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Published on 16.4.2024 in Vol 26 (2024)

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Original Paper

Satoshi Nishioka 1 , PhD ;
Satoshi Watabe 1 , BSc ;
Yuki Yanagisawa 1 , PhD ;
Kyoko Sayama 1 , MSc ;
Hayato Kizaki 1 , MSc ;
Shungo Imai 1 , PhD ;
Mitsuhiro Someya 2 , BSc ;
Ryoo Taniguchi 2 , PhD ;
Shuntaro Yada 3 , PhD ;
Eiji Aramaki 3 , PhD ;
Satoko Hori 1 , PhD

1 Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan

2 Nakajima Pharmacy, Hokkaido, Japan

3 Nara Institute of Science and Technology, Nara, Japan

Corresponding Author:

Satoko Hori, PhD

Division of Drug Informatics

Keio University Faculty of Pharmacy

1-5-30 Shibakoen

Tokyo, 105-8512

Phone: 81 3 5400 2650

Email: [email protected]

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed.

Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives.

Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

Introduction

Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ]. Thus, more efficient approaches for this purpose are needed.

It has been recognized that patients’ voices concerning adverse events represent an important source of information. Several studies have indicated that the number, severity, and time of occurrence of adverse events might be underevaluated by physicians [ 12 - 15 ]. Thus, patient-reported outcomes (PROs) have recently received more attention in the drug evaluation process, reflecting patients’ real voices. Various kinds of PRO measures have been developed and investigated in different disease populations [ 16 , 17 ]. Health care authorities have also encouraged the pharmaceutical industry to use PROs for drug evaluation [ 18 , 19 ], and it is becoming more common to take PRO assessment results into consideration for drug marketing approval [ 20 , 21 ]. Similar trends can be seen in the clinical management of individual patients. Thus, health care professionals have an interest in understanding how to appropriately gather patients’ concerns in order to improve safety management and clinical decisions [ 22 - 24 ].

The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients. In addition, there have been some studies on adverse event severity grading based on health care records [ 36 - 39 ], but none has yet aimed to extract clinically important adverse event signals that require medical intervention from patients’ narratives. It is important to know whether deep learning models could contribute to the detection of such important adverse event signals from concern texts generated by individual patients.

To address this question, we have developed deep learning models to detect adverse event signals from individual patients with cancer based on patients’ blog articles in online communities, following other types of natural language processing–related previous work [ 40 , 41 ]. One deep learning model focused on the specific symptom of hand-foot syndrome (HFS), which is one of the typical side effects of anticancer treatments [ 42 ], and another focused on a broad range of adverse events that impact patients’ activities of daily living [ 43 ]. We showed that our models can provide good performance scores in targeting adverse event signals. However, the evaluation relied on patients’ narratives from the patients’ blog data used for deep learning model training, so further evaluation is needed to ensure the validity and applicability of the models to other texts regarding patients’ concerns. In addition, the blog data source did not contain medical information, so it was not feasible to assess whether the models could contribute to the extraction of clinically important adverse event signals.

To address these challenges, we focused on pharmaceutical care records written by pharmacists at community pharmacies. The gold standard format for pharmaceutical care records in Japan is the SOAP (subjective, objective, assessment, plan)-based document that follows the “problem-oriented system” concept proposed by Weed [ 44 ] in 1968. Pharmacists track patients’ subjective concerns in the S column, provide objective information or observations in the O column, give their assessment from the pharmacist perspective in the A column, and suggest a plan for moving forward in the P column [ 45 , 46 ]. We considered that SOAP-based pharmaceutical care records could be a unique data source suitable for further evaluation of our deep learning models because they contain both patients’ concerns and professional health care records by pharmacists, including the medication prescription history with time stamps. Therefore, this study was designed to assess whether our deep learning models could extract clinically important adverse event signals that require intervention by medical professionals from these records. We also aimed to evaluate the characteristics of the models when applied to patients’ subjective information noted in the pharmaceutical care records, as there have been only a few studies on the application of deep learning models to patients’ concerns recorded during pharmacists’ daily work [ 47 - 49 ].

Here, we report the results of applying our deep learning models to patients’ concern text data in pharmaceutical care records, focusing on patients receiving anticancer treatment.

Data Source

The original data source was 2,276,494 pharmaceutical care records for 303,179 patients, created from April 2020 to December 2021 at community pharmacies belonging to the Nakajima Pharmacy Group in Japan [ 50 ]. To focus on patients with cancer, records of patients with at least 1 prescription for an anticancer drug were retrieved by sorting individual drug codes (YJ codes) used in Japan (YJ codes starting with 42 refer to anticancer drugs). Records in the S column (ie, S records) were collected from the patients with cancer as the text data of patients’ concerns for deep learning model analysis.

Deep Learning Models

The deep learning models used for this research were those that we constructed based on patients’ narratives in blog articles posted in an online community and that showed the best performance score in each task in our previous work (ie, a Bidirectional Encoder Representations From Transformers [BERT]–based model for HFS signal extraction [ 42 ] and a T5-based model for adverse event signal extraction [ 43 ]). BERT [ 26 ] and T5 [ 51 ] both belong to a type of deep learning model that has recently shown high performance in several studies [ 29 , 52 ]. Hereafter, we refer to the deep learning model for HFS signals as the HFS model, the model for any adverse event signals as All AE (ie, all or any adverse events) model, and the model for adverse event signals limited to patients’ activities of daily living as the AE-L (adverse events limiting patients’ daily lives) model. It was also confirmed that these deep learning models showed similar or higher performance scores for the HFS, All AE, or AE-L identification tasks using 1000 S records randomly extracted from the data source of this study compared to the values obtained in our previous work [ 42 , 43 ] (the performance scores of sentence-level tasks from our previous work are comparable, as the mean number of words in the sentences in the data source in our previous work was 32.7 [SD 33.9], which is close to that of the S records used in this study, 38.8 [SD 29.4]). The method and results of the performance-level check are described in detail in Multimedia Appendix 1 [ 42 , 43 ]. We applied the deep learning models to all text data in this study without any adjustment in setting parameters from those used in constructing them based on patient-authored texts in our previous work [ 42 , 43 ].

Evaluation of Extracted S Records by the Deep Learning Models

In this study, we focused on the evaluation of S records that our deep learning models extracted as HFS or AE-L positive. Each positive S record was assessed as if it was a true adverse event signal, a sort of adverse event symptom, whether or not an intervention was made by health care professionals. We also investigated the kind of anticancer treatment prescription in connection with each adverse event signal identified in S records.

To assess whether an extracted positive S record was a true adverse event signal, we used the same annotation guidelines as in our previous work [ 43 ]. In brief, each S record was treated as an “adverse event signal” if any untoward medical occurrence happened to the patient, regardless of the cause. For the AE-L model only, if a positive S record was confirmed as an adverse event signal, it was further categorized into 1 or more of the following adverse event symptoms: “fatigue,” “nausea,” “vomiting,” “diarrhea,” “constipation,” “appetite loss,” “pain or numbness,” “rash or itchy,” “hair loss,” “menstrual irregularity,” “fever,” “taste disorder,” “dizziness,” “sleep disorder,” “edema,” or “others.”

For the assessment of interventions by health care professionals and anticancer treatment prescriptions, information from the O, A, and P columns and drug prescription history in the data source were investigated for the extracted positive S records. The interventions by health care professionals were categorized in any of the following: “adding symptomatic treatment for the adverse event signal,” “dose reduction or discontinuation of causative anticancer treatment,” “consultation with physician,” “others,” or “no intervention (ie, just following up the adverse event signal).” The actions categorized in “others” were further evaluated individually. For this assessment, we also randomly extracted 200 S records and evaluated them in the same way for comparison with the results from the deep learning model. Prescription history of anticancer treatment was analyzed by primary category of mechanism of action (MoA) with subcategories if applicable (eg, target molecule for kinase inhibitors).

Applicability Check to Other Text Data Including Patients’ Concerns

To check the applicability of our deep learning models to data from a different source, interview transcripts from patients with cancer were also evaluated. The interview transcripts were created by the Database of Individual Patient Experiences-Japan (DIPEx-Japan) [ 53 ]. DIPEx-Japan divides the interview transcripts into sections for each topic, such as “onset of disease” and “treatment,” and posts the processed texts on its website. Processing is conducted by accredited researchers based on qualitative research methods established by the University of Oxford [ 54 ]. In this study, interview text data created from interviews with 52 patients with breast cancer conducted from January 2008 to October 2018 were used to assess whether our deep learning models can extract adverse event signals from this source. In total, 508 interview transcripts were included with the approval of DIPEx-Japan.

Ethical Considerations

This study was conducted with anonymized data following approval by the ethics committee of the Keio University Faculty of Pharmacy (210914-1 and 230217-1) and in accordance with relevant guidelines and regulations and the Declaration of Helsinki. Informed consent specific to this study was waived due to the retrospective observational design of the study with the approval of the ethics committee of the Keio University Faculty of Pharmacy. To respect the will of each individual stakeholder, however, we provided patients and pharmacists of the pharmacy group with an opportunity to refuse the sharing of their pharmaceutical care records by posting an overview of this study at each pharmacy store or on their web page regarding the analysis using pharmaceutical care records. Interview transcripts from DIPEx-Japan were provided through a data sharing arrangement for using narrative data for research and education. Consent for interview transcription and its sharing from DIPEx-Japan was obtained from the participants when the interviews were recorded.

From the original data source of 2,180,902 pharmaceutical care records for 291,150 patients, S records written by pharmacists for patients with a history of at least 1 prescription of an anticancer drug were extracted. This yielded 30,784 S records for 2479 patients with cancer ( Table 1 ). The mean and median number of words in the S records were 38.8 (SD 29.4) and 32 (IQR 20-50), respectively. We applied our deep learning models, HFS, All AE, and AE-L, to these 30,784 S records for the evaluation of the deep learning models for adverse event signal detection.

For interview transcripts created by DIPEx-Japan, the mean and median number of words were 428.9 (SD 160.9) and 416 (IQR 308-526), respectively, in the 508 transcripts for 52 patients with breast cancer.

a SOAP: subjective, objective, assessment, plan.

b S: subjective.

Application of the HFS Model

First, we applied the HFS model to the S records for patients with cancer. The BERT-based model was used for this research as it showed the best performance score in our previous work [ 42 ].

S Records Extracted as HFS Positive

The S records extracted as HFS positive by the HFS model ( Table 2 ) amounted to 167 (0.5%) records for 119 (4.8%) patients. A majority of the patients had 1 HFS-positive record in their S records (n=91, 76.5%), while 2 patients had as many as 6 (1.7%) HFS-positive records. When we examined whether the extracted S records were true adverse event signals or not, 152 records were confirmed to be adverse event signals, while the other 15 records were false-positives. All the false-positive S records were descriptions about the absence of symptoms or confirmation of improving condition (eg, “no diarrhea, mouth ulcers, or limb pain so far” or “the skin on the soles of my feet has calmed down a lot with this ointment”). Some examples of S records that were predicted as HFS positive by the model are shown in Table S1 in Multimedia Appendix 2 .

The same examination was conducted with interview transcripts from DIPEx-Japan. Only 1 (0.2%) transcript was extracted as HFS positive by the HFS model, and it was a true adverse event signal (100%). The actual transcript extracted as HFS positive is shown in Table S2 in Multimedia Appendix 2 .

a S: subjective.

b HFS: hand-foot syndrome.

c All false-positive S records were denial of symptoms or confirmation of improving condition.

Interventions by Health Care Professionals

The 167 S records extracted as HFS positive as well as 200 randomly selected records were checked for interventions by health care professionals ( Figure 1 ). The proportion showing any action by health care professionals was 64.1% for 167 HFS-positive S records compared to 13% for the 200 random S records. Among the actions taken for HFS positives, “adding symptomatic treatment” was the most common, accounting for around half (n=79, 47.3%), followed by “other” (n=18, 10.8%). Most “other” actions were educational guidance from pharmacists, such as instructions on moisturizing, nail care, or application of ointment and advice on daily living (eg, “avoid tight socks”).

Anticancer Drugs Prescribed

The types of anticancer drugs prescribed for HFS-positive patients are summarized based on the prescription histories in Table 3 . For the 152 adverse event signals identified by the HFS model in the previous section, the most common MoA class of anticancer drugs used for the patients was antimetabolite (n=62, 40.8%), specifically fluoropyrimidines (n=59, 38.8%). Kinase inhibitors were next (n=49, 32.2%), with epidermal growth factor receptor (EGFR) inhibitors and multikinase inhibitors as major subgroups (n=28, 18.4% and n=14, 9.2%, respectively). The third and fourth most common MoAs were aromatase inhibitors (n=24, 15.8%) and antiandrogen or estrogen drugs (n=7, 4.6% each) for hormone therapy.

a EGFR: epidermal growth factor receptor.

b VEGF: vascular endothelial growth factor.

c HER2: human epidermal growth factor receptor-2.

d CDK4/6: cyclin-dependent kinase 4/6.

Application of the All AE or AE-L model

The All AE and AE-L models were also applied to the same S records for patients with cancer. The T5-based model was used for this research as it gave the best performance score in our previous work [ 43 ].

S Records Extracted as All AE or AE-L positive

The numbers of S records extracted as positive were 7604 (24.7%) for 1797 patients and 196 (0.6%) for 142 patients for All AE and AE-L, respectively. In the case of All AE, patients tended to have multiple adverse event positives in their S records (n=1315, 73.2% of patients had at least 2 positives). In the case of AE-L, most patients had only 1 AE-L positive (n=104, 73.2%), and the largest number of AE-L positives for 1 patient was 4 (2.8%; Table 4 ).

We focused on AE-L evaluation due to its greater importance from a medical viewpoint and lower workload for manual assessment, considering the number of positive S records. Of the 197 AE-L–positive S records, it was confirmed that 157 (80.1%) records accurately extracted adverse event signals, while 39 (19.9%) records were false-positives that did not include any adverse event signals ( Table 4 ). The contents of the 39 false-positives were all descriptions about the absence of symptoms or confirmation of improving condition, showing a similar tendency to the HFS false-positives (eg, “The diarrhea has calmed down so far. Symptoms in hands and feet are currently fine” and “No symptoms for the following: upset in stomach, diarrhea, nausea, abdominal pain, abdominal pain or stomach cramps, constipation”). Examples of S records that were predicted as AE-L positive are shown in Table S3 in Multimedia Appendix 2 .

The deep learning models were also applied to interview transcripts from DIPEx-Japan in the same manner. The deep learning models identified 84 (16.5%) and 18 (3.5%) transcripts as All AE or AE-L positive, respectively. Of the 84 All AE–positive transcripts, 73 (86.9%) were true adverse event signals. The false-positives of All AE (n=11, 13.1%) were categorized into any of the following 3 types: explanations about the disease or its prognosis, stories when their cancer was discovered, or emotional changes that did not include clear adverse event mentions. With regard to AE-L, all the 18 (100%) positives were true adverse event signals (Table S4 in Multimedia Appendix 2 ). Examples of actual transcripts extracted as All AE or AE-L positive are shown in Table S5 in Multimedia Appendix 2 .

b All AE: all (or any of) adverse event.

c AE-L: adverse events limiting patients’ daily lives.

d All false-positive S records were denial of symptoms or confirmation of improving condition.

Whether or not interventions were made by health care professionals was investigated for the 196 AE-L–positive S records. As in the HFS model evaluation, data from 200 randomly selected S records were used for comparison ( Figure 2 ). In total, 91 (46.4%) records in the 196 AE-L–positive records were accompanied by an intervention, while the corresponding figure in the 200 random records was 26 (13%) records. The most common action in response to adverse event signals identified by the AE-L model was “adding symptomatic treatment” (n=71, 36.2%), followed by “other” (n=11, 5.6%). “Other” included educational guidance from pharmacists, inquiries from pharmacists to physicians, or recommendations for patients to visit a doctor.

The types of anticancer drugs prescribed for patients with adverse event signals identified by the AE-L model were summarized based on the prescription histories ( Table 5 ). In connection with the 157 adverse event signals, the most common MoA of the prescribed anticancer drug was antimetabolite (n=62, 39.5%) and fluoropyrimidine (n=53, 33.8%), which accounted for the majority. Kinase inhibitor (n=31, 19.7%) was the next largest category with multikinase inhibitor (n=14, 8.9%) as the major subgroup. These were followed by antiandrogen (n=27, 17.2%), antiestrogen (n=10, 6.4%), and aromatase inhibitor (n=10, 6.4%) for hormone therapy.

b JAK: janus kinase.

c VEGF: vascular endothelial growth factor.

d BTK: bruton tyrosine kinase.

e FLT3: FMS-like tyrosine kinase-3.

f PARP: poly-ADP ribose polymerase.

g CDK4/6: cyclin-dependent kinase 4/6.

h CD20: cluster of differentiation 20.

Adverse Event Symptoms

For the 157 adverse event signals identified by the AE-L model, the symptoms were categorized according to the predefined guideline in our previous work [ 43 ]. “Pain or numbness” (n=57, 36.3%) accounted for the largest proportion followed by “fever” (n=46, 29.3%) and “nausea” (n=40, 25.5%; Table 6 ). Symptoms classified as “others” included chills, tinnitus, running tears, dry or peeling skin, and frequent urination. When comparing the proportion of the symptoms associated with or without interventions by health care professionals, a trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). On the other hand, a smaller proportion was observed in “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes).

This study was designed to evaluate our deep learning models, previously constructed based on patient-authored texts posted in an online community, by applying them to pharmaceutical care records that contain both patients’ subjective concerns and medical information created by pharmacists. Based on the results, we discuss whether these deep learning models can extract clinically important adverse event signals that require medical intervention, and what characteristics they show when applied to data on patients’ concerns in pharmaceutical care records.

Performance for Adverse Event Signal Extraction

The first requirement for the deep learning models is to extract adverse event signals from patients’ narratives precisely. In this study, we evaluated the proportion of true adverse event signals in positive S records extracted by the HFS or AE-L model. True adverse event signals amounted to 152 (91%) and 157 (80.1%) for the HFS and AE-L models, respectively ( Tables 2 and 4 ). Given that the proportion of true adverse event signals in 200 randomly extracted S records without deep learning models was 54 (27%; categories other than “no adverse event” in Figures 1 and 2 ), the HFS and AE-L models were able to concentrate S records with adverse event mentions. Although 15 (9%) for the HFS model and 39 (19.9%) for the AE-L model were false-positives, it was confirmed all of the false-positive records described a lack of symptoms or confirmation of improving condition. We considered that such false-positives are due to the unique feature of pharmaceutical care records, where pharmacists might proactively interview patients about potential side effects of their medications. As the data set of blog articles we used to construct the deep learning models included few such cases (especially comments on lack of symptoms), our models seemed unable to exclude them correctly. Even though we confirmed that the proportion of true “adverse event” signals extracted from the S records by the HFS or AE-L model was more than 80%, the performance scores to extract true “HFS” or “AE-L” signals were not so high based on the performance check using 1000 randomly extracted S records ( F 1 -scores were 0.50 and 0.22 for true HFS and AE-L signals, respectively; Table S1 in Multimedia Appendix 1 ). It is considered that the performance to extract true HFS and AE-L signals was relatively low due to the short length of texts in the S records, providing less context to judge the impact on patients’ daily lives, especially for the AE-L model (the mean word number of the S records was 38.8 [SD 29.4; Table 1 ], similar to the sentence-level tasks in our previous work [ 42 , 43 ]). However, we consider a true adverse event signal proportion of more than 80% in this study represents a promising outcome, as this is the first attempt to apply our deep learning models to a different source of patients’ concern data, and the extracted positive cases would be worthy of evaluation by a medical professional, as the potential adverse events could be caused by drugs taken by the patients.

When the deep learning models were applied to DIPEx-Japan interview transcripts, including patients’ concerns, the proportion of true adverse event signals was also more than 80% (for All AE: n=73, 86.9% and for HFS and AE-L: n=18, 100%). The difference in the results between pharmaceutical care S records and DIPEx-Japan interview transcripts was the features of false-positives, descriptions about lack of symptoms or confirmation of improving condition in S records versus explanations about disease or its prognosis, stories about when their cancer was discovered, or emotional changes in interview transcripts. This is considered due to the difference in the nature of the data source; the pharmaceutical care records were generated in a real-time manner by pharmacists through their daily work, where adverse event signals are proactively monitored, while the interview transcripts were purely based on patients’ retrospective memories. Our deep learning models were able to extract true adverse event signals with an accuracy of more than 80% from both text data sources in spite of the difference in their nature. When looking at future implementation of the deep learning models in society (discussed in the Potential for Deep Learning Model Implementation in Society section), it may be desirable to further adjust deep learning models to reduce false-positives depending upon the features of the data source.

Identification of Important Adverse Events Requiring Medical Intervention

To assess whether the models could extract clinically important adverse event signals, we investigated interventions by health care professionals connected with the adverse event signals that are identified by our deep learning models. In the 200 randomly extracted S records, only 26 (13%) consisted of adverse event signals, leading to any intervention by health care professionals. On the other hand, the proportion of signals associated with interventions was increased to 107 (64.1%) and 91 (46.4%) in the S records extracted as positive by the HFS and AE-L models, respectively ( Figures 1 and 2 ). These results suggest that both deep learning models can screen clinically important adverse event signals that require intervention from health care professionals. The performance level in screening adverse event signals requiring medical intervention was higher in the HFS model than in the AE-L model (n=107, 64.1% vs n=91, 46.4%; Figures 1 and 2 ). Since the target events were specific and adverse event signals of HFS were narrowly defined, which is one of the typical side effects of some anticancer drugs, we consider that health care providers paid special attention to HFS-related signals and took action proactively. In both deep learning models, similar trends were observed in actions taken by health care professionals in response to extracted adverse event signals; common actions were attempts to manage adverse event symptoms by symptomatic treatment or other mild interventions, including educational guidance from pharmacists or recommendations for patients to visit a doctor. More direct interventions focused on the causative drugs (ie, “dose reduction or discontinuation of anticancer treatment”) amounted to less than 5%; 7 (4.2%) for the HFS model and 6 (3.1%) for the AE-L model ( Figures 1 and 2 ). Thus, it appears that our deep learning models can contribute to screening mild to moderate adverse event signals that require preventive actions such as symptomatic treatments or professional advice from health care providers, especially for patients with less sensitivity to adverse event signals or who have few opportunities to visit clinics and pharmacies.

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Based on the drug prescription history associated with S records extracted as HFS or AE-L positive, the type and duration of anticancer drugs taken by patients experiencing the adverse event signals were investigated. For the HFS model, the most common MoA of anticancer drug was antimetabolite (fluoropyrimidine: n=59, 38.8%), followed by kinase inhibitors (n=49, 32.2%, of which EGFR inhibitors and multikinase inhibitors accounted for n=28, 18.4% and n=14, 9.2%, respectively) and aromatase inhibitors (n=24, 15.8%; Table 3 ). It is known that fluoropyrimidine and multikinase inhibitors are typical HFS-inducing drugs [ 55 - 58 ], suggesting that the HFS model accurately extracted HFS side effect signals derived from these drugs. Note that symptoms such as acneiform rash, xerosis, eczema, paronychia, changes in the nails, arthralgia, or stiffness of limb joints, which are common side effects of EGFR inhibitors or aromatase inhibitors [ 59 , 60 ], might be extracted as closely related expressions to those of HFS signals. When looking at the MoA of anticancer drugs for patients with adverse event signals identified by the AE-L model, antimetabolite (fluoropyrimidine) was the most common one (n=53, 33.8%), as in the case of those identified by the HFS model, followed by kinase inhibitors (n=31, 19.7%) and antiandrogens (n=27, 17.2%; Table 5 ). Since the AE-L model targets a broad range of adverse event symptoms, it is difficult to rationalize the relationship between the adverse event signals and types of anticancer drugs. However, the type of anticancer drugs would presumably closely correspond to the standard treatments of the cancer types of the patients. Based on the prescribed anticancer drugs, we can infer that a large percentage of the patients had breast or lung cancer, indicating that our study results were based on data from such a population. Thus, a possible direction for the expansion of this research would be adjusting the deep learning models by additional training with expressions for typical side effects associated with standard treatments of other cancer types. To interpret these results correctly, it should be noted that we could not investigate anticancer treatments conducted outside of the pharmacies (eg, the time-course relationship with intravenously administered drugs would be missed, as the administration will be done at hospitals). To further evaluate how useful this model is in side effect signal monitoring for patients with cancer, comprehensive medical information for the eligible patients would be required.

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Among the adverse event signals identified by the AE-L model, the type of symptom was categorized according to a predefined annotation guideline that we previously developed [ 43 ]. The most frequently recorded adverse event signals identified by the AE-L model were “pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%; Table 6 ). Since the pharmaceutical care records had information about interventions by health care professionals, the frequency of the presence or absence of the interventions for each symptom was examined. A trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). There seem to be 2 possible explanations for this: these symptoms are of high importance and require early medical intervention or effective symptomatic treatments are available for these symptoms in clinical practice so that medical intervention is an easy option. On the other hand, a trend for a smaller proportion of adverse event signals to result in interventions was observed for “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes). The reason for this may be the lack of effective symptomatic treatments or the difficulty of judging whether the severity of these symptoms justifies medical intervention by health care providers. In either case, there may be room for improvement in the quality of medical care for these symptoms. We expect that our research will contribute to a quality improvement in safety monitoring in clinical practice by supporting adverse event signal detection in a cost-effective manner.

Potential for Deep Learning Model Implementation in Society

Although we evaluated our deep learning models using pharmaceutical care records in this study, the main target of future implementation of our deep learning models in society would be narrative texts that patients directly write to record their daily experiences. For example, the application of these deep learning models to electronic media where patients record their daily experiences in their lives with disease (eg, health care–related e-communities and disease diary applications) could enable information about adverse event signal onset that patients experience to be provided to health care providers in a timely manner. Adverse event signals can automatically be identified and shared with health care providers based on the concern texts that patients post to any platform. This system will have the advantage that health care providers can efficiently grasp safety-related events that patients experience outside of clinic visits so that they can conduct more focused or personalized interactions with patients at their clinic visits. However, consideration should be given to avoid an excessive burden on health care providers. For instance, limiting the sharing of adverse event signals to those of high severity or summarizing adverse event signals over a week rather than sharing each one in a real-time manner may be reasonable approaches for medical staff. We also need to think about how to encourage patients to record their daily experiences using electronic tools. Not only technical progress and support but also the establishment of an ecosystem where both patients and medical staff can feel benefit will be required. Prospective studies with deep learning models to follow up patients in the long term and evaluate outcomes will be needed. We primarily looked at patient-authored texts as targets of implementation, but our deep learning models may also be worth using medical data including patients’ subjective concerns, such as pharmaceutical care S records. As this study confirmed that our deep learning models are applicable to patients’ concern texts tracked by pharmacists, it should be possible to use them to analyze other “patient voice-like” medical text data that have not been actively investigated so far.

Limitations

First, the major limitation of this study was that we were not able to collect complete medical information of the patients. Although we designed this study to analyze patients’ concerns extracted by the deep learning models and their relationship with medical information contained in the pharmaceutical care records, some information could not be tracked (eg, missing history of medical interventions or anticancer treatment at hospitals as well as diagnosis of patients’ primary cancers). Second, there might be a data creation bias in S records for patients’ concerns by pharmacists. For example, symptoms that have little impact on intervention decisions might less likely be recorded by them. It should be also noted that the characteristics of S records may not be consistent at different community pharmacies.

Conclusions

Our deep learning models were able to screen clinically important adverse event signals that require intervention by health care professionals from patients’ concerns in pharmaceutical care records. Thus, these models have the potential to support real-time adverse event monitoring of individual patients taking anticancer treatments in an efficient manner. We also confirmed that these deep learning models constructed based on patient-authored texts could be applied to patients’ subjective information recorded by pharmacists through their daily work. Further research may help to expand the applicability of the deep learning models for implementation in society or for analysis of data on patients’ concerns accumulated in professional records at pharmacies or hospitals.

Acknowledgments

This work was supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI; grant 21H03170) and Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (CREST; grant JPMJCR22N1), Japan. Mr Yuki Yokokawa and Ms Sakura Yokoyama at our laboratory advised SN about the structure of pharmaceutical care records. This study would not have been feasible without the high quality of pharmaceutical care records created by many individual pharmacists at Nakajima Pharmacy Group through their daily work.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

SN and SH designed the study. SN retrieved the subjective records of patients with cancer from the data source for the application of deep learning models and organized other data for subsequent evaluations. SN ran the deep learning models with the support of SW. SN, YY, and KS checked the adverse event signals for each subjective record that was extracted as positive by the models for hand-foot syndrome or adverse events limiting patients’ daily lives and evaluated the adverse event signal symptoms, details of interventions taken by health care professionals, and types of anticancer drugs prescribed for patients based on available data from the data source. HK and SI advised on the study concept and process. MS and RT provided pharmaceutical records at their community pharmacies along with advice on how to use and interpret them. SY and EA supervised the natural language processing research as specialists. SH supervised the study overall. SN drafted and finalized the paper. All authors reviewed and approved the paper.

Conflicts of Interest

SN is an employee of Daiichi Sankyo Co, Ltd. All other authors declare no conflicts of interest.

Performance evaluation of deep learning models.

Examples of S records and sample interview transcripts.

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Abbreviations

Edited by G Eysenbach; submitted 25.12.23; peer-reviewed by CY Wang, L Guo; comments to author 24.01.24; revised version received 14.02.24; accepted 09.03.24; published 16.04.24.

©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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How to Write a Medical Research Paper

Last Updated: February 5, 2024 Approved

This article was co-authored by Chris M. Matsko, MD . Dr. Chris M. Matsko is a retired physician based in Pittsburgh, Pennsylvania. With over 25 years of medical research experience, Dr. Matsko was awarded the Pittsburgh Cornell University Leadership Award for Excellence. He holds a BS in Nutritional Science from Cornell University and an MD from the Temple University School of Medicine in 2007. Dr. Matsko earned a Research Writing Certification from the American Medical Writers Association (AMWA) in 2016 and a Medical Writing & Editing Certification from the University of Chicago in 2017. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, 89% of readers who voted found the article helpful, earning it our reader-approved status. This article has been viewed 202,076 times.

Writing a medical research paper is similar to writing other research papers in that you want to use reliable sources, write in a clear and organized style, and offer a strong argument for all conclusions you present. In some cases the research you discuss will be data you have actually collected to answer your research questions. Understanding proper formatting, citations, and style will help you write and informative and respected paper.

Researching Your Paper

Pick something that really interests you to make the research more fun.
Choose a topic that has unanswered questions and propose solutions.

Step 2 Determine what kind of research paper you are going to write.

Quantitative studies consist of original research performed by the writer. These research papers will need to include sections like Hypothesis (or Research Question), Previous Findings, Method, Limitations, Results, Discussion, and Application.
Synthesis papers review the research already published and analyze it. They find weaknesses and strengths in the research, apply it to a specific situation, and then indicate a direction for future research.

Keep track of your sources. Write down all publication information necessary for citation: author, title of article, title of book or journal, publisher, edition, date published, volume number, issue number, page number, and anything else pertaining to your source. A program like Endnote can help you keep track of your sources.
Take detailed notes as you read. Paraphrase information in your own words or if you copy directly from the article or book, indicate that these are direct quotes by using quotation marks to prevent plagiarism.
Be sure to keep all of your notes with the correct source.
Your professor and librarians can also help you find good resources.

Keep all of your notes in a physical folder or in a digitized form on the computer.
Start to form the basic outline of your paper using the notes you have collected.

Writing Your Paper

Start with bullet points and then add in notes you've taken from references that support your ideas. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source
A common way to format research papers is to follow the IMRAD format. This dictates the structure of your paper in the following order: I ntroduction, M ethods, R esults, a nd D iscussion. [2] X Research source
The outline is just the basic structure of your paper. Don't worry if you have to rearrange a few times to get it right.
Ask others to look over your outline and get feedback on the organization.
Know the audience you are writing for and adjust your style accordingly. [3] X Research source

Use a standard font type and size, such as Times New Roman 12 point font.
Double-space your paper.
If necessary, create a cover page. Most schools require a cover page of some sort. Include your main title, running title (often a shortened version of your main title), author's name, course name, and semester.

Break up information into sections and subsections and address one main point per section.
Include any figures or data tables that support your main ideas.
For a quantitative study, state the methods used to obtain results.

Step 4 Write the conclusion and discussion.

Clearly state and summarize the main points of your research paper.
Discuss how this research contributes to the field and why it is important. [4] X Research source
Highlight potential applications of the theory if appropriate.
Propose future directions that build upon the research you have presented. [5] X Research source
Keep the introduction and discussion short, and spend more time explaining the methods and results.

State why the problem is important to address.
Discuss what is currently known and what is lacking in the field.
State the objective of your paper.
Keep the introduction short.

Highlight the purpose of the paper and the main conclusions.
State why your conclusions are important.
Be concise in your summary of the paper.
Show that you have a solid study design and a high-quality data set.
Abstracts are usually one paragraph and between 250 – 500 words.

Unless otherwise directed, use the American Medical Association (AMA) style guide to properly format citations.
Add citations at end of a sentence to indicate that you are using someone else's idea. Use these throughout your research paper as needed. They include the author's last name, year of publication, and page number.
Compile your reference list and add it to the end of your paper.
Use a citation program if you have access to one to simplify the process.

Continually revise your paper to make sure it is structured in a logical way.
Proofread your paper for spelling and grammatical errors.
Make sure you are following the proper formatting guidelines provided for the paper.
Have others read your paper to proofread and check for clarity. Revise as needed.

Expert Q&A

Ask your professor for help if you are stuck or confused about any part of your research paper. They are familiar with the style and structure of papers and can provide you with more resources. Thanks Helpful 0 Not Helpful 0
Refer to your professor's specific guidelines. Some instructors modify parts of a research paper to better fit their assignment. Others may request supplementary details, such as a synopsis for your research project . Thanks Helpful 0 Not Helpful 0
Set aside blocks of time specifically for writing each day. Thanks Helpful 0 Not Helpful 0

Do not plagiarize. Plagiarism is using someone else's work, words, or ideas and presenting them as your own. It is important to cite all sources in your research paper, both through internal citations and on your reference page. Thanks Helpful 4 Not Helpful 2

↑ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3178846/
↑ http://owl.excelsior.edu/research-and-citations/outlining/outlining-imrad/
↑ http://china.elsevier.com/ElsevierDNN/Portals/7/How%20to%20write%20a%20world-class%20paper.pdf
↑ http://intqhc.oxfordjournals.org/content/16/3/191
↑ http://www.ruf.rice.edu/~bioslabs/tools/report/reportform.html#form

About This Article

To write a medical research paper, research your topic thoroughly and compile your data. Next, organize your notes and create a strong outline that breaks up the information into sections and subsections, addressing one main point per section. Write the results and discussion sections first to go over your findings, then write the introduction to state your objective and provide background information. Finally, write the abstract, which concisely summarizes the article by highlighting the main points. For tips on formatting and using citations, read on! Did this summary help you? Yes No

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Medical Research Paper Topics

This page provides a comprehensive list of medical research paper topics divided into 20 categories, each with 10 unique subjects. The categories span across various subfields, including anatomy and physiology, diseases, epidemiology, health and fitness, health disparities, healthcare, kinesiology, mental health, pharmacology, and veterinary medicine. Additionally, the page offers expert advice on choosing the right topic and crafting an impactful medical research paper. It also introduces iResearchNet’s writing services, which are designed to assist students in creating high-quality, custom medical research papers.

200 Medical Research Paper Topics:

Medical research is a vast and diverse field, offering a plethora of topics for students and researchers to explore. The choice of topic can significantly influence the direction of your research and the impact of your findings. Therefore, it’s crucial to choose a topic that not only interests you but also aligns with your academic and career goals. To assist you in this endeavor, we have compiled a comprehensive list of medical research paper topics, divided into 20 categories, each with 10 unique topics.

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1. Anatomy and Physiology:

The role of the lymphatic system in maintaining homeostasis.
The impact of aging on muscle function and structure.
The physiological effects of stress on the human body.
The role of the endocrine system in metabolism.
The impact of exercise on cardiovascular health.
The role of the nervous system in sensation and perception.
The physiological effects of sleep deprivation.
The role of the digestive system in nutrient absorption.
The impact of genetic factors on human physiology.
The role of the immune system in disease prevention.

2. Diseases:

The genetic and environmental factors contributing to the development of cancer.
The impact of lifestyle factors on the prevalence of heart disease.
The role of vaccination in the prevention of infectious diseases.
The challenges in the management and treatment of diabetes.
The impact of the human microbiome on disease development.
The role of inflammation in the pathogenesis of autoimmune diseases.
The challenges in the diagnosis and treatment of Alzheimer’s disease.
The impact of antibiotic resistance on the treatment of bacterial infections.
The role of genetic mutations in the development of rare diseases.
The impact of viral evolution on the spread of infectious diseases.

3. Epidemiology:

The impact of socioeconomic factors on the prevalence of infectious diseases.
The role of vaccination in the prevention of disease outbreaks.
The impact of climate change on the spread of vector-borne diseases.
The role of epidemiological studies in the development of public health policies.
The impact of population density on the spread of infectious diseases.
The role of epidemiology in the identification of risk factors for chronic diseases.
The impact of global travel on the spread of infectious diseases.
The role of epidemiological data in the management of disease outbreaks.
The impact of demographic factors on disease prevalence.
The role of epidemiology in the study of health disparities.

4. Health and Fitness:

The impact of physical activity on mental health.
The role of diet in the prevention of chronic diseases.
The impact of sedentary lifestyle on health.
The role of physical fitness in the prevention of heart disease.
The impact of obesity on health and lifespan.
The role of exercise in the management of stress.
The impact of nutrition on cognitive function.
The role of physical activity in the management of chronic diseases.
The impact of sleep on health and wellbeing.
The role of lifestyle interventions in the prevention of diabetes.

5. Health Disparities:

The impact of socioeconomic status on health outcomes.
The role of healthcare access in health disparities.
The impact of racial and ethnic disparities on health outcomes.
The role of social determinants of health in health disparities.
The impact of health literacy on health outcomes.
The role of cultural competence in reducing health disparities.
The impact of gender disparities on health outcomes.
The role of health policy in addressing health disparities.
The impact of environmental factors on health disparities.
The role of education in reducing health disparities.

6. Healthcare:

The impact of healthcare reform on patient outcomes.
The role of telemedicine in improving healthcare access.
The impact of health information technology on patient care.
The role of healthcare quality in patient satisfaction.
The impact of healthcare costs on health outcomes.
The role of healthcare leadership in improving patient care.
The impact of health insurance on healthcare access.
The role of healthcare innovation in improving patient outcomes.
The impact of healthcare policy on patient care.
The role of healthcare management in improving healthcare quality.

7. Kinesiology:

The impact of physical activity on muscle function.
The role of biomechanics in injury prevention.
The impact of exercise on cognitive function.
The role of kinesiology in the prevention of chronic diseases.
The impact of physical fitness on health outcomes.
The role of kinesiology in the management of sports injuries.
The impact of exercise physiology on athletic performance.
The role of kinesiology in the promotion of physical activity.
The impact of motor control on athletic performance.
The role of kinesiology in the study of human movement.

8. Mental Health:

The impact of stress on mental health.
The role of psychotherapy in the treatment of mental disorders.
The impact of mental illness on quality of life.
The role of mental health promotion in the prevention of mental disorders.
The impact of stigma on mental health outcomes.
The role of mental health services in the treatment of mental disorders.
The impact of mental health policy on mental health services.
The role of mental health literacy in the prevention of mental disorders.
The impact of mental health disparities on mental health outcomes.
The role of mental health research in improving mental health care.

9. Pharmacology:

The impact of drug interactions on patient safety.
The role of pharmacokinetics in drug development.
The impact of pharmacogenomics on personalized medicine.
The role of pharmacology in the treatment of diseases.
The impact of drug resistance on the treatment of infectious diseases.
The role of pharmacology in the management of pain.
The impact of drug abuse on public health.
The role of pharmacology in the prevention of drug interactions.
The impact of drug development on patient care.
The role of pharmacology in the study of drug action.

10. Veterinary Medicine:

The impact of zoonotic diseases on public health.
The role of veterinary medicine in the prevention of animal diseases.
The impact of veterinary medicine on food safety.
The role of veterinary medicine in the treatment of animal diseases.
The impact of veterinary public health on human health.
The role of veterinary medicine in the prevention of zoonotic diseases.
The impact of animal health on public health.
The role of veterinary medicine in the promotion of animal welfare.
The impact of veterinary medicine on animal behavior.
The role of veterinary medicine in the study of animal physiology.

11. Neurology:

The impact of neurodegenerative diseases on public health.
The role of neurology in the treatment of neurological disorders.
The impact of neurological research on patient care.
The role of neurology in the study of the nervous system.
The impact of neurological disorders on quality of life.
The role of neurology in the diagnosis of neurological disorders.
The impact of neurological diseases on mental health.
The role of neurology in the prevention of neurological diseases.
The impact of neurological disorders on cognitive function.
The role of neurology in the study of brain function.

12. Oncology:

The impact of cancer on public health.
The role of oncology in the treatment of cancer.
The impact of oncological research on patient care.
The role of oncology in the study of cancer.
The impact of cancer on quality of life.
The role of oncology in the diagnosis of cancer.
The impact of cancer on mental health.
The role of oncology in the prevention of cancer.
The impact of cancer on physical health.
The role of oncology in the study of cancer biology.

13. Cardiology:

The impact of heart disease on public health.
The role of cardiology in the treatment of heart disease.
The impact of cardiological research on patient care.
The role of cardiology in the study of the cardiovascular system.
The impact of heart disease on quality of life.
The role of cardiology in the diagnosis of heart disease.
The impact of heart disease on mental health.
The role of cardiology in the prevention of heart disease.
The impact of heart disease on physical health.
The role of cardiology in the study of heart function.

14. Endocrinology:

The impact of endocrine disorders on public health.
The role of endocrinology in the treatment of endocrine disorders.
The impact of endocrinological research on patient care.
The role of endocrinology in the study of the endocrine system.
The impact of endocrine disorders on quality of life.
The role of endocrinology in the diagnosis of endocrine disorders.
The impact of endocrine disorders on mental health.
The role of endocrinology in the prevention of endocrine disorders.
The impact of endocrine disorders on physical health.
The role of endocrinology in the study of hormone function.

15. Gastroenterology:

The impact of gastrointestinal disorders on public health.
The role of gastroenterology in the treatment of gastrointestinal disorders.
The impact of gastroenterological research on patient care.
The role of gastroenterology in the study of the gastrointestinal system.
The impact of gastrointestinal disorders on quality of life.
The role of gastroenterology in the diagnosis of gastrointestinal disorders.
The impact of gastrointestinal disorders on mental health.
The role of gastroenterology in the prevention of gastrointestinal disorders.
The impact of gastrointestinal disorders on physical health.
The role of gastroenterology in the study of digestive function.

16. Dermatology:

The impact of skin disorders on public health.
The role of dermatology in the treatment of skin disorders.
The impact of dermatological research on patient care.
The role of dermatology in the study of the skin.
The impact of skin disorders on quality of life.
The role of dermatology in the diagnosis of skin disorders.
The impact of skin disorders on mental health.
The role of dermatology in the prevention of skin disorders.
The impact of skin disorders on physical appearance.
The role of dermatology in the study of skin health.

17. Pulmonology:

The impact of respiratory disorders on public health.
The role of pulmonology in the treatment of respiratory disorders.
The impact of pulmonological research on patient care.
The role of pulmonology in the study of the respiratory system.
The impact of respiratory disorders on quality of life.
The role of pulmonology in the diagnosis of respiratory disorders.
The impact of respiratory disorders on mental health.
The role of pulmonology in the prevention of respiratory disorders.
The impact of respiratory disorders on physical health.
The role of pulmonology in the study of lung function.

18. Pediatrics:

The impact of childhood diseases on public health.
The role of pediatrics in the treatment of childhood diseases.
The impact of pediatric research on patient care.
The role of pediatrics in the study of childhood diseases.
The impact of childhood diseases on quality of life.
The role of pediatrics in the diagnosis of childhood diseases.
The impact of childhood diseases on mental health.
The role of pediatrics in the prevention of childhood diseases.
The impact of childhood diseases on physical health.
The role of pediatrics in the study of child health.

19. Geriatrics:

The impact of aging on public health.
The role of geriatrics in the treatment of age-related diseases.
The impact of geriatric research on patient care.
The role of geriatrics in the study of aging.
The impact of aging on quality of life.
The role of geriatrics in the diagnosis of age-related diseases.
The impact of aging on mental health.
The role of geriatrics in the prevention of age-related diseases.
The impact of aging on physical health.
The role of geriatrics in the study of the aging process.

20. Psychiatry:

The impact of mental disorders on public health.
The role of psychiatry in the treatment of mental disorders.
The impact of psychiatric research on patient care.
The role of psychiatry in the study of mental disorders.
The impact of mental disorders on quality of life.
The role of psychiatry in the diagnosis of mental disorders.
The impact of mental disorders on social interactions.
The role of psychiatry in the prevention of mental disorders.
The impact of mental disorders on physical health.
The role of psychiatry in the study of mental health.

The field of medical research is vast and diverse, offering a multitude of topics for exploration. The choice of a research topic is a critical step in the research process, and it can significantly influence the direction of your research and the impact of your findings. Therefore, it’s crucial to choose a topic that not only interests you but also aligns with your academic and career goals. This comprehensive list of medical research paper topics provides a starting point for your research journey. Remember, the best research topic is one that is original, manageable, and meaningful.

Introduction to Medical Research

Medical research is a broad and multifaceted field that encompasses a wide range of topics and disciplines. It is the backbone of modern healthcare and plays a crucial role in improving the quality of life for people around the world. Medical research is the driving force behind new discoveries, innovative treatments, and the development of policies that shape public health.

At its core, medical research seeks to understand the complexities of the human body, the diseases that afflict it, and the various external factors that influence health. It is a field that is constantly evolving, with new technologies and methodologies continually pushing the boundaries of what we know about health and disease.

One of the key aspects of medical research is its interdisciplinary nature. It draws upon a variety of fields, including biology, chemistry, physics, and even social sciences. This interdisciplinary approach allows for a more comprehensive understanding of health and disease, as it considers not only the biological aspects but also the social, environmental, and psychological factors that influence health.

For instance, research in anatomy and physiology delves into the structure and function of the human body, providing the foundation for understanding health and disease. Studies in diseases and epidemiology, on the other hand, focus on the patterns, causes, and effects of health and disease conditions in defined populations. Health and fitness research explores the impact of lifestyle choices on health, while health disparities research examines the inequalities in health outcomes and healthcare that exist among different population groups.

Research in healthcare and kinesiology often intersects, examining how healthcare services and physical activity can improve health outcomes. Mental health research is a rapidly growing field, addressing a wide range of mental health disorders and the impact they have on overall health. Pharmacology research is vital for the development of new drugs and therapies, while veterinary medicine research not only improves the health of animals but can also provide insights into human health through comparative medicine.

Choosing a topic for a medical research paper can be a daunting task given the breadth and depth of the field. However, it also offers a wealth of opportunities to delve into a topic that is not only interesting but can also contribute to the betterment of health and healthcare.

How to Choose Medical Research Paper Topics

Choosing a topic for your medical research paper is a critical step in your academic journey. The topic you select will guide your research direction, influence your motivation, and determine the depth of your understanding of the subject matter. Therefore, it’s essential to choose wisely and consider various factors before making a decision. Here are ten comprehensive tips to help you select the right medical research paper topic:

Identify Your Interests: The first step in choosing a research topic is to consider your interests within the field of medicine. Are you fascinated by neurology, passionate about public health, or intrigued by the complexities of surgery? Identifying your interests can help you narrow down potential topics. When you choose a topic you’re passionate about, the research process becomes less of a task and more of an exploration. You’ll find yourself more engaged, and the enthusiasm will reflect in the quality of your research.
Consider the Scope: The scope of your topic is an important factor to consider. A topic that’s too broad can make your research overwhelming, as you may have to sift through an enormous amount of information and struggle to cover all aspects within the constraints of your paper. On the other hand, a topic that’s too narrow might not provide enough material for a comprehensive study. Strive for a balance where your topic is specific enough to manage but broad enough to explore in depth.
Check for Resources: Before finalizing a topic, ensure there are sufficient resources available for your research. These resources could include books, scholarly articles, reputable online sources, and expert interviews. Conduct a preliminary literature review to gauge the availability of sources. A lack of resources can lead to a weak paper, while an abundance of resources can provide multiple perspectives and strengthen your research.
Relevance to Current Times: Choosing a topic that is relevant to current medical issues or ongoing research can make your paper more engaging and significant. It allows you to contribute to the existing body of knowledge and possibly influence future research or policy. Whether it’s a new treatment method, an emerging disease, or a controversial medical practice, current topics can spark interest and debate.
Consult with Your Instructor: Your instructor or advisor can be a valuable resource when choosing a topic. They can provide guidance, help you refine a broad topic, expand a narrow one, and provide feedback on your initial ideas. They can also point you towards resources or research areas you might not have considered.
Consider the Target Audience: Always keep your target audience in mind when choosing a research topic. The topic should be interesting and accessible to them. If you’re writing for a class, consider what you’ve already discussed and what themes your instructor has emphasized. If you’re writing for publication, consider the interests and level of expertise of the readers of the journal.
Practicality: Consider the practical aspects of your research. If your research involves primary data collection, consider the feasibility of the methods you plan to use. Do you have access to the necessary equipment or population? Is your study ethical and approved by an ethics committee? Practical considerations can significantly influence your choice of topic.
Originality: While it’s important to build upon existing research, strive to bring a unique perspective to your topic. This could involve studying a well-researched topic from a new angle, exploring a less-studied area of medical research, or proposing a new hypothesis. Originality can make your research stand out and contribute to the advancement of your field.
Alignment with Course Objectives: Your research topic should align with the objectives of your course or curriculum. This ensures that your research paper will not only be interesting but also academically beneficial. It can demonstrate your understanding ofthe course material and your ability to apply it in a real-world context.
Future Career Relevance: If you have a clear idea of your future career path, consider choosing a topic that could be beneficial in your professional life. This could mean researching a topic related to a field you’d like to specialize in, a current issue in your future profession, or a novel area of study that could give you a competitive edge in your career.

In addition to these tips, it’s also important to consider the timeline of your research. Some topics may require more time to research than others, especially if they involve complex experiments or hard-to-reach populations. Make sure you choose a topic that you can reasonably research and write about within your given timeframe.

Also, consider the potential impact of your research. While it’s not always possible to predict how influential a research paper will be, you can consider whether the topic has the potential to affect policy, influence clinical practice, or lead to new research questions. Choosing a topic with potential impact can be particularly important if you plan to pursue a career in academia or research.

Remember, choosing a research topic is not a decision to be taken lightly. It’s the foundation of your research paper and can significantly influence your enjoyment of the writing process, your grade, and even your future career. Take your time, consider your options, and choose a topic that you are passionate about, meets practical considerations, and has the potential to contribute to the field of medical science.

Finally, be flexible. Research is a dynamic process, and it’s okay to refine or even change your topic as you delve deeper into your research. The most important thing is to stay curious, open-minded, and dedicated to uncovering new knowledge. With the right topic, research can be a rewarding journey of discovery.

How to Write a Medical Research Paper

Writing a medical research paper is a comprehensive task that demands a profound understanding of the subject matter, a systematic approach, and a lucid writing style. This process can be quite daunting, especially for those who are new to it. However, with the right guidance and a well-structured plan, it can be a rewarding experience. Here are ten detailed tips to guide you through the process:

Understand the Assignment Thoroughly: The first step in writing a medical research paper is to fully comprehend the assignment. What is the specific question you’re being asked to address? What is the scope of the research paper? What are the formatting requirements? Are there any specific sources you need to use? Understanding the assignment in its entirety will help you focus your research and ensure you meet all the requirements. It’s crucial to clarify any doubts with your professor or advisor at this stage to avoid any misunderstandings later on.
Choose a Suitable Topic: If you have the freedom to choose your own topic, select one that genuinely interests you. Your passion for the subject will come through in your writing, making the process more enjoyable and less of a chore. Make sure the topic is relevant to your field of study and is something you can manage within the given timeframe and word limit. A well-chosen topic can make the difference between a paper that is a joy to research and write and one that is a burdensome chore.
Conduct Thorough Research: The next step is to conduct comprehensive research. Use reputable sources such as academic journals, textbooks, and government health websites to gather information. Make sure to use the most recent data and research available, as the medical field is constantly evolving and it’s important to stay up-to-date. Keep track of your sources as you go along for easy referencing later. This will also save you a lot of time when you are compiling your bibliography.
Create an Outline: An outline is a roadmap for your research paper. It should include an introduction, a body with several points or arguments, and a conclusion. Each point should be supported by evidence from your research. An outline will help structure your thoughts and ideas, making your writing process smoother and more organized. It also allows you to see the flow of your arguments and make sure that everything is in a logical order.
Write a Strong Thesis Statement: Your thesis statement is the backbone of your research paper. It should clearly state the main idea or argument of your paper. It should be concise, specific, and arguable. The thesis statement will guide your writing and keep your argument focused. Spend some time crafting a strong thesis statement. It’s worth the effort as it will give direction to your research and writing.
Start Writing: Once you have your outline and thesis statement, you can start writing. Begin with a draft. Don’t worry about making it perfect the first time around. Just get your ideas down on paper. You can revise and improve it later. Remember, writing is a process. It’s okay to have multiple drafts. The important thing is to start writing.
Use Clear, Concise Language: Medical research papers should be written in a clear, concise, and formal style. Avoid jargon and complex sentences. Make sure your ideas are expressed clearly and logically. Remember, your goal is to communicate your research and ideas, not to impress with big words or complex sentences.
Cite Your Sources: Always cite your sources. This gives credit to the original authors and allows readers to follow up on your research. Be sure to use the citation style required by your professor or the journal you are submitting to. Proper citation is not just about avoiding plagiarism. It also lends credibility to yourpaper and demonstrates the breadth of your research.
Revise and Edit: Once you’ve finished your draft, take the time to revise and edit. This is where you refine your arguments, clarify your thoughts, and polish your language. Look for any inconsistencies, grammatical errors, or areas that could be clarified or improved. Consider having a peer or mentor review your paper. They can provide valuable feedback and catch errors you might have missed. Remember, good writing is rewriting. Don’t be afraid to make changes, and don’t be discouraged if your first draft isn’t perfect.
Proofread: Finally, proofread your paper. Check for any spelling, grammar, or punctuation errors. Make sure all citations and references are correct. Proofreading is a crucial step in the writing process. Even the most well-researched and well-written paper can lose credibility if it’s full of errors. Consider using a proofreading tool or ask someone else to proofread your paper. A fresh pair of eyes can often catch mistakes you might have overlooked.

In addition to these tips, remember to take breaks during your writing process. Writing a medical research paper is a significant undertaking, and it’s important to avoid burnout. Take time to rest and refresh your mind. This will help you maintain your focus and energy levels.

Also, keep in mind that writing a research paper is not just about the end product. It’s also about the process. It’s an opportunity to learn more about a topic you’re interested in, to improve your research and writing skills, and to contribute to your field of study. Approach it with curiosity and enthusiasm, and you’ll find that writing a medical research paper can be a rewarding experience.

Writing a medical research paper is a systematic process that requires careful planning, thorough research, and meticulous writing. By following these tips, you can produce a paper that is informative, insightful, and contributes to your field of study. Remember, every writer faces challenges along the way, so don’t be discouraged if you encounter difficulties. With persistence and dedication, you can write a successful medical research paper.

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Navigating the complex world of academic writing can be a daunting task, especially when you’re dealing with intricate topics in the field of medical research. That’s where iResearchNet comes in. As a leading provider of academic writing services, we’re here to support you every step of the way. Our services are designed to help you produce high-quality, well-researched, and professionally formatted papers that meet the rigorous standards of academic writing. Here’s a detailed look at the features that set our services apart:

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Title: nuclei instance segmentation of cryosectioned h&e stained histological images using triple u-net architecture.

Abstract: Nuclei instance segmentation is crucial in oncological diagnosis and cancer pathology research. H&E stained images are commonly used for medical diagnosis, but pre-processing is necessary before using them for image processing tasks. Two principal pre-processing methods are formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS). While FFPE is widely used, it is time-consuming, while FS samples can be processed quickly. Analyzing H&E stained images derived from fast sample preparation, staining, and scanning can pose difficulties due to the swift process, which can result in the degradation of image quality. This paper proposes a method that leverages the unique optical characteristics of H&E stained images. A three-branch U-Net architecture has been implemented, where each branch contributes to the final segmentation results. The process includes applying watershed algorithm to separate overlapping regions and enhance accuracy. The Triple U-Net architecture comprises an RGB branch, a Hematoxylin branch, and a Segmentation branch. This study focuses on a novel dataset named CryoNuSeg. The results obtained through robust experiments outperform the state-of-the-art results across various metrics. The benchmark score for this dataset is AJI 52.5 and PQ 47.7, achieved through the implementation of U-Net Architecture. However, the proposed Triple U-Net architecture achieves an AJI score of 67.41 and PQ of 50.56. The proposed architecture improves more on AJI than other evaluation metrics, which further justifies the superiority of the Triple U-Net architecture over the baseline U-Net model, as AJI is a more strict evaluation metric. The use of the three-branch U-Net model, followed by watershed post-processing, significantly surpasses the benchmark scores, showing substantial improvement in the AJI score

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Embryo Development and Selection: Advances in Genetics

Editorial: Embryo Development and Selection: Advances in Genetics Provisionally Accepted

1 Guilin Medical University, China
2 Hainan Medical University, China

The final, formatted version of the article will be published soon.

Assisted reproductive technologies (ART) are being utilized with increasing frequency across the globe to support individuals who face challenges in conceiving naturally. ART procedures facilitate pregnancy by extracting eggs from a woman's ovaries, fusing them with sperm in a laboratory setting, and subsequently reintroducing them into the woman's body. The efficacy of ART is subject to a multitude of factors, encompassing genetic considerations, historical medical and reproductive data, the specific medications employed during ART treatment, and any complications that may arise during pregnancy. The focus of this research topic is the selection of embryos during the ART process, and an exploration of the factors that influence embryo development and the overall success rate of ART. 1. Abu et al. evaluated the impact of supplementing a single-dose GnRH agonist to the standard progestogen regimen for luteal phase support in IVF treatments. Compared to using progestogens alone, they found that adding a GnRH agonist improved the overall IVF outcomes.2. Andreescu has conducted a review on the dysregulation of immune responses between the mother and fetus, a factor that heightens the risk of embryo rejection and reproductive failure.This paper provides an exhaustive overview of the current literature on the influence of Calcineurin inhibitors and anti-TNF treatment in enhancing the live birth rate post embryo transfer. It concludes that the suppression of immunological rejection and the promotion of immunological tolerance are vital in safeguarding embryos and averting immunological assaults. Therefore, it is important to exercise caution while selecting use of any immunosuppressive therapy in pregnancy. indicates that the risks associated with angular pregnancy following ART may not be as severe as previously thought. With regular, close monitoring, the majority of these cases can be managed expectantly, often resulting in live births. 5. Zhu and colleagues conducted a study to identify the risk factors that influence the occurrence of heterotopic pregnancies following IVF-ET. They also examined the outcomes of pregnancies after surgical intervention for heterotopic pregnancies. Their research indicates that a history of ectopic pregnancy, multiple abortions, tubal infertility, and multiple-embryo transfer may significantly increase the risk of a subsequent heterotopic pregnancy after IVF-ET.The study also found that for patients with heterotopic pregnancies who undergo surgery, factors such as a shorter operation duration, a smaller ectopic mass, and a location in the ampulla of the fallopian tube are associated with a more favorable reproductive prognosis. IVF success rates are influenced by a multitude of factors, including genetic considerations, historical medical and reproductive data, the types of medications used during ART treatment, and complications that may arise during pregnancy. The research papers discussed here offer significant insights into improving the success rates of IVF. They underscore the efficacy of a combined treatment approach using N-Acetylcysteine and Gonadotropins, which has been shown to enhance ovarian response to superovulation drugs and improve the quality of blastocysts in older women. The papers also identify surgical time, the size of ectopic masses, and their location in the ampulla of the fallopian tube as key factors in determining the reproductive prognosis in patients with ectopic pregnancies. Additionally, the use of GnRH agonists in the luteal phase has been found to increase IVF success rates. The papers also discuss the evaluation of cornual pregnancy following ART. The aim of these studies is to reduce the risks associated with assisted reproduction and increase pregnancy rates, thereby providing invaluable information for clinical IVF centers. The referenced studies investigate the influence of several factors on the success rate of IVF. These factors encompass the patient's medical history, the treatment protocol, the medications administered during treatment, and complications encountered during pregnancy.The insights and research outcomes presented are of immense value for improving the success rates of assisted reproductive techniques.

Keywords: assisted reproductive technologies, Pregnancy rates, Embryo development, Embryo selection, Influence factor

Received: 13 Apr 2024; Accepted: 22 Apr 2024.

Copyright: © 2024 Liu and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Yanlin Ma, Hainan Medical University, Haikou, 571199, Hainan Province, China

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Fatal Traffic Risks With a Total Solar Eclipse in the US

1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Guidance on energy and macronutrients across the lifespan

by Pennington Biomedical Research Center

In the long history of recommendations for nutritional intake, current research is trending toward the concept of "food as medicine"—a philosophy in which food and nutrition are positioned within interventions to support health and wellness. In the paper " Guidance on Energy and Macronutrients Across the Lifespan ," Pennington Biomedical Research Center's Dr. Steven Heymsfield shares the latest clarity and recommendations in the rich and storied history of energy and macronutrient intake.

The research paper by Dr. Heymsfield and colleague Dr. Sue Shapses, Professor of Nutritional Sciences at Rutgers University and Director of the Next Center at the New Jersey Institute for Food, Nutrition and Health, was recently published in the New England Journal of Medicine , showcasing recommendations with increased clarity for protein, fat, carbohydrates, fiber and water intake at various stages in the human lifespan.

"Couple with the amount and pattern of the foods people eat, the primary macronutrients of protein, carbohydrates and fat can shape the major determinates of health throughout the lifespan," said Dr. Heymsfield, who is a professor of Metabolism & Body Composition at Pennington Biomedical.

"Even considering the incredible diversity of traits and nutritional needs across the global population, we can potentially provide effective care for all patients, including the growing number of patients with diet-related diseases, so long as we recognize the subtle effects of the key macronutrients."

Throughout the research document, the authors frequently reference the original, historic research for which they are providing the latest incarnation and related knowledge. Focusing primarily on energy and three macronutrients—protein, carbohydrates and fat, and their subsequent substrates— amino acids , glucose and free fatty acids , the paper shows how these can fuel growth and maintenance throughout life.

For optimal health, the study provides dietary reference intakes for the three micronutrients at various stages: 0 to 6 months, 7 months to slightly less than a year old, one year to three, four to eight years, nine to 13 years, 14 to 18 years, over 19 years, and then additional recommendations for pregnancy and lactation.

The research goes on to provide recommendations to patients and caregivers on healthy eating patterns consistent with the energy and macronutrient guidelines and includes an online calculator .

While the energy requirements and variable needs for the three main macronutrients and multiple micronutrients vary across the nine life stage groups, there are overarching nutritional goals for patients when choosing healthy food patterns. A variety of healthy meal pattern examples are available, but reoccurring components feature the inclusion of vegetables of all types, whole fruits, fat-free or low-fat dairy, lean meats, seafood, eggs, beans, and nuts, plant- and seafood-based oils, and grains, with at least half of those being whole grains.

The need to incorporate the three main macronutrient groups and micronutrients into the diets of the various life stage groups is a matrix that is further complicated as varying financial resources, personal preferences, cultural backgrounds and ethnic food traditions are accounted for. The paper structures a priority framework, offering better insights into those diets that can be tailored for specific diet-related chronic conditions, such as obesity or type 2 diabetes.

"The legacy of research into dietary nutrition continues to refine what we know about our bodies and the capacity for a tailored diet, featuring key macronutrients to support our long-term health," said Dr. John Kirwan, Executive Director of Pennington Biomedical Research Center. "Dr. Heymsfield's recent paper in the New England Journal of Medicine is the latest contribution to this research history of contributing to the knowledge base, and further promotes the notion of 'food as medicine'—delivering the potential to improve health across the lifespan with bespoke, nutrient-rich diets."

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Medicinal Cannabis: History, Pharmacology, And Implications for the Acute Care Setting

The authors review the historical use of medicinal cannabis and discuss the agent’s pharmacology and pharmacokinetics, select evidence on medicinal uses, and the implications of evolving regulations on the acute care hospital setting.

INTRODUCTION

Medicinal cannabis, or medicinal marijuana, is a therapy that has garnered much national attention in recent years. Controversies surrounding legal, ethical, and societal implications associated with use; safe administration, packaging, and dispensing; adverse health consequences and deaths attributed to marijuana intoxication; and therapeutic indications based on limited clinical data represent some of the complexities associated with this treatment. Marijuana is currently recognized by the U.S. Drug Enforcement Agency’s (DEA’s) Comprehensive Drug Abuse Prevention and Control Act (Controlled Substances Act) of 1970 as a Schedule I controlled substance, defined as having a high potential for abuse, no currently accepted medicinal use in treatment in the United States, and a lack of accepted safety data for use of the treatment under medical supervision. 1

Cannabis is the most commonly cultivated, trafficked, and abused illicit drug worldwide; according to the World Health Organization (WHO), marijuana consumption has an annual prevalence rate of approximately 147 million individuals or nearly 2.5% of the global population. 2 In 2014, approximately 22.2 million Americans 12 years of age or older reported current cannabis use, with 8.4% of this population reporting use within the previous month. 3 , 4 General cannabis use, both for recreational and medicinal purposes, has garnered increasing acceptance across the country as evidenced by legislative actions, ballot measures, and public opinion polls; an October 2016 Gallup poll on American’s views on legalizing cannabis indicated that 60% of the population surveyed believed the substance should be legalized. 5 Further, a recent Quinnipiac University poll concluded 54% of American voters surveyed would favor the legalization of cannabis without additional constraints, while 81% of respondents favored legalization of cannabis for medicinal purposes. 6 Limited data suggest that health care providers also may consider this therapy in certain circumstances. 7 – 9 In the United States, cannabis is approved for medicinal use in 28 states, the District of Columbia, Guam, and Puerto Rico as of January 2017. 10

The use and acceptance of medicinal cannabis continues to evolve, as shown by the growing number of states now permitting use for specific medical indications. The Food and Drug Administration (FDA) has considered how it might support the scientific rigor of medicinal cannabis claims, and the review of public data regarding safety and abuse potential is ongoing. 11 , 12 The purpose of this article is to review the historical significance of the use of medicinal cannabis and to discuss its pharmacology, pharmacokinetics, and select evidence on medicinal uses, as well as to describe the implications of evolving medicinal cannabis regulations and their effects on the acute care hospital setting.

HISTORICAL SIGNIFICANCE

Cannabis is a plant-based, or botanical, product with origins tracing back to the ancient world. Evidence suggesting its use more than 5,000 years ago in what is now Romania has been described extensively. 13 There is only one direct source of evidence (Δ 6 -tetrahydrocannabinol [Δ 6 -THC] in ashes) that cannabis was first used medicinally around 400 ad . 14 In the U.S., cannabis was widely utilized as a patent medicine during the 19th and early 20th centuries, described in the United States Pharmacopoeia for the first time in 1850. Federal restriction of cannabis use and cannabis sale first occurred in 1937 with the passage of the Marihuana Tax Act. 15 , 16 Subsequent to the act of 1937, cannabis was dropped from the United States Pharmacopoeia in 1942, with legal penalties for possession increasing in 1951 and 1956 with the enactment of the Boggs and Narcotic Control Acts, respectively, and prohibition under federal law occurring with the Controlled Substances Act of 1970. 1 , 17 , 18 Beyond criminalization, these legislative actions contributed to creating limitations on research by restricting procurement of cannabis for academic purposes.

In 1996, California became the first state to permit legal access to and use of botanical cannabis for medicinal purposes under physician supervision with the enactment of the Compassionate Use Act. As previously stated, as of January 1, 2017, 28 states as well as Washington, D.C., Guam, and Puerto Rico will have enacted legislation governing medicinal cannabis sale and distribution; 21 states and the District of Columbia will have decriminalized marijuana and eliminated prohibition for possession of small amounts, while eight states, including Alaska, California, Colorado, Maine, Massachusetts, Nevada, Oregon, and Washington, as well as the District of Columbia, will have legalized use of marijuana for adult recreation. 10 , 19

THE MEDICINAL CANNABIS DEBATE

As a Schedule I controlled substance with no accepted medicinal use, high abuse potential, concerns for dependence, and lack of accepted safety for use under medical supervision—along with a national stigma surrounding the potential harms and implication of cannabis use as a gateway drug to other substances—transitioning from a vilified substance to one with therapeutic merits has been controversial. The United States Pharmacopoeia and the FDA have considered the complexities of regulating this plant-based therapy, including the numerous compounds and complex interactions between substances in this product, and how it might fit into the current regulatory framework of drugs in United States. 11 , 12 , 17

The emergence of interest in botanical medicinal cannabis is thought by many to be a collateral effect of the opioid abuse epidemic; public perception surrounding the use of medicinal cannabis suggests that this plant-based therapy is viewed as not much different than a botanical drug product or supplement used for health or relief of symptoms if disease persists. Like some herbal preparations or supplements, however, medicinal cannabis may similarly pose health risks associated with its use, including psychoactive, intoxicating, and impairing effects, which have not been completely elucidated through clinical trials. Proponents argue that there is evidence to support botanical medicinal cannabis in the treatment of a variety of conditions, particularly when symptoms are refractory to other therapies; that beneficial cannabinoids exist, as evidenced by single-entity agents derived from cannabis containing the compounds THC and cannabidiol (CBD); that cannabis is relatively safe, with few deaths reported from use; that therapy is self-titratable by the patient; and that therapy is relatively inexpensive compared with pharmaceutical agents. 20 – 22 Opponents of medicinal cannabis use argue, in part, that well-designed randomized trials to confirm benefits and harms are lacking; that it has not been subject to the rigors of the FDA approval process; that standardization in potency or quantity of pharmacologically active constituents is absent; that adverse health effects relate not only to smoking cannabis but to unmasking mental health disorders, impairing coordination, and affecting judgment; that standardization does not exist for product packaging and controls to prevent inadvertent use by minors or pets; that there is a potential for dependence, addiction, and abuse; and that costs pose a potential burden. 23 – 25

Regardless of personal views and perceptions, to deny or disregard the implications of use of this substance on patient health and the infrastructure of the health care system is irresponsible; clinicians must be aware of these implications and informed about how this therapy may influence practice in a variety of health care settings, including acute care.

PHARMACOLOGY

Endocannabinoids (eCBs) and their receptors are found throughout the human body: nervous system, internal organs, connective tissues, glands, and immune cells. The eCB system has a homeostatic role, having been characterized as “eat, sleep, relax, forget, and protect.” 26 It is known that eCBs have a role in the pathology of many disorders while also serving a protective function in certain medical conditions. 27 It has been proposed that migraine, fibromyalgia, irritable bowel syndrome, and related conditions represent clinical eCB deficiency syndromes (CEDS). Deficiencies in eCB signaling could be also involved in the pathogenesis of depression. In human studies, eCB system deficiencies have been implicated in schizophrenia, multiple sclerosis (MS), Huntington’s disease, Parkinson’s disease, anorexia, chronic motion sickness, and failure to thrive in infants. 28

The eCB system represents a microcosm of psycho-neuroimmunology or “mind–body” medicine. The eCB system consists of receptors, endogenous ligands, and ligand metabolic enzymes. A variety of physiological processes occur when cannabinoid receptors are stimulated. Cannabinoid receptor type 1 (CB 1 ) is the most abundant G-protein–coupled receptor. It is expressed in the central nervous system, with particularly dense expression in (ranked in order): the substantia nigra, globus pallidus, hippocampus, cerebral cortex, putamen, caudate, cerebellum, and amygdala. CB 1 is also expressed in non-neuronal cells, such as adipocytes and hepatocytes, connective and musculoskeletal tissues, and the gonads. CB 2 is principally associated with cells governing immune function, although it may also be expressed in the central nervous system.

The most well-known eCB ligands are N-arachidonyl-ethanolamide (anandamide or AEA) and sn-2-arachidonoyl-glycerol (2-AG). AEA and 2-AG are released upon demand from cell membrane phospholipid precursors. This “classic” eCB system has expanded with the discovery of secondary receptors, ligands, and ligand metabolic enzymes. For example, AEA, 2-AG, N-arachidonoyl glycine (NAGly), and the phytocannabinoids Δ 9 -THC and CBD may also serve, to different extents, as ligands at GPR55, GPR18, GPR119, and several transient receptor potential ion channels (e.g., TRPV1, TRPV2, TRPA1, TRPM8) that have actions similar to capsaicin. 28 The effects of AEA and 2-AG can be enhanced by “entourage compounds” that inhibit their hydrolysis via substrate competition, and thereby prolong their action through synergy and augmentation. Entourage compounds include N-palmitylethanolamide (PEA), N-oleoylethanolamide (SEA), and cis-9-octadecenoamide (OEA or oleamide) and may represent a novel route for molecular regulation of endogenous cannabinoid activity. 29

Additional noncannabinoid targets are also linked to cannabis. G-protein–coupled receptors provide noncompetitive inhibition at mu and delta opioid receptors as well as norepinephrine, dopamine, and serotonin. Ligand-gated ion channels create allosteric antagonism at serotonin and nicotinic receptors, and enhance activation of glycine receptors. Inhibition of calcium, potassium, and sodium channels by noncompetitive antagonism occurs at nonspecific ion channels and activation of PPARα and PPARγ at the peroxisome proliferator-activated receptors is influenced by AEA. 30

THC is known to be the major psychoactive component of cannabis mediated by activation of the CB 1 receptors in the central nervous system; however, this very mechanism limits its use due to untoward adverse effects. It is now accepted that other phytocannabinoids with weak or no psychoactivity have promise as therapeutic agents in humans. The cannabinoid that has sparked the most interest as a nonpsychoactive component is CBD. 31 Unlike THC, CBD elicits its pharmacological effects without exerting any significant intrinsic activity on CB 1 and CB 2 receptors. Several activities give CBD a high potential for therapeutic use, including antiepileptic, anxiolytic, antipsychotic, anti-inflammatory, and neuroprotective effects. CBD in combination with THC has received regulatory approvals in several European countries and is under study in registered trials with the FDA. And, some states have passed legislation to allow for the use of majority CBD preparations of cannabis for certain pathological conditions, despite lack of standardization of CBD content and optimal route of administration for effect. 32 Specific applications of CBD have recently emerged in pain (chronic and neuropathic), diabetes, cancer, and neurodegenerative diseases, such as Huntington’s disease. Animal studies indicate that a high dose of CBD inhibits the effects of lower doses of THC. Moreover, clinical studies suggest that oral or oromucosal CBD may prolong and/or intensify the effects of THC. Finally, preliminary clinical trials suggest that high-dose oral CBD (150–600 mg per day) may exert a therapeutic effect for epilepsy, insomnia, and social anxiety disorder. Nonetheless, such doses of CBD have also been shown to cause sedation. 33

PHARMACOKINETICS AND ADMINISTRATION

The three most common methods of administration are inhalation via smoking, inhalation via vaporization, and ingestion of edible products. The method of administration can impact the onset, intensity, and duration of psychoactive effects; effects on organ systems; and the addictive potential and negative consequences associated with use. 34

Cannabinoid pharmacokinetic research has been challenging; low analyte concentrations, rapid and extensive metabolism, and physicochemical characteristics hinder the separation of compounds of interest from biological matrices and from each other. The net effect is lower drug recovery due to adsorption of compounds of interest to multiple surfaces. 35 The primary psychoactive constituent of marijuana—Δ 9 -THC—is rapidly transferred from lungs to blood during smoking. In a randomized controlled trial conducted by Huestis and colleagues, THC was detected in plasma immediately after the first inhalation of marijuana smoke, attesting to the efficient absorption of THC from the lungs. THC levels rose rapidly and peaked prior to the end of smoking. 36 Although smoking is the most common cannabis administration route, the use of vaporization is increasing rapidly. Vaporization provides effects similar to smoking while reducing exposure to the byproducts of combustion and possible carcinogens and decreasing adverse respiratory syndromes. THC is highly lipophilic, distributing rapidly to highly perfused tissues and later to fat. 37 A trial of 11 healthy subjects administered Δ 9 -THC intravenously, by smoking, and by mouth demonstrated that plasma profiles of THC after smoking and intravenous injection were similar, whereas plasma levels after oral doses were low and irregular, indicating slow and erratic absorption. The time courses of plasma concentrations and clinical “high” were of the same order for intravenous injection and smoking, with prompt onset and steady decline over a four-hour period. After oral THC, the onset of clinical effects was slower and lasted longer, but effects occurred at much lower plasma concentrations than they did after the other two methods of administration. 38

Cannabinoids are usually inhaled or taken orally; the rectal route, sublingual administration, transdermal delivery, eye drops, and aerosols have been used in only a few studies and are of little relevance in practice today. The pharmacokinetics of THC vary as a function of its route of administration. Inhalation of THC causes a maximum plasma concentration within minutes and psychotropic effects within seconds to a few minutes. These effects reach their maximum after 15 to 30 minutes and taper off within two to three hours. Following oral ingestion, psychotropic effects manifest within 30 to 90 minutes, reach their maximum effect after two to three hours, and last for about four to 12 hours, depending on the dose. 39

Within the shifting legal landscape of medical cannabis, different methods of cannabis administration have important public health implications. A survey using data from Qualtrics and Facebook showed that individuals in states with medical cannabis laws had a significantly higher likelihood of ever having used the substance with a history of vaporizing marijuana (odds ratio [OR], 2.04; 99% confidence interval [CI], 1.62–2.58) and a history of oral administration of edible marijuana (OR, 1.78; 99% CI, 1.39–2.26) than those in states without such laws. Longer duration of medical cannabis status and higher dispensary density were also significantly associated with use of vaporized and edible forms of marijuana. Medical cannabis laws are related to state-level patterns of utilization of alternative methods of cannabis administration. 34

DRUG INTERACTIONS

Metabolic and pharmacodynamic interactions may exist between medical cannabis and other pharmaceuticals. Quantification of the in vitro metabolism of exogenous cannabinoids, including THC, CBD, and cannabinol (CBN), indicates hepatic cytochrome 450 (CYP450) isoenzymes 2C9 and 3A4 play a significant role in the primary metabolism of THC and CBN, whereas 2C19 and 3A4 and may be responsible for metabolism of CBD. 40 Limited clinical trials quantifying the effect of the exogenous cannabinoids on the metabolism of other medications exist; however, drug interaction data may be gleaned from the prescribing information from cannabinoid-derived pharmaceutical products such as Sativex (GW Pharmaceuticals, United Kingdom) and dronabinol (Marinol, AbbVie [United States]). 41 , 42 Concomitant administration of ketoconazole with oromucosal cannabis extract containing THC and CBD resulted in an increase in the maximum serum concentration and area under the curve for both THC and CBD by 1.2-fold to 1.8-fold and twofold, respectively; coadministration of rifampin is associated with a reduction in THC and CBD levels. 40 , 41 In clinical trials, dronabinol use was not associated with clinically significant drug interactions, although additive pharmacodynamic effects are possible when it is coadministered with other agents having similar physiological effects (e.g., sedatives, alcohol, and antihistamines may increase sedation; tricyclic antidepressants, stimulants, and sympathomimetics may increase tachycardia). 41 Additionally, smoking cannabis may increase theophylline metabolism, as is also seen after smoking tobacco. 40 , 42

ADVERSE EFFECTS

Much of what is known about the adverse effects of medicinal cannabis comes from studies of recreational users of marijuana. 43 Short-term use of cannabis has led to impaired short-term memory; impaired motor coordination; altered judgment; and paranoia or psychosis at high doses. 44 Long-term or heavy use of cannabis, especially in individuals who begin using as adolescents, has lead to addiction; altered brain development; cognitive impairment; poor educational outcomes (e.g., dropping out of school); and diminished life satisfaction. 45 Long-term or heavy use of cannabis is also associated with chronic bronchitis and an increased risk of chronic psychosis-related health disorders, including schizophrenia and variants of depression, in persons with a predisposition to such disorders. 46 – 48 Vascular conditions, including myocardial infarction, stroke, and transient ischemic attack, have also been associated with cannabis use. 49 – 51 The use of cannabis for management of symptoms in neurodegenerative diseases, such as Parkinson’s, Alzheimer’s, and MS, has provided data related to impaired cognition in these individuals. 52 , 53

A systematic review of published trials on the use of medical cannabinoids over a 40-year period was conducted to quantify adverse effects of this therapy. 54 A total of 31 studies evaluating the use of medicinal cannabis, including 23 randomized controlled trials and eight observational studies, was included. In the randomized trials, the median duration of cannabinoid exposure was two weeks, with a range between eight hours and 12 months. Of patients assigned to active treatment in these trials, a total of 4,779 adverse effects were reported; 96.6% (4,615) of these were not deemed by authors to be serious. The most common serious adverse effects included relapsing MS (9.1%; 15 events), vomiting (9.8%; 16 events), and urinary tract infections (9.1%; 15 events). No significant differences in the rates of serious adverse events between individuals receiving medical cannabis and controls were identified (relative risk, 1.04; 95% CI, 0.78–1.39). The most commonly reported non-serious adverse event was dizziness, with an occurrence rate of 15.5% (714 events) among people exposed to cannabinoids. 54

Other negative adverse effects reported with acute cannabis use include hyperemesis syndrome, impaired coordination and performance, anxiety, suicidal ideations or tendencies, and psychotic symptoms, whereas chronic effects may include mood disturbances, exacerbation of psychotic disorders, cannabis use disorders, withdrawal syndrome, and neurocognitive impairments, as well as cardiovascular and respiratory conditions. 52 Long-term studies evaluating adverse effects of chronic medicinal cannabis use are needed to conclusively evaluate the risks when used for an extended period of time.

MEDICINAL USES

Cannabis and cannabinoid agents are widely used to alleviate symptoms or treat disease, but their efficacy for specific indications is not well established. For chronic pain, the analgesic effect remains unclear. A systematic review of randomized controlled trials was conducted examining cannabinoids in the treatment of chronic noncancer pain, including smoked cannabis, oromucosal extracts of cannabis-based medicine, nabilone, dronabinol, and a novel THC analogue. 55 Pain conditions included neuropathic pain, fibromyalgia, rheumatoid arthritis, and mixed chronic pain. Fifteen of the 18 included trials demonstrated a significant analgesic effect of cannabinoids compared with placebo. Cannabinoid use was generally well tolerated; adverse effects most commonly reported were mild to moderate in severity. Overall, evidence suggests that cannabinoids are safe and moderately effective in neuropathic pain with preliminary evidence of efficacy in fibromyalgia and rheumatoid arthritis. 55

While there is not enough evidence to suggest routine use of medicinal cannabis for alleviating chemotherapy-related nausea and vomiting by national or international cancer societies, therapeutic agents based on THC (e.g., dronabinol) have been approved for use as an antiemetic in the United States for a number of years. Only recently has the efficacy and safety of cannabis-based medicines in managing nausea and vomiting due to chemotherapy been evaluated. In a review of 23 randomized, controlled trials, patients who received cannabis-based products experienced less nausea and vomiting than subjects who received placebo. 56 The proportion of people experiencing nausea and vomiting who received cannabis-based products was similar to those receiving conventional antiemetics. Subjects using cannabis-based products experienced side effects such as “feeling high,” dizziness, sedation, and dysphoria and dropped out of the studies at a higher rate due to adverse effects compared with participants receiving either placebo or conventional antiemetics. In crossover trials in which patients received cannabis-based products and conventional antiemetics, patients preferred the cannabis-based medicines. Cannabis-based medications may be useful for treating chemotherapy-induced nausea and vomiting that responds poorly to conventional antiemetics. However, the trials produced low to moderate quality evidence and reflected chemotherapy agents and antiemetics that were available in the 1980s and 1990s.

With regard to the management of neurological disorders, including epilepsy and MS, a Cochrane review of four clinical trials that included 48 epileptic patients using CBD as an adjunct treatment to other antiepileptic medications concluded that there were no serious adverse effects associated with CBD use but that no reliable conclusions on the efficacy and safety of the therapy can be drawn from this limited evidence. 57 The American Academy of Neurology (AAN) has issued a Summary of Systematic Reviews for Clinicians that indicates oral cannabis extract is effective for reducing patient-reported spasticity scores and central pain or painful spasms when used for MS. 58 THC is probably effective for reducing patient-reported spasticity scores but is likely ineffective for reducing objective measures of spasticity at 15 weeks, the AAN found; there is limited evidence to support the use of cannabis extracts for treatment of Huntington’s disease, levodopa-induced dyskinesias in patients with Parkinson’s disease, or reducing tic severity in Tourette’s. 58

In older patients, medical cannabinoids have shown no efficacy on dyskinesia, breathlessness, and chemotherapy-induced nausea and vomiting. Some evidence has shown that THC might be useful in treatment of anorexia and behavioral symptoms in patients with dementia. The most common adverse events reported during cannabinoid treatment in older adults were sedation-like symptoms. 59

Despite limited clinical evidence, a number of medical conditions and associated symptoms have been approved by state legislatures as qualifying conditions for medicinal cannabis use. Table 1 contains a summary of medicinal cannabis indications by state, including select disease states and qualifying debilitating medical conditions or symptoms. 10 , 60 , 61 The most common conditions accepted by states that allow medicinal cannabis relate to relief of the symptoms of cancer, glaucoma, human immunodeficiency virus/acquired immunodeficiency syndrome, and MS. A total of 28 states, the District of Columbia, Guam, and Puerto Rico now allow comprehensive public medical marijuana and cannabis programs. 10 The National Conference of State Legislatures uses the following criteria to determine if a program is comprehensive:

Medicinal Cannabis Indications for Use by State 10 , 60 , 61

1 = State law additionally covers any condition where treatment with medical cannabis would be beneficial, according to the patient’s physician

2 = State law covers any severe condition refractory to other medical treatment

3 = Additional restrictions on the use for this indication exist in this state

4 = State law requires providers to certify the existence of a qualifying disease and symptom

HIV/AIDS = human immunodeficiency virus/acquired immunodeficiency syndrome

Table adapted with permission from the Marijuana Policy Project; 60 table is not all-encompassing and other medical conditions for use may exist. The reader should refer to individual state laws regarding medicinal cannabis for specific details of approved conditions for use. In addition, states may permit the addition of approved indications; list is subject to change.

Protection from criminal penalties for using marijuana for a medical purpose;
Access to marijuana through home cultivation, dispensaries, or some other system that is likely to be implemented;
Allows a variety of strains, including more than those labeled as “low THC;” and
Allows either smoking or vaporization of some kind of marijuana products, plant material, or extract.

Some of the most common policy questions regarding medical cannabis now include how to regulate its recommendation and indications for use; dispensing, including quality and standardization of cultivars or strains, labeling, packaging, and role of the pharmacist or health care professional in education or administration; and registration of approved patients and providers.

REGULATORY IMPLICATIONS OF MEDICINAL CANNABIS

The regulation of cannabis therapy is complex and unique; possession, cultivation, and distribution of this substance, regardless of purpose, remain illegal at the federal level, while states that permit medicinal cannabis use have established individual laws and restrictions on the sale of cannabis for medical purposes. In a 2013 U.S. Department of Justice memorandum to all U.S. attorneys, Deputy Attorney General James M. Cole noted that despite the enactment of state laws authorizing marijuana production and sale having a regulatory structure that is counter to the usual joint efforts of federal authorities working together with local jurisdictions, prosecution of individuals cultivating and distributing marijuana to seriously ill individuals for medicinal purpose has not been identified as a federal priority. 62

There are, however, other regulatory implications to consider based on the federal restriction of cannabis. Physicians cannot legally “prescribe” medicinal cannabis therapy, given its Schedule I classification, but rather in accordance with state laws may certify or recommend patients for treatment. Medical cannabis expenses are not reimbursable through government medical assistance programs or private health insurers. As previously described, the Schedule I listing of cannabis according to federal law and DEA regulations has led to difficulties in access for research purposes; nonpractitioner researchers can register with the DEA more easily to study substances in Schedules II–V compared with Schedule I substances. 63 Beyond issues related to procurement of the substance for research purposes, other limitations in cannabis research also exist. For example, the Center for Medicinal Cannabis Research at the University of California–San Diego had access to funding, marijuana at different THC levels, and approval for a number of clinical research trials, and yet failed to recruit an adequate number of patients to conduct five major trials, which were subsequently canceled. 64 Unforeseen factors, including the prohibition of driving during the clinical trials, deterred patients from trial enrollment. The limited availability of clinical research to support or refute therapeutic claims and indications for use of cannabis for medicinal purposes has frequently left both state legislative authorities and clinicians to rely on anecdotal evidence, which has not been subjected to the same rigors of peer review and scrutiny as well-conducted, randomized trials, to validate the safety and efficacy of medicinal cannabis therapy. Furthermore, although individual single-entity pharmaceutical medications, such as dronabinol, have been isolated, evaluated, and approved for use by the FDA, a plant cannot be patented and mass produced by a corporate entity. 65 Despite this limitation, some corporations, including GW Pharmaceuticals, are mass producing cannabis plants and extracting complex mixtures or single cannabinoids for clinical trials. 65 The complex pharmacology related to the numerous substances and interactions among chemicals in the cannabis plant coupled with environmental variables in cultivation further complicate regulation, standardization, purity, and potency as a botanical drug product.

RELEVANCE TO HOSPITAL PRACTITIONERS

Although the public has largely accepted medicinal cannabis therapy as having a benefit when used under a provider’s supervision, the implications of the use of this substance when patients transition into the acute care setting are additionally complex and multifaceted. The Schedule I designation of cannabis causes hospitals and other care settings that receive federal funding, either through Medicare reimbursement or other federal grants or programs, to pause to consider the potential for loss of these funds should the federal government intercede and take action if patients are permitted to use this therapy on campus. Similarly, licensed practitioners registered to certify patients for state medicinal cannabis programs may have comparable concerns regarding jeopardizing their federal DEA registrations and ability to prescribe other controlled substances as well as jeopardizing Medicare reimbursements. In 2009, U.S. Attorney General Eric Holder recommended that enforcement of federal marijuana laws not be a priority in states that have enacted medicinal cannabis programs and are enforcing the rules and regulations of such a program; despite this, concerns persist.

The argument for or against the use of medicinal cannabis in the acute care setting encompasses both legal and ethical considerations, with the argument against use perhaps seeming obvious on its surface. States adopting medical cannabis laws may advise patients to utilize the therapy only in their own residence and not to transport the substances unless absolutely necessary. 66 Further, many acute care institutions have policies prohibiting smoking on facility grounds, thus restricting the smoking of cannabis, regardless of purpose or indication. Of note, several Canadian hospitals, including Montreal’s Jewish General Hospital and Quebec’s Centre Hospitalier Universitaire de Sherbrooke, have permitted inpatient cannabis use via vaporization; the pharmacy departments of the respective institutions control and dispense cannabis much like opioids for pain. Canada has adopted national regulations to control and standardize dried cannabis for medical use. 67 , 68 There are complicated logistics for self-administration of medicinal cannabis by the patient or caregiver; in particular, many hospitals have policies on self-administration of medicines that permit patients to use their own medications only after identification and labeling by pharmacy personnel. The argument can be made that an herb- or plant-based entity cannot be identified by pharmacy personnel as is commonly done for traditional medicines, although medicinal cannabis dispensed through state programs must be labeled in accordance with state laws. Dispensing and storage concerns, including an evaluation of where and how this product should be stored (e.g., within the pharmacy department and treated as a controlled substance, by security personnel, or with the patient); who should administer it, and implications or violations of federal law by those administering treatment; what pharmaceutical preparations should be permitted (e.g., smoked, vaporized, edible); and how it should be charted in the medical record represent other logistical concerns. Inpatient use of medicinal cannabis also carries implications for nursing and medical staff members. The therapy cannot be prescribed, and states may require physicians authorizing patient use to be registered with local programs. In a transition into the acute care setting from the community setting, a different clinician who is not registered could be responsible for the patient’s care; that clinician would be restricted in ordering continuation of therapy.

Despite the complexities in the logistics of continuing medicinal cannabis in the acute care setting, proponents of palliative care and continuity of care argue that prohibiting medicinal cannabis use disrupts treatment of chronic and debilitating medical conditions. Patients have been denied this therapy during acute care hospitalizations for reasons stated above. 69 Permission to use medicinal cannabis in the acute care setting may be dependent on state legislation and restrictions imposed by such laws. Legislation in Minnesota, as one example, has been amended to permit hospitals as facilities that can dispense and control cannabis use; similar legislative actions protecting nurses from criminal, civil, or disciplinary action when administering medical cannabis to qualified patients have been enacted in Connecticut and Maine. 70 – 73 Proposed legislation to remove restrictions on the certification of patients to receive medicinal cannabis by doctors at the Department of Veterans Affairs was struck down in June; prohibitions continue on the use of this therapy even in facilities located in states permitting medicinal cannabis use. 74

Despite lingering controversy, use of botanical cannabis for medicinal purposes represents the revival of a plant with historical significance reemerging in present day health care. Legislation governing use of medicinal cannabis continues to evolve rapidly, necessitating that pharmacists and other clinicians keep abreast of new or changing state regulations and institutional implications. Ultimately, as the medicinal cannabis landscape continues to evolve, hospitals, acute care facilities, clinics, hospices, and long-term care centers need to consider the implications, address logistical concerns, and explore the feasibility of permitting patient access to this treatment. Whether national policy—particularly with a new presidential administration—will offer some clarity or further complicate regulation of this treatment remains to be seen.

Disclosures: The authors report no commercial or financial interests in regard to this article.

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Key facts about the abortion debate in America

A woman receives medication to terminate her pregnancy at a reproductive health clinic in Albuquerque, New Mexico, on June 23, 2022, the day before the Supreme Court overturned Roe v. Wade, which had guaranteed a constitutional right to an abortion for nearly 50 years.

The U.S. Supreme Court’s June 2022 ruling to overturn Roe v. Wade – the decision that had guaranteed a constitutional right to an abortion for nearly 50 years – has shifted the legal battle over abortion to the states, with some prohibiting the procedure and others moving to safeguard it.

As the nation’s post-Roe chapter begins, here are key facts about Americans’ views on abortion, based on two Pew Research Center polls: one conducted from June 25-July 4 , just after this year’s high court ruling, and one conducted in March , before an earlier leaked draft of the opinion became public.

This analysis primarily draws from two Pew Research Center surveys, one surveying 10,441 U.S. adults conducted March 7-13, 2022, and another surveying 6,174 U.S. adults conducted June 27-July 4, 2022. Here are the questions used for the March survey , along with responses, and the questions used for the survey from June and July , along with responses.

Everyone who took part in these surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

A majority of the U.S. public disapproves of the Supreme Court’s decision to overturn Roe. About six-in-ten adults (57%) disapprove of the court’s decision that the U.S. Constitution does not guarantee a right to abortion and that abortion laws can be set by states, including 43% who strongly disapprove, according to the summer survey. About four-in-ten (41%) approve, including 25% who strongly approve.

A bar chart showing that the Supreme Court’s decision to overturn Roe v. Wade draws more strong disapproval among Democrats than strong approval among Republicans

About eight-in-ten Democrats and Democratic-leaning independents (82%) disapprove of the court’s decision, including nearly two-thirds (66%) who strongly disapprove. Most Republicans and GOP leaners (70%) approve , including 48% who strongly approve.

Most women (62%) disapprove of the decision to end the federal right to an abortion. More than twice as many women strongly disapprove of the court’s decision (47%) as strongly approve of it (21%). Opinion among men is more divided: 52% disapprove (37% strongly), while 47% approve (28% strongly).

About six-in-ten Americans (62%) say abortion should be legal in all or most cases, according to the summer survey – little changed since the March survey conducted just before the ruling. That includes 29% of Americans who say it should be legal in all cases and 33% who say it should be legal in most cases. About a third of U.S. adults (36%) say abortion should be illegal in all (8%) or most (28%) cases.

A line graph showing public views of abortion from 1995-2022

Generally, Americans’ views of whether abortion should be legal remained relatively unchanged in the past few years , though support fluctuated somewhat in previous decades.

Relatively few Americans take an absolutist view on the legality of abortion – either supporting or opposing it at all times, regardless of circumstances. The March survey found that support or opposition to abortion varies substantially depending on such circumstances as when an abortion takes place during a pregnancy, whether the pregnancy is life-threatening or whether a baby would have severe health problems.

While Republicans’ and Democrats’ views on the legality of abortion have long differed, the 46 percentage point partisan gap today is considerably larger than it was in the recent past, according to the survey conducted after the court’s ruling. The wider gap has been largely driven by Democrats: Today, 84% of Democrats say abortion should be legal in all or most cases, up from 72% in 2016 and 63% in 2007. Republicans’ views have shown far less change over time: Currently, 38% of Republicans say abortion should be legal in all or most cases, nearly identical to the 39% who said this in 2007.

A line graph showing that the partisan gap in views of whether abortion should be legal remains wide

However, the partisan divisions over whether abortion should generally be legal tell only part of the story. According to the March survey, sizable shares of Democrats favor restrictions on abortion under certain circumstances, while majorities of Republicans favor abortion being legal in some situations , such as in cases of rape or when the pregnancy is life-threatening.

There are wide religious divides in views of whether abortion should be legal , the summer survey found. An overwhelming share of religiously unaffiliated adults (83%) say abortion should be legal in all or most cases, as do six-in-ten Catholics. Protestants are divided in their views: 48% say it should be legal in all or most cases, while 50% say it should be illegal in all or most cases. Majorities of Black Protestants (71%) and White non-evangelical Protestants (61%) take the position that abortion should be legal in all or most cases, while about three-quarters of White evangelicals (73%) say it should be illegal in all (20%) or most cases (53%).

A bar chart showing that there are deep religious divisions in views of abortion

In the March survey, 72% of White evangelicals said that the statement “human life begins at conception, so a fetus is a person with rights” reflected their views extremely or very well . That’s much greater than the share of White non-evangelical Protestants (32%), Black Protestants (38%) and Catholics (44%) who said the same. Overall, 38% of Americans said that statement matched their views extremely or very well.

Catholics, meanwhile, are divided along religious and political lines in their attitudes about abortion, according to the same survey. Catholics who attend Mass regularly are among the country’s strongest opponents of abortion being legal, and they are also more likely than those who attend less frequently to believe that life begins at conception and that a fetus has rights. Catholic Republicans, meanwhile, are far more conservative on a range of abortion questions than are Catholic Democrats.

Women (66%) are more likely than men (57%) to say abortion should be legal in most or all cases, according to the survey conducted after the court’s ruling.

More than half of U.S. adults – including 60% of women and 51% of men – said in March that women should have a greater say than men in setting abortion policy . Just 3% of U.S. adults said men should have more influence over abortion policy than women, with the remainder (39%) saying women and men should have equal say.

The March survey also found that by some measures, women report being closer to the abortion issue than men . For example, women were more likely than men to say they had given “a lot” of thought to issues around abortion prior to taking the survey (40% vs. 30%). They were also considerably more likely than men to say they personally knew someone (such as a close friend, family member or themselves) who had had an abortion (66% vs. 51%) – a gender gap that was evident across age groups, political parties and religious groups.

Relatively few Americans view the morality of abortion in stark terms , the March survey found. Overall, just 7% of all U.S. adults say having an abortion is morally acceptable in all cases, and 13% say it is morally wrong in all cases. A third say that having an abortion is morally wrong in most cases, while about a quarter (24%) say it is morally acceptable in most cases. An additional 21% do not consider having an abortion a moral issue.

A table showing that there are wide religious and partisan differences in views of the morality of abortion

Among Republicans, most (68%) say that having an abortion is morally wrong either in most (48%) or all cases (20%). Only about three-in-ten Democrats (29%) hold a similar view. Instead, about four-in-ten Democrats say having an abortion is morally acceptable in most (32%) or all (11%) cases, while an additional 28% say it is not a moral issue.

White evangelical Protestants overwhelmingly say having an abortion is morally wrong in most (51%) or all cases (30%). A slim majority of Catholics (53%) also view having an abortion as morally wrong, but many also say it is morally acceptable in most (24%) or all cases (4%), or that it is not a moral issue (17%). Among religiously unaffiliated Americans, about three-quarters see having an abortion as morally acceptable (45%) or not a moral issue (32%).

Religion & Abortion

What the data says about abortion in the U.S.

Support for legal abortion is widespread in many countries, especially in europe, nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, by more than two-to-one, americans say medication abortion should be legal in their state, most latinos say democrats care about them and work hard for their vote, far fewer say so of gop, most popular.

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Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Original Paper

Corresponding Author:

Introduction

Data Source

Deep Learning Models

Evaluation of Extracted S Records by the Deep Learning Models

Applicability Check to Other Text Data Including Patients’ Concerns

Ethical Considerations

Application of the HFS Model

S Records Extracted as HFS Positive

Interventions by Health Care Professionals

Anticancer Drugs Prescribed

Application of the All AE or AE-L model

S Records Extracted as All AE or AE-L positive

Adverse Event Symptoms

Performance for Adverse Event Signal Extraction

Identification of Important Adverse Events Requiring Medical Intervention

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Potential for Deep Learning Model Implementation in Society

Limitations

Conclusions

Acknowledgments

Data Availability

Authors' Contributions

Conflicts of Interest

Abbreviations

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