age or gender (Sober 1980). Nor does standard physiology provide these norms (Ereshefsky 2009), not in the least part because physiological accounts typically provide idealized and simplified descriptions of organs and their functions, but not of their natural states (Wachbroit 1994). Rachel Cooper (2002) compellingly argues that coming up with an acceptable conception of normal function (and in turn dysfunction) is the major problem with Boorsian-style accounts, arguing that his analysis should focus on disposition to malfunction instead. This argument utilizes counterexamples such as activities that interfere with normal functioning such as taking contraceptive pills that are not diseases, as well as examples of persons with chronic diseases controlled by drugs who function normally as a result. Elselijn Kingma (2007, 2010) has critiqued Boorse’s appeal to reference classes as objectively discoverable, arguing that these cannot be established without reference to normative judgments. A further issue often noted with regard to naturalistic accounts of disease (for example, that of Lennox 1995) is the underlying assumption that biological fitness (survival and reproduction) is the goal of human life, and along with this that medicine is only considered to be interested in biological fitness, rather than other human goals and values, some of which might indeed run contrary to or make no difference in terms of the goal of biological fitness, such as relief of pain.

An alternative approach in the philosophical literature to naturalist/descriptivist/objectivist definitions of disease and health can roughly be termed “normative” or “constructivist”. Most proponents agree that we must define the terms “disease” and “health” explicitly and that our definitions are a function of our values (Margolis 1976; Goosens 1980; Sedgewick 1982; Engelhardt 1986). Hence defining various disease conditions is not merely a matter of discovering patterns in nature, but requires a series of normative value judgments and invention of appropriate terms to describe such conditions. Conversely, health involves shared judgments about what we value and what we want to be able to do; disease is a divergence from these social norms. Normativists believe that their definitions are valid not only philosophically but also reflect actual usage of the terminology associated with disease and health both in common language and among medical professionals. They also claim that this approach more adequately explains how certain conditions can come to be viewed in different ways over the course of history as our values changed despite relatively few changes in our underlying biological theories about the condition, for example homosexuality. Further, they are able to accommodate examples of so-called folk illnesses or culture-bound syndromes such as ghost sickness among some Native American tribes, the evil eye in many Mediterranean cultures, or susto in Latin and South American cultures, as their theories explicitly allow for cross-cultural differences in understandings of disease and health.

However normativism also generates a series of typical criticisms: it cannot cope adequately with cases where there is general agreement that a state is undesirable (such as alcoholism or morbid obesity) but no similar general agreement that the state is actually a disease condition (Ershefsky 2009). Another classic objection is that normative accounts do not allow us to make retrospective judgments about the validity of disease categories such as “drapetomania” (a disease which was commonly diagnosed among American slaves in the 19 th century, with the main symptom being the tendency to run away) (Cartwright 1851). The normativist can point to changes in values to explain the abandonment of belief in this disease condition, but would not be able to claim that the doctors were in any sense “wrong” to consider drapetomania to be a disease. Hence there is more involved in our everyday usage of the terms “disease” and “health” than just value or normative conditions.

Hybrid theories of health and disease attempt to overcome the gaps in both the naturalistic and normative approaches, by hybridizing aspects of both theories (Reznek 1987; Wakefield 1992; Caplan 1992). For instance Jerome Wakefield (1992, 1996, 2007), writing about psychiatric conditions in particular, notes that a condition should be considered a disease if it both causes harm to the person or otherwise contributes diminished value, and the condition results from some internal mechanism failing to perform its natural function (hence for instance much of what is diagnosed as “depression” would fail to count as a disease condition). Whereas the normativist is committed to calling any undesirable state a disease condition, these hybrid criteria rule out calling conditions “diseases” which are non-biological,. Then various marginal cases might be considered to be healthy rather than potentially described as diseased, and hence might not be eligible for treatment within conventional medicine. Examples include those organs or structures that no longer have a function due to evolutionary processes cannot malfunction and so cannot be diseased. Many hybrid approaches also retain too many assumptions about their naturalistic components, and hence are criticized for relying on a notion of natural function which cannot be supported by biology.

The concept of health has been relatively undertheorized in comparison to those of disease and illness, perhaps in part because it raises even more complicated issues than these concepts describing its absence. One could be a straightforward naturalist about health, and define it as being a product of a functional biology; however this argument would run afoul of the same criticisms of naturalism recounted above (see Hare 1986). The source for the classic definition of health comes from the Constitution of the World Health Organization (WHO) which defines health

a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity. (WHO 1948: preamble)

Notice that according to this formulation, health is not just the absence of disease but a positive state of well-being and flourishing (notoriously ambiguous concepts in themselves). Although quality of life is often cited as critical to definitions and theories of health, many commentators are wary of the expansiveness of a definition similar to the WHO’s terminology, as it seems to encompass many things beyond the health of the individual which could contribute (or diminish) his or her “well-being”.

A more narrow definition of health takes its rightful domain as being the state which medicine aims to restore, and its opposite to be “unhealth” or falling short of being healthy, rather than disease as such (Kass 1975). Under such a definition, medicine should not engage in aesthetic surgery or elective terminations of pregnancy or similar procedures which do not (strictly speaking) seek to restore health. Caroline Whitbeck (1981) has defined health in terms of the psychological and physiological capacities of an individual that allow him or her to pursue a wide range of goals and projects. Hence her account is a type of hybrid approach, since she places biological capacities at the core of her definition of health but only in so far as they help individuals to flourish and live their lives as they wish to do. The concept of health here is much more than the absence of disease; for instance, one could have a high level of health while still suffering from a particular disease condition.

One much discussed philosophical approach to defining health is that of Georges Canguilhem (1991, based on work in the early 1940s), who argued against equating it with normality. He noted that the concept of a norm could not be defined objectively in a manner that could be determined using scientific methods. Physiology deals with the science of norms, but even scientifically-based medical approaches should not focus solely on norms, contrary to for instance the ideal vision of medicine according to Claude Bernard (1865). The history of how the distinction between the normal and pathological became so entrenched is explored in detail in Michel Foucault’s now classic work (1963). Both Foucault and Canguilhem sought to reveal how values have been built into the epistemological framework underlying modern medicine.

One of the key points in Canguilhem’s argument is that our usage of the term “normal” often conflates two distinct meanings: the usual or typical, and that which is as it ought to be. Consequently, he argues that there can be no purely scientific or objective definition of the normal that allows us to take the theories of physiology and apply them in medical practice, and accordingly we cannot define health as normality either. Instead, according to him, health is that which confers a survival value, particularly adaptability within a set of environmental conditions: “to be in good health is being able to fall sick and recover; it is a biological luxury” (1991: 199). Disease, then, is reduction in the levels of tolerance for the vagaries of the environment. As Mary Tiles (1993) has noted, this emphasis on health rather than normality is a particularly useful tool for enriching contemporary debates over preventative medicine and more generally the trend toward the development of a positive conception of health. Havi Carel (2007, 2008) has contributed to this strand of thought, developing a phenomenological notion of health which emphasizes that health should be understood as the lived experience of one's own body rather than as simply statistically normal bodily functioning in abstract biological terms. Hence she develops an expressly revisionist project, emphasizing that a phenomenological perspective accommodates cases where someone is ill (in biological terms) but healthy, such as in chronic illness.

A number of authors have made even more extreme claims, arguing that seeking concepts of disease is bound to be a failed effort. For instance, Peter Schwartz (2007) claims that there is not an underlying general concept of disease within the biomedical sciences that is coherent enough to be analyzed, and that different concepts of disease might be useful within different contexts. Some philosophers have argued that to seek correct definitions for “disease” and “health” is distracting and irrelevant when it comes to clinical decisions: as Germund Hesslow puts it, “the health/disease distinction is irrelevant for most decisions and represents a conceptual straightjacket [sic]” (1993: 1). The key is whether or not a particular state is desirable to its bearer, and not whether the person actually has a disease or defect. For instance, the term “malady” has been proposed as a more appropriate alternative to “disease” (Clouser, Culver, and Gert 1981), and which should be extended to include all illnesses, injuries, handicaps, dysfunctions, and even asymptomatic conditions. A malady is present when there is something wrong with a person; regardless of the cause (mental or physical), to be a malady, the condition must be part of its bearer and not distinct or external to him or her. The clear advantage of this approach is that it unifies a range of phenomena and descriptions that seem intuitively to be related. The disadvantages include that it relies in part on an objectivist approach to disease, and hence suffers from some of the difficulties detailed above that plague some versions of naturalism (for a provocative reaction to this debate, see Worrall and Worrall 2001).

An alternative approach to defining disease and health has been described by Marc Ereshefsky (2009) in terms of making distinct state descriptions (descriptions of physiological or psychological states while avoiding any claims about naturalness, functionality, or normality), and normative claims (explicit judgments about whether we value or disvalue a particular physiological or psychological state). This approach has the advantages of allowing more clarity about controversial “disease” conditions as it avoids the need to apply the term explicitly. It also forces us to pinpoint the key issues that matter to understanding and treating someone suffering from ill health. But perhaps most persuasively, he argues that this approach allows us to distinguish the current state of a human from those we wish to promote or diminish, whereas the terms “disease” and “health” do not adequately highlight this critical distinction.

In short, philosophers of medicine continue to debate a range of accounts: in broad outline, the most vigorous disagreement centers on whether more objective, biologically-based, and generalizable accounts are preferable to those that incorporate social and experiential perspectives. It is clear that none satisfy all of the desiderata of a complete and robust philosophical account that also can be useful for practitioners; although some would dispute whether the latter should be a requirement, many believe that philosophy of medicine should be responsive to and helpful for actual clinical practices.

Some disease categories are far from straightforward in terms of being recognized, named, classified, and made legitimate both within medicine itself and for the wider society. In recent times there have been long-standing debates over a range of conditions including Lyme disease, fibromyalgia, and chronic fatigue syndrome (CFS), to name just a few (for extended historical discussions of these and related conditions, see Aronowitz 1998, 2001; Shorter 2008). Take CFS as an example: its main symptoms are fatigue after exertion over a period lasting at least six months, but sufferers can have a wide array of complaints in diverse systems of the body; the range of severity is as wide as the range of symptoms. The condition has been associated with several other controversial syndromes and sometimes equated to with them, most notably myalgic encephalitis and fibromyalgia, as well as other illnesses of inexact definition such as multiple chemical sensitivity and irritable bowel syndrome; more popular (and derogatory) labels also have been attached to it such as yuppie flu. Definitive evidence as to the cause or basis of CFS has remained elusive, and in the absence of causal explanations, accurate diagnoses and effective treatments often have been difficult to obtain. Thus the illness has been perceived by many as being illegitimate because of difficulties in proving the existence of a discrete disease condition, given the lack of traditional forms of clinical evidence for it, and it has had different statuses in different locales (see Ankeny and Mackenzie 2016). These issues severely impact on the lives of those affected by this condition, and on the care that is thought to be appropriate to be made available to them.

Mental illnesses (and the term “mental health” itself) also have traditionally posed considerable problems for categorization and conceptualization for both medical practitioners and philosophers of medicine. Many authors advocate the case that it is critical to make a distinction between mental and physical illness (Macklin 1972), particularly because of the moral implications associated with labeling a condition as mental or psychological. Psychiatry is a field which has historically been loaded with value judgments, many of which were quite dubious. There is a long history of using mental illness as a way to categorize behaviors which are socially deviant as well as those conditions of ill health with no apparent organic cause and which do not otherwise fit into our dominant biomedical model. Many scholars (e.g., Ritchie 1989; Gaines 1992; Mezzich et al. 1996; Horwitz and Wakefield 2007; Demazeux and Singy 2015) have critiqued approaches and the underlying assumptions of the various editions of the Diagnostic and Statistical Manual published by the American Psychiatric Association, which is a “bible” for psychiatric conditions for many practitioners and also has considerable public influence for instance on who can seek care. Key examples of contested issues within the DSM include the highly politicized nature of the processes of revision across various editions, various cultural, sexist, and gender biases inherent in specific diagnostic categories, and the relatively weak reliability and validity of the classification system.

One key question is whether the biomedical model is the most appropriate approach to psychological or mental conditions and their treatment. Some theorists have argued in favor of naturalistic accounts of disease, notably Thomas Szasz (1961, 1973, 1987). As a result, he famously claimed that “mental diseases” are a myth and do not exist because they do not result from tissue damage; in his view, all diseases must be correlated with this sort of physical damage. He thus argues that the concept of mental illness is a prescriptive concept used as though it were a merely descriptive one, and also a justificatory concept masquerading as an explanatory one. These conclusions lead him to a highly critical analysis of psychiatric practices, and to reclassifying such forms of suffering as “problems in living” rather than diseases. However it is not always clear in his account what his evidence for these claims is, and in particular whether he is making an in principle objection or one that is grounded in the history of the mistreatment of people with mental illnesses, and the disservice done to them in part because of the adoption of the medical model. In addition, some have noted that some psychiatric conditions do in fact correlate with physiologically detectable and other types of biological abnormalities. For instance twin studies have demonstrated that genetics is a major factor in the etiology of schizophrenia among other conditions typically considered to be psychiatric, although clearly not all conditions that are diagnosable according to contemporary psychiatric standards fit this model.

A prominent functionalist approach to mental disorders more recently has been that of Wakefield (1992, 1996, 2007), as discussed above, who argues that mental disorders are best understood as “harmful” dysfunctions, which permits a supposedly value-free foundation in terms of biological function gauged in evolutionary terms) with judgments coming in only in terms of the judgment of whether certain dysfunctions are harmful to their bearers. Such accounts have been criticized along lines similar to analyses of Boorsian accounts by emphasizing that function and dysfunction cannot in fact be defined independently of value terms, but Wakefield’s account also has been questioned in terms of its practical implications (e.g., Sadler and Agich 1995) and whether malfunction is a necessary component of mental disorder (Murphy and Woolfolk 2000).

Other authors, notably George Engel (1977), have argued for the need to unify our understandings of mental and physical illness under a broader, biopsychosocial model. Such a model would focus clinicians to take account of both the physical, psychological, and social factors that contribute to ill health, in contrast to the traditional biomedical model which is faulted for being overly reductionistic rather than holistic. Such an account, it is claimed, would be more effective in dealing with borderline cases including people who are told they are in need of treatment due to abnormal lab results or similar but who are feeling well, as well as those who appear to have no underlying somatic disease condition but are feeling unwell. Hence this type of account does not draw any sharp distinction between the physical and the mental (or even the social), leaving the question of appropriate therapies or approaches as a matter to be decided by the doctor and his or her patient. Engel compellingly defended this type of account as more appropriate not only for clinical work but for research and teaching in medicine. It is arguable that it has implicitly (and often explicitly) been adopted in much of current-day medical practice and teaching, although it is less clear whether it has had much influence in biomedical research, much of which tends to remain more reductionistic in its nature.

There is no widely accepted notion of what a scientific theory is. The logical positivists thought that theories are sets of propositions, formalizable in first-order logic, at one point, and as classes of set-theoretic models at another. For our purposes here one can distinguish two senses of theory, a narrower and a broader sense. In the narrower sense, a theory comprises a set of symbols and concepts used to represent the entities in a domain of discourse as well as a set of simple general-purpose principles that describe the behavior of these entities in abstract terms. In the broader sense, theory refers to any statement or set of statements used to explain the phenomena of a given domain.

In medicine one can find theories in both the narrower and the broader sense. Humorism, for instance, holds that the human body is filled with four basic substances or “humors”: black bile, yellow bile, phlegm, and blood. The humors are in balance in a healthy person; diseases are explained by excesses or deficiencies in one or more humors. Humorism has ancient origins and influenced Western medicine well into the 18th century. Eastern medicine has analogous systems of thought. Indian Ayurveda medicine, for example, is a theory of the three primary humors wind, bile, and phlegm, and diseases are similarly understood as imbalances in humors (Magner 2002).

In contemporary Western medicine, such highly unifying and general theories play a limited role, however. Evolutionary and Darwinian medicine may well constitute exceptions but these are at best emergent fields at present (see Méthot 2011). Contemporary Western medical researchers and practitioners instead seek to explain medical outcomes using mechanistic hypotheses about their causes—symptoms by hypotheses about diseases, diseases by hypotheses about antecedents, epidemics by hypotheses about changes in environmental or behavioral conditions (Thagard 2006). What distinguishes these contemporary medical theories from the ancient approaches is that the causes of symptoms, diseases, and epidemics can in principle be as multifarious as the outcomes themselves; in the ancient approaches, lack of humoral balance was the only possible cause. In contemporary Western medicine, there is no presupposition concerning number, form, or mode of action of the causes that explain the outcome other than there being some cause or set of causes responsible.

Not every cause is equally explanatory. A given person’s death can be described as one by cardiac arrest, pulmonary embolism or lung cancer, for instance. The lung cancer may have had a genetic mutation, the deposition of carcinogens in lung tissue and smoking in its causal history. The smoking, in turn, was caused by the smoker’s proneness to addictive behavior, peer pressure and socio-economic environment, let us suppose. Which of the many candidate hypotheses of the form “ X causes (or caused) Y ”, where Y refers to the patient’s death, does best explain the outcome? There is no absolute answer to this question. The goodness of a medical explanation depends in part on the context in which it is given (see entry on scientific explanation ). When asked “Why did Y happen?” a coroner might refer to the pulmonary embolism, the patient’s physician to the lung cancer and an epidemiologist to the patient’s tobacco consumption. The adequacy of a medical explanation is related to our ability to intervene on the factor in question. A pulmonary embolism can be prevented by screening the patient for blood clots. The accumulation of carcinogens in lung tissue can be prevented by stopping smoking. By contrast, even though certain kinds of genetic mutations are in the causal history of any cancer, the mutation is not at present of much explanatory interest to most clinicians, as this is not a factor on which they can easily intervene. There is considerable current medical research to identify mutations associated with various subtypes of cancer and using these to develop targeted therapies and interventions, as well as to provide more accurate prognostic information. Medical explanation, thus, is closely related to our instrumental interests in controlling, preventing and controlling outcomes (Whitbeck 1977).

One issue that is currently debated in the philosophy of medicine is the desirability (or lack thereof) of citing information about the mechanisms responsible for a medical outcome to explain this outcome. While mechanisms are usually characterized in causal terms (e.g., Glennan 2002; Woodward 2002; Steel 2008), it is not the case that every cause acts through or is a part of some mechanism, which is understood as a more or less complex arrangement of causal factors that are productive of change (e.g., Machamer et al. 2000). Absences, such as lack of sunlight, can cause medical outcomes but are not related to them through continuous mechanisms from cause to effect (Reiss 2012). Neuroscientific explanations are often acceptable despite the lack of knowledge or false assumptions about mechanisms (Weber 2008). However, we may ask whether mechanistic explanations are generally preferable to non-mechanistic causal explanations.

Many medical researchers and philosophers of medicine subscribe to a reductionist paradigm, according to which bottom-up explanations that focus on the generative physiological mechanisms for medical outcomes are the only acceptable ones or at least always preferable. Indeed, macro-level claims such as “Smoking causes lung cancer” seem to raise more questions than they answer: Why does smoking have adverse health consequences? To prevent these consequences, is it necessary to stop smoking? Is it possible to produce cigarettes the smoking of which has fewer or no adverse consequences? What is the best policy to improve morbidity and mortality from lung cancer? Knowing that it is specific carcinogens in tobacco smoke and genetic susceptibility that are jointly responsible for the onset of the disease helps to address many of these questions.

Nevertheless it would be wrong to assume that we cannot explain outcomes without full knowledge of the mechanisms responsible. When, in the mid-1950s, smoking was established as a cause of lung cancer, it was certainly possible to explain lung cancer epidemics in many countries where people had exchanged pipe smoking for cigarette smoking half a century earlier—even though the mechanism of action was not understood at the time. Differences in lung cancer incidence between men and women or between different countries can be explained with reference to different smoking behaviors. Policy interventions, in this case the addition of warning labels to cigarette packets, could not wait until sufficient mechanistic knowledge was available, nor did they have to wait.

For reasons such as these, a number of philosophers of medicine have proposed to adopt an “explanatory pluralism” for medicine (De Vreese et al. 2010; Campaner 2012). If nothing else, this is certainly a position that is consistent with the explanatory practices in the field.

As in many fields, debates over reductionism versus holism are rife in medicine both with reference to medical research and practice, and the terms often are used rather loosely to mean a range of things (for a related discussion see entry on reductionism in biology ). In the broadest terms, reductionistic approaches to disease look for fundamental mechanisms or processes that are the underlying causes of that disease. In recent years in light of large-scale genomic sequencing initiatives notably the Human Genome Project, there has been considerable emphasis on reducing diseases to the genetic or molecular level. Those who advocate more holistic approaches note that reductionism leaves out important information based on the patient’s experiences of the disease at the phenotypic level, and such information is critical to pursuit of effective treatments. Many diseases typically viewed as “genetic” have proven to be extremely difficult in practice to reduce to unified disease entities with singular (or simple) genetic causes, including mental illnesses (Harris and Schaffner 1992), cystic fibrosis (Ankeny 2002), and Alzheimer’s disease (Dekkers and Rikkert 2008). As Catherine Dekeuwer (2015) notes, given that there probably is genetic variation in susceptibility to virtually all diseases, there is no clear demarcation between genetic diseases and diseases for which there are genetic risk factors; hence she argues that our tendency to focus on genetic determinants of disease may reinforce folk notions of the geneticization of both people and of human behavior.

With regard to research, critics of reductionism point out that there has been an overemphasis on the pursuit of genetic or molecular level explanations of disease to the neglect of alternative levels of explanation. Further, such limitations are highly detrimental to patients, especially because there are not likely to be short-term cures or treatments for most genetic diseases, perhaps beyond avoiding having children carrying particular genes in the first instance (see for instance Hubbard and Wald 1999), although this domain of medicine is rapidly changing as new treatments are developed and the understandings of the effects of genomic mutations improve. Focusing overly or solely on the genetic level results in a process which the sociologist Abby Lippmann (1991) terms “geneticization”, namely reducing the differences between individuals to their DNA, and in turn viewing genetics as the most promising approach to curing disease, rather than viewing people and the illnesses that they suffer at a phenotypic and much more environmentally-situated level. In addition, as Elisabeth Lloyd (2002) argues, higher levels of social organization that are culturally sanctioned have unrecognized causal effects on health, and hence medical research should not be restricted solely to the molecular level.

Fred Gifford (1990) claims that although all phenotypic traits are the result of an interaction between genes and the environment within which they are expressed, nonetheless it makes sense to distinguish certain traits as “genetic”; he argues in terms of populations that if it is genetic differences that make the differences in that trait variable in a given population, and if genetic traits can be individuated in a way that matches what some genetic factors cause specifically, then a trait (including a disease trait) can be understood as genetic. Kelly Smith (1992) disputes this, noting that the second condition depends on an extremely problematic distinction between causes (in this case genes) and mere conditions (e.g., epigenetic factors). Lisa Gannett (1999) argues for a “pragmatic” account of genetic explanation, claiming that when a disease is classed as “genetic”, the reasons for singling out genes as causes over other conditions necessarily include pragmatic dimensions inasmuch as they are relative to a given causal background (which includes both genetic and nongenetic factors), relative to a population, and relative to our present state of knowledge. More recently it has been argued that although explanatory reduction cannot be defended on metaphysical grounds, reductive explanations might be indispensable ways to address certain questions in the most accurate, adequate, and efficient ways (van Bouwel et al. 2011).

“Evidence-based medicine” (EBM) describes a movement that was started (under that name) in the early 1990s by a group of epidemiologists at McMaster University in Hamilton, Canada, as a reaction against what was perceived as an over-reliance on clinical judgment and experience in making treatment decisions for patients. According to a widely cited definition:

Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. (Sackett et al. 1996: 312)

Such a definition has bite only when the concept of evidence used is relatively narrow. In particular, it should not allow clinical judgment and experience to count as “best evidence”.

To this effect, proponents of EBM have developed so-called “hierarchies of evidence” that categorize different research methods with respect to their supposed quality. While there is no universally accepted hierarchy, the different proposed hierarchies all agree in the priority they give to randomized controlled trials (RCTs) and reviews thereof. A typical hierarchy looks as follows (Weightman et al. 2005):

Evidence produced by RCTs has thus been called the “gold standard” of evidence in EBM (e.g., by Timmermans and Berg 2003).

In an RCT, a population of individuals who might benefit from a new medical treatment are divided into a treatment group—the group whose members receive the new treatment—and one or several control groups—groups whose members receive either an alternative or “standard” treatment or a placebo. Individual patients are assigned to a group by means of a random process such as the flip of a coin. A placebo is an intervention that resembles the new treatment in all respects except that it has no known ingredients active for the condition under investigation (i.e., it is some kind of “sugar pill”). Patients, researchers, nurses, and analysts are all blinded with respect to treatment status of all patients until after the analysis. After a period of time, a pre-determined outcome variable is observed and the values of the variable are compared between the groups. If the value of the outcome variable differs between different treatment groups at the desired level of statistical significance, the treatment is judged to be effective.

Proponents of EBM regard RCTs as reliable means to judge treatment efficacy because they can help to control for a variety of (though not all) biases and confounders. If, for instance, the symptoms of a patient or group of patients improve after an intervention, this may be due to spontaneous remission rather than the treatment. An experimental design that compares a treatment group with one or several control groups is therefore better able to control for this confounder than a simple “before-and-after” design. Similarly, a design in which the allocation to treatment and control groups is done by a non-random process, it is possible that healthier patients end up in the treatment and less healthy patients in the control group. If so, the measured improvement may be due to the health status of the patients rather than the intervention. Especially if the allocation is done by a medical researcher who has a stake in the matter (for instance because she has developed the new treatment), allocation decisions may consciously or subconsciously be influenced by expectations about who will profit from the intervention and thus create unbalanced groups. Allocation by a random process helps to control this source of bias.

No one denies that RCTs are powerful experimental designs—and that their power stems from the ability to control numerous sources of bias and confounding. However, to refer to RCTs as the “gold standard” of evidence suggests that they are more. Specifically, one may be led to assume that RCTs are necessary for reliable causal inference or that RCTs are guaranteed to deliver reliable results. A number of philosophers of medicine have in the past decade or so argued that these stronger claims do not hold to scrutiny.

In particular, the following claims have been criticized:

The logic of statistical significance tests requires randomization (Fisher 1935). Ronald Fisher invoked his famous tea lady thought experiment in order to make plausible that significance testing works only with randomized allocation. Suppose an English lady claims that she is able to tell whether tea or milk was poured into the cup first and we would like to test this assertion. If she gets it right each time in a series of eight cups (four “milk first” and four “tea first” cups), this result may be due to her usually sharp sense of taste. But it may also be for indefinitely many other reasons: she may know that milk was poured first in the first four cups and correctly identified the first four as “milk first” cups; the “milk first” cups differ in color or shape from the “tea first” cups or have any other visually identifiable features; a confederate recorded which cups were “milk first” and signals her; and so on. Fisher now argues that only if the allocation of tea to cups was done at random, the probability of the lady getting all eight cups right is correctly identified as the probability of her getting it right if she were to guess, having no discriminatory ability (which in this case is 1/70). Therefore, we can judge that she really does have an unusual discriminatory ability or something very unlikely must have happened (i.e., an event with the probability 1/70). But this is incorrect. In fact, there is still an indefinite number of ways in which she got the result even though she does not have a good sense of taste. If a confederate signals her the correct answer, the probability of her getting it right is very close to 1 independently of her discriminatory ability (Worrall 2007a). A good experiment would prevent this, but this has to do with other aspects of the experimental design, not randomization.
Randomization controls for all confounders, known and unknown (Fisher 1935; Giere 1984). Many variables affect a patient’s probability of recovery: her gender, age, co-morbidities, genetic factors, compliance with the treatment regime, psychological factors and many more. If we want to judge that an observed difference in recovery rates between treatment groups is due to the intervention rather than these other factors, we have to make sure that the probability distribution of causal factors is the same between the different groups. Randomization is supposed to ensure this. However, for any finite test population size (and many RCTs do indeed have relatively small numbers of patients), it remains possible that treatment groups are unbalanced: old patients ending up in one group, younger in the other etc. While it is the case that the larger the number of patients in the RCT, the less likely it is that the groups are unbalanced with respect to any given factor, if there are many possible factors affecting the outcome it is actually very likely that some of them are unbalanced. Thus, in practice if it is noticed after randomization that the two groups are unbalanced with respect to a variable that is thought to affect the outcome outcome, then the groups are re-randomized or adjusted (Worrall 2002)
It is possible to “prove” the results of an RCT to be correct (Cartwright 1989; cf. Worrall 2007b). Every scientist, at some point in his career, learns that one cannot judge X to be a cause of Y just because X and Y are correlated. According to a prominent theory of causation, viz. the probabilistic theory, causation is a form of correlation after all. Very roughly, the probabilistic theory holds that X causes Y just in case X and Y are correlated and all sources of confounding have been controlled (Reiss 2007). It can now be shown that under the probabilistic theory and a host of other assumptions (including the assumption that randomization has been successful in that the treatment groups are balanced with respect to prognostic factors), if the treatment status variable is correlated with the outcome variable, then the treatment must cause the outcome (Cartwright 2007). To give RCTs a special status in EBM on the basis of this reasoning would be to commit a logical mistake, however. The argument can only show that if all the assumptions behind an RCT are satisfied , the RCT will give a causally correct result. It does not show that RCTs are the only way to generate provably correct results. Indeed, it can relatively easily be shown that observational studies that identify so-called instrumental variables are similarly provably correct under a certain set of assumptions (Reiss 2005).

A final but very important issue is that of the external validity of the RCT results. Even under ideal conditions (i.e., when medical researchers have very strong reasons to presume the assumptions under which an RCT works to be satisfied), the RCT can only establish that the treatment is effective in the test population . Typical test populations differ from the target populations (i.e., those populations for whom the treatment has been developed and who will eventually receive the treatment) in more or less systematic ways. For example, many RCTs will exclude elderly patients or patients with co-morbidities but the treatment will be marketed to these patients. For financial reasons, many RCTs are nowadays conducted in developing countries whereas the treatments are mainly or exclusively marketed to patients in developed countries. Whereas the protocols for conducting an RCT are very strict and detailed, there are no good guidelines how to make treatment decisions when the patient at hand belongs to a population that differs from the population in which the RCT was conducted (e.g., Cartwright 2011).

There are in fact two problems of external validity in the application of RCT results. On the one hand there is the population-level problem of making an inference from test to target population . On the other hand, there is the problem of making an inference from population to individual . The RCT provides evidence for a population-level claim: “In population p (the test population) intervention X is effective in the treatment of condition Y ”. For this claim to be true, the treatment must be on average effective, which allows the effectiveness to vary among the individuals in the population. Indeed, it is possible that the intervention is effective (and beneficial) on average but ineffective or positively harmful in some individuals (i.e., members of some subpopulations). Proponents of EBM to some extent oversell their case when they write that EBM

de-emphasizes intuition, unsystematic clinical experience, and pathophysiologic rationale… and [instead] stresses the examination of evidence from clinical research. (Evidence-Based Medicine Working Group 1992)

because inferences from test to target population and from any population to the individual receiving the treatment are necessarily based on clinical judgment.

John Worrall argues that, at the end of the day, RCTs are a powerful means to control selection bias, but no more than that (Worrall 2002, 2007a,b). As he uses the term, selection bias occurs when treatment and control group are unbalanced with respect to some prognostic factors because a medical researcher has selected which patients will receive which treatment. Selection bias in this sense obviously cannot occur in an RCT because in an RCT the allocation is made by a random process. But it is also clear that randomization is at best sufficient but not necessary to achieve the result. A large number of alternative designs may be used to the same effect: allocation can be made by a strict, albeit non-random protocol; allocation is made by non-experts who are unrelated to the treatment development and therefore have no expectations concerning outcomes; treatment and control groups are deliberately matched (again by persons who have nothing at stake or according to some protocol); and so on.

A controversial issue is the role of mechanistic knowledge, that is, knowledge about the biological and physiological mechanisms responsible for medical outcomes (and thus treatment efficacy) should play in EBM. As mentioned above, the RCT provides evidence for black-box causal claims of the form “In population p , intervention X is effective in the treatment of condition Y ”. As we have seen, proponents of EBM also believe EBM to de-emphasize patho-physiologic rationale (a different term for “mechanistic knowledge”). Nevertheless, a number of philosophers of medicine have pointed out that mechanistic knowledge is in fact important in EBM or that it should receive more attention. Federica Russo and Jon Williamson have, for instance, argued that causal claims need both statistical evidence as well as evidence about the mechanisms that connect an intervention with the outcome variable in order to be established (Russo and Williamson 2007). Others disagree (Reiss 2012) or qualify the claim (Gillies 2011; Howick 2011a; Illari 2011). Further, it has been pointed out that mechanistic knowledge plays an important role in the design and preparation of an RCT, as well as in the interpretation and application of RCT results (La Caze 2011; Solomon 2015). Especially when it comes to extrapolating research results from a test to another population, mechanistic knowledge is supposed to be vital (Steel 2008; see also next section). On the other hand, knowledge about mechanisms is often highly problematic and should not be relied on too heavily in applications (Andersen 2012).

New therapies are often trialed using animal models before they are tested on humans in a randomized trial. Animal models also play important roles in establishing whether or not a substance is toxic for humans. The International Agency for Research on Cancer (IARC), for example, classifies substances with respect to the quality of the evidence for their carcinogenicity into five groups. Evidence from animal models is referred to in the characterization of each group (IARC 2006). This raises questions about how such extrapolations from animal models to humans work, and how reliable they are.

Animal models are widely used in biomedical research because experimental interventions on animals are easier to conduct and cheaper than experiments on humans. Both kinds of experiments involve ethical dilemmas, but animal experimentation is usually regarded as less problematic from an ethical point of view than experimentation with humans. At any rate, the number of animals killed, maimed, or made sick in biomedical research is much higher than the number of humans adversely affected in this research.

There is a fundamental inferential problem in transferring what has been learned in any model (whether human, animal, or whatever) to some target population of interest has been described as the “experimenter’s circle” (Steel 2008). The problem is essentially this. What is true of a model can be presumed to be true of the target only to the extent that the model is similar to the target in relevant respects. The reason we experiment on models in the first place is, however, that the model differs in important respects from the target (if animals were just like humans, we would not find experiments on the former to be ethically less problematic than experiments on the latter). Extrapolation—the inference from model to target—is therefore only worthwhile to the extent that there are significant limitations in our ability to study the target directly. If so, there can be no good grounds to decide whether a model is a good one for the target. To do so, we would have to investigate whether the target is relevantly similar to the model; but if we could do so, there would be no reason to study the model in the first place.

This inferential problem has led some commentators to maintain highly skeptical views concerning our ability to use animals as models for humans in biomedical research. Hugh LaFollette and Niall Shanks argue that animal models cannot be reliably used for extrapolation at all, but at best only heuristically, as sources of hypotheses that have to be tested on humans (LaFollette and Shanks 1997). They introduce two terms to make their argument: causal analogue model (CAM) and hypothetical analogue model (HAM). The former can be used to make reliable predictions about target populations of interest; the latter only heuristically. The main premise in their argument that animal models in biomedical research are at best HAMs but not CAMs is that for a model to be a CAM there cannot be causally relevant disanalogies between model and target—a condition which is rarely if ever met by animal models (again, this is why we study animals in the laboratory in the first place).

Daniel Steel (2008: ch. 5) argues that LaFollette and Shanks’ condition for reliable extrapolation is too stringent. Whether a claim about a model can be extrapolated depends, he argues, also on the strength of the claim to be exported. It is one thing, say, to reason from

x % of the members of population p will show symptoms of poisoning after ingesting substance S

x % of the members of population $q \ne p$ will show symptoms of poisoning after ingesting substance S ,

quite another to reason from the quantitative claim to a qualitative claim such as “Substance S is poisonous for the members of q ”.

Steel’s own reconstruction of how extrapolation works in the biomedical sciences is called comparative process tracing . He assumes that causes C (such as medical interventions or the ingestion of toxic substances) bring about their effects E (such as the appearance of symptoms or improvements or deteriorations of symptoms) through a series of steps or stages. To trace a causal process means to investigate through what set of stages C brings about E . Process tracing is comparative when the set of stages through which C brings about E in one species or population is compared to the set through which it does so (if it does so indeed) in another.

Comparative process tracing would be futile if, in order to know that C causes E in the target species or population, we would have to compare all the stages of the process between model and target. This is because in order to do so, we would have to know all stages of the process through which C causes E , but if we did, we would already know that C causes E . This brings us back to the extrapolator’s circle. Steel now argues that comparative process tracing avoids the extrapolator’s circle by demanding processes to be compared only at stages where they are likely to differ and assuming that differences between model and target matter only to stages that are downstream from where they obtain. Thus, if we compare an intermediate stage of the process which obtains in the model with that stage in the target and find them to be relevantly similar, then the only differences that may still obtain will be downstream from this stage. We therefore do not require knowledge of the entire process from C to E in the target, and the extrapolator’s circle is successfully avoided.

How useful comparative process tracing is as a method for extrapolation for the biomedical sciences depends on how reliable the assumption that only downstream differences matter to extrapolation is, the reliability with which stages where there might be differences between model and target can be identified and the reliability of our mechanistic knowledge more generally. If, say, our reasons for supposing that C causes E through a series of stages X , Y , Z in the model, or that X and Z are the stages where model and target are likely to differ, are not very strong, then the method does not get off the ground. This is an issue that depends on the quality of the existing knowledge about a given case and cannot be addressed for the biomedical sciences as a whole. There are certainly some examples of well-established causal claims where it is known only that C causes E but the details of the causal process are entirely beyond our current grasp (Reiss forthcoming-a).

An alternative to comparative process tracing that has been proposed is extrapolation by knowledge of causal capacities . If C has a causal capacity to bring about E , then C causes E in a somewhat stable or invariant manner. Specifically, C will then continue to contribute to the production of E even when disturbing factors are present (Cartwright 1989). To establish that C has the causal capacity to cause E therefore means to show that C ’s causing E is independent of the background in which C and E occur to some extent. And therefore, if C causes E in a model species or population and C has the causal capacity to bring about E , then there is some reason to believe that C causes E also in the target species or population (for a defense, see Cartwright 2011).

The usefulness of the method of extrapolation by causal capacities depends, among other things, on the extent to which biomedical factors can be characterized as having capacities. Many biomedical causes do indeed have some degree of stability. The sickle cell trait is 50% protective against mild clinical malaria, 75% protective against admission to the hospital for malaria, and almost 90% protective against severe or complicated malaria (Williams et al. 2005). These figures suggest a reading along the lines of,

in the presence of the sickle cell trait (a preventer of/disturbing factor for malaria), infection with Plasmodium malaria continues to affect outcomes consistently. (Reiss 2015b: 19)

But there is a high degree of interaction with other factors as well. Whether or not a substance is toxic for an organism depends on minute details of its metabolic system, and unless the conditions are just right, the organism may not be affected by the substance at all. To what extent this method will be successful therefore similarly case-dependent as comparative process tracing.

As we can see, there is no general answer to the question whether or not animal studies are valuable from a purely epistemic (as opposed to ethical, economic, or combined) view. Other authors have developed a practice-based taxonomy of animal modes to allow more accurate assessment of the epistemic merits and shortcomings, and predictive capacities of specific modeling practices (Degeling and Johnson 2013). There is much evidence that species differ enormously with respect to their susceptibility to have toxic reactions to substances. Thus, while it is very likely that for any one toxin, there is some species that is predictive of the human response, it is often hard to tell which one is most appropriate for any particular toxin. A species that predicts the human response well for one substance may be a bad model for another. However, some authors suggest that extrapolations from animal models have been made successfully in at least some cases (Steel 2008 discusses the extrapolation of claims concerning the carcinogenicity of aflatoxin from Fisher rats to humans; see Reiss 2010a for a critical appraisal and Steel 2013 for a response).

Frequently, in the biomedical sciences, reliable animal or other non-human models are not available and RCTs on humans are infeasible for ethical or practical reasons. In these and other cases, biomedical hypotheses can be established using observational methods. As we have seen in Section 5 , evidence-based medicine regards observational methods as generally less reliable than RCTs and other experimental methods. This is because observational studies are subject to a host of confounders and biases that can be controlled when the hypothesis is tested by a—well-designed and well-conducted—RCT. But it is not the case that observational methods cannot deliver reliable results. In fact, it is well possible that the medical knowledge that has been established observationally by far exceeds the knowledge that comes from RCTs. Here are some examples of medical interventions that are widely accepted as effective but whose effectiveness has not been tested using RCTs: penicillin in the treatment of pneumonia, aspirin for mild headache, diuretics for heart failure, appendectomy for acute appendicitis and cholecystectomy for gallstone disease (Worrall 2007a: 986); automatic external defibrillation to start a stopped heart, tracheostomy to open a blocked air passage, the Heimlich maneuver to dislodge an obstruction in the breathing passages, rabies vaccines and epinephrine in the treatment of anaphylactic shock (Howick 2011b, 40).

Observational studies often begin by reporting a recorded correlation between a medical outcome of interest and one or a set of independent variables: lung cancer rates are higher in groups of smokers than in groups of non-smokers, liver cancer rates are higher in populations that tend to consume food that has been contaminated with aflatoxin than in populations whose food is uncontaminated, to give a few examples. That smoking causes lung cancer, or aflatoxin cancer of the liver, would indeed account for the observed correlations. But so would a variety of other hypotheses. Generally, if two variables X and Y are correlated, it may be the case that X causes Y , Y causes X or a common factor Z causes both X and Y (or a combination of these). In the smoking/lung cancer case, all three hypotheses were invoked as possible accounts of the data. Ronald Fisher famously proposed that it may be the case that early stages of bronchial carcinoma cause an individual to crave cigarettes, and he provided some evidence that both smoking behavior and susceptibility to lung cancer have a common genetic basis (Fisher 1958). Moreover, it is possible that the correlation itself is spurious—that the data are correlated as per some measure of correlation such as Pearson’s coefficient, but that the underlying variables are not in fact correlated in the population of interest. Selection bias is normally understood as the bias that obtains when individuals self-select into the observed population and the reasons for which they do so are correlated with the outcome variable. If an observational study examines only hospitalized patients and smokers are more likely to be in hospital for reasons that have nothing to do with lung cancer, then smoking and lung cancer can be correlated in the data even if the variables are independent in the general population. Mismeasurement and diagnostic error provide another account of spurious correlation. Suppose tuberculosis was on the rise a generation or so after many people traded pipe smoking for cigarette smoking. Then, if it was difficult to distinguish a death from tuberculosis from a death from lung cancer because necropsy techniques were not sufficiently well developed, the data might again show a correlation even though the population variables are uncorrelated.

Retrospective observational studies work by ruling out alternative hypotheses such as these ex post rather than controlling for them ex ante as RCTs do (Reiss 2015a). In an RCT, mismeasurement should not obtain because the protocol specifies measurement procedures for the outcome variables in great detail in advance. Selection bias should not obtain because patients are randomized into treatment groups. Once allocated to a group, they are prevented from obtaining another treatment elsewhere, and researchers make sure that patients comply with the treatment regime. But there are equivalent means to rule out these possibilities in observational settings. While it may well be the case that early stages of cancer cause a craving for cigarettes, this hypothesis cannot explain the protective effect that stopping smoking has. At the time of the smoking/lung cancer controversy in the mid-1950s, misdiagnosis was indeed a problem. However, it could be shown that in order to account for the observed rise in lung cancer incidence, the diagnostic error at autopsy among older people would have to have been an order of magnitude higher than the diagnostic error among younger people (Gilliam 1955). Mismeasurement could therefore also be ruled out. Similar considerations helped to rule out other alternative hypotheses (Cornfield et al. 1959).

Even if one were to believe, with the proponents of EBM, that observational studies are generally less reliable than RCTs, medicine could—fairly obviously—not do without them. There are large numbers of pressing questions that could not be addressed by an RCT for ethical, financial and other practical reasons. No-one would seriously consider testing a proposition such as “Aflatoxin causes cancer of the liver (in humans)” by an RCT. This is not merely because of the straightforward ethical issues involved in deliberately exposing humans to a potential carcinogen for the sake of medical progress. It is also because exposure to low levels of aflatoxin can take many years or even decades to produce symptoms. The ability of researchers to control food intake in a large group of experimental subjects for a very long has evident practical financial and limitations. RCTs can also not be used when researchers or patients or both cannot be blinded, and many medical interventions do require the doctor’s or the patient’s knowledge of details about the intervention.

Moreover, it is not clear that RCTs are always more reliable than observational studies to answer questions both methods are able to address. Whether or not a study is reliable depends on whether or not confounders and biases have in fact been eliminated, not by which method they have been eliminated. Issues concerning the reliability of a method can be entangled with issues concerning its ability to address the research question the biomedical scientist seeks to answer. Both RCTs and observational studies in the biomedical sciences are typically employed to test rather complex hypotheses about the safety and efficacy of medical interventions. It may well be that some of the issues are more reliably treated by one method and others by the other.

A famous controversy in which the results from observational studies and those from RCTs conflicted was that over the benefits and safety of hormone replacement therapy (HRT) in the early 2000s (Vandenbroucke 2009). HRT seemed protective for coronary heart disease in observational studies, whereas RCTs indicated an increase in the first years of use. For breast cancer, combined hormone preparations showed a smaller risk in an RCT than in observational studies. In the end it turned out that the timescale of the effects was responsible, and that because of the way they are typically run, observational studies got some issues right and RCTs others:

The observational studies had picked up a true signal for the women closer to menopause. In the randomised trial, that signal was diluted because fewer women close to menopause were enrolled… The randomised trials had it right for coronary heart disease but failed to sufficiently focus on women close to menopause for breast cancer. The main reasons for the discrepancies were changes of the effects of HRT over different times… (Vandenbroucke 2009: 1234)

Case reports remain extremely popular in medicine both as publications to communicate within the field and for pedagogical purposes. In short, a case report describes a medical problem experienced by one or more patient, usually involving the presentation of an illness or similar that in some way difficult to explain or categorize based on existing understandings of disease or understandings of physiology and pathology. Cases in medicine take highly standardized forms of presentation which are inculcated in health care professionals during their education, and many have commented on their highly standardized narrative structure and its epistemic and other implications (Hunter 1991; Hurwitz 2006). Cases typically provide details on the presentation of the disease, diagnosis, treatment, and outcomes for the patient, with a focus on practice-based observations and clinical care (rather than the results of randomized controlled trials or other experimental methodologies). One of the purposes of cases is to gather detailed information including facts that may not be immediately relevant, but that could prove to be (Ankeny 2011). Thus the information contained in the case and the case itself can be useful over the long term particularly if it can be systematically combined with other cases into larger datasets.

Single cases are seen by some as problematic as a form of evidence particularly in the era of EBM, because they often focus on highly unusual manifestations of illness and disease, rather than typical or repeatedly observed conditions that might support generalizable rules. This feature has led some to describe medicine as a “science of particulars” (Gorovitz and MacIntyre 1976), or as an art rather than a science (Pellegrino 1979), particularly in processes of diagnosis (see Section 9 ). However standard accounts of EBM include the case series as a type of evidence, which involves the aggregation of individual cases of patients with similar attributes (e.g., who received the same treatment or therapy) who are tracked over time using descriptive data and without utilizing particular hypotheses to look for evidence of cause and effect. EBM does place the case series quite low in its hierarchy of evidence but nonetheless it is acknowledged that cases have potential usefulness especially where forms of evidence that rate more highly are not available, as may often be the case where human patients are concerned due to practical or ethical reasons, or where the available evidence at higher levels has been produced in a manner that is methodologically or otherwise flawed.

Cases can serve other purposes: for instance analyses of cases can provide working hypotheses about casual attribution that can ground further tests of causal relations (Ankeny 2014), which in turn allows use of more traditional methodologies such as RCTs, cohort studies, and so on to explore these causal hypotheses. In the context of clinical care, cases can allow health care providers to identify a cause that can be manipulated to cure (or prevent) the condition in question, in order to treat ill patients, even in the absence of more rigorous forms of evidence.

Diagnosis is the process through which a clinician determines what is wrong with a patient who is ill or ailing in some way. Although a critical part of the practice of medicine, it has been relatively neglected in the literature of philosophy of medicine particularly in comparison to more statistically-based methods for evaluating evidence in other fields (Stanley and Campos 2013). The key philosophical issues that arise in this context relate to how such determinations can be made in a manner that is accurate given the high amount of uncertainty and complexity often associated with the human condition, and hence involve logical, epistemological, and ontological issues. The usual way of proceeding in a clinical setting is to ask the patient to articulate what is ailing him or her, and thus to use a standardized reporting format to detail various symptoms which represent subjective manifestations of the illness or disease. In addition, clinicians perform various tests and examinations that allow more objective manifestations or signs to be recorded, such as heart rate, blood pressure and count, reflexes, and so on. A perennial debate in the philosophy of medicine is what constitutes symptoms and signs and whether they are in fact distinct, which relates to deeper issues about the realism of disease conditions as discussed above ( Section 2 ).

The tricky part of the process is to find a means for mapping these symptoms and signs onto a particular disease condition. Some would advocate that this process is no different than usual methods in philosophy of science for hypothesis generation and testing based on evidence, and this type of model fits with what is termed differential diagnosis. Differential diagnosis involves a set of hypothetical explanations for a particular condition which come to be ruled in (or out) based on the evidence together with additional data that is collected, hence relying on a form of reasoning via decision nodes or algorithmic pathways (Stanley and Campos 2013). However, the details of the rules of reasoning that underlie this sort of process remain largely unarticulated, as does the amount of “tacit” knowledge that may contribute to diagnostic reasoning.

There are various ways in which diagnosis is taught and operationalized in clinical settings: in some subspecialties in particular, “pattern” recognition often using pictorial representations seems to be common, and hence diagnosis is a form of recognizing repeated patterns. However this approach can be dangerous particularly among novices, given the large number of similar patterns among common diseases. Some have claimed that the making of a diagnosis is both a deontic act and computable, and that diagnoses are relative only inasmuch as they occur in a complex context which in turn makes them a social practice (Sadegh-Zadeh 2011). Computer-assisted diagnostic techniques have improved and are used increasingly in clinical settings; Kenneth Schaffner (1981) provided an early analysis of the criteria which an ideal diagnostic logic would need to satisfy (for updated discussions see Schaffner 1993, 2010, and for arguments about the limitations of such types of diagnosis see Wartofsky 1986). In recent years there is a relative consensus among medical professionals and those involved in medical informatics that medical diagnosis almost certainly relies on some form of “fuzzy logic” (e.g., Sadegh-Zadeh 2000; Barro and Marin 2002).

As we have seen in Section 5 , hierarchies of evidence in evidence-based medicine rank study results from “systematic” clinical research such as RCTs and observational studies higher than “unsystematic” expert opinion. The epidemiologists who initiated the formal EBM movement in the early 1990s had good reason to be skeptical about expert opinion. When therapies are subjected to systematic tests, tradition and expert opinion are sometimes shown to be flawed. John Worrall discusses three examples: grommets for glue ear, ventricular ectopic beats repressing substances such as encainide or flecainide for cardiac arrest, and routine fetal heart rate monitoring to prevent infant death (Worrall 2007a: 985). In each case we have a procedure the effectiveness of which is indicated by common sense and knowledge about the patho-physiological pathways—glue ear is a condition produced by a build-up of fluid in the middle ear that is unable to drain away because of pressure differentials, grommets act by letting air into the middle ear and thereby equalizing pressure, for instance—but which, when tested by a randomized trial, turns out to be ineffective at best and positively harmful in the worst case.

Misjudgments concerning the efficacy of therapies for purely epistemic reasons are not the only worry that one might have about expert opinion. Medical experts and patients are in what economists call a principal-agent relationship. The principal—in this case, the patient—desires the delivery of a certain good or service—in this case, his health. He instructs an agent—in this case, the doctor—with it, because he lacks the expertise to produce the good himself. The good can only be produced with uncertainty: no therapy is 100% effective. Moreover, the success at delivering the good depends in part on the agent’s effort. The doctor may not always choose the optimal therapy for a patient (we can suppose that it takes some effort to select the optimal therapy for a patient), and any therapy can be implemented sloppily. Moreover, lacking expertise, the patient cannot observe the level of effort a doctor puts in. He therefore cannot design a contract that makes payments dependent on level of effort (much less on success, as success is in part influenced by factors outside of either party’s control). Agents therefore have an incentive to cheat: not to put in the level of effort required to select and deliver the optimal therapy from a patient’s point of view.

If patients and doctors were perfectly rational and motivated only by their own material welfare, and in the absence of regulation, there simply wouldn’t be a market for health services. Doctors would choose therapies that are best for them and not for patients, and patients would anticipate this behavior and stop seeking doctor’s services in the first place. In our world, neither patients nor doctors are particularly rational, nor are they motivated purely by self-interest, there are ethical codes such as the modern form of the Hippocratic Oath, and the health sector is one of the most regulated industries of all. All this does not, however, change the incentive structure in which doctors and other providers of health services operate. Because they and not the patients are experts, they have incentives to choose therapies that are in their best interests and not in the patients’ interests.

There is a further complication. Many, probably most, doctors have connections to the pharmaceutical industry in one form or another. According to one study, 94% of U.S. physicians receive financial benefits from the pharmaceutical industry (Bekelman et al. 2003). Even if we suppose that doctors do not prescribe a therapy because they are paid to do so, marketing efforts directed at them will influence treatment recommendations, if only because they know certain pills better than others, or because some treatments are at the top of their heads.

For all these reasons, the EBM principle that treatment decisions should be based on the best available evidence from systematic research does not come out of nowhere. If, say, there is an RCT or an observational study that reports that treatment X is more effective at relieving symptoms S than treatment Y , it would seem bad to recommend a patient who suffers from S to take Y because his GP doesn’t know about X , doesn’t know the study result, personally profits from prescribing Y or is inattentive. However, while these are all bad reasons to recommend Y over X in the light of the study result, there may be a variety of good reasons.

As discussed in Section 5 , RCTs and many observational studies are population-level studies, which produce average results that are not straightforwardly applicable to individuals. If, say, treatment X reduces the risk of suffering from some adverse event over a period of time by 50% in population p , that is, the risk ratio (RR) for this treatment is 50%, then there may be no individual in p for whom the treatment halves the risk. Instead, the RR may vary dramatically among the subpopulations of p , and it may well be the case that Y is more effective than X for some subpopulations.

The same is true of side effects. Tonelli (2006) discusses a case where a patient who suffers from multiple sclerosis receives a treatment that does seem to alleviate her symptoms, but since she has started taking it, she has been plagued by severe episodes of depression. Clinical trial results indicate that the drug is effective in treating multiple sclerosis, and no adverse psychiatric effects have been reported. Her GP and her psychiatrist now debate whether to continue the treatment. There are various reasons why the clinical studies do not show evidence of mental health effects: the trial subjects weren’t properly screened for depression; adverse effects were found but not reported; the adverse effects were not statistically significant—but they may have been clinically significant for some subpopulations; the side effects only obtain in populations that differ from the trial populations.

This case shows that a treatment’s effectiveness in relieving the symptoms of the disease for which it was prescribed is not the only consideration when making a treatment decision. The goal of the treatment is to improve the patient’s wellbeing, which is well recognized by the proponents of EBM. A patient’s wellbeing has many components, of course, and the symptoms of any given disease are at best one element in its determination. This is another reason why clinical judgment must be exercised in the derivation of a treatment recommendation.

Unfortunately, experts—like all humans—are notoriously bad decision makers. Cognitive psychologists have established a large number of cognitive biases to which human experts are subject: they suffer from overconfidence (e.g., Dawes and Mulford 1996) and hindsight bias (e.g., Fischhoff 1975; Hugh and Dekker 2009); are regularly outperformed by simple mechanical algorithms (e.g., Grove and Meehl 1996); commit the conjunction fallacy (Tversky and Kahneman 1983; Rao 2009), and many others.

To give an example for a simple mechanical algorithm outperforming experts, consider the Goldberg Rule, according to which a patient is to be qualified as neurotic if $x = (\textrm{L} + \textrm{Pa}+ \textrm{Sc}) - (\textrm{Hy} + \textrm{Pt}) > 45$ (where L is a validity scale and Pa, Sc, Hy, and Pt are clinical scales of a Minnesota Multiphasic Personality Inventory or MMPI test) and as psychotic otherwise. Lewis Goldberg tested the rule on a set of MMPI profiles from 861 patients who had been diagnosed by the psychiatric staff in their hospital or clinic and found it to be 70% accurate; clinicians’ accuracy ranged from 55% to 67% (Goldberg 1968; for a discussion, see Bishop and Trout 2005).

There is no one strategy to deal with the various biases and interests that affect clinicians’ judgments. Better numeracy and statistical training at universities can help to eliminate some cognitive biases (Gigerenzer 2014). Computer-aided medical diagnosis and decision making may alleviate others. No training or computer program can make normative judgments, however, and neither will help with adverse incentive structures and financial interests. These difficulties also beset committees of medical experts to which we are turning next.

One way to help overcome expert bias is by making medical decisions not dependent on individual expert judgments but instead have groups of experts coming to some form of aggregate judgment. The U.S. National Institutes of Health, for instance, used to organize so-called consensus conferences designed to resolve scientific controversy. Panel members are chosen from clinicians, researchers, methodologists and the general public. Federal employees are not eligible, nor are researchers who have published on the subject at hand or have financial conflicts of interest (Solomon 2007). These exclusions are intended to contribute to controlling government influences as well as any biases due to financial or intellectual interests.

Consensus conferences and other mechanisms for reaching group judgments are clearly no panacea. Miriam Solomon (2015), for instance, argues that consensus conferences tend to “miss the window of epistemic opportunity” in that they often take place after the medical community has already settled an issue. More important in the present context is the observation that while these conferences possibly help to control some forms of partiality, they are ineffective in reducing others and may be responsible for the introduction of new biases. One concern is that panel members may read the existing evidence selectively, for instance, because of weighing salient studies or studies that are available to them more heavily. Another is that phenomena such as groupthink (Janis 1982) and peer pressure may influence results. In an NIH consensus conference panel members have to come to a verdict after only two days of hearings and deliberations. Under these conditions it is certainly possible that more outspoken panel members or those who perform well under extreme pressure have a undue influence on results. Moreover, it is not clear that excluding clinicians who have published on the issue at hand is always such a good idea. After all, it is not implausible to maintain that those scientists who actively work on a research topic are those who best understand it and therefore can make the best informed judgments. For these and other reasons, Solomon (2007, 2015) explores the consequences of judgment aggregation. In this process group members typically do not deliberate but instead cast their opinions which are then aggregated using some pre-determined procedure. The majority rule would be a simple example of such a procedure.

Coming to a group judgment using a mechanical procedure such as majority vote has a number of advantages. First, there are epistemic advantages that can be illustrated by Condorcet’s Jury Theorem. This theorem shows that if (a) the judgment concerns a proposition that can either be true or false, (b) jury members have an independent probability $>.5$ that they get the judgment right, (c) the individual judgments are aggregated using majority vote, then the larger the jury, the more likely it is to reach the correct group judgment. Under these conditions, then, a committee of experts is likely to make a better judgment than a single expert. Moreover, in the absence of deliberation and pressure to come to a unanimous results, and when voting is secret, the influence of groupthink, peer pressure etc. is attenuated or eliminated.

When conditions (a)–(c) do not hold, results are more ambiguous or even negative. When experts are not reliable, i.e., the individual probability of getting the judgment right is $<.5$, the larger the group, the less likely is it to reach a correct group judgment and the optimal group size is a single expert. When the outcome can have more than two values, inconsistent results can obtain. This can easily be demonstrated with an example in which there are three possible outcomes and three experts. Suppose, for instance, that a panel has to decide which of three treatments A , B and C is the most effective in treating some disease. The individual panel members have the following individual rankings:

Expert I: $A > B > C$

Expert II: $B > C > A$

Expert III: $C > A > B$,

where “>” means “more effective”. There is now a majority that holds that A is more effective than B (I&III), a majority that holds that B is more effective than C (I&II) and a majority that holds that C is more effective than A (II&III). More generally, whenever there are logical relations among the propositions to be decided (in this case: $A > B$ and $B > C$ implies that $A > C$), there are at least three panel members, and votes are aggregated by the majority rule, inconsistencies can arise at the group level (Pettit 2001).

The majority rule is of course only one way to aggregate judgments. The Delphi method (e.g., Dalkey and Helmer 1963; for an application to medicine, see Jones and Hunter 1995) applies to cases where the task is to provide a numerical estimate of some variable of interest (say, the risk difference a new treatment makes). Experts answer questionnaires in several rounds. After each round, a facilitator provides an anonymous summary of the experts’ estimates from the previous round and the reasons given for their judgments. Experts are thus supposed to be encouraged to revise their earlier answers in light of other experts’ estimates and justifications. During this process the range of the estimate will often decrease, and it is hoped that the group will converge towards the correct answer. The process is stopped after a pre-determined stopping criterion such as number of rounds, achievement of consensus, stability of results, and an average of the estimates of the final round is used as result.

Solomon (2011, 2015) raises a fundamental issue concerning group judgments that is entirely independent of the specific method used. She argues that we do not often find group judgment methods to determine the truth of scientific hypotheses or estimates of variables in the natural sciences (though see Staley 2004). If there is uncertainty about, say, which of two alternative hypotheses is true or what value a natural constant has, scientists go out and test, experiment, measure. Controversies, in other words, are settled on the basis of evidence, not (individual or group) opinion. Shouldn’t we, with the advancement of evidence-based medicine, expect the same to happen in medicine? Consequently, she recommends more widespread use of mechanical techniques for amalgamating evidence such as meta-analysis in lieu of consensus conferences and the like.

The frequency of NIH consensus conferences has indeed markedly declined in recent years (Solomon 2011, 2015). But this is of course no reason to maintain that group judgments are no longer needed. Consensus conferences may be the wrong tool for the purposes of the NIH, or the NIH may have a mistaken view about the ability of evidence to settle disputes adequately. Indeed, there are at least two reasons to believe that group judgment procedures are here to stay.

The first reason is that, as we have seen above, medical decisions are always in part decisions about normative matters. No treatment is entirely without side effects and so if judgments about efficacy are to be of practical guidance, they must include a weighing of benefit (alleviation of disease symptoms) against cost (suffering from side effects)—even if economic costs and benefits are not to be taken into consideration. Second, government agencies such as the U.S. Food and Drug Administration (FDA) have to decide whether new treatments should be licensed to be marketed. These decisions often have significant consequences, and democracies tend to prefer to be able to hold someone accountable for making them. Drug approval therefore cannot be determined on the basis of evidence according to some mechanical algorithm.

Biddle (2007) discusses epistemological and moral issues of drug approval in the context of a case study on Vioxx, an analgesic. Vioxx was approved by the FDA in 1999 but five years later pulled from the market by its manufacturer Merck due to safety concerns. It is estimated that some 55,000 people died from taking the drug (Harris 2005). Biddle observes that the FDA is not sufficiently independent of the pharmaceutical industry to make unbiased decisions likely. Many of the members of the FDA’s drug approval committees have financial conflicts of interests (often in the form of receiving benefits from the pharmaceutical company whose drug is to be approved), and a large number of employees of the FDA are dependent on the “user fees” industry pays to help cover the cost of drug approval. To solve these problems of conflicts of interests, Biddle proposes to institute an adversarial system in which two groups of advocates, a group of representatives of the manufacturer and a group of independent scientists, would argue before a panel of judges over whether a drug should be allowed on the market. The panel of judges in this model also consists of independent FDA or university scientists. He argues that the adversarial system would better acknowledge the fact that an increasing number of medical researchers have financial ties to the pharmaceutical industry by treating them as advocates rather than disinterested experts. (See also Reiss and Wieten 2015, Reiss forthcoming-b.)

There is no doubt that medical research is shaped by various external values, in ways similar to the value ladeness that is well-recognized in other areas of science (see entry on scientific objectivity ). Many of these values create a variety of ethical dilemmas relating to equity of access to health care and similar. Even in recent years once medical research has been made more inclusive, this trend has introduced a host of additional philosophical and ethical issues (Epstein 2007). For our purposes, we will focus on the implications of the systematic exclusion of certain types of individuals, groups, or diseases from research for future research as well as clinical medical practice in terms of the validity of evidence produced and decisions made based on that evidence.

In traditional medical research, it was generally assumed that white male participants could be used as the basis of generalizations that in turn could be extrapolated to all other populations, including minorities and females (Dresser 1992). Reviews of the literature indicate that women in particular have been excluded (especially older women), and that research on women has usually been related to reproductive function and capacity (Inborn and Whittle 2001). Such types of research have been argued to fail the ideals of quality medical research as well as evidence-based health care (Dodds 2008). Although some improvements have been made in recent years, there remain certain forms of blanket exclusions for instance of women of childbearing age or pregnant women in many types of medical research. These types of systematic exclusion are highly problematic especially because there is clear evidence of critical differences between men and women with regard to a range of factors relating to receptivity to therapies for both biological and social reasons.

In the case of minorities such as African-Americans in the United States, even when research trials seek to recruit them, a range of factors may contribute to them not being involved in medical and other types of research studies. These include distrust due to historical and institutional racism including research performed without consent; lack of understanding about research and consent; social stigma; financial considerations; and lack of culturally-sensitive recruitment methods by researchers (e.g., Huang and Coker 2010). Such gaps in medical research potentially lead to use of treatments or therapies that may in fact be harmful for particular groups, and may result in the withholding of therapies that might be beneficial.

Medical research also is affected by which conditions or diseases are selected for investigation (Reiss and Kitcher 2008): perhaps most notoriously, “orphan” diseases which are either rare, common only in minority populations, or only present in certain developing world or other lower socioeconomic settings, are often neglected for drug and other therapy development because it is perceived that there will not be a viable commercial market for any products on which research might be done due to the at-risk or affected population (and hence such potential products are often termed “orphan drugs”). In some cases patients may pursue “off-label” use of drugs which are approved for a condition other than the one they have, because approval for an “orphan” disease is unlikely due to cost and demand; however such off-label uses of drugs even when overseen by a physician typically result in lack of consistent collection of evidence and absence of typical risk-benefit regulatory considerations utilized when a drug is approved for particular purposes.

A final way in which our knowledge in medicine generated by research is potentially adversely affected by values is through the funding patterns connected with research. As implied above, pharmaceutical companies sponsor a considerable portion of drug trials and have a variety of interests at stake in these investments well beyond the gathering of evidence for the effectiveness (or lack thereof) of a particular product. There is consistent evidence that negative research results typically are suppressed when sponsored by industry (Lexchin 2012a), leading to a bias in what is reported and thus what evidence is available on which to make prescribing and treatment decisions. Bias also has been found in a number of other areas: within the study itself in the choice of research question or topic of investigation, in the choice of doses or drugs against which the drug under study is to be compared, in the control over trial design and various changes in protocols, and in decisions to terminate clinical trials early, and in the reinterpretation of data, as well as in the publication of data such as restrictions on publication rights, use of fake journals, favoring journal supplements and symposia rather than peer review venues, the use of ghostwriting, and in the details of the reporting of results and outcomes (Sismondo 2008; Reiss 2010b; Lexchin 2012b). All of these issues weaken the evidence base on which clinical care judgments are made, and also lead to potentially adverse effects for patients.

In order to evaluate medical outcomes quantitatively, they have to be measured. There are numerous reasons for aiming to quantify medical outcomes. We may want to compare two or more treatments with respect to their efficacy at relieving certain symptoms or their ability to prevent deaths due to a certain disease. When resources are scarce, we may not only want to invest in treatments that are efficacious (that is, they do improve patient morbidity, mortality or both) but also efficient (that is, it is more efficacious than other treatments relative to the cost of procuring it). For matters of international comparison, development and international justice, we also want to have measures of disease burden : Which of a number of tropical diseases has the highest cost in terms of increased morbidity and mortality? For each research dollar spent on treatments for disease X , how much can we expect to reduce the morbidity and mortality it causes?

Clinical trials now often report so-called patient-reported outcome measures or PROMs. A PROM is a questionnaire given to patients to evaluate certain aspects of their quality of life, functioning or health status after a medical intervention without interpretation of the patient’s response by a clinician or other people. It might ask, for example, how difficult patients find it to climb up a flight of stairs after hip surgery or whether or not a cancer treatment helps them to pursue their hobbies. The main goal of a PROM is the assessment of treatment benefit or risk in cases where the medical outcome is best known by the patient or best measured from the patient perspective.

PROMs can vary considerably in length and complexity depending on the concept that is being measured. In simple, straightforward cases (e.g., intensity of a certain kind of pain), a single question might suffice. In others, it may be necessary to address several aspects of a more complex functioning with a number of questions each. Either way, the design of the questionnaire should make sure that the instrument reliably measures the concept of interest. The FDA distinguishes the following six measurement properties or “tests” (FDA 2009: 11):

Test-retest or intra-interviewer reliability (“Are the scores stable over time when no change is expected in the concept of interest?”)
Internal consistency (“Is there a high correlation between the responses that purport to measure the same concept?”)
Inter-interviewer reliability (“Is there agreement among responses when the PROM is administered by two or more different interviewers?”)
Content validity (“Is there evidence that the instrument measures the concept of interest?”)
Construct validity (“Is there evidence that the relationships between responses conform to expectations?”)
Ability to detect change (“Is there evidence that the instrument can identify differences in scores over time in individuals or groups who have changed with respect to the concept of interest?”).

Despite their plausibility, these tests are not methodologically innocuous. Content validity, for instance, is assessed on the basis of qualitative research in the form of patient interviews, focus groups, and qualitative cognitive interviewing (the latter refers to a method that asks respondents to think aloud and describe their thought processes as they answer the instrument questions and involves follow-up questions in a field test interview to gain a better understanding of how patients interpret questions). This qualitative research aims to develop questions with standardized meanings that are shared between patients and clinicians. Arguably, however, there will always be differences in interpreting phrases such as “bodily pain” or “difficulty in lifting one”s arm’ because they refer to a patient’s experiences, and these will differ from patient to patient and, in a given patient, from time to time (Rapkin and Schwartz 2004). Moreover, there may be good philosophical reasons to allow for the expression of a sufficient array of legitimate perspectives on health and quality of life instead of insisting on a standardization of meaning across patients and contexts (McClimans 2010). Similarly, internal consistency can be desirable only to the extent that the concept is a relatively simple one and different questions really do address the same concept. It is of less relevance when the disorder is heterogeneous (McClimans and Browne 2011). These kinds of worries can be raised with respect to each of the measurement tests. Finally, there is an issue when several PROMs that address a given disorder or treatment exist. Different PROMs will score differently with respect to the different tests, and there is no universally valid schema to weigh their relative importance ( ibid .).

Disability-adjusted life years or DALYs aim to measure burden of disease. The measure was originally developed by Harvard University for the World Bank and World Health Organization (WHO) in 1990 and is now widely used by heath policy researchers for comparisons between countries and over time and as a tool for policy making. It can also be used to measure the effectiveness of interventions, though these are usually health policy rather than medical interventions narrowly construed. The WHO makes regular global disease burden estimates in terms of DALYs at regional and global level for more than 135 causes of disease and injury (Mathers et al. 2002).

The principal idea behind DALYs is simple. If a woman in Guatemala dies of Chagas disease at age 63, this adds 20 DALYs to the global disease burden because her death is 20 years “premature” as compared to Japanese life expectancy (which is taken as the standard because it is the highest world wide). If a man in Hamburg has an accident that confines him to the wheelchair for the rest of his life, this contributes 0.57 DALYs for each of his remaining life years because the weight for paraplegia is 0.57. Every kind of disease or impairment is thus given a number between 0 and 1 (where 0 = full health and 1 = death) that makes it comparable to other conditions. For example, blindness has a weight of 0.43. As blindness contributes less to the burden of disease than paraplegia, this means that blindness is regarded as the less severe of the two in terms of its reduction of functional capacity (Prüss-Üstün et al. 2003).

The simple idea is complicated by two adjustments, however. Typical burden of disease studies weigh an impairment differently depending on the age of the person whose functional capacity is impaired by the disease or disability. Blindness, say, has a greater impact on the burden of disease if it occurs at age 20 than if it occurs at very young or older ages (Prüss-Üstün et al. 2003). Moreover, if the man who has the accident now can be expected to live with the disease for 30 years, future years of disability are discounted by a factor. The further into the future a disability occurs, the less it contributes to the disease burden ( ibid .).

The adequacy of any socio-economic indicator has to be evaluated in the light of the purpose that it is meant to serve (Reiss 2008). If DALYs are supposed to measure the everyday concept “burden of disease”, we may criticize, for instance, that the indicator fails to take account of societal, cultural, climatic and other variations within which the disease or disability occurs. Being paraplegic, say, is less burdensome when it occurs in societies that spend more resources on making public buildings and transport wheelchair accessible, that display more tolerance towards the handicapped, or in flatter than in hillier regions. Arguably, therefore, DALYs measure ill-health rather than disease burden (Anand and Hanson 1997). Similarly, because ill-health is measured as a percentage, a disease occurring in a person who is already handicapped contributes less to the measure than the same disease occurring in an otherwise comparable but not handicapped person. If DALYs are used to make public health decisions, however, it might be better to prioritize those individuals who are least well off instead of those who are relatively better off ( ibid .).

The WHO is very explicit that numerous choices made in the construction of the DALY measure are value-based (Murray 1994; Prüss-Üstün et al. 2003). Clearly, there is no matter of fact whether paraplegia constitutes a more severe impairment of someone’s functional capacity than blindness, much less the precise extent to which it contributes to the burden of disease. The same is true of the duration of the time lost due to premature death, age weights and time preference. While any given choice will, due to its value-laden nature, be controversial, the WHO makes some efforts to represent societal preferences instead of, say, a priori philosophical arguments. For example, the disability weights used in the 2003 World Health Survey were based on health state valuations from large representative population samples in over 70 countries (Prüss-Üstün et al. 2003: Ch. 3). Similarly, age weights are based on empirical studies that have indicated there is a broad social preference to value a year lived by a young adult more highly than a year lived by a young child, or lived at older ages (Murray 1996).

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Narrative Medicine: Every Patient Has a Story

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Reflective writing also provides a “safe space” for students to discuss the stresses of medical school and their professional fears, she added.

Jake Measom, a fourth-year medical student at UNR, said that participating in the narrative medicine scholarly concentration has pushed him to be more creative in his approach to patient care. He also sees narrative medicine as a “remedy to burnout,” noting that while the practice of medicine can sometimes feel monotonous, narrative medicine reminds him that “there’s a story to be had everywhere.”

“It not only makes me a better physician in the sense of being able to listen better and be more compassionate,” he said, “it also helps you gain a better understanding of who you are as a person.”

Storytelling as a means of coping

“ First you get your coat. I don’t care if you don’t remember where you left it, you find it. If there was a lot of blood, you ask someone to go quickly to the basement to get you a new set of scrubs. You put on your coat and you go into the bathroom. You look in the mirror and you say it. You use the mother’s name and you use her child’s name. You may not adjust this part in any way.”

That’s an excerpt from “How to Tell a Mother Her Child Is Dead,” which was published last September in the New York Times in the Sunday Review Opinion section. Authored by Naomi Rosenberg, MD, a physician at Temple University Hospital, the piece is a heart-wrenching example of how narrative medicine can serve as an outlet for coping with the harrowing experiences that providers regularly encounter.

Pulitzer Prize-winning journalist Michael Vitez encouraged Rosenberg to submit the piece in his new role as director of narrative medicine at Temple University Lewis Katz School of Medicine. After retiring from a 30-year career as a reporter at the Philadelphia Inquirer , Vitez approached the school’s dean about using his skills to help students, faculty, and patients translate their experiences into words. The idea morphed into Temple’s new Narrative Medicine Program, which launched in 2016.

Currently, the Temple program is fairly unstructured, with students and faculty working one-on-one with Vitez on their narrative pieces. For example, Vitez said a third-year medical student recently sent him a poem she wrote after an especially difficult day in her psychiatric rotation: “It helped her process her emotions and turn a really bad day into something really valuable,” he noted. Eventually, Temple hopes to offer a certificate and master’s degree in narrative medicine.

“I believe that stories have an incredible power,” said Vitez. “Understanding what a good story is and learning how to interview and ask questions will help you connect with your patients, understand them, and build relationships with them.”

Jay Baruch, MD, associate professor of emergency medicine at Brown’s Warren Alpert Medical School and faculty advisor to the narrative medicine course there, likewise maintains that the type of creative thinking often associated with the arts and humanities—and that narrative medicine often promotes—deserves a more central role in medical education.

“[Students and physicians] need to know the anatomy of a patient’s story just as much as the anatomy of the human body,” he said.

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‘The Scientist and the Poet’ is an essay, written by English professor Cantor 1 in The New Atlantis , which draws on tensions between how scientists and poets see the world. He writes, ‘Poets generally seem to be unsympathetic to science; they question its capacity to tell us the full truth about our world.’ This is a notion worth considering for medical doctors and leaders in healthcare. Why would the poets think that objective science would be less than revealing of the full truth? The piece moves through excerpts of works from famous Romantic poets, beginning with Goethe—a poet and scientist—and ends alarmingly with reflections from Shelley’s Frankenstein . 2 Cantor’s critique of science through Frankenstein is that science is ‘good’, provided it is oriented towards the humane.

In Frankenstein , science is a victim of its own power over nature. In the novel, the protagonist Victor Frankenstein tells of his dedication to science, his study of chemistry and natural philosophy at the university, and his commitment to scientific research. But in the course of practicing science, somehow the power of science escapes Victor’s control. Cantor 1 concludes:

The basic lesson Frankenstein can teach us is this: science can tell us how to do something, but it cannot tell us whether we should do it. To explore that question, we must step outside the narrow range of science’s purely technical questions and look at the full human context and consequences of what we are doing .

What Cantor is capturing here is a philosophical—not a scientific—truth: ‘good’ medicine should be concerned with the health of the human person, 3 not just the stuff they are made of. If good medicine is caring for persons, not bodies, then physicians have something relevant to learn about persons from the poet, novelist, essayist, philosopher, theologian and the patient-person before them. Yet, medical journals, education and training have a difficult time incorporating personhood from literature, poetry, history, anthropology, archaeology, philosophy and religion into the practice of medicine. Why is this? Is there some rule that medicine should only draw on the natural sciences of mathematics, physics, chemistry and biology? Interestingly, the prominent and respected JAMA includes a very small section titled ‘Humanities,’ which appears dead last, after all the scientific-technical medical studies. A charitable view would suggest that ‘Humanities’ is last because medicine’s goals are found in the humanities, in persons.

Perhaps the tension between the scientist and the poet can be resolved through a union of the two views: the physical (the body) and the metaphysical (the person). But what can unite these disparate perspectives? I propose philosophy—the ancient discipline by which mankind has inquired about the world in all its dimensions—has the power to unite the sciences and the humanities together again.

Today, medical leaders are participating in an industry dominated by the production of science and technology. But what is scientifically possible for the body and what is humane for the person are different questions which medicine must answer together. My aim for this essay is to ask medical leaders a basic question in the philosophical spirit of Socrates: ‘What is medicine for?’

The ancient philosopher Aristotle teaches us that medicine is for health, not in relation to the body alone, but in relation to the person who seeks happiness in human life through the pursuit of virtuous activities. In his Nicomachean Ethics he says, ‘For the life of the man who is active in accordance with virtue will be happy’, and ‘By human virtue we mean not that of the body’ . 4 So, we discover that virtue, not bodily health, is what provides happiness to the person. Before Aristotle, Socrates states through Plato’s Apology, ‘I say that it is the greatest good for a man to discuss virtue every day’. 5 For Socrates, virtue is the greatest good in life, and for Aristotle, the virtuous person is a happy person. So, according to these three ancient philosophers—Socrates, Plato and Aristotle—it is virtue, not health, that is essential to the happiness and well-being of persons.

Now what should we make of ‘health’ in this schema of virtue and happiness of the person? Is medicine only concerned with ‘bodily health’ as the final good of the profession, or does medicine further encompass the ‘health of the person’ as this final good? Aristotle does not resolve this question. If medicine is oriented towards the health of persons, then the metaphysical goods of health (such as virtue and character, which bring happiness) are essential to the practice of medicine. For this reason, the Socratic philosophers spoke of virtue as the end of mankind, not bodily health. To make bodily health the end of medicine would be to ultimately arrive at death as the end of medicine—a tragic end. If, however, the end of medicine is persons seeking virtue in life, then medicine is not a tragic profession. Taking the goal of medicine to be health and happiness of human persons, what then should medicine learn from the wisdom of the humanities about persons, which cannot be taught through the sciences about bodies?

To begin to explore this question today, medical leaders should go back in time—for the humanities love history—to London on the 28th of May in the year 1934 to Sadler’s Wells Theatre. ‘The Rock,’ by the great poet Eliot 6 is making its debut. It is a time of great industrial and scientific progress in Europe, much like today, but the opening stanzas of this play arrest the listening audience with what has become lost in progress:

The Eagle soars in the summit of Heaven, The Hunter with his dogs pursues his circuit. O perpetual revolution of configured stars, O perpetual recurrence of determined seasons, O world of spring and autumn, birth and dying The endless cycle of idea and action, Endless invention, endless experiment, Brings knowledge of motion, but not of stillness; Knowledge of speech, but not of silence; Knowledge of words, and ignorance of the Word. All our knowledge brings us nearer to our ignorance, All our ignorance brings us nearer to death, But nearness to death, no nearer to God. Where is the life we have lost in living? Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? The cycles of heaven in twenty centuries Bring us farther from God and nearer to the dust. 6

In Eliot’s poem, we find that science and industry are perpetually busy, participating in the ‘cycle of idea and action, endless invention, endless experiment’. But there is a tragic punchline to this progress, a paradox that arrests the modern sensibility in the closing stanzas of the poem: ‘Where is the life we have lost in living? Where is the wisdom we have lost in knowledge ? Where is the knowledge we have lost in information ?’

Information should lead us to knowledge, then to wisdom and from wisdom to life. This is how the ancient philosophers mentioned earlier thought about life. Life is more than just bodily biological status; it holds an almost timeless, transcendental, divine, and metaphysical quality. The poet, however, shows that humanity, through its achievements, is not moving upwards towards higher truths, towards God and Life, but is moving downwards ‘nearer to our ignorance’ and ‘farther from God and nearer to dust (death)’. 6 It is a mortifying vision of the goals of science and technology achieved apart from wisdom. Could Eliot’s poem also be a mortifying vision of modern medicine?

Consider an analogy. Modern scientific medicine is like a massive boat propelled by cutting-edge technology. The boat is impressive, moves expediently, travels great distances, but in which direction and towards what destination? Science and technology alone cannot know. The metaphysical thing you do not see, which directs the boat to its proper, good destination, is the rudder of philosophy. This philosophical rudder is essential for aligning the direction of science and technology with the good goals of medicine. Who is attending to the philosophic rudder of medicine today?

The powerful and profitable tools of science and technology, though necessary to medicine as a profession, have created a myopic lens through which medicine’s gaze has been captivated for at least a century and probably much longer. 7 Steinbeck, 8 in The Grapes of Wrath, reflects on the distorted nature of man who is unwisely bound up in technology and biochemistry separate from virtue:

Carbon is not a man, nor salt nor water, nor calcium. He is all these, but he is much more, much more; and the land is so much more than its analysis. That man who is more than his chemistry…that man who is more than his elements knows the land that is more than its analysis. But the machine man, driving a dead tractor on land he does not know and love, understands only chemistry; and he is contemptuous of the land and of himself.

Aiming at the goal of bodily health, sought primarily through scientific and technological means—and absent the wisdom of personhood—only frustrates physicians and their patients when bodily health is not achieved. When bodily health fails—in spite of real fourfold and sixfold per capita increases in medical expenses and technology in the UK and US, respectively, over the past 50 years 9 10 —the disenchantment of medicine 11 naturally follows. Ironically, medicine’s disenchantment has been wrought through extraordinary bodily health gains under this scientific model of medicine. But this model of healthcare ultimately fails when our bodily health fails, as it naturally does with the passing of time. When our bodily health fails, in spite of the best scientific and technological treatments, the physician often becomes contemptuous of the patient and of himself because he is unable to achieve bodily health as the supposed goal of medicine. 12 Instead, practitioners of biomedical healthcare ought to concern themselves with the overall health and happiness of human persons. This requires medicine to also be wise, which means that medicine cannot only study the sciences, but must incorporate the humanities as well.

Nineteenth century theologian and philosopher John Henry Newman wrote to medical faculty and students at a medical school in Dublin about the danger of medicine seeing itself only through a scientific-technical lens, studying only science aimed solely at bodily health. He wrote, ‘Men, whose minds are possessed with some one object, take exaggerated views of its importance, are feverish in the pursuit of it, make it the measure of things which are utterly foreign to it, and are startled and despondent if it happens to fail them’. 13 His antidote for this scientific view of medicine aimed only at bodily health was to ensure the perspective of the medical trainee also included theology and religion to inform the metaphysics of personhood in the clinician. Newman 13 writes that one should strive for an ‘enlargement of the mind’ and that ‘there is no enlargement, unless there be a comparison of ideas one with another’ where a properly formed person ‘possesses the knowledge, not only of things, but also of their mutual and true relations; knowledge, not merely considered as acquirement, but as philosophy’ . Newman writes that this is obtained by having an exposure to the entirety of the liberal arts, including the sciences and the humanities. He cautioned that if medicine were to become ‘self-contained’ within the scientific-technical biomedical silo, absent the humanities, it would only leave the physician unable to be able to become wise.

Policy expert and physician Bulger 14 stated in a 2000 JAMA piece, ‘The greatest challenge facing the academic healthcare community is to restore the marriage between humanistic concerns and scientific and technical excellence in healthcare delivery practices’. The erosion of the humanities in the practice of medicine is associated with the accompanying loss of meaning in medicine and associated levels of physician burnout, 15 loss of the personal relationship between physician and patient due to the ‘medical gaze’ of the physician, 16 and the overall disenchantment of medicine practiced as a hard science. These shortcomings in medicine today can be further evinced through the growing estrangement we see between the physician and the patient, and the associated breakdown in human trust between them. 17 We also believe the loss of spirituality and religion within the teaching and practice of medicine has mostly reduced medicine to a merely scientific endeavour, devoid of greater meaning and purpose. 18 Loss of faith in the integrity of medicine as a moral practice is at stake in the eyes of society. 19 20

All these modern medical wounds have accumulated alongside the massive proliferation of science and technology and healthcare spending. Therefore, we should not expect additional scientific, technical progress and spending to deliver us from our current medical condition. Scientific information, knowledge and technical tools do have the power to diagnose conditions of the body and aid the body in these conditions, but they do not have the power to heal medicine itself because science cannot tell us how medicine ought to be. Science can tell us what is; science alone cannot answer deeper human and philosophical questions of meaning, morality, purpose and ends. 4 In treating whole human persons, not just the bodies, medicine and physicians must be more than technically competent; they must also be wise. Medicine requires more than information and knowledge because medicine is a practice requiring physicians not only to be wise but to practice wisdom.

In his Nicomachean Ethics, Aristotle wrote, ‘Practical wisdom… is concerned with things human and things about which it is possible to deliberate… but no one deliberates about things invariable, nor about things which have not an end’. 4 Here, Aristotle is expressing the essence of a profession which requires wisdom, or deliberation, in aiming at what is good. Aristotle himself says it this way, ‘Practical wisdom cannot be scientific knowledge nor art, [but]… is that true and reasoned state of capacity to act with regard to things that are good or bad for mankind’. 4 Here, we find a helpful philosophical definition of health for physicians: acting with regard to what is good for mankind. In the case of medicine and health, wise physicians must deliberate and act towards what is good for not only their patients’ health, but their neighbours’ health as well. This requires applying knowledge of the highest order, which is what Aristotle calls practical wisdom or phronesis.

Episteme, techne and phronesis are three Greek words describing different categories of knowledge. Thinking about medicine, episteme is bare scientific knowledge: ‘the facts’. Techne involves technology and ‘know-how’. Phronesis, however, embodies the moral grounds of an activity and requires virtue, ethics and judgement. Modern medicine is excellent at both episteme and techne, but knowledge related to wisdom found in phronesis is largely absent from the profession. This is because modern medicine is productive—busy, knowing and doing—but in the midst of all this activity it has neglected the rudder of medicine, philosophy, which directs medicine to its proper ends. 21

Borrowing from CS Lewis, philosopher Kreeft provides a nautical illustration. He describes sailing orders given to captains of a fleet of ships. These leaders first need to know the answers to three types of questions: How are the ships to cooperate? How to stay shipshape? What is the mission 22 ? Modern medicine focuses on the first two questions, but the most important answers are found in the third: What is the mission? Why are they at sea? What is their purpose? These are questions of meaning and purpose, transcendental questions of ultimate goods explored through the humanities, which require wisdom to answer. To the philosopher’s mind—and to the good physician leader—these are the first and most important questions we should be asking. Modern medicine, however, is often last to ask these deeply philosophical questions. Why?

First, the answers to these questions of ultimate meaning lay not only in the humanities at large but in many of the major religions of the world and in the study of theology and through philosophy, from which modern medicine has tragically divorced itself. 23 Second, modern medicine itself is terrified of moral disagreement, so it avoids these deeper questions of virtue, meaning and purpose as they create massive conflict within the profession. Medicine, however, cannot avoid these types of moral-theological questions of meaning and purpose if it is to be wise regarding the health and happiness of human persons. Third, the commercial–financial aspects of modern medicine are compelling forces that distract the physician from deeper, more philosophical questions, that are bound up in medicine that cares for patients as persons, not just bodies of persons. 3

The phronesis—wise practice—of medicine cannot be performed in a merely technoscience model absent philosophy. If science and technology are busy working, then philosophy is necessary to come along side to examine this work. 24 Medical leaders therefore need to develop a philosophical lens of medicine to examine their work. Without such a lens, medical leaders rob themselves of a proper understanding of medicine’s goals related to the health and happiness of human persons, both individually and collectively, in society.

Medicine is a unique vocation that combines philosophic wisdom in the humanities with scientific knowledge and technical skills towards the goal of health and happiness of human persons in society. Good physicians must not only be scientifically and technically competent, but they must also become wise concerning humankind’s ultimate goods: ‘what is good for themselves and what is good for men in general’. 4 Philosophy and the humanities are therefore necessary for physicians to be able to become wise persons in order to be able to wisely practice medicine on persons. 25–31

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Aristotle TJ ,
Tredennick H
Cooper JM ,
Hutchinson DS
Steinbeck J
Pettinger T
Telesford I ,
Rakshit S ,
McGough M , et al
Misselbrook D
Collier KM ,
Saint S , et al
Gunderman RB
Ventres W ,
Wolenberg K , et al
Pellegrino ED
Stempsey WE

Contributors KC, as the sole author, is responsible for the overall content as guarantor. She accepts full responsibility for the finished work and controlled the decision to publish.

Funding The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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10 Successful Medical School Essays

Importance of medicine quality in achieving universal health coverage

Sachiko ozawa.

1 Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States of America

2 Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States of America

Colleen R. Higgins

Tatenda t. yemeke, jude i. nwokike.

3 Promoting the Quality of Medicines (PQM) Program, United States Pharmacopeial Convention (USP), Rockville, MD, United States of America

Lawrence Evans

Mustapha hajjou, victor s. pribluda, associated data.

Data for the indicators from the Global Health Observatory can be found at https://apps.who.int/gho/data/node.imr . Data for the World Bank Worldwide Governance Indicators can be found at https://info.worldbank.org/governance/wgi/ . Data on the prevalence of substandard and falsified medicines for each country analyzed here can be found in the Supporting Information of this manuscript.

To assess the importance of ensuring medicine quality in order to achieve universal health coverage (UHC).

We developed a systems map connecting medicines quality assurance systems with UHC goals to illustrate the ensuing impact of quality-assured medicines in the implementation of UHC. The association between UHC and medicine quality was further examined in the context of essential medicines in low- and middle-income countries (LMICs) by analyzing data on reported prevalence of substandard and falsified essential medicines and established indicators for UHC. Finally, we examined the health and economic savings of improving antimalarial quality in four countries in sub-Saharan Africa: the Democratic Republic of the Congo (DRC), Nigeria, Uganda, and Zambia.

A systems perspective demonstrates how quality assurance of medicines supports dimensions of UHC. Across 63 LMICs, the reported prevalence of substandard and falsified essential medicines was found to be negatively associated with both an indicator for coverage of essential services ( p = 0.05) and with an indicator for government effectiveness ( p = 0.04). We estimated that investing in improving the quality of antimalarials by 10% would result in annual savings of $8.3 million in Zambia, $14 million in Uganda, $79 million in two DRC regions, and $598 million in Nigeria, and was more impactful compared to other potential investments we examined. Costs of substandard and falsified antimalarials per malaria case ranged from $7 to $86, while costs per death due to poor-quality antimalarials ranged from $14,000 to $72,000.

Medicines quality assurance systems play a critical role in reaching UHC goals. By ensuring the quality of essential medicines, they help deliver effective treatments that lead to less illness and result in health care savings that can be reinvested towards UHC.

Introduction

The goal of Universal Health Coverage (UHC) is to ensure that all people obtain the health services they need without suffering financial hardship when paying for them [ 1 ]. Sustainable Development Goal 3.8 supports UHC by aiming to achieve “access to safe, effective, quality, and affordable essential medicines and vaccines for all” [ 2 ]. A successful UHC system is a result of the combination of quality health services and expanding coverage of affordable care.

Quality health services cannot be delivered without quality-assured medicines. Ensuring medicine quality is paramount in providing safe and effective health care and reducing overall health care costs. The World Health Organization (WHO) estimates that, on average, 1 in 10 medical products circulating in low- and middle-income countries (LMICs) is substandard or falsified [ 3 ]. WHO defines substandard medicines as “authorized medical products that fail to meet their quality standards, specifications, or both [ 4 ].” Medical products that “deliberately and fraudulently misrepresent their identity, composition, or source” are classified as falsified [ 4 ]. A recent meta-analysis found that 13.6% (95% CI, 11.0–16.3%) of essential medicines in LMICs were either substandard or falsified [ 5 ], with other literature reviews reporting a comparable range [ 3 , 6 – 8 ]. Large percentages of poor-quality antimalarials (19.1%) and antibiotics (12.4%) were found, with the highest reported prevalence observed in Africa (18.7%) and Asia (13.7%) [ 5 ]. Despite the growing evidence of the problem, the importance of quality-assured medicines and the challenge of ensuring their quality is rarely discussed in UHC planning.

The pharmaceutical system operates within a complex health system, where pharmaceutical good governance can be viewed as a component of health systems strengthening necessary to support UHC [ 9 ]. Ensuring the delivery of quality-assured medicines also requires strengthened governance of medicine procurement systems. Medicine procurement for the public sector is typically handled by the government, where purchase and supply chain delivery are shared between medicines and other health service commodities. Medicines are a leading source of health system inefficiency due to the pervasiveness of inappropriate use, variable quality on the market, and high-priced brand name medicines being preferred over generics despite their bioequivalence [ 9 ]. In addition, availability of unregistered medicines presents alternatives for patients to access and use medicines that are neither included in health systems nor covered on insurance plans. Improving financing schemes and strengthening governance of medicine procurement systems is essential to increasing financial protection and access to health services, which are core tenets of UHC [ 10 ]. Increased health care utilization alone will not result in better outcomes if the quality of services is low [ 11 ]. Similarly, the full benefits of expanded coverage may not be realized without also ensuring the quality of covered medicines [ 12 ].

Financing and procurement of medicines play an important role in UHC schemes, where medicine quality needs to be safeguarded [ 9 ]. Globally, a quarter of all health expenditures are spent on medicines [ 13 ]. The Lancet ’s Commission on Essential Medicines Policies estimated that between $13 and $25 per capita (US$77.4 to $151.9 billion) is required to finance a basic package of essential medicines in all LMICs [ 13 ]. However, the majority of low-income countries and over a quarter of middle-income countries spend less [ 13 ]. Moreover, medicines are often paid out-of-pocket in many countries, putting individuals and households at risk of having poor access to treatments and/or becoming poor due to their costs [ 14 – 16 ]. UHC can improve population health by providing quality health services and quality-assured medicines, while preventing catastrophic medical expenditures for the world’s poorest communities.

This study uses systems mapping to conceptually illustrate the benefits of ensuring medicine quality in UHC. We subsequently examined the association between UHC and medicine quality in the context of essential medicines in LMICs using data on substandard and falsified medicines prevalence and UHC indicators. The study also demonstrates the health and economic costs of poor-quality medicines that could be averted by quality assurance interventions where savings could be reinvested in UHC, using antimalarials as a case study.

Materials and methods

Systems map linking medicine quality with uhc.

Systems mapping has been increasingly used in health to help understand indirect effects in complex systems [ 17 – 20 ]. With more detail displaying associations between variables than in a conceptual framework, systems mapping can show how different parts of a system fit together and interact, which make it a useful tool for understanding linkages that are less frequently explored. An advantage of a systems approach, such as systems mapping, is that it takes the entire system into consideration, which can facilitate understanding of indirect effects and unintended consequences [ 17 ]. Taking a systems perspective is particularly useful for medicine quality due to the variety of processes and stakeholders involved, from medicine manufacturing, to purchase and utilization throughout the supply chain. As medicine quality is not commonly emphasized in UHC planning and policies, we use a systems approach to make a conceptual linkage between medicine quality assurance and UHC, allowing us to explore the potential secondary and tertiary effects of medicine quality assurance systems and interventions on UHC.

This research mapped the series of stages where quality assurance processes and interventions are necessary to ensure the quality of medicines reaching patients, illustrating the flow of medicines from manufacturing to utilization. We illustrated the linkages between health systems and health insurance processes, and the resulting benefits to patients. The systems map also illustrates the three dimensions of UHC, demonstrating how medicine quality assurance processes and interventions, and the ensuing benefits, relate to the UHC dimensions [ 10 ]. Finally, we depicted interventions that target quality assurance processes to illustrate some of the key levers required to ensure quality-assured products in UHC. We developed the systems map through an iterative process. First, the study team mapped the system map elements based on existing literature. We then solicited feedback on the map from external stakeholders with expertise in health systems and medicine quality assurance, including experts from the Promoting the Quality of Medicines (PQM) program at the United States Pharmacopeial Convention (USP), United States Agency for International Development (USAID), and academics at the University of North Carolina at Chapel Hill (UNC). We subsequently revised the systems map based on conceptual feedback by including additional content and linkages between the map elements, and made structural revisions to improve comprehension and readability of the map.

Association between UHC and medicine quality

To relate the conceptual linkage between UHC and medicine quality portrayed through our systems map to real world indicators, we investigated the potential association between UHC and medicine quality indicators utilizing existing data. Two indicators from the WHO were used to determine progress towards UHC: an indicator on essential services coverage; and an indicator on the proportion of the population with large household expenditures on health as a share of total household expenditures [ 21 ]. The WHO indicator on coverage of essential services was calculated from 16 tracer indicators measuring average insurance coverage of interventions in areas such as maternal and child health, infectious diseases, and non-communicable diseases [ 22 ]. The WHO indicator for large health expenditures measured the proportion of the population that spends over 10% of their household expenditures on health services.

In addition, two indicators for government effectiveness and regulatory quality were abstracted from the World Governance Indicators of the World Bank [ 23 ]. The government effectiveness measure combined perceptions of the quality of a country’s public and civil services, independence from political pressures, as well as formulation and commitment to policies. This indicator is also used by USAID to help inform strategic decisions and assess a country’s path to self-reliance [ 24 ]. The second regulatory quality indicator from the World Bank assessed the perception of the soundness of a government’s policies and control over private sector practices. We also retrieved under-five mortality rates for each country from the United Nations International Children's Emergency Fund (UNICEF) [ 25 ].

We examined how these indicators were associated with estimated prevalence of poor-quality medicines for each country, using data on reported prevalence of substandard and falsified essential medicines among 63 LMICs previously gathered from a systematic literature review and meta-analysis [ 5 ]. Substandard and falsified medicines prevalence was defined as the number of failed samples over the total number of samples of essential medicines chemically tested and publicly reported within each country ( S1 Table ). We examined the association between country specific reported prevalence of substandard and falsified medicines and the proportion of essential services covered, government effectiveness, regulatory quality, large health expenditures, and under-five mortality. We used simple linear regression models given the small sample size for a multi-country analysis, using prevalence of substandard and falsified medicines as the dependent variable. We conducted visual tests for linearity and Breusch Pagan tests for heteroskadasticity. Gross Domestic Product (GDP) per capita was assessed as a main confounder in each analysis.

Health and economic impact of poor-quality antimalarials

To illustrate the potential health and economic impact that can result from ensuring the quality of antimalarials, we analyzed the current landscape of the impact of poor-quality antimalarials in a case study. Four countries were included in our analysis: the Democratic Republic of the Congo (DRC), Nigeria, Uganda, and Zambia [ 26 – 28 ]. Malaria is associated with high levels of morbidity and mortality in LMICs, and medicines to treat malaria (i.e. antimalarials) are, within the therapies surveyed, one of the medications most commonly tested and found to be of poor quality [ 5 ]. UHC planning in malaria endemic countries would naturally entail decisions on what malaria medications and services to make available, and what populations will receive these benefits.

Estimates of country-specific impact were obtained from the Substandard and Falsified Antimalarial Research Impact (SAFARI) model, an agent-based model we built that simulates malaria disease progression, care seeking, and outcomes for children under age five [ 26 – 28 ]. The SAFARI model was adapted and run separately for each country using country-specific demographic and epidemiological inputs. Methods for the development of the SAFARI model and individual country data are described in detail in other publications [ 26 – 28 ]. We simulated child agents who sought malaria treatment and received either quality-assured or poor-quality antimalarials. Poor-quality antimalarials reduced treatment efficacy and increased the likelihood of agents progressing to severe malaria. The model tracked agent children’s health over the course of treatments leading to hospitalization, neurological sequelae, death, further treatment, or recovery ( S1 Fig ). The costs incurred from these events were recorded as both direct costs to patients and facilities for the treatment of malaria, as well as the indirect productivity losses incurred from time spent seeking treatment, life lived with a neurological disorder caused by severe malaria, and early death due to malaria.

In order to compare the impact of poor-quality antimalarials across countries, we calculated the potential savings in each country if the reported prevalence of substandard and falsified antimalarials were to be reduced by 10%. We estimated this impact by comparing a baseline scenario using the reported prevalence of substandard and falsified antimalarials, to a scenario where 10% more antimalarials were quality-assured. This provided an estimate of the health and economic benefits a health system would experience by investing in quality assurance mechanisms that increased the supply and utilization of quality-assured antimalarials and reduced use of substandard and falsified antimalarials by 10%. The savings are estimated in fewer deaths, fewer hospitalizations, lower costs of care, and productivity gains simulated when poor-quality antimalarials are replaced with quality-assured antimalarials. The economic impact of poor-quality antimalarials was further assessed at an individual level to demonstrate the cost of poor-quality medicines per malaria case, and the cost attributable to substandard and falsified medicines for each additional malaria death, hospitalization, and disability-adjusted life year (DALY). This was calculated by taking the total savings in direct costs and productivity losses averted from reducing the prevalence of substandard and falsified antimalarials by 10% and dividing it by estimated reductions in deaths, hospitalizations, and DALYs.

To contextualize the impact of improving medicine quality, we compared a scenario in which no poor-quality antimalarials were present, to other options that governments may consider in reducing malaria burden. We simulated two additional interventions: a scenario in which there were no stockouts of antimalarials, and one in which only Artemisinin-based Combination Therapies (ACTs) were taken as first-line therapy. Moreover, low-quality anti-infectives can induce further costs to health systems and society by contributing to the development of antimicrobial resistance. We simulated the impact of widespread ACT resistance in a hypothetical scenario where the efficacy of ACTs were reduced to efficacy of other malaria treatments, impacting the duration and severity of malarial illness among children.

Fig 1 presents the systems map of associations between medicine quality and essential components of UHC. We first mapped the essential steps and processes involved in order for beneficiaries to receive quality-assured medicines (blue ovals). This illustrates the movement of medicines from manufacturing, procurement, and through the supply chain to reach health facilities, after which beneficiaries who seek health care can obtain and utilize medicines. We then mapped the resulting benefits–when beneficiaries utilize quality-assured medicines, as opposed to no medicines or poor-quality medicines, beneficiaries can be healthier with a shorter duration of illness and milder symptoms, thus needing less additional health care and having the possibility to return to work earlier (pink rectangles) [ 26 – 29 ]. For example, using a substandard medicine with inadequate amounts of active pharmaceutical ingredients could add days to recovery time or be completely ineffective, requiring a patient to seek additional care or suffer a longer duration of illness without proper treatment, increasing the severity of disease [ 26 – 28 ]. With quality-assured medicines, beneficiaries may be cured of illness, and may even avert disability or death [ 26 – 28 ].

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Quality-assured medicines bring further benefits (orange rectangles). Appropriate use of quality-assured medicines contributes to maintaining medicine efficacy by delaying the development of antimicrobial resistance [ 29 , 30 ]. When beneficiaries require less health care because they have access and utilize quality-assured medicines, they decrease the risk of becoming poor due to additional expenses for medicines and health care [ 31 ]. Healthier beneficiaries are also more productive in society, resulting in productivity gains. Ensuring medicine quality thus contributes to the overall goal of UHC to ensure health care access without suffering financial hardship when paying for them.

Averting the need for additional health care when beneficiaries are healthier with quality-assured medicines can trigger a number of health system mechanisms (purple rectangles). When beneficiaries require less health care, the health system is less burdened and the quality of health services could improve [ 32 ]. Healthier beneficiaries utilizing quality-assured medicines can also trigger UHC mechanisms (green rectangles). As beneficiaries require less care, health care costs for health insurers decrease, and those savings could be reinvested back into the system [ 33 ]. Health insurers can reinvest these savings into three dimensions of UHC–as illustrated by the cube in Fig 1 –by covering more medicines and services, covering more beneficiaries, or covering more costs incurred by individuals to reduce cost-sharing for medicines and health services.

Regulatory oversight and quality assurance mechanisms throughout the supply chain reinforce the system that ensures patients can obtain and use quality-assured medicines. These are the backbone of the key interventions (yellow rectangles) within the system, broken down to be those affecting medicine delivery, administration, or UHC. These interventions include best practices (e.g. good manufacturing, distribution, and storage practices), policies (e.g. for medicine registration, pre-qualification requirements, and procurement), regulations (e.g. post-market surveillance, including customs screening and inspections), and education (e.g. health education on medicines and advocacy campaigns) [ 34 – 36 ]. Within UHC, we include the role of formulary management, where bioequivalence studies can trigger changes in formularies [ 37 , 38 ]. This can subsequently change insurance coverage for generic medicines, affecting prescribing practices and costs to health insurers and beneficiaries [ 39 – 42 ]. Getting quality-assured generic medicines on the preferred list of formularies is an example of a medicine quality assurance intervention that can save costs to health insurers and affect UHC [ 41 , 42 ].

The systems map connects the mechanisms to build effective medicines quality assurance systems with the health and economic benefits that quality-assured medicines can bring to UHC, highlighting the need for investments in strengthening these systems to achieve UHC.

We sought evidence to further describe the linkages between medicine quality and UHC by examining the association between existing data on the reported prevalence of substandard and falsified medicines ( S1 Table ) and UHC indicators among 63 LMICs ( Table 1 ). Each individual indicator was first regressed on prevalence of substandard and falsified medicines while controlling for GDP per capita. This confounder was only found to yield a strong relationship when included with the indicator for large health expenditures. Visual tests for linearity were conducted and yielded no grounds to reject the linearity assumption. Breusch Pagan tests for heteroskadasticity were performed on each model, each resulting in insignificant p-values (>0.05). With no evidence to reject homoskedasticity, we present the results of simple linear regressions.

GDP: gross domestic product

1 Data from the most recent year available were used for each country for each indicator: coverage of essential services: 2015; large household expenditure: various years between 1998 and 2015; government effectiveness: 2017; regulatory quality: 2017; under-five mortality rate: 2017.

2 Data were retrieved from the Global Health Observatory repository

3 Measured on a linear scale between -2.5 and 2.5

Across countries, we found that coverage of essential services by health insurance schemes was found to be negatively associated with reported prevalence of poor-quality medicines ( p value 0.054; Fig 2B ). The direction of the relationship could indicate that countries that have higher coverage of essential services by health insurance were likely to have lower reported prevalence of poor-quality medicines. However, the effect size was small and not significant at a 0.05 alpha level. Countries with stronger capacity to provide greater health insurance coverage could be in a better position to effectively regulate the medicines supply chain, thus preventing the availability of poor-quality medicines. Coverage of essential services has a further benefit when combined with medicines quality through streamlined, well-regulated processes. This mitigates the need to seek potentially compromised medicines from the informal system.

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Regulatory quality (C) and government effectiveness (D) are measured on a linear scale between -2.5 and 2.5.

A negative association was also found between reported prevalence of substandard and falsified medicines and indicators for government effectiveness and regulatory quality, where higher reported prevalence of poor-quality medicines was associated with lower governmental effectiveness ( p value 0.048; Fig 2C ) and lower regulatory quality ( p value 0.046; Fig 2D ). The direction of this association reflects the expected inverse relationship between these parameters, where an increase in regulatory quality or government effectiveness scores was associated with a decrease in prevalence of poor-quality medicines, though with a small effect size. In addition, reported prevalence of substandard and falsified medicines was shown to have a slightly positive but not significant correlation with under-five mortality ( p value 0.056; Fig 2A ).

No significant association was found between large health expenditures and reported prevalence of substandard and falsified medicines ( p value 0.335) after controlling for GDP per capita. This lack of observable association does not indicate that poor-quality medicines do not result in larger household expenditures. It instead suggests that, given the data available, poor-quality medicines may not be the main driver of enhanced expenditures where other factors are at play. Furthermore, data on catastrophic health spending was not available for all countries, resulting in a lower number of observations for this analysis compared to others (51 compared to 63 countries).

Table 2 summarizes the results of the SAFARI model, showing the monetary and health savings that could result if the prevalence of poor-quality antimalarials in each modeled country were reduced by 10%. The reported prevalence of substandard and falsified antimalarials in the four countries ranged from 10.3% in Zambia to 22% in Uganda [ 5 ]. By improving the quality of antimalarials through removing 1 in 10 poor-quality ones, we simulated fewer deaths annually– 8,255 averted in Nigeria, 507 in Uganda, 208 in Zambia, and 667 and 4,764 in the Kinshasa and Katanga regions in DRC. This means that ensuring that 10% more antimalarials are quality-assured can result in 22 fewer deaths per day in Nigeria and 3 fewer deaths a week in Zambia.

GDP: Gross domestic product, DRC: Democratic Republic of the Congo, SF: substandard and falsified, DALYs: disability adjusted life years

1 Table results were calculated by comparing baseline results for the health and economic burden of malaria to a scenario in which only high quality antimalarials were available.

2 Costs are presented in 2017 USD. Lifetime costs were discounted at 3%.

3 DALY estimates were not included in the DRC version of the SAFARI model.

4 Estimated by dividing the costs of substandard and falsified antimalarials by the number of malaria cases in each country.

We estimated that each country would experience substantial savings by improving the quality of antimalarials for children. By replacing 10% of the current substandard and falsified antimalarials with quality-assured antimalarials, we simulated savings of $8.3 million in Zambia, $14 million in Uganda, $598 million in Nigeria, $68 million in Katanga and $11 million in Kinshasa. Direct costs incurred by public facilities and by patients were estimated to contribute between 3.3% (Nigeria) and 23.8% (Kinshasa) of total savings ($813 thousand to $20 million) across countries. The model results suggest that assuring the quality of antimalarials would result in considerable savings in productivity, largely composed of the potential economic productivity that a child would contribute over a lifetime if death or disability due to poor-quality antimalarials were averted. We estimated that reducing poor-quality antimalarials by 10% would reduce productivity losses annually by $7.5 million in Zambia and $578 million in Nigeria.

We estimated that substandard and falsified antimalarials contribute between $7 per malaria case in Zambia, and $86 per malaria case in the Katanga region in DRC. Each additional death due to poor quality antimalarials approximately cost between $14,300 per death in Katanga, and $72,500 per death in Nigeria. Each additional pediatric malaria hospitalization attributable to substandard and falsified medicines cost between $584 per hospitalization in Kinshasa, and $26,800 per hospitalization in Nigeria. The costs at an individual level are heavily influenced by the cost of care, amount of care seeking, number of malaria cases, and the GDP per capita in each country.

Fig 3 compares the potential economic impact of three simulated interventions (no substandard and falsified antimalarials, no stockouts, and replacing all antimalarials with ACT treatments) and one negative scenario (emergence of antimalarial resistance) in each country. Among the different investments that governments could make towards malaria burden reduction, ensuring the quality of all antimalarials was found to be the most impactful course in DRC, Uganda, and Zambia, while in Nigeria it was second to preventing stockouts. In addition, the negative impact of antimalarial resistance, a potential consequence of recurrent use of poor-quality medicines, could have a substantial negative impact, costing countries nearly $10 million in Zambia to $839 million in Nigeria.

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Nigeria is depicted separately due to scale. Costs are in 2017 USD.

Despite the many differences between countries, this study consistently observed that substandard and falsified essential medicines were associated with key components of UHC such as coverage of essential medicines, government effectiveness, and regulatory quality. Using systems mapping, we illustrated the conceptual link between UHC and regulatory measures to improve the quality of medicines. With the conceptual mapping as guidance, we demonstrated that a relationship can be observed between medicine quality and UHC indicators within existing data. We then showed the importance to a health system of investing in the quality of medicines, and the potential savings that could be reinvested in UHC dimensions (i.e. covering more beneficiaries in health insurance schemes, covering more services, or reducing out-of-pocket expenditures for patients). We show that improving antimalarial quality would not only save millions by improving outcomes—it can also help avert the immense costs associated with potential development of drug resistance [ 43 ]. Using high-quality medicines results in shorter and less severe illness, leading to fewer fatalities and less opportunity for the spread of contagious diseases. With less time spent sick, and less money spent on care resulting from poor-quality medicines, patients can be more economically productive and less likely to use up their resources to pay for health care. Moreover, improving medicine quality can have a dual impact by reducing inequities, since providing only high-quality antimalarials has been shown to have greater benefits among poor and rural populations [ 44 ].

Interventions aimed at ensuring medicine quality, such as good practices in manufacturing and distribution, or establishing an essential medicines list, strengthen institutions necessary for maintaining continued access to quality-assured medicines and quality services [ 45 – 48 ]. UHC financing schemes can create incentives for providers to prescribe and dispense medicines procured through systems or processes that ensure their quality. As coverage of services and populations increases, this system may require continuous strengthening to satisfy the increased demand for quality-assured medicines. Some quality assurance and regulatory interventions for medicines could also result in cost savings to health insurers, facilitating greater coverage of essential services. For example, data from bioequivalence studies of quality-assured generic medicines may guide and facilitate registration and subsequent procurement of cheaper generics [ 37 , 38 ], resulting in cost savings.

Recent literature has drawn attention to potential negative unintended consequences in UHC implementation, where incentives for procuring cheaper medicines to meet increased demand under UHC can lead to arbitrage opportunities and proliferation of suppliers of cheaper, non-quality-assured medicines [ 49 , 50 ]. Therefore, ensuring medicine quality within UHC requires planning and continued attention to regulatory processes such as good registration practices and post-market surveillance strategies, as well as robust quality assurance mechanisms. There are also concerns that the costs of medicine quality assurance activities could result in higher medicine prices, making them less affordable. However, other literature has argued that it is feasible to attain UHC with affordable quality medicines through a mix of quality assurance interventions and incentive schemes [ 51 ], citing successes such as the WHO prequalification program (WHO PQP) and the Medicines Patent Pool in increasing access to affordable, quality-assured medicines [ 52 , 53 ]. Our study builds upon this prior literature by illustrating the multiple conceptual linkages and connections between medicines quality assurance systems and UHC processes. Further, our study shows how quality-assured medicines can improve health outcomes and reduce the financial costs of implementing UHC by preventing costs associated with utilization of poor-quality medicines. Hence, evaluations of the potential costs of quality assurance systems in UHC schemes, including possible higher medicine costs, should include the value of the averted health and economic costs of substandard and falsified medicines that would ensue if there are no investments in quality assurance systems. Accrued savings in the health system from utilizing quality-assured medicines could then be reinvested in strengthening various UHC dimensions, including reducing out-of-pocket expenses for patients and making medicines more affordable.

Our analysis has a number of limitations. Agent-based models provide results that depend on the quality of data inputs. Because data on substandard and falsified medicines, care-seeking, and costs for malaria in LMICs are limited, we performed extensive literature searches and analysis of the most recent quality data for our inputs. Epidemiological data and cost inputs were probabilistically ranged to account for uncertainty in outcomes [ 26 ]. In addition, our data analysis was limited by data availability across countries where combined indicators were only available for 51 to 63 countries. Given this small number of data points, many confounders could not be controlled for, including country differences in underlying wealth, wealth distribution, population composition, distribution of health services, and political systems. Although the prevalence of substandard and falsified medicines was searched systematically, the prevalence we report were based on a limited number of medicine quality studies performed in each country. In addition, our literature analysis does not include data generated by medicines regulatory authorities through their own post-marketing surveillance processes, which are not readily made publically available.

Despite these limitations, this study contributes to emerging analyses regarding the role that medicines quality assurance systems play in UHC, and conceptually highlights the importance of ensuring medicine quality in order to achieve UHC goals. Our systems map can be used as a conceptual and advocacy tool to make a case for the importance of investing in medicine quality assurance with UHC among a broad range of stakeholders. The system mapping approach can also be adapted and customized to explore medicine quality assurance and UHC within local specific contexts. Cost-savings implications of ensuring medicine quality and providing UHC can assist policy makers, governments, civil society, and other stakeholders, when prioritizing health interventions and engaging with health insurers and other financing agencies. In addition, this analysis highlights the critical role that Medicines Regulatory Authorities play in UHC through several key functions, such as enforcement of good practices for manufacturing, registration, distribution and storage; establishing pharmacovigilance and medicines quality post-marketing surveillance programs; and enforcing regulatory actions to withdraw poor-quality medicines from circulation. Thus, ensuring the use of quality-assured medicines is critical in UHC and investing in medicine quality assurance systems and interventions is vital for the long-term success of UHC planning in LMICs.

Supporting information

Funding statement.

Research reported in this publication was supported by the United States Agency for International Development (USAID) through the Promoting the Quality of Medicines (PQM) Program via GHS-A-00-09-00003-00. Members of the PQM program are co-authors on this manuscript and contributed to its preparation and the analysis within.

Data Availability

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Medicine and Social Justice: Essays on the Distribution of Health Care (2nd edn)

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Introduction Medicine and Social Justice: Essays on the Distribution of Health Care

Published: September 2012
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This book discusses the issue of social justice in medicine through an in-depth analysis of both philosophical theory and health care policy and practice. It explores a wide range of approaches to the issues of justice in health care, as well as the connections between theoretical accounts of justice and observations of justice (and injustice) in practice. It considers why it is important for a health care system to be ideally just, and what conditions of social justice or injustice in the background society affect justice in health care. The relevant theoretical discussion of justice draws on Aristotle, who defines “justice” as treating like cases alike and different cases differently. Some chapters also draw on John Rawls’s influential theory of justice to address issues ranging from resource allocation in medicine and public health to poverty, equality, democracy, and the human right to health care, along with medical malpractice and tort reform.

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International Student
Essay Writing Center
Sample Essays

Sample Medical School Essays

Applying to medical school is an exciting decision, but the application process is very competitive. This means when it comes to your application you need to ensure you’ve put your best foot forward and done everything you can to stand out from other applicants. One great way to provide additional information on why you have decided to pursue a career in medicine and why you’re qualified, is your medical school essay. Read these samples to get a good idea on how you can write your own top-notch essay.

This section contains five sample medical school essays

Medical School Sample Essay One
Medical School Sample Essay Two
Medical School Sample Essay Three
Medical School Sample Essay Four
Medical School Sample Essay Five

Medical School Essay One

When I was twelve years old, a drunk driver hit the car my mother was driving while I was in the backseat. I have very few memories of the accident, but I do faintly recall a serious but calming face as I was gently lifted out of the car. The paramedic held my hand as we traveled to the hospital. I was in the hospital for several weeks and that same paramedic came to visit me almost every day. During my stay, I also got to know the various doctors and nurses in the hospital on a personal level. I remember feeling anxiety about my condition, but not sadness or even fear. It seemed to me that those around me, particularly my family, were more fearful of what might happen to me than I was. I don’t believe it was innocence or ignorance, but rather a trust in the abilities of my doctors. It was as if my doctors and I had a silent bond. Now that I’m older I fear death and sickness in a more intense way than I remember experiencing it as a child. My experience as a child sparked a keen interest in how we approach pediatric care, especially as it relates to our psychological and emotional support of children facing serious medical conditions. It was here that I experienced first-hand the power and compassion of medicine, not only in healing but also in bringing unlikely individuals together, such as adults and children, in uncommon yet profound ways. And it was here that I began to take seriously the possibility of becoming a pediatric surgeon.

My interest was sparked even more when, as an undergraduate, I was asked to assist in a study one of my professors was conducting on how children experience and process fear and the prospect of death. This professor was not in the medical field; rather, her background is in cultural anthropology. I was very honored to be part of this project at such an early stage of my career. During the study, we discovered that children face death in extremely different ways than adults do. We found that children facing fatal illnesses are very aware of their condition, even when it hasn’t been fully explained to them, and on the whole were willing to fight their illnesses, but were also more accepting of their potential fate than many adults facing similar diagnoses. We concluded our study by asking whether and to what extent this discovery should impact the type of care given to children in contrast to adults. I am eager to continue this sort of research as I pursue my medical career. The intersection of medicine, psychology, and socialization or culture (in this case, the social variables differentiating adults from children) is quite fascinating and is a field that is in need of better research.

Although much headway has been made in this area in the past twenty or so years, I feel there is a still a tendency in medicine to treat diseases the same way no matter who the patient is. We are slowly learning that procedures and drugs are not always universally effective. Not only must we alter our care of patients depending upon these cultural and social factors, we may also need to alter our entire emotional and psychological approach to them as well.

It is for this reason that I’m applying to the Johns Hopkins School of Medicine, as it has one of the top programs for pediatric surgery in the country, as well as several renowned researchers delving into the social, generational, and cultural questions in which I’m interested. My approach to medicine will be multidisciplinary, which is evidenced by the fact that I’m already double-majoring in early childhood psychology and pre-med, with a minor in cultural anthropology. This is the type of extraordinary care that I received as a child—care that seemed to approach my injuries with a much larger and deeper picture than that which pure medicine cannot offer—and it is this sort of care I want to provide my future patients. I turned what might have been a debilitating event in my life—a devastating car accident—into the inspiration that has shaped my life since. I am driven and passionate. And while I know that the pediatric surgery program at Johns Hopkins will likely be the second biggest challenge I will face in my life, I know that I am up for it. I am ready to be challenged and prove to myself what I’ve been telling myself since that fateful car accident: I will be a doctor.

Tips for a Successful Medical School Essay

If you’re applying through AMCAS, remember to keep your essay more general rather than tailored to a specific medical school, because your essay will be seen by multiple schools.
AMCAS essays are limited to 5300 characters—not words! This includes spaces.
Make sure the information you include in your essay doesn't conflict with the information in your other application materials.
In general, provide additional information that isn’t found in your other application materials. Look at the essay as an opportunity to tell your story rather than a burden.
Keep the interview in mind as you write. You will most likely be asked questions regarding your essay during the interview, so think about the experiences you want to talk about.
When you are copying and pasting from a word processor to the AMCAS application online, formatting and font will be lost. Don’t waste your time making it look nice. Be sure to look through the essay once you’ve copied it into AMCAS and edit appropriately for any odd characters that result from pasting.
Avoid overly controversial topics. While it is fine to take a position and back up your position with evidence, you don’t want to sound narrow-minded.
Revise, revise, revise. Have multiple readers look at your essay and make suggestions. Go over your essay yourself many times and rewrite it several times until you feel that it communicates your message effectively and creatively.
Make the opening sentence memorable. Admissions officers will read dozens of personal statements in a day. You must say something at the very beginning to catch their attention, encourage them to read the essay in detail, and make yourself stand out from the crowd.
Character traits to portray in your essay include: maturity, intellect, critical thinking skills, leadership, tolerance, perseverance, and sincerity.

Medical School Essay Two

If you had told me ten years ago that I would be writing this essay and planning for yet another ten years into the future, part of me would have been surprised. I am a planner and a maker of to-do lists, and it has always been my plan to follow in the steps of my father and become a physician. This plan was derailed when I was called to active duty to serve in Iraq as part of the War on Terror.

I joined the National Guard before graduating high school and continued my service when I began college. My goal was to receive training that would be valuable for my future medical career, as I was working in the field of emergency health care. It was also a way to help me pay for college. When I was called to active duty in Iraq for my first deployment, I was forced to withdraw from school, and my deployment was subsequently extended. I spent a total of 24 months deployed overseas, where I provided in-the-field medical support to our combat troops. While the experience was invaluable not only in terms of my future medical career but also in terms of developing leadership and creative thinking skills, it put my undergraduate studies on hold for over two years. Consequently, my carefully-planned journey towards medical school and a medical career was thrown off course. Thus, while ten-year plans are valuable, I have learned from experience how easily such plans can dissolve in situations that are beyond one’s control, as well as the value of perseverance and flexibility.

Eventually, I returned to school. Despite my best efforts to graduate within two years, it took me another three years, as I suffered greatly from post-traumatic stress disorder following my time in Iraq. I considered abandoning my dream of becoming a physician altogether, since I was several years behind my peers with whom I had taken biology and chemistry classes before my deployment. Thanks to the unceasing encouragement of my academic advisor, who even stayed in contact with me when I was overseas, I gathered my strength and courage and began studying for the MCAT. To my surprise, my score was beyond satisfactory and while I am several years behind my original ten-year plan, I am now applying to Brown University’s School of Medicine.

I can describe my new ten-year plan, but I will do so with both optimism and also caution, knowing that I will inevitably face unforeseen complications and will need to adapt appropriately. One of the many insights I gained as a member of the National Guard and by serving in war-time was the incredible creativity medical specialists in the Armed Forces employ to deliver health care services to our wounded soldiers on the ground. I was part of a team that was saving lives under incredibly difficult circumstances—sometimes while under heavy fire and with only the most basic of resources. I am now interested in how I can use these skills to deliver health care in similar circumstances where basic medical infrastructure is lacking. While there is seemingly little in common between the deserts of Fallujah and rural Wyoming, where I’m currently working as a volunteer first responder in a small town located more than 60 miles from the nearest hospital, I see a lot of potential uses for the skills that I gained as a National Guardsman. As I learned from my father, who worked with Doctors Without Borders for a number of years, there is quite a bit in common between my field of knowledge from the military and working in post-conflict zones. I feel I have a unique experience from which to draw as I embark on my medical school journey, experiences that can be applied both here and abroad.

In ten years’ time, I hope to be trained in the field of emergency medicine, which, surprisingly, is a specialization that is actually lacking here in the United States as compared to similarly developed countries. I hope to conduct research in the field of health care infrastructure and work with government agencies and legislators to find creative solutions to improving access to emergency facilities in currently underserved areas of the United States, with an aim towards providing comprehensive policy reports and recommendations on how the US can once again be the world leader in health outcomes. While the problems inherent in our health care system are not one-dimensional and require a dynamic approach, one of the solutions as I see it is to think less in terms of state-of-the-art facilities and more in terms of access to primary care. Much of the care that I provide as a first responder and volunteer is extremely effective and also relatively cheap. More money is always helpful when facing a complex social and political problem, but we must think of solutions above and beyond more money and more taxes. In ten years I want to be a key player in the health care debate in this country and offering innovative solutions to delivering high quality and cost-effective health care to all our nation’s citizens, especially to those in rural and otherwise underserved areas.

Of course, my policy interests do not replace my passion for helping others and delivering emergency medicine. As a doctor, I hope to continue serving in areas of the country that, for one reason or another, are lagging behind in basic health care infrastructure. Eventually, I would also like to take my knowledge and talents abroad and serve in the Peace Corps or Doctors Without Borders.

In short, I see the role of physicians in society as multifunctional: they are not only doctors who heal, they are also leaders, innovators, social scientists, and patriots. Although my path to medical school has not always been the most direct, my varied and circuitous journey has given me a set of skills and experiences that many otherwise qualified applicants lack. I have no doubt that the next ten years will be similarly unpredictable, but I can assure you that no matter what obstacles I face, my goal will remain the same. I sincerely hope to begin the next phase of my journey at Brown University. Thank you for your kind attention.

Additional Tips for a Successful Medical School Essay

Regardless of the prompt, you should always address the question of why you want to go to medical school in your essay.
Try to always give concrete examples rather than make general statements. If you say that you have perseverance, describe an event in your life that demonstrates perseverance.
There should be an overall message or theme in your essay. In the example above, the theme is overcoming unexpected obstacles.
Make sure you check and recheck for spelling and grammar!
Unless you’re very sure you can pull it off, it is usually not a good idea to use humor or to employ the skills you learned in creative writing class in your personal statement. While you want to paint a picture, you don’t want to be too poetic or literary.
Turn potential weaknesses into positives. As in the example above, address any potential weaknesses in your application and make them strengths, if possible. If you have low MCAT scores or something else that can’t be easily explained or turned into a positive, simply don’t mention it.

Medical School Essay Three

The roots of my desire to become a physician are, thankfully, not around the bedside of a sick family member or in a hospital, but rather on a 10-acre plot of land outside of a small town in Northwest Arkansas. I loved raising and exhibiting cattle, so every morning before the bus arrived at 7 a.m. I was in the barn feeding, checking cattle for any health issues and washing the show heifers. These early mornings and my experiences on a farm not only taught me the value of hard work, but ignited my interest in the body, albeit bovine at the time. It was by a working chute that I learned the functions of reproductive hormones as we utilized them for assisted reproduction and artificial insemination; it was by giving vaccinations to prevent infection that I learned about bacteria and the germ theory of disease; it was beside a stillborn calf before the sun had risen that I was exposed to the frailty of life.

Facing the realities of disease and death daily from an early age, I developed a strong sense of pragmatism out of necessity. There is no place for abstractions or euphemisms about life and death when treating a calf’s pneumonia in the pouring rain during winter. Witnessing the sometimes harsh realities of life on a farm did not instill within me an attitude of jaded inevitability of death. Instead, it germinated a responsibility to protect life to the best of my abilities, cure what ailments I can and alleviate as much suffering as possible while recognizing that sometimes nothing can be done.

I first approached human health at the age of nine through beef nutrition and food safety. Learning the roles of nutrients such as zinc, iron, protein and B-vitamins in the human body as well as the dangers of food-borne illness through the Beef Ambassador program shifted my interest in the body to a new species. Talking with consumers about every facet of the origins of food, I realized that the topics that most interested me were those that pertained to human health. In college, while I connected with people over samples of beef and answered their questions, I also realized that it is not enough simply to have adequate knowledge. Ultimately knowledge is of little use if it is not digestible to those who receive it. So my goal as a future clinical physician is not only to illuminate the source of an affliction and provide treatment for patients, but take care to ensure the need for understanding by both patient and family is met.

I saw this combination of care and understanding while volunteering in an emergency room, where I was also exposed to other aspects and players in the medical field. While assisting a nurse perform a bladder scan and witnessing technicians carry out an echocardiogram or CT scan, I learned the important roles that other professionals who do not wear white coats have in today’s medical field. Medicine is a team sport, and coordinating the efforts of each of these players is crucial for the successful execution of patient care. It is my goal to serve as the leader of this healthcare unit and unify a team of professionals to provide the highest quality care for patients. Perhaps most importantly my time at the VA showed me the power a smile and an open ear can have with people. On the long walk to radiology, talking with patients about their military service and families always seemed to take their mind off the reason for their visit, if only for a few minutes. This served as a reminder that we are helping people with pasts and dreams, rather than simply remedying patients’ symptoms.

Growing up in a small town, I never held aspirations of world travel when I was young. But my time abroad revealed to me the state of healthcare in developing countries and fostered a previously unknown interest in global health. During my first trip abroad to Ghana, my roommate became ill with a severe case of traveler’s diarrhea. In the rural north of the country near the Sahara, the options for healthcare were limited; he told me how our professor was forced to bribe employees to bypass long lines and even recounted how doctors took a bag of saline off the line of another patient to give to him. During a service trip to a rural community in Nicaragua, I encountered patients with preventable and easily treatable diseases that, due to poverty and lack of access, were left untreated for months or years at a time. I was discouraged by the state of healthcare in these countries and wondered what could be done to help. I plan to continue to help provide access to healthcare in rural parts of developing countries, and hopefully as a physician with an agricultural background I can approach public health and food security issues in a multifaceted and holistic manner.

My time on a cattle farm taught me how to work hard to pursue my interests, but also fueled my appetite for knowledge about the body and instilled within me a firm sense of practicality. Whether in a clinic, operating room or pursuing public and global health projects, I plan to bring this work ethic and pragmatism to all of my endeavors. My agricultural upbringing has produced a foundation of skills and values that I am confident will readily transplant into my chosen career. Farming is my early passion, but medicine is my future.

Medical School Essay Four

I am a white, cisgender, and heterosexual female who has been afforded many privileges: I was raised by parents with significant financial resources, I have traveled the world, and I received top-quality high school and college educations. I do not wish to be addressed or recognized in any special way; all I ask is to be treated with respect.

As for my geographic origin, I was born and raised in the rural state of Maine. Since graduating from college, I have been living in my home state, working and giving back to the community that has given me so much. I could not be happier here; I love the down-to-earth people, the unhurried pace of life, and the easy access to the outdoors. While I am certainly excited to move elsewhere in the country for medical school and continue to explore new places, I will always self-identify as a Mainer as being from Maine is something I take great pride in. I am proud of my family ties to the state (which date back to the 1890’s), I am proud of the state’s commitment to preserving its natural beauty, and I am particularly proud of my slight Maine accent (we don’t pronounce our r’s). From the rocky coastline and rugged ski mountains to the locally-grown food and great restaurants, it is no wonder Maine is nicknamed, "Vacationland.” Yet, Maine is so much more than just a tourist destination. The state is dotted with wonderful communities in which to live, communities like the one where I grew up.

Perhaps not surprisingly, I plan to return to Maine after residency. I want to raise a family and establish my medical practice here. We certainly could use more doctors! Even though Maine is a terrific place to live, the state is facing a significant doctor shortage. Today, we are meeting less than half of our need for primary care providers. To make matters worse, many of our physicians are close to retirement age. Yet, according to the AAMC, only 53 Maine residents matriculated into medical school last year! Undoubtedly, Maine is in need of young doctors who are committed to working long term in underserved areas. As my primary career goal is to return to my much adored home state and do my part to help fill this need, I have a vested interest in learning more about rural medicine during medical school.

I was raised in Cumberland, Maine, a coastal town of 7,000 just north of Portland. With its single stoplight and general store (where it would be unusual to visit without running into someone you know), Cumberland is the epitome of a small New England town. It truly was the perfect place to grow up. According to the most recent census, nearly a third of the town’s population is under 18 and more than 75% of households contain children, two statistics which speak to the family-centric nature of Cumberland’s community. Recently rated Maine's safest town, Cumberland is the type of place where you allow your kindergartener to bike alone to school, leave your house unlocked while at work, and bring home-cooked food to your sick neighbors and their children. Growing up in such a safe, close-knit, and supportive community instilled in me the core values of compassion, trustworthiness, and citizenship. These three values guide me every day and will continue to guide me through medical school and my career in medicine.

As a medical student and eventual physician, my compassion will guide me to become a provider who cares for more than just the physical well-being of my patients. I will also commit myself to my patients’ emotional, spiritual, and social well-being and make it a priority to take into account the unique values and beliefs of each patient. By also demonstrating my trustworthiness during every encounter, I will develop strong interpersonal relationships with those whom I serve. As a doctor once wisely said, “A patient does not care how much you know until he knows how much you care.”

My citizenship will guide me to serve my community and to encourage my classmates and colleagues to do the same. We will be taught in medical school to be healers, scientists, and educators. I believe that, in addition, as students and as physicians, we have the responsibility to use our medical knowledge, research skills, and teaching abilities to benefit more than just our patients. We must also commit ourselves to improving the health and wellness of those living in our communities by participating in public events (i.e by donating our medical services), lobbying for better access to healthcare for the underprivileged, and promoting wellness campaigns. As a medical student and eventual physician, my compassion, trustworthiness, and citizenship will drive me to improve the lives of as many individuals as I can.

Cumberland instilled in me important core values and afforded me a wonderful childhood. However, I recognize that my hometown is not perfect. For one, the population is shockingly homogenous, at least as far as demographics go. As of the 2010 census, 97.2% of the residents of Cumberland were white. Only 4.1% of residents speak a language other than English at home and even fewer were born in another country. Essentially everybody who identified with a religion identified as some denomination of Christian. My family was one of maybe five Jewish families in the town. Additionally, nearly all the town’s residents graduated from high school (98.1%), are free of disability (93.8%), and live above the poverty line (95.8%). Efforts to attract diverse families to Cumberland is one improvement that I believe would make the community a better place in which to live. Diversity in background (and in thought) is desirable in any community as living, learning, and working alongside diverse individuals helps us develop new perspectives, enhances our social development, provides us with a larger frame of reference, and improves our understanding of our place in society.

Medical School Essay Five

“How many of you received the flu vaccine this year?” I asked my Bricks 4 Kidz class, where I volunteer to teach elementary students introductory science and math principles using Lego blocks. “What’s a flu vaccine?” they asked in confusion. Surprised, I briefly explained the influenza vaccine and its purpose for protection. My connection to children and their health extends to medical offices, clinics and communities where I have gained experience and insight into medicine, confirming my goal of becoming a physician.

My motivation to pursue a career in medicine developed when my mother, who was diagnosed with Lupus, underwent a kidney transplant surgery and suffered multiple complications. I recall the fear and anxiety I felt as a child because I misunderstood her chronic disease. This prompted me to learn more about the science of medicine. In high school, I observed patients plagued with acute and chronic kidney disease while briefly exploring various fields of medicine through a Mentorship in Medicine summer program at my local hospital. In addition to shadowing nephrologists in a hospital and clinical setting, I scrubbed into the operating room, viewed the radiology department, celebrated the miracle of birth in the delivery room, and quietly observed a partial autopsy in pathology. I saw many patients confused about their diagnoses. I was impressed by the compassion of the physicians and the time they took to reassure and educate their patients.

Further experiences in medicine throughout and after college shaped a desire to practice in underserved areas. While coloring and reading with children in the patient area at a Family Health Center, I witnessed family medicine physicians diligently serve patients from low-income communities. On a medical/dental mission trip to the Philippines, I partnered with local doctors to serve and distribute medical supplies to rural schools and communities. At one impoverished village, I held a malnourished two-year old boy suffering from cerebral palsy and cardiorespiratory disease. His family could not afford to take him to the nearest pediatrician, a few hours away by car, for treatment. Overwhelmed, I cried as we left the village. Many people were suffering through pain and disease due to limited access to medicine. But this is not rare; there are many people suffering due to inadequate access/accessibility around the world, even in my hometown. One physician may not be able to change the status of underserved communities, however, one can alleviate some of the suffering.

Dr. X, my mentor and supervisor, taught me that the practice of medicine is both a science and an art. As a medical assistant in a pediatric office, I am learning about the patient-physician relationship and the meaningful connection with people that medicine provides. I interact with patients and their families daily. Newborn twins were one of the first patients I helped, and I look forward to seeing their development at successive visits. A young boy who endured a major cardiac surgery was another patient I connected with, seeing his smiling face in the office often as he transitioned from the hospital to his home. I also helped many excited, college-bound teenagers with requests for medical records in order to matriculate. This is the art of medicine – the ability to build relationships with patients and have an important and influential role in their lives, from birth to adulthood and beyond.

In addition, medicine encompasses patient-centered care, such as considering and addressing concerns. While taking patient vitals, I grew discouraged when parents refused the influenza vaccine and could not understand their choices. With my experience in scientific research, I conducted an informal yet insightful study. Over one hundred families were surveyed about their specific reasons for refusing the flu vaccine. I sought feedback on patients’ level of understanding about vaccinations and its interactions with the human immune system. Through this project, I learned the importance of understanding patient’s concerns in order to reassure them through medicine. I also learned the value of communicating with patients, such as explaining the purpose of a recommended vaccine. I hope to further this by attending medical school to become a physician focused on patient-centered care, learning from and teaching my community.

Children have been a common thread in my pursuit of medicine, from perceiving medicine through child-like eyes to interacting daily with children in a medical office. My diverse experiences in patient interaction and the practice of medicine inspire me to become a physician, a path that requires perseverance and passion. Physicians are life-long learners and teachers, educating others whether it is on vaccinations or various diseases. This vocation also requires preparation, and I eagerly look forward to continually learning and growing in medical school and beyond.

To learn more about what to expect from the study of medicine, check out our Study Medicine in the US section.

Heilbrunn Timeline of Art History Essays

Medicine in the middle ages.

Sarcophagus with a Greek Physician

Ampulla (Flask) of Saint Menas

Processional Cross

Reliquary Casket with Scenes from the Martyrdom of Saint Thomas Becket

Reliquary Pendant with Queen Margaret of Sicily Blessed by Bishop Reginald of Bath

Arm Reliquary

"Physician Preparing an Elixir", Folio from a Materia Medica of Dioscorides

'Abdullah ibn al-Fadl

The Hours of Jeanne d'Evreux, Queen of France

Jean Pucelle

The Prayer Book of Bonne of Luxembourg, Duchess of Normandy

Attributed to Jean Le Noir , and Workshop

Shoe Reliquary

Manuscript Illumination with the Birth of the Virgin in an Initial G, from a Gradual

Don Silvestro de' Gherarducci

Pilgrim's Badge of the Shrine of St. Thomas Becket at Canterbury

Pharmacy Jar with the Arms of the Hospital of Santa Maria della Scala

Apothecary jar (orciuolo)

perhaps workshop of Giunta di Tugio

Saint Fiacre

Martyrdom of Saint Agatha in an Initial D

Sano di Pietro (Ansano di Pietro di Mencio)

Pendant Capsule in the Form of a Tau Cross, with the Trinity and the Virgin and Child

Saint Anthony Abbot

Attributed to Nikolaus von Hagenau

Saints Christopher, Eustace, and Erasmus (Three Helper Saints)

Tilman Riemenschneider

The Miraculous Communion of Saint Catherine of Siena

Giovanni di Paolo (Giovanni di Paolo di Grazia)

Sigrid Goldiner Department of Medieval Art and The Cloisters, The Metropolitan Museum of Art

January 2012

In the second century, Origen wrote, “For those who are adorned with religion use physicians as servants of God, knowing that He himself gave medical knowledge to men, just as He himself assigned both herbs and other things to grow on the earth.”

The practice of medicine in the Middle Ages was rooted in the Greek tradition . Hippocrates, considered the “father of Medicine,” described the body as made up of four humors—yellow bile, phlegm, black bile, and blood—and controlled by the four elements—fire, water, earth, and air. The body could be purged of excess by bleeding, cupping, and leeching—medical practices that continued throughout the Middle Ages.

In 65 A.D. , Dioscorides, a Greek, wrote his Materia Medica ( 13.152.6 ). This was a practical text dealing with the medicinal use of more than 600 plants. In the second century, Galen synthesized much of what has been attributed to Hippocrates. To further his understanding of bodily functions, he performed animal and even human dissections and was able to demonstrate that the arteries carried blood rather than air. Galenic theories had great longevity, prevailing in western Europe until the sixteenth century.

The Arabs were the great translators and synthesizers of medical texts. Many Greek texts were translated first into Arabic and then into Hebrew. Consequently, Arabs and Jews were renowned for the practice of medicine, and Arabic and Jewish doctors were often employed by kings (for example, James II of Aragon [died 1327]).

One cannot overestimate the importance of medicinal plants in the Middle Ages. Although the original text of Dioscorides is lost, there are many surviving copies. His texts formed the basis of much of the herbal medicine practiced until 1500. Some plants were used for specific disorders, while others were credited with curing multiple diseases. In many cases, draughts were made up of many different herbs. No monastic garden would have been complete without medicinal plants, and it was to monasteries that the sick went to obtain such herbs. Additionally, people might have gone to the local witch or to the apothecary for healing potions .

By the twelfth century, there were medical schools throughout Europe. The most famous was the school of Salerno in southern Italy , reputedly founded by a Christian, an Arab, and a Jew. A health spa as early as the second century, Salerno was surprisingly free of clerical control, even though it was very close to the famous and very powerful monastery of Monte Cassino. The medical faculty at Salerno permitted women to study there.

The medical school at Montpellier traces its roots back to the tenth century, though the university was not founded until 1289. Count Guilhem VIII of Montpellier (1157–1202) permitted anyone who had a medical license to teach there, regardless of religion or background. By 1340, the university at Montpellier included a school of anatomy .

In 1140, Roger of Sicily forbade anyone from practicing medicine without a license, indicating that doctors were clearly under some form of regulation. In the late Middle Ages, apothecary shops opened in important towns. Interestingly, these shops also sold artists’ paints and supplies, and apothecaries and artists shared a guild—the Guild of Saint Luke.

Physicians were trained in the art of diagnosis—often shown in manuscripts holding a urine flask up for inspection ( 54.1.2 , Hours of Jeanne d’Evreux , marginal illustration, fol. 143), or feeling a pulse. In fact, in the sixth century, Cassiodorus wrote that “for a skilled physician the pulsing of the veins reveals [to his fingers] the patient’s ailment just as the appearance of urine indicates it to his eyes.” Observation, palpation, feeling the pulse, and urine examination would be the tools of the doctor throughout the Middle Ages.

Surgery such as amputations, cauterization, removal of cataracts, dental extractions, and even trepanning (perforating the skull to relieve pressure on the brain) were practiced. Surgeons would have relied on opiates for anesthesia and doused wounds with wine as a form of antiseptic.

Many people would have sought out the local healer for care, or might have gone to the barber to be bled or even leeched. Midwives took care of childbirth ( 21.168 ) and childhood ailments. For the sick and dying, there were hospitals. Although many large monasteries did have hospitals attached to them—for example, Saint Bartholemew’s in London and the Hotel Dieu in Paris—and all would have had at least a small infirmary where sick and dying monks could be cared for, it is unclear just how much time the monks dedicated to care of the sick. The medicus in a monastery would have devoted himself to prayer, the laying on of hands, exorcizing of demons, and of course the dispensing of herbal medicine. The hospital of Santa Maria della Scala in Siena was initially administered by the canons of the cathedral ( 23.166 ; 16.154.5 ). It was renowned for its efficient administration and, supported by wealthy patrons, was richly endowed with works of art ( 1975.1.2488 ; 32.100.95 ). Many communities had hospitals to care for the sick that were independent of monasteries.

Some of the most notorious illnesses of the Middle Ages were the plague (the Black Death), leprosy, and Saint Anthony’s fire. From 1346, the plague ravaged Europe, and rich and poor alike succumbed with terrifying speed ( 69.86 ). Pneumonic plague attacked the lungs and bubonic plague produced the characteristic buboes; there was no cure for either form. The only hope for those who escaped the dread disease was prayer or pilgrimage. While leprosy ( 54.1.2 , Hours of Jeanne d’Evreux, Louis Feeding the Lepers , fol. 123v) was very disfiguring and therefore sufferers were feared and kept apart, in fact, leprosy has a very slow incubation period and may not have been as contagious as it was believed. Lepers were obliged to live outside a town or village and to carry a bell to warn people of their approach. Many medieval parish churches in England have leper “squints” that allowed a leper to see the Mass and even receive the sacrament without coming into contact with other parishioners.

Sufferers from St. Anthony’s fire were afflicted with burning extremities. As the disease, caused by the ingestion of tainted rye, progressed, the bright red extremities—hands, feet. and whole limbs—could become gangrenous and fall off. There were many Antonine hospitals to which patients flocked ( 1990.283a,b ). These hospitals, dedicated to Saint Anthony Abbot ( 1988.159 ), gave patients a mixture called Saint Vinage. and cooling herbs such as verbena and sage were applied to soothe the burning heat. Amputations of the affected limbs were also performed.

Many people died of much less dramatic diseases. Women often died in childbirth or succumbed to postpartum infections. Children frequently did not live into adulthood. Laborers must have had multiple problems, such as accidents, osteoarthritis, and fractures. Kidney disease, dental problems, hemorrhoids, and heart disease would have been common. Battle-related injuries were frequent and often fatal.

The most important exemplar for any healer was Jesus himself. The Gospels recount that Jesus healed the blind, caused the paralyzed to walk, cast out devils from the possessed, healed a woman with an issue of blood, and even raised the dead. The healing touch was appropriated by English and French kings, and many miraculous cures were attributed to the royal laying-on of hands. In England, for example, the King’s Touch was believed to heal scrofula, a form of tuberculosis. Prayers to Christ, the Virgin, and saints were always considered the most efficacious form of help. Saint Margaret was invoked for help in childbirth ( 47.101.65 ); Saint Fiacre ( 25.120.227 ; 17.190.353 ) for relief from hemorrhoids. Pilgrimage to a shrine might also lead to miraculous healing. Often these sites and the relics they displayed were related to specific diseases and to specific saints .

Objects associated with the shrine of Saint Thomas Becket attest to the importance of Canterbury as a pilgrimage site where many sick people received miraculous cures. Becket was described as “the best physician of virtuous sick people” and the thirteenth-century windows at Canterbury provide a vivid record of miraculous cures of blindness, leprosy, drowning, madness, and the plague. At Canterbury, the saint’s blood was believed to be particularly beneficial—ampullae containing blood mixed with water were distributed at the shrine ( 2001.310 ). Canterbury seems to have been a particularly important pilgrimage destination for people suffering from bleeding disorders—perhaps because of the blood shed by Thomas at his martyrdom ( 17.190.520 ).

Pilgrims arriving at their destination would be able to touch the relics and even carry home with them secondary relics—perhaps a piece of cloth that had been applied to a reliquary, or an ampulla of liquid that had been poured over a tomb ( 17.194.2291 ). These secondary relics could then be used to heal those who were too ill to make the journey. Ultimately, the power of faith was potent medicine for the sick in the Middle Ages.

Goldiner, Sigrid. “Medicine in the Middle Ages.” In Heilbrunn Timeline of Art History . New York: The Metropolitan Museum of Art, 2000–. http://www.metmuseum.org/toah/hd/medm/hd_medm.htm (January 2012)

Additional Essays by Sigrid Goldiner

Goldiner, Sigrid. “ Art and Death in the Middle Ages .” (originally published October 2001, last revised February 2010)

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Giunta di Tugio
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transitive verb

composition

attempt , try , endeavor , essay , strive mean to make an effort to accomplish an end.

attempt stresses the initiation or beginning of an effort.

try is often close to attempt but may stress effort or experiment made in the hope of testing or proving something.

endeavor heightens the implications of exertion and difficulty.

essay implies difficulty but also suggests tentative trying or experimenting.

strive implies great exertion against great difficulty and specifically suggests persistent effort.

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