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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

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Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

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The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

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Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

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A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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How to Write a Conclusion for a Research Paper

Last Updated: June 29, 2023 Approved

This article was co-authored by Christopher Taylor, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article received 42 testimonials and 82% of readers who voted found it helpful, earning it our reader-approved status. This article has been viewed 2,257,949 times.

The conclusion of a research paper needs to summarize the content and purpose of the paper without seeming too wooden or dry. Every basic conclusion must share several key elements, but there are also several tactics you can play around with to craft a more effective conclusion and several you should avoid to prevent yourself from weakening your paper's conclusion. Here are some writing tips to keep in mind when creating a conclusion for your next research paper.

Sample Conclusions

Writing a basic conclusion.

• Do not spend a great amount of time or space restating your topic.
• A good research paper will make the importance of your topic apparent, so you do not need to write an elaborate defense of your topic in the conclusion.
• Usually a single sentence is all you need to restate your topic.
• An example would be if you were writing a paper on the epidemiology of infectious disease, you might say something like "Tuberculosis is a widespread infectious disease that affects millions of people worldwide every year."
• Yet another example from the humanities would be a paper about the Italian Renaissance: "The Italian Renaissance was an explosion of art and ideas centered around artists, writers, and thinkers in Florence."

• A thesis is a narrowed, focused view on the topic at hand.
• This statement should be rephrased from the thesis you included in your introduction. It should not be identical or too similar to the sentence you originally used.
• Try re-wording your thesis statement in a way that complements your summary of the topic of your paper in your first sentence of your conclusion.
• An example of a good thesis statement, going back to the paper on tuberculosis, would be "Tuberculosis is a widespread disease that affects millions of people worldwide every year. Due to the alarming rate of the spread of tuberculosis, particularly in poor countries, medical professionals are implementing new strategies for the diagnosis, treatment, and containment of this disease ."

• A good way to go about this is to re-read the topic sentence of each major paragraph or section in the body of your paper.
• Find a way to briefly restate each point mentioned in each topic sentence in your conclusion. Do not repeat any of the supporting details used within your body paragraphs.
• Under most circumstances, you should avoid writing new information in your conclusion. This is especially true if the information is vital to the argument or research presented in your paper.
• For example, in the TB paper you could summarize the information. "Tuberculosis is a widespread disease that affects millions of people worldwide. Due to the alarming rate of the spread of tuberculosis, particularly in poor countries, medical professionals are implementing new strategies for the diagnosis, treatment, and containment of this disease. In developing countries, such as those in Africa and Southeast Asia, the rate of TB infections is soaring. Crowded conditions, poor sanitation, and lack of access to medical care are all compounding factors in the spread of the disease. Medical experts, such as those from the World Health Organization are now starting campaigns to go into communities in developing countries and provide diagnostic testing and treatments. However, the treatments for TB are very harsh and have many side effects. This leads to patient non-compliance and spread of multi-drug resistant strains of the disease."

• Note that this is not needed for all research papers.
• If you already fully explained what the points in your paper mean or why they are significant, you do not need to go into them in much detail in your conclusion. Simply restating your thesis or the significance of your topic should suffice.
• It is always best practice to address important issues and fully explain your points in the body of your paper. The point of a conclusion to a research paper is to summarize your argument for the reader and, perhaps, to call the reader to action if needed.

• Note that a call for action is not essential to all conclusions. A research paper on literary criticism, for instance, is less likely to need a call for action than a paper on the effect that television has on toddlers and young children.
• A paper that is more likely to call readers to action is one that addresses a public or scientific need. Let's go back to our example of tuberculosis. This is a very serious disease that is spreading quickly and with antibiotic-resistant forms.
• A call to action in this research paper would be a follow-up statement that might be along the lines of "Despite new efforts to diagnose and contain the disease, more research is needed to develop new antibiotics that will treat the most resistant strains of tuberculosis and ease the side effects of current treatments."

• For example, if you are writing a history paper, then you might discuss how the historical topic you discussed matters today. If you are writing about a foreign country, then you might use the conclusion to discuss how the information you shared may help readers understand their own country.

Making Your Conclusion as Effective as Possible

• Since this sort of conclusion is so basic, you must aim to synthesize the information rather than merely summarizing it.
• Instead of merely repeating things you already said, rephrase your thesis and supporting points in a way that ties them all together.
• By doing so, you make your research paper seem like a "complete thought" rather than a collection of random and vaguely related ideas.

• Ask a question in your introduction. In your conclusion, restate the question and provide a direct answer.
• Write an anecdote or story in your introduction but do not share the ending. Instead, write the conclusion to the anecdote in the conclusion of your paper.
• For example, if you wanted to get more creative and put a more humanistic spin on a paper on tuberculosis, you might start your introduction with a story about a person with the disease, and refer to that story in your conclusion. For example, you could say something like this before you re-state your thesis in your conclusion: "Patient X was unable to complete the treatment for tuberculosis due to severe side effects and unfortunately succumbed to the disease."
• Use the same concepts and images introduced in your introduction in your conclusion. The images may or may not appear at other points throughout the research paper.

• Include enough information about your topic to back the statement up but do not get too carried away with excess detail.
• If your research did not provide you with a clear-cut answer to a question posed in your thesis, do not be afraid to indicate as much.
• Restate your initial hypothesis and indicate whether you still believe it or if the research you performed has begun swaying your opinion.
• Indicate that an answer may still exist and that further research could shed more light on the topic at hand.

• This may not be appropriate for all types of research papers. Most research papers, such as one on effective treatment for diseases, will have the information to make the case for a particular argument already in the paper.
• A good example of a paper that might ask a question of the reader in the ending is one about a social issue, such as poverty or government policy.
• Ask a question that will directly get at the heart or purpose of the paper. This question is often the same question, or some version of it, that you may have started with when you began your research.
• Make sure that the question can be answered by the evidence presented in your paper.
• If desired you can briefly summarize the answer after stating the question. You could also leave the question hanging for the reader to answer, though.

• Even without a call to action, you can still make a recommendation to your reader.
• For instance, if you are writing about a topic like third-world poverty, you can various ways for the reader to assist in the problem without necessarily calling for more research.
• Another example would be, in a paper about treatment for drug-resistant tuberculosis, you could suggest donating to the World Health Organization or research foundations that are developing new treatments for the disease.

Avoiding Common Pitfalls

• These sayings usually sound stiff, unnatural, or trite when used in writing.
• Moreover, using a phrase like "in conclusion" to begin your conclusion is a little too straightforward and tends to lead to a weak conclusion. A strong conclusion can stand on its own without being labeled as such.

• Always state the main argument or thesis in the introduction. A research paper is an analytical discussion of an academic topic, not a mystery novel.
• A good, effective research paper will allow your reader to follow your main argument from start to finish.
• This is why it is best practice to start your paper with an introduction that states your main argument and to end the paper with a conclusion that re-states your thesis for re-iteration.

• All significant information should be introduced in the body of the paper.
• Supporting evidence expands the topic of your paper by making it appear more detailed. A conclusion should narrow the topic to a more general point.
• A conclusion should only summarize what you have already stated in the body of your paper.
• You may suggest further research or a call to action, but you should not bring in any new evidence or facts in the conclusion.

• Most often, a shift in tone occurs when a research paper with an academic tone gives an emotional or sentimental conclusion.
• Even if the topic of the paper is of personal significance for you, you should not indicate as much in your paper.
• If you want to give your paper a more humanistic slant, you could start and end your paper with a story or anecdote that would give your topic more personal meaning to the reader.
• This tone should be consistent throughout the paper, however.

• Apologetic statements include phrases like "I may not be an expert" or "This is only my opinion."
• Statements like this can usually be avoided by refraining from writing in the first-person.
• Avoid any statements in the first-person. First-person is generally considered to be informal and does not fit with the formal tone of a research paper.

Community Q&A

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• ↑ http://owl.english.purdue.edu/owl/resource/724/04/
• ↑ http://www.crlsresearchguide.org/18_Writing_Conclusion.asp
• ↑ http://writing.wisc.edu/Handbook/PlanResearchPaper.html#conclusion
• ↑ http://writingcenter.unc.edu/handouts/conclusions/
• ↑ http://writing2.richmond.edu/writing/wweb/conclude.html

About This Article

To write a conclusion for a research paper, start by restating your thesis statement to remind your readers what your main topic is and bring everything full circle. Then, briefly summarize all of the main points you made throughout your paper, which will help remind your readers of everything they learned. You might also want to include a call to action if you think more research or work needs to be done on your topic by writing something like, "Despite efforts to contain the disease, more research is needed to develop antibiotics." Finally, end your conclusion by explaining the broader context of your topic and why your readers should care about it, which will help them understand why your topic is relevant and important. For tips from our Academic co-author, like how to avoid common pitfalls when writing your conclusion, scroll down! Did this summary help you? Yes No

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The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points derived from the findings of your study and, if applicable, where you recommend new areas for future research. For most college-level research papers, two or three well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in describing the key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  your answer to the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [first identified in your literature review section] has been addressed by your research and why this contribution is significant.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers an opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The general function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by clearly summarizing the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. However, make sure that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your paper.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, concise language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique, new, or crucial contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem but that further investigations should take place beyond the scope of your investigation.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data [this is opposite of the introduction, which begins with general discussion of the context and ends with a detailed description of the research problem]. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way. If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge leading to positive change.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good source would be from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results from your study to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion framed around the implications and significance of your findings [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study. They should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social and behavioral sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority as a researcher by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader about the study's validity and realiability.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining that they are reaching the end of your paper. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper. This why the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and position your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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In a short paper—even a research paper—you don’t need to provide an exhaustive summary as part of your conclusion. But you do need to make some kind of transition between your final body paragraph and your concluding paragraph. This may come in the form of a few sentences of summary. Or it may come in the form of a sentence that brings your readers back to your thesis or main idea and reminds your readers where you began and how far you have traveled.

So, for example, in a paper about the relationship between ADHD and rejection sensitivity, Vanessa Roser begins by introducing readers to the fact that researchers have studied the relationship between the two conditions and then provides her explanation of that relationship. Here’s her thesis: “While socialization may indeed be an important factor in RS, I argue that individuals with ADHD may also possess a neurological predisposition to RS that is exacerbated by the differing executive and emotional regulation characteristic of ADHD.”

In her final paragraph, Roser reminds us of where she started by echoing her thesis: “This literature demonstrates that, as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Highlight the “so what”  

At the beginning of your paper, you explain to your readers what’s at stake—why they should care about the argument you’re making. In your conclusion, you can bring readers back to those stakes by reminding them why your argument is important in the first place. You can also draft a few sentences that put those stakes into a new or broader context.

In the conclusion to her paper about ADHD and RS, Roser echoes the stakes she established in her introduction—that research into connections between ADHD and RS has led to contradictory results, raising questions about the “behavioral mediation hypothesis.”

She writes, “as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Leave your readers with the “now what”  

After the “what” and the “so what,” you should leave your reader with some final thoughts. If you have written a strong introduction, your readers will know why you have been arguing what you have been arguing—and why they should care. And if you’ve made a good case for your thesis, then your readers should be in a position to see things in a new way, understand new questions, or be ready for something that they weren’t ready for before they read your paper.

In her conclusion, Roser offers two “now what” statements. First, she explains that it is important to recognize that the flawed behavioral mediation hypothesis “seems to place a degree of fault on the individual. It implies that individuals with ADHD must have elicited such frequent or intense rejection by virtue of their inadequate social skills, erasing the possibility that they may simply possess a natural sensitivity to emotion.” She then highlights the broader implications for treatment of people with ADHD, noting that recognizing the actual connection between rejection sensitivity and ADHD “has profound implications for understanding how individuals with ADHD might best be treated in educational settings, by counselors, family, peers, or even society as a whole.”

To find your own “now what” for your essay’s conclusion, try asking yourself these questions:

• What can my readers now understand, see in a new light, or grapple with that they would not have understood in the same way before reading my paper? Are we a step closer to understanding a larger phenomenon or to understanding why what was at stake is so important?  
• What questions can I now raise that would not have made sense at the beginning of my paper? Questions for further research? Other ways that this topic could be approached?  
• Are there other applications for my research? Could my questions be asked about different data in a different context? Could I use my methods to answer a different question?  
• What action should be taken in light of this argument? What action do I predict will be taken or could lead to a solution?  
• What larger context might my argument be a part of?  

What to avoid in your conclusion  

• a complete restatement of all that you have said in your paper.  
• a substantial counterargument that you do not have space to refute; you should introduce counterarguments before your conclusion.  
• an apology for what you have not said. If you need to explain the scope of your paper, you should do this sooner—but don’t apologize for what you have not discussed in your paper.  
• fake transitions like “in conclusion” that are followed by sentences that aren’t actually conclusions. (“In conclusion, I have now demonstrated that my thesis is correct.”)
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Research Paper Conclusion – Writing Guide and Examples

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Research Paper Conclusion

Definition:

A research paper conclusion is the final section of a research paper that summarizes the key findings, significance, and implications of the research. It is the writer’s opportunity to synthesize the information presented in the paper, draw conclusions, and make recommendations for future research or actions.

The conclusion should provide a clear and concise summary of the research paper, reiterating the research question or problem, the main results, and the significance of the findings. It should also discuss the limitations of the study and suggest areas for further research.

Parts of Research Paper Conclusion

The parts of a research paper conclusion typically include:

Restatement of the Thesis

The conclusion should begin by restating the thesis statement from the introduction in a different way. This helps to remind the reader of the main argument or purpose of the research.

Summary of Key Findings

The conclusion should summarize the main findings of the research, highlighting the most important results and conclusions. This section should be brief and to the point.

Implications and Significance

In this section, the researcher should explain the implications and significance of the research findings. This may include discussing the potential impact on the field or industry, highlighting new insights or knowledge gained, or pointing out areas for future research.

Limitations and Recommendations

It is important to acknowledge any limitations or weaknesses of the research and to make recommendations for how these could be addressed in future studies. This shows that the researcher is aware of the potential limitations of their work and is committed to improving the quality of research in their field.

Concluding Statement

The conclusion should end with a strong concluding statement that leaves a lasting impression on the reader. This could be a call to action, a recommendation for further research, or a final thought on the topic.

How to Write Research Paper Conclusion

Here are some steps you can follow to write an effective research paper conclusion:

• Restate the research problem or question: Begin by restating the research problem or question that you aimed to answer in your research. This will remind the reader of the purpose of your study.
• Summarize the main points: Summarize the key findings and results of your research. This can be done by highlighting the most important aspects of your research and the evidence that supports them.
• Discuss the implications: Discuss the implications of your findings for the research area and any potential applications of your research. You should also mention any limitations of your research that may affect the interpretation of your findings.
• Provide a conclusion : Provide a concise conclusion that summarizes the main points of your paper and emphasizes the significance of your research. This should be a strong and clear statement that leaves a lasting impression on the reader.
• Offer suggestions for future research: Lastly, offer suggestions for future research that could build on your findings and contribute to further advancements in the field.

Remember that the conclusion should be brief and to the point, while still effectively summarizing the key findings and implications of your research.

Example of Research Paper Conclusion

Here’s an example of a research paper conclusion:

Conclusion :

In conclusion, our study aimed to investigate the relationship between social media use and mental health among college students. Our findings suggest that there is a significant association between social media use and increased levels of anxiety and depression among college students. This highlights the need for increased awareness and education about the potential negative effects of social media use on mental health, particularly among college students.

Despite the limitations of our study, such as the small sample size and self-reported data, our findings have important implications for future research and practice. Future studies should aim to replicate our findings in larger, more diverse samples, and investigate the potential mechanisms underlying the association between social media use and mental health. In addition, interventions should be developed to promote healthy social media use among college students, such as mindfulness-based approaches and social media detox programs.

Overall, our study contributes to the growing body of research on the impact of social media on mental health, and highlights the importance of addressing this issue in the context of higher education. By raising awareness and promoting healthy social media use among college students, we can help to reduce the negative impact of social media on mental health and improve the well-being of young adults.

Purpose of Research Paper Conclusion

The purpose of a research paper conclusion is to provide a summary and synthesis of the key findings, significance, and implications of the research presented in the paper. The conclusion serves as the final opportunity for the writer to convey their message and leave a lasting impression on the reader.

The conclusion should restate the research problem or question, summarize the main results of the research, and explain their significance. It should also acknowledge the limitations of the study and suggest areas for future research or action.

Overall, the purpose of the conclusion is to provide a sense of closure to the research paper and to emphasize the importance of the research and its potential impact. It should leave the reader with a clear understanding of the main findings and why they matter. The conclusion serves as the writer’s opportunity to showcase their contribution to the field and to inspire further research and action.

When to Write Research Paper Conclusion

The conclusion of a research paper should be written after the body of the paper has been completed. It should not be written until the writer has thoroughly analyzed and interpreted their findings and has written a complete and cohesive discussion of the research.

Before writing the conclusion, the writer should review their research paper and consider the key points that they want to convey to the reader. They should also review the research question, hypotheses, and methodology to ensure that they have addressed all of the necessary components of the research.

Once the writer has a clear understanding of the main findings and their significance, they can begin writing the conclusion. The conclusion should be written in a clear and concise manner, and should reiterate the main points of the research while also providing insights and recommendations for future research or action.

Characteristics of Research Paper Conclusion

The characteristics of a research paper conclusion include:

• Clear and concise: The conclusion should be written in a clear and concise manner, summarizing the key findings and their significance.
• Comprehensive: The conclusion should address all of the main points of the research paper, including the research question or problem, the methodology, the main results, and their implications.
• Future-oriented : The conclusion should provide insights and recommendations for future research or action, based on the findings of the research.
• Impressive : The conclusion should leave a lasting impression on the reader, emphasizing the importance of the research and its potential impact.
• Objective : The conclusion should be based on the evidence presented in the research paper, and should avoid personal biases or opinions.
• Unique : The conclusion should be unique to the research paper and should not simply repeat information from the introduction or body of the paper.

Advantages of Research Paper Conclusion

The advantages of a research paper conclusion include:

• Summarizing the key findings : The conclusion provides a summary of the main findings of the research, making it easier for the reader to understand the key points of the study.
• Emphasizing the significance of the research: The conclusion emphasizes the importance of the research and its potential impact, making it more likely that readers will take the research seriously and consider its implications.
• Providing recommendations for future research or action : The conclusion suggests practical recommendations for future research or action, based on the findings of the study.
• Providing closure to the research paper : The conclusion provides a sense of closure to the research paper, tying together the different sections of the paper and leaving a lasting impression on the reader.
• Demonstrating the writer’s contribution to the field : The conclusion provides the writer with an opportunity to showcase their contribution to the field and to inspire further research and action.

Limitations of Research Paper Conclusion

While the conclusion of a research paper has many advantages, it also has some limitations that should be considered, including:

• I nability to address all aspects of the research: Due to the limited space available in the conclusion, it may not be possible to address all aspects of the research in detail.
• Subjectivity : While the conclusion should be objective, it may be influenced by the writer’s personal biases or opinions.
• Lack of new information: The conclusion should not introduce new information that has not been discussed in the body of the research paper.
• Lack of generalizability: The conclusions drawn from the research may not be applicable to other contexts or populations, limiting the generalizability of the study.
• Misinterpretation by the reader: The reader may misinterpret the conclusions drawn from the research, leading to a misunderstanding of the findings.

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• Writing Tips

How to Write a Conclusion for a Research Paper

3-minute read

• 29th August 2023

If you’re writing a research paper, the conclusion is your opportunity to summarize your findings and leave a lasting impression on your readers. In this post, we’ll take you through how to write an effective conclusion for a research paper and how you can:

·   Reword your thesis statement

·   Highlight the significance of your research

·   Discuss limitations

·   Connect to the introduction

·   End with a thought-provoking statement

Rewording Your Thesis Statement

Begin your conclusion by restating your thesis statement in a way that is slightly different from the wording used in the introduction. Avoid presenting new information or evidence in your conclusion. Just summarize the main points and arguments of your essay and keep this part as concise as possible. Remember that you’ve already covered the in-depth analyses and investigations in the main body paragraphs of your essay, so it’s not necessary to restate these details in the conclusion.

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Highlighting the Significance of Your Research

The conclusion is a good place to emphasize the implications of your research . Avoid ambiguous or vague language such as “I think” or “maybe,” which could weaken your position. Clearly explain why your research is significant and how it contributes to the broader field of study.

Here’s an example from a (fictional) study on the impact of social media on mental health:

Discussing Limitations

Although it’s important to emphasize the significance of your study, you can also use the conclusion to briefly address any limitations you discovered while conducting your research, such as time constraints or a shortage of resources. Doing this demonstrates a balanced and honest approach to your research.

Connecting to the Introduction

In your conclusion, you can circle back to your introduction , perhaps by referring to a quote or anecdote you discussed earlier. If you end your paper on a similar note to how you began it, you will create a sense of cohesion for the reader and remind them of the meaning and significance of your research.

Ending With a Thought-Provoking Statement

Consider ending your paper with a thought-provoking and memorable statement that relates to the impact of your research questions or hypothesis. This statement can be a call to action, a philosophical question, or a prediction for the future (positive or negative). Here’s an example that uses the same topic as above (social media and mental health):

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

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There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

Conclusions

What this handout is about.

This handout will explain the functions of conclusions, offer strategies for writing effective ones, help you evaluate conclusions you’ve drafted, and suggest approaches to avoid.

About conclusions

Introductions and conclusions can be difficult to write, but they’re worth investing time in. They can have a significant influence on a reader’s experience of your paper.

Just as your introduction acts as a bridge that transports your readers from their own lives into the “place” of your analysis, your conclusion can provide a bridge to help your readers make the transition back to their daily lives. Such a conclusion will help them see why all your analysis and information should matter to them after they put the paper down.

Your conclusion is your chance to have the last word on the subject. The conclusion allows you to have the final say on the issues you have raised in your paper, to synthesize your thoughts, to demonstrate the importance of your ideas, and to propel your reader to a new view of the subject. It is also your opportunity to make a good final impression and to end on a positive note.

Your conclusion can go beyond the confines of the assignment. The conclusion pushes beyond the boundaries of the prompt and allows you to consider broader issues, make new connections, and elaborate on the significance of your findings.

Your conclusion should make your readers glad they read your paper. Your conclusion gives your reader something to take away that will help them see things differently or appreciate your topic in personally relevant ways. It can suggest broader implications that will not only interest your reader, but also enrich your reader’s life in some way. It is your gift to the reader.

Strategies for writing an effective conclusion

One or more of the following strategies may help you write an effective conclusion:

• Play the “So What” Game. If you’re stuck and feel like your conclusion isn’t saying anything new or interesting, ask a friend to read it with you. Whenever you make a statement from your conclusion, ask the friend to say, “So what?” or “Why should anybody care?” Then ponder that question and answer it. Here’s how it might go: You: Basically, I’m just saying that education was important to Douglass. Friend: So what? You: Well, it was important because it was a key to him feeling like a free and equal citizen. Friend: Why should anybody care? You: That’s important because plantation owners tried to keep slaves from being educated so that they could maintain control. When Douglass obtained an education, he undermined that control personally. You can also use this strategy on your own, asking yourself “So What?” as you develop your ideas or your draft.
• Return to the theme or themes in the introduction. This strategy brings the reader full circle. For example, if you begin by describing a scenario, you can end with the same scenario as proof that your essay is helpful in creating a new understanding. You may also refer to the introductory paragraph by using key words or parallel concepts and images that you also used in the introduction.
• Synthesize, don’t summarize. Include a brief summary of the paper’s main points, but don’t simply repeat things that were in your paper. Instead, show your reader how the points you made and the support and examples you used fit together. Pull it all together.
• Include a provocative insight or quotation from the research or reading you did for your paper.
• Propose a course of action, a solution to an issue, or questions for further study. This can redirect your reader’s thought process and help them to apply your info and ideas to their own life or to see the broader implications.
• Point to broader implications. For example, if your paper examines the Greensboro sit-ins or another event in the Civil Rights Movement, you could point out its impact on the Civil Rights Movement as a whole. A paper about the style of writer Virginia Woolf could point to her influence on other writers or on later feminists.

Strategies to avoid

• Beginning with an unnecessary, overused phrase such as “in conclusion,” “in summary,” or “in closing.” Although these phrases can work in speeches, they come across as wooden and trite in writing.
• Stating the thesis for the very first time in the conclusion.
• Introducing a new idea or subtopic in your conclusion.
• Ending with a rephrased thesis statement without any substantive changes.
• Making sentimental, emotional appeals that are out of character with the rest of an analytical paper.
• Including evidence (quotations, statistics, etc.) that should be in the body of the paper.

Four kinds of ineffective conclusions

• The “That’s My Story and I’m Sticking to It” Conclusion. This conclusion just restates the thesis and is usually painfully short. It does not push the ideas forward. People write this kind of conclusion when they can’t think of anything else to say. Example: In conclusion, Frederick Douglass was, as we have seen, a pioneer in American education, proving that education was a major force for social change with regard to slavery.
• The “Sherlock Holmes” Conclusion. Sometimes writers will state the thesis for the very first time in the conclusion. You might be tempted to use this strategy if you don’t want to give everything away too early in your paper. You may think it would be more dramatic to keep the reader in the dark until the end and then “wow” them with your main idea, as in a Sherlock Holmes mystery. The reader, however, does not expect a mystery, but an analytical discussion of your topic in an academic style, with the main argument (thesis) stated up front. Example: (After a paper that lists numerous incidents from the book but never says what these incidents reveal about Douglass and his views on education): So, as the evidence above demonstrates, Douglass saw education as a way to undermine the slaveholders’ power and also an important step toward freedom.
• The “America the Beautiful”/”I Am Woman”/”We Shall Overcome” Conclusion. This kind of conclusion usually draws on emotion to make its appeal, but while this emotion and even sentimentality may be very heartfelt, it is usually out of character with the rest of an analytical paper. A more sophisticated commentary, rather than emotional praise, would be a more fitting tribute to the topic. Example: Because of the efforts of fine Americans like Frederick Douglass, countless others have seen the shining beacon of light that is education. His example was a torch that lit the way for others. Frederick Douglass was truly an American hero.
• The “Grab Bag” Conclusion. This kind of conclusion includes extra information that the writer found or thought of but couldn’t integrate into the main paper. You may find it hard to leave out details that you discovered after hours of research and thought, but adding random facts and bits of evidence at the end of an otherwise-well-organized essay can just create confusion. Example: In addition to being an educational pioneer, Frederick Douglass provides an interesting case study for masculinity in the American South. He also offers historians an interesting glimpse into slave resistance when he confronts Covey, the overseer. His relationships with female relatives reveal the importance of family in the slave community.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Douglass, Frederick. 1995. Narrative of the Life of Frederick Douglass, an American Slave, Written by Himself. New York: Dover.

Hamilton College. n.d. “Conclusions.” Writing Center. Accessed June 14, 2019. https://www.hamilton.edu//academics/centers/writing/writing-resources/conclusions .

Holewa, Randa. 2004. “Strategies for Writing a Conclusion.” LEO: Literacy Education Online. Last updated February 19, 2004. https://leo.stcloudstate.edu/acadwrite/conclude.html.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Writing a Paper: Conclusions

Writing a conclusion.

A conclusion is an important part of the paper; it provides closure for the reader while reminding the reader of the contents and importance of the paper. It accomplishes this by stepping back from the specifics in order to view the bigger picture of the document. In other words, it is reminding the reader of the main argument. For most course papers, it is usually one paragraph that simply and succinctly restates the main ideas and arguments, pulling everything together to help clarify the thesis of the paper. A conclusion does not introduce new ideas; instead, it should clarify the intent and importance of the paper. It can also suggest possible future research on the topic.

An Easy Checklist for Writing a Conclusion

It is important to remind the reader of the thesis of the paper so he is reminded of the argument and solutions you proposed.

Think of the main points as puzzle pieces, and the conclusion is where they all fit together to create a bigger picture. The reader should walk away with the bigger picture in mind.

Make sure that the paper places its findings in the context of real social change.

Make sure the reader has a distinct sense that the paper has come to an end. It is important to not leave the reader hanging. (You don’t want her to have flip-the-page syndrome, where the reader turns the page, expecting the paper to continue. The paper should naturally come to an end.)

No new ideas should be introduced in the conclusion. It is simply a review of the material that is already present in the paper. The only new idea would be the suggesting of a direction for future research.

Conclusion Example

As addressed in my analysis of recent research, the advantages of a later starting time for high school students significantly outweigh the disadvantages. A later starting time would allow teens more time to sleep--something that is important for their physical and mental health--and ultimately improve their academic performance and behavior. The added transportation costs that result from this change can be absorbed through energy savings. The beneficial effects on the students’ academic performance and behavior validate this decision, but its effect on student motivation is still unknown. I would encourage an in-depth look at the reactions of students to such a change. This sort of study would help determine the actual effects of a later start time on the time management and sleep habits of students.

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How to Write a Conclusion for a Research Paper

Table of Contents

Writing a conclusion for a research paper is a critical step that often determines the overall impact and impression the paper leaves on the reader. While some may view the conclusion as a mere formality, it is actually an opportunity to wrap up the main points, provide closure, and leave a lasting impression. In this article, we will explore the importance of a well-crafted conclusion and discuss various tips and strategies to help you write an engaging and impactful conclusion for your research paper.

Introduction

Before delving into the specifics of writing a conclusion, it is important to understand why it is such a crucial component of a research paper. The conclusion serves to summarize the main points of the paper and reemphasize their significance. A well-written conclusion can leave the reader satisfied and inspired, while a poorly executed one may undermine the credibility of the entire paper. Therefore, it is essential to give careful thought and attention to crafting an effective conclusion.

When writing a research paper, the conclusion acts as the final destination for the reader. It is the point where all the information, arguments, and evidence presented throughout the paper converge. Just as a traveler reaches the end of a journey, the reader reaches the conclusion to find closure and a sense of fulfillment. This is why the conclusion should not be taken lightly; it is a critical opportunity to leave a lasting impact on the reader.

Moreover, the conclusion is not merely a repetition of the introduction or a summary of the main points. It goes beyond that by providing a deeper understanding of the research findings and their implications. It allows the writer to reflect on the significance of their work and its potential contributions to the field. By doing so, the conclusion elevates the research paper from a mere collection of facts to a thought-provoking piece of scholarship.

In the following sections, we will explore various strategies and techniques for crafting a compelling conclusion. By understanding the importance of the conclusion and learning how to write one effectively, you will be equipped to create impactful research papers.

Structuring the Conclusion

In order to create an effective conclusion, it is important to consider its structure. A well-structured conclusion should begin by restating the thesis statement and summarizing the main points of the paper. It should then move on to provide a concise synthesis of the key findings and arguments, highlighting their implications and relevance. Finally, the conclusion should end with a thought-provoking statement that leaves the reader with a lasting impression.

Additionally, using phrases like "this research demonstrates," "the findings show," or "it is clear that" can help to highlight the significance of your research and emphasize your main conclusions.

Tips for Writing an Engaging Conclusion

Writing an engaging conclusion requires careful consideration and attention to detail. Here are some tips to help you create an impactful conclusion for your research paper:

• Revisit the Introduction: Start your conclusion by referencing your introduction. Remind the reader of the research question or problem you initially posed and show how your research has addressed it.
• Summarize Your Main Points: Provide a concise summary of the main points and arguments presented in your paper. Be sure to restate your thesis statement and highlight the key findings.
• Offer a Fresh Perspective: Use the conclusion as an opportunity to provide a fresh perspective or offer insights that go beyond the main body of the paper. This will leave the reader with something new to consider.
• Leave a Lasting Impression: End your conclusion with a thought-provoking statement or a call to action. This will leave a lasting impression on the reader and encourage further exploration of the research topic.

Addressing Counter Arguments In Conclusion

While crafting your conclusion, you can address any potential counterarguments or limitations of your research. This will demonstrate that you have considered alternative perspectives and have taken them into account in your conclusions. By acknowledging potential counterarguments, you can strengthen the credibility and validity of your research. And by openly discussing limitations, you demonstrate transparency and honesty in your research process.

Language and Tone To Be Used In Conclusion

The language and tone of your conclusion play a crucial role in shaping the overall impression of your research paper. It is important to use clear and concise language that is appropriate for the academic context. Avoid using overly informal or colloquial language that may undermine the credibility of your research. Additionally, consider the tone of your conclusion – it should be professional, confident, and persuasive, while still maintaining a respectful and objective tone.

When it comes to the language used in your conclusion, precision is key. You want to ensure that your ideas are communicated effectively and that there is no room for misinterpretation. Using clear and concise language will not only make your conclusion easier to understand but will also demonstrate your command of the subject matter.

Furthermore, it is important to strike the right balance between formality and accessibility. While academic writing typically requires a more formal tone, you should still aim to make your conclusion accessible to a wider audience. This means avoiding jargon or technical terms that may confuse readers who are not familiar with the subject matter. Instead, opt for language that is clear and straightforward, allowing anyone to grasp the main points of your research.

Another aspect to consider is the tone of your conclusion. The tone should reflect the confidence you have in your research findings and the strength of your argument. By adopting a professional and confident tone, you are more likely to convince your readers of the validity and importance of your research. However, it is crucial to strike a balance and avoid sounding arrogant or dismissive of opposing viewpoints. Maintaining a respectful and objective tone will help you engage with your audience in a more persuasive manner.

Moreover, the tone of your conclusion should align with the overall tone of your research paper. Consistency in tone throughout your paper will create a cohesive and unified piece of writing.

Common Mistakes to Avoid While Writing a Conclusion

When writing a conclusion, there are several common mistakes that researchers often make. By being aware of these pitfalls, you can avoid them and create a more effective conclusion for your research paper. Some common mistakes include:

• Repeating the Introduction: A conclusion should not simply be a reworded version of the introduction. While it is important to revisit the main points, try to present them in a fresh and broader perspective, by foregrounding the implications/impacts of your research.
• Introducing New Information: The conclusion should not introduce any new information or arguments. Instead, it should focus on summarizing and synthesizing the main points presented in the paper.
• Being Vague or General: Avoid using vague or general statements in your conclusion. Instead, be specific and provide concrete examples or evidence to support your main points.
• Ending Abruptly: A conclusion should provide a sense of closure and completeness. Avoid ending your conclusion abruptly or leaving the reader with unanswered questions.

Editing and Revising the Conclusion

Just like the rest of your research paper, the conclusion should go through a thorough editing and revising process. This will help to ensure clarity, coherence, and impact in the conclusion. As you revise your conclusion, consider the following:

• Check for Consistency: Ensure that your conclusion aligns with the main body of the paper and does not introduce any new or contradictory information.
• Eliminate Redundancy: Remove any repetitive or redundant information in your conclusion. Instead, focus on presenting the key points in a concise and engaging manner.
• Proofread for Clarity: Read your conclusion aloud or ask someone else to read it to ensure that it is clear and understandable. Check for any grammatical or spelling errors that may distract the reader.
• Seek Feedback: Consider sharing your conclusion with peers or mentors to get their feedback and insights. This can help you strengthen your conclusion and make it more impactful.

How to Write Conclusion as a Call to Action

Finally, consider using your conclusion as a call to action. Encourage the reader to take further action, such as conducting additional research or considering the implications of your findings. By providing a clear call to action, you can inspire the reader to actively engage with your research and continue the conversation on the topic.

Adapting to Different Research Paper Types

It is important to adapt your conclusion approach based on the type of research paper you are writing. Different research paper types may require different strategies and approaches to writing the conclusion. For example, a scientific research paper may focus more on summarizing the key findings and implications, while a persuasive research paper may emphasize the call to action and the potential impact of the research. Tailor your conclusion to suit the specific goals and requirements of your research paper.

Final Thoughts

A well-crafted conclusion can leave a lasting impression on the reader and enhance the impact of your research. By following the tips and strategies outlined in this article, you can create an engaging and impactful conclusion that effectively summarizes your main points, addresses potential counterarguments, and leaves the reader with a sense of closure and inspiration. Embrace the importance of the conclusion and view it as an opportunity to showcase the significance and relevance of your research.

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How to Write a Conclusion for a Research Paper

Find out which type of conclusion best suits your research, how to write it step-by-step, and common mistakes to avoid.

When writing a research paper, it can be challenging to make your point after providing an extensive amount of information. For this reason, a well-organized conclusion is essential. 

A research paper’s conclusion should be a brief summary of the paper’s substance and objectives; what you present in your research paper can gain impact by having a strong conclusion section.

In this Mind The Graph article, you will learn how to write a conclusion for a research report in a way that inspires action and helps the readers to better understand your research paper. This article will provide you the definition and some broad principles before providing step-by-step guidance.

What is a conclusion for a research paper and why is it important?

A conclusion is where you summarize the main points and, if appropriate, make new research suggestions. It is not merely a summary of the key points discussed or a rehash of your research question.

The reader is expected to comprehend from the article’s conclusion why your study should be significant to them after reading it. A conclusion of one or two well-developed paragraphs is appropriate for the majority of research papers; however, in a few unusual cases, more paragraphs may be required to highlight significant findings and their importance.

Just as the introduction is responsible for giving the reader a first impression on the subject, the conclusion is the chance to make a final impression by summarizing major information of your research paper and, most often, giving a different point of view on significant implications.

Adding a strong conclusion to your research paper is important because it’s a possibility to give the reader the comprehension of your research topic. Given that the reader is now fully informed on the subject, the conclusion also gives you a chance to restate the research problem effectively and concisely.

Examples of conclusions for a research paper

Now that you are aware of what a conclusion is and its significance for a research paper, it is time to provide you with some excellent samples of well-structured conclusions so you may get knowledge about the type of conclusion you can use for your research paper.

Argumentative Research Paper Conclusion

The most convincing arguments from your research paper should be added to the conclusion if you want to compose a strong argumentative conclusion.

Additionally, if your thesis statement expresses your perspective on the subject, you should think about restarting it as well as including any other pertinent information.

Example: As a result of the sixth extinction, which is currently affecting Earth, many species are vanishing every day. There are at least three strategies that people could employ to keep them from going extinct entirely in the ensuing fifty years. More recycling options, innovative plastic production techniques, and species preservation could save lives.

Analytical Research Paper Conclusion

The first thing you should do is reiterate your thesis and list the main elements of your arguments.

There should undoubtedly be a spotlight on a bigger context in the analytical research paper conclusion, which is the key distinction between it and other types of conclusions. It means you can add some meaning to the findings.

Example: Elon Musk has revolutionized the way we drive, pay for things, and even fly. His innovations are solely motivated by the desire to simplify things, but they inevitably alter the course of history. When Musk was a student, he had his first idea for PayPal, which is now among the most widely used methods of online payment. Likewise with Tesla automobiles.

Comparative Research Paper Conclusion

The conclusion of a comparative essay should be deeply analytical. To clearly express your conclusions, you must be very thorough when reviewing the data. Furthermore, the sources must be reliable.

A paraphrased thesis statement and a few sentences describing the significance of your study research are also required, as per normal.

Example: Gas-powered vehicles are ineffective and inefficient compared to electric vehicles. Not only do they emit fewer pollutants, but the drivers also get there more quickly. Additionally, gas cars cost more to maintain. Everything stems from the details of the far more straightforward engines used in electric cars.

How to write a conclusion for a research paper

In this section, you will learn how to write a conclusion for a research paper effectively and properly. These few easy steps will enable you to write the most convincing conclusion to your research paper.

1. Remember about the main topic

The statement must be written clearly and concisely to be effective, just one sentence. Remember that your conclusion should be concise and precise, expressing only the most important elements.

2. Reaffirm your thesis

Restate the research paper’s thesis after that. This can be done by going back to the original thesis that you presented in the research’s introduction. The thesis statement in your conclusion must be expressed differently from how it was in the introduction. This section can also be written effectively in a single sentence.

3. Sum important points in a summary

It’s time to make a list of the important arguments in your research paper. This phase can be made simpler by reading over your research and emphasizing only the main ideas and evidence.

Remember that the conclusion should not contain any new information. Focus only on the concepts you cover in your paper’s main body as a result. And also, keep in mind that this brief summary reminds your readers of the importance of the topic you are researching.

4. Emphasize the importance

At this stage, you can genuinely express a few words about how significant your arguments are. A succinct but impactful sentence can successfully achieve its aim. You could also attempt to examine this circumstance from a wider perspective.

Give an example of how your discoveries have affected a certain field. It would be beneficial if you made an effort to answer the question, “So what?” if there was any ambiguity.

5. Finish up your argument

As you wrap up your conclusion, consider posing a question or a call to action that will encourage readers to consider your point of view even further. This sentence can also answer any queries that were not addressed in the paper’s body paragraphs.

In addition, if there is an unresolved question in the main body, this is a fantastic area to comment on.

Common mistakes you should avoid

After learning the fundamentals of producing a strong research paper conclusion, it’s time to learn the common mistakes to avoid.

• Weak conclusion: If your ending is weak, readers will feel dissatisfied and disappointed. Writing ambiguous closing lines for essays also lowers the quality of the paper and the capacity of your arguments to support your main topic.
• Abrupt conclusion: Your research has to be an expression of your writing as a whole, not just a section. Therefore, make sure your thoughts are fully stated.
• Adding new information: Only your research should only be summarized in the conclusion. As the conclusion cannot contain extra information, make sure to offer all of your conclusions and supporting evidence in the body paragraphs.
• Absence of focus: A conclusion needs to be concise and well-focused. Avoid concluding the research with inane or superfluous details.
• Absurd length: Research must be of a proper length—neither too long nor too short. If you write more than is necessary, you can miss the point, which is to revisit the paper’s argument straightforwardly. Additionally, if you write too little, your readers will think you’re being negligent. It should be written in at least one or two whole paragraphs.

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How to Write a Research Methodology for a Research Paper

Ever find yourself stuck when trying to write the discussion part of your research paper? Don't worry, it happens to a lot of people. 

The discussion section is super important in your research paper . It's where you explain what your results mean. But turning all that data into a clear and meaningful story? That's not easy.

Guess what? MyPerfectWords.com has come up with a solution. 

This blog is your guide to writing an outstanding discussion section. We'll guide you step by step with useful tips to make sure your research stands out.

So, let’s get started!

• 1. What Exactly is a Discussion Section in the Research Paper?
• 2. How to Write the Discussion Section of a Research Paper?
• 3. Examples of Good Discussion for a Research Paper
• 4. Mistakes to Avoid in Your Research Paper's Discussion 

What Exactly is a Discussion Section in the Research Paper?

In a research paper, the discussion section is where you explain what your results really mean. It's like answering the questions, "So what?" and "What's the big picture?" 

The discussion section is your chance to help your readers understand why your findings are important and how they fit into the larger context. It's more than just summarizing; it's about making your research understandable and meaningful to others.

Importance of the Discussion Section

The discussion section isn't just a formality; it's the heart of your research paper. This is where your findings transform from data into knowledge. 

Let's break down why it's so crucial:

• Interpretation of Results : The discussion is where you get to tell readers what your results really mean. You go into the details, helping them understand the story behind the numbers or findings.
• Connecting the Dots : You connect different parts of your research, showing how they relate. This helps your readers see the bigger picture.
• Relevance to the Big Picture : You get to highlight why your research matters. How does it contribute to the broader understanding of the topic? This is your time to make your research significant.
• Addressing Limitations : In the discussion, you can acknowledge any limitations in your study and discuss how they might impact your results.
• Suggestions for Further Research : The discussion is where you suggest areas for future exploration. It's like passing the baton to the next researcher, indicating where more work could be done.

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How to Write the Discussion Section of a Research Paper?

The Discussion section in a research paper plays a vital role in interpreting findings and formulating a conclusion . Given below are the main components of the discussion section:

• Quick Summary: A brief recap of your main findings.
• Interpretation: Significance and meaning of your results in relation to your research question.
• Literature Review : Connecting your findings with previous research or similar studies.
• Limitations: Discussing any study limitations, addressing potential concerns.
• Implications: Broader implications of your findings, considering practical and theoretical aspects.
• Alternative Explanations: Evaluating alternative interpretations, demonstrating a comprehensive analysis.
• Connecting to Hypotheses : Summarizing how your result section aligns or diverges from your initial hypotheses.

Now let’s explore the steps to write an effective discussion section that will effectively communicate the significance of your research:

Step 1: Get Started with a Quick Summary

Start by quickly telling your readers the main things you found in your research. Don't explain them in detail just yet; just give a simple overview. 

This helps your readers get the big picture before diving into the details.

Step 2: Interpret Your Results

In the next step, talk about what your findings really mean. Share why the information you gathered is important. Connect each result to the questions you were trying to answer and the goals you set for your research.

Step 3: Relate to Existing Literature

In this step, link up your discoveries with what other researchers have already figured out. 

Share if your results are similar to or different from what's been found before. This helps give more background to your study and shows you know what other scientists have been up to.

Step 4: Address Limitations Honestly

Every study has its limitations. Acknowledge them openly in your discussion. This not only shows transparency but also helps readers interpret your results more accurately.

Step 5: Discuss the Implications

Explore the implications of your findings. How do they contribute to the field? What real-world applications or changes might they suggest?

Dig into why your discoveries are important. How do they help the subject you studied? 

This step is like looking at the bigger picture and asking, "So, what can we do with this information?"

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Step 6: Consider Alternative Explanations

After discussing the implications, challenge yourself by exploring alternative explanations for your results. 

Discuss different perspectives and show that you've considered multiple angles.

Step 7: Connect to Your Hypotheses or Research Questions

For the last step, revisit your initial hypotheses or research questions. Explain whether your results support what you thought might happen or if they surprised you. 

Examples of Good Discussion for a Research Paper

Learning from well-crafted discussions can significantly enhance your own writing. Given below are some examples to help you understand how to write your own.

Discussion for a Research Paper Example Pdf

Discussion for a Medical Research Paper

Discussion Section for a Qualitative Research Paper

Mistakes to Avoid in Your Research Paper's Discussion 

Writing the discussion section of your research paper can be tricky. To make sure you're on the right track, be mindful of these common mistakes:

• Overstating or Overinterpreting Results

Avoid making your findings sound more groundbreaking than they are. Stick to what your data actually shows, and don't exaggerate.

• Neglecting Alternative Explanations 

Failing to consider other possible explanations for your results can weaken your discussion. Always explore alternative perspectives to present a well-rounded view.

• Ignoring Limitations 

Don't sweep limitations under the rug. Acknowledge them openly and discuss how they might affect the validity or generalizability of your results.

• Being Overly Technical or Jargon-laden

Remember that your audience may not be experts in your specific field. Avoid using overly technical language or excessive jargon that could alienate your readers.

• Disregarding the 'So What' Factor

Always explain the significance of your findings. Don't leave your readers wondering why your research matters or how it contributes to the broader understanding of the subject.

• Rushing the Conclusion

The conclusion section of your discussion is critical. Don't rush it. Summarize the key points and leave your readers with a strong understanding of the significance of your research.

So, there you have it —writing a discussion and conclusion section isn't easy, but avoiding some common mistakes can make it much smoother. 

Remember to keep it real with your results, think about what else could explain things, and don't forget about any limits in your study.

But if you're feeling stuck, MyPerfectWords.com is here for you. 

Our team of experts knows their way around discussions. Whether you need some guidance or want someone to handle the writing for you, we've got your back.

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How Do I Write a Dissertation Conclusion?

Table of Contents

Conclusions play a vital role in any academic writing. They involve summarising your research findings, discussing the research project, and suggesting the final thought in the dissertation for further research.

In this blog, we will discuss the details of the dissertation’s conclusion, including its structure, key elements, purpose, length, and more.

Read on to learn the strategies and hacks our experts offering dissertation help have dished out for you. 

What is a Dissertation Conclusion ?

The conclusion is considered the final chapter of a dissertation. It contains the main argument and findings with wider implications for the question of research. It is also the shortest chapter.

The conclusion of the dissertation matters to the reader because it makes the reader understand what this research aims to display. Another important aspect is why the research paper would matter to future researchers.

The conclusion is not only about the dissertation summary or rewriting the research problem. It is a synthesis of the main findings. Writing a conclusion for a dissertation focuses on the important findings to demonstrate your overall understanding of the research problem to the reader. These include:

• Present some important words to make a lasting impression
• Summarise your main contribution to the research work
• Try to convey the larger implications to create a broader context
• Elaborate the ideas of your findings

If you need personalised guidance in the UK, MyAssignmentHelp can help you with writing a conclusion for a dissertation.

How to Structure Dissertation Conclusions for Your Research Project?

Struggling to find a conclusion for dissertation example? Confused about how to write a dissertation conclusion? Structuring the conclusion for your dissertation requires your attention, timing, and dedication. To get a better view of the dissertation, let’s break down the structure of the conclusion. You can follow this typical structure to cover the points in the implications of your research:

• Write a brief dissertation introduction section . The conclusion chapter should start with a brief introduction of the entire dissertation. This section should contain the information that the reader is trying to find out in the chapter.
• Elaborate on the overall findings about the main body of the research paper : The second step is to discuss the overall findings for future research. Try to relate the research aims and research questions. Maybe you have discussed everything in the discussion chapter. However, summarising those concepts helps readers to focus on broader findings.
• Understand the limitations of your research study . This will help you critically reflect on the limitations of your research. A few examples of limitations are budget constraints, time constraints, lack of research equipment, and limited data access.
• Create recommendations for future research . Include data points and analysis findings, but do not include anything directly related to the research questions.
• Wrap up the content while maintaining the research focus : Wrap up the conclusion with the information that you included on your dissertation’s conclusion page. Include key takeaways. Don’t include any new information here. Try to finish within one paragraph.

If you find it challenging to structure the conclusion of your dissertation, you can choose to buy dissertation conclusion content from us. 

Key Elements to Include in Your Conclusion

Writing a conclusion dissertation requires key elements to focus on:

• Start the conclusion with an introductory remark : Effectively starting the concluding paragraph is the best start to writing an introductory paragraph of your conclusion.
• Establish a link to your introduction : Introduction plays an important role in all academic writing or empirical research. It should contain a research aim and a literature review. When you write the concluding paragraph, restate points from the introduction, but that should not sound repetitive.
• Try to grab readers’ attention : An emotional connection towards the end of the dissertation takes the reader back to your website to read the topic again and again.
• Conclude with a closing line : Don’t conclude the conclusion ambiguously. It may create a bad impression on the reader.
• Encourage with an ending note : Conclude the conclusion with a clear and precise note, leaving the reader with a deep thought. 

If you are confused about concluding your research, get help from our dissertation editors in the UK. 

Crafting a Dissertation Conclusion with Purpose

The main purpose of the conclusion is to provide answers to the research questions in practical terms. Another important aspect is restating important arguments. Here, you will get the final chance to demonstrate the significance of the issue in the chapter of discussion. The importance of the conclusion in the dissertation is given below:

• The final chapter enables you to demonstrate the way outcomes have matched what you have expected,
• Review the activities that you have included to support your argument,
• Inform the readers about unusual things that you have observed during research. Demonstrate the knowledge that you have gained from your research field,
• You can clarify your position.

If you still feel confused, contact MyAssignmentHelp, where our team of experts will help you conclude your dissertation.

What is the Ideal Length of a Dissertation Conclusions?

You have already discussed the introduction and body of the research paper. Now, it’s time to conclude the dissertation. Let’s check in detail:

• The ideal length of a conclusion is 5-7% of your overall word count.
• It should state the main research findings and recommendations for future research. You can restate the thesis statement.
• But do not copy and paste it from the introduction. Use different words in it.
• Review the body of your dissertation and take key notes from there to create a strong conclusion.
• Change the wording of the main focus of your research idea so that it does not sound repetitive.
• Clarify why your is important for other researchers
• Recommendation plays a vital role to the reader. Use relevant questions for further research. You can ask yourself about:
• What is the bigger issue which will draw the readers’ attention?
• Do you want your reader to take certain actions?
• What do your readers want to do in reaction to your research paper?

We know that it is tough to writing a dissertation, including the introduction, evidence, examples, and all. So, to avoid such daunting situations, you can hire MyassignmentHelp for support.

How to Write Dissertation Conclusions? Key Considerations

We have covered the conclusion of the dissertation, how to structure it, its key elements, purpose, and ideal length, along with examples. Here are the key considerations to create a top-notch conclusion for your dissertation:

• Avoid digressing. Although this may vary throughout colleges, the conclusion chapter typically makes up 5-7% of the total word count, so you need to be succinct. Edit this chapter carefully, emphasising its clarity and conciseness.
• When claiming your study’s contribution, exercise extreme caution. Nothing will cause the marker to look away more quickly than inflated or false claims. Make your claims with firmness but humility.
• Use plain language that even a knowledgeable layperson may understand. Keep in mind that not all of your readers will be subject matter experts in your sector, so you should write in an approachable manner. Remember that you are the expert on your subject, so be sure to provide readers with clear explanations.

If you want a conclusion for the dissertation example, here is the list to go through and take ideas.

Showcase of Dissertation Conclusion Examples for Future Research

Are you looking for a ‘dissertation conclusion example UK?’ So, you are in the right place. Let’s check the examples:

• Advice: Confirm that your dissertation is relevant to your field of research.

Example : This history project consists of conventional ideas about the relationship between medicine, politics, and romanticism. It unfolds romantic literature and biopolitical immunity as mutual and co-productive processes.

• Advice: Avoid just pasting your thesis into your conclusion. Put it again in new terms

Example : In this dissertation, I proposed that the Romantic era’s unique combination of post-revolutionary politics and smallpox preventative breakthroughs marked the point of critical mass for biopolitical immunity. Specifically, I illustrated how the French Revolution and the discovery of vaccination in the 1790s brought about a reevaluation of the connection between the state and bodies.

• Advice: Reiterating key arguments from your introduction at the end of your thesis is a good idea.

Example : I looked at how other writers, like Mary Shelley and William Wordsworth, increasingly imagined the body politic literally—that is, as an integrated political collective made up of bodies whose immunity to political and medical ills was essential to a healthy state—and how Mary Wollstonecraft envisions an ideal medico-political state in both her fiction and political writings.

Crafting a Flawless Conclusion of Dissertation Chapters for Further Research

Crafting a flawless concluding chapter requires the main findings, the purpose of the investigation, the importance of the investigation, recommendations, and contribution to the knowledge. 

Let’s check in detail about how you can organise your discussion chapters:

The Purpose of Investigation : Here, you will find a summary of your findings. Include information about your contribution to accomplishing the desired objectives.

The Importance of Investigation : Always demonstrate the significance of the investigation performed on a specific topic.

All about Recommendations : You can include your advice for better investigation procedures in such topics.

Contribution to Knowledge : Describe the knowledge and skills you have gained while participating in research. Include details to specify the undertakings that have helped you accomplish the desired academic objectives.

Conclusion,

We hope you now know how to write a strong dissertation conclusion. Presenting your final thoughts in dissertation and translating them into an effective conclusion requires a lot of hard work and energy. But with the right guidance and support, you can complete the conclusion. Make sure to check the concluding remarks for dissertation to submit a paper that leaves the reader curious to know more.  

And, if you still fumble while writing a dissertation conclusion, you can always fall back on MyAssignmenthelp. 

Let MyAssignmenthelp Guide You with Dissertation Writing

If you still feel confused about concluding your dissertation, you are not alone in this journey. You can ask our experts for a ‘dissertation conclusion example UK’ as per university guidelines. We provide world-class online academic writing help at the cheapest price. You can also use our conclusion generator for instant results. Our executives are available 24* 7 for urgent assistance. So share your details today.

Hi, I am Mark, a Literature writer by profession. Fueled by a lifelong passion for Literature, story, and creative expression, I went on to get a PhD in creative writing. Over all these years, my passion has helped me manage a publication of my write ups in prominent websites and e-magazines. I have also been working part-time as a writing expert for myassignmenthelp.com for 5+ years now. It’s fun to guide students on academic write ups and bag those top grades like a pro. Apart from my professional life, I am a big-time foodie and travel enthusiast in my personal life. So, when I am not working, I am probably travelling places to try regional delicacies and sharing my experiences with people through my blog. 

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There’s No Evidence of a Retirement Crisis

The New York Times stretches too far.

Last month, The New York Times printed an article about BlackRock BLK CEO Larry Fink, bearing the headline “He Wants to Address the Retirement Crisis Looming in the U.S.” (The online version carried a different title.)

The argument that the United States faces an impending retirement crisis has been struck many times, over many years. “Unless we as a society do a better job of investing,” said Eli Broad of SunAmerica in a 1994 Associated Press article, “future retirees face a drastically reduced standard of living.” Later in that article, Merrill Lynch’s Daniel Tully fretted that “members of the baby boom generation” could endure lower “living standards” than the retirees of the 1990s.

A decade later, The Coming Retirement Crisis, published in 2004 by Forbes , anticipated a “slow-motion crisis as baby boomers head into retirement.” Ten years after that, in 2014, came the video Broken Eggs: The Looming Retirement Crisis in America. There is no shortage of such examples. Until now, though, I had not seen a major newspaper cross that line.

The Research

In 1994, the nation’s retirement structure was changing rapidly. Over the previous eight years, the number of defined-benefit plans had fallen by more than 50%, from 172,642 to 74,422. Meanwhile, the number of 401(k) accounts was soaring. Baby boomers were the first defined-contribution generation. Unlike their predecessors, they would largely be planning for retirement on their own.

As baby boomers are currently between the ages of 60 and 78, enough time has passed to reveal the early results of this experiment. Fortunately for our purpose, the US Census Bureau publishes a history of median national incomes, sorted by sex and age. (The data used in this column can be found in Table P-8 within this link .)

Unfortunately for our purposes, although the Census Bureau’s research is regarded as the gold standard for US income data, it is nevertheless suspect. For example, the Bureau reports that in 2019, the median real income for female workers from ages 25 through 34 ballooned by 9%. Meanwhile, income for male workers of that age was slightly lower. The next year, it then states, the female workers’ income was flat, while the group’s males increased by 3%.

I’m not buying. Young American working women did not suddenly receive a decade’s worth of real income growth in a single year, even as their male counterparts went nowhere. The Census Bureau’s measurements contain the amount of fluctuation associated with small sample sizes, but for cohorts of 20 million.

The Age Groups’ Results

Thus, I would strongly caution against attempting to decipher the wiggles of the following illustration, which shows the results since 1994 for each of the Census Bureau’s seven age groups. (With these calculations, I combined the female and male results within each age group, to simplify the presentation. Each cohort is indexed, with an initial value of 100 assigned to its 1994 total. Thus, the chart shows changes in relative income, rather than the levels of absolute income—which, for reference, are highest for the 45 to 54 group and by far the lowest for the 15 to 24 group.)

That caveat aside, the overall trend is instructive.

Income Changes Over Time, by Age Group

Over the past 30 years, the median real income for every cohort has improved—for retirees as well as workers. Indeed, retirees have fared somewhat better than the norm. The 65 to 74 group, charted in purple, has enjoyed the second-highest overall gain among the seven cohorts, while the 75-plus crowd places fourth.

One potential concern is the purple line’s recent downturn. Does this indicate that, at long last, retirees’ problems have surfaced? To answer my rhetorical question, I think not. For one thing, as previously written, the data are lumpy, which makes interpretation hazardous. For another, the age group suffered two unusual shocks, just as the research period ended. First, covid-19′s arrival sent many older workers into early retirement and/or cost them their part-time jobs. Second, 2022′s high inflation eroded their purchasing power that year.

However, in 2023—which does not appear in the illustration, as those figures have not yet been released—the Social Security Administration gave retirees an 8.7% cost-of-living raise, while the Consumer Price Index rose by only 3.4%. That makes for a one-year after-inflation raise of 5% for Social Security benefits, which will almost have certainly reversed that near-term trend.

Where’s the Beef?

But I digress. The point isn’t that the evidence has been tried and found lacking. It is instead that it really hasn’t been offered at all. For several decades, people worried about future retirees have argued their case based on broad trends, such as the waning of defined-benefit plans, surveys suggesting that workers don’t own enough investment assets, and the ever-increasing length of retirement as Americans live longer. Those are all worthwhile topics, but they represent questions to be addressed, not answers to be delivered.

To put the matter another way, unless they are accompanied by specific supporting evidence, demographic arguments are insufficient. I should know. I spent much of my investment youth hearing that stock market returns would be strong until about 2010 but would then collapse as baby boomers became more conservative and sold their equity shares. Of course, the opposite occurred. When they were supposed to be declining, stocks instead soared.

None of this should be mistaken for complacency. My feelings about the transition from defined-benefit plans to defined-contribution plans are decidedly mixed . The current system possesses weaknesses as well as strengths . Nor do I discount the demographic concerns. The world is becoming older rather than younger. That shift will have profound implications, including with retirement funding.

But there is a difference between speculation and certainty, particularly when the speculation has existed for several decades. With its headline, The New York Times presented an assertion as a fact. It should not have done so.

The opinions expressed here are the author’s. Morningstar values diversity of thought and publishes a broad range of viewpoints.

The author or authors do not own shares in any securities mentioned in this article. Find out about Morningstar’s editorial policies .

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About the author, john rekenthaler.

John Rekenthaler is vice president, research for Morningstar Research Services LLC, a wholly owned subsidiary of Morningstar, Inc.

Rekenthaler joined Morningstar in 1988 and has served in several capacities. He has overseen Morningstar's research methodologies, led thought leadership initiatives such as the Global Investor Experience report that assesses the experiences of mutual fund investors globally, and been involved in a variety of new development efforts. He currently writes regular columns for Morningstar.com and Morningstar magazine.

Rekenthaler previously served as president of Morningstar Associates, LLC, a registered investment advisor and wholly owned subsidiary of Morningstar, Inc. During his tenure, he has also led the company’s retirement advice business, building it from a start-up operation to one of the largest independent advice and guidance providers in the retirement industry.

Before his role at Morningstar Associates, he was the firm's director of research, where he helped to develop Morningstar's quantitative methodologies, such as the Morningstar Rating for funds, the Morningstar Style Box, and industry sector classifications. He also served as editor of Morningstar Mutual Funds and Morningstar FundInvestor.

Rekenthaler holds a bachelor's degree in English from the University of Pennsylvania and a Master of Business Administration from the University of Chicago Booth School of Business, from which he graduated with high honors as a Wallman Scholar.

When Low Risk Means High Risk

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• Open access
• Published: 17 April 2024

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

• Weiwei Zhang 1 &
• Xinchun Li 1  

BMC Health Services Research volume  24 , Article number:  477 ( 2024 ) Cite this article

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Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.

This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.

A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.

Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.

The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Peer Review reports

Introduction

The outbreak of severe acute respiratory syndrome (SARS) in 2003 was the first global public health emergency of the 21st century. From SARS to the coronavirus disease (COVID-19) pandemic at the end of 2019, followed shortly by the monkeypox epidemic of 2022, the global community has witnessed eight major public health events within the span of only 20 years [ 1 ]. These events are all characterized by high infection and fatality rates. For example, the number of confirmed COVID-19 cases worldwide is over 700 million, and the number of deaths has exceeded 7 million [ 2 ]. Every major public health emergency typically consists of four stages: incubation, outbreak, peak, and decline. During the outbreak and transmission, surges in the number of infected individuals and the number of critically ill patients led to a corresponding increase in the urgent demand for intensive care unit (ICU) medical resources. ICU healthcare resources provide material security for rescue work during major public health events as they allow critically ill patients to be treated, which decreases the case fatality rate and facilitates the prevention and control of epidemics. Nevertheless, in actual cases of prevention and control, the surge in patients has often led to shortages of ICU healthcare resources and a short-term mismatch of supply and demand, which are problems that have occurred several times in different regions. These issues can drastically impact anti-epidemic frontline healthcare workers and the treatment outcomes of infected patients. According to COVID-19 data from recent years, many infected individuals take about two weeks to progress from mild to severe disease. As the peak of severe cases tends to lag behind that of infected cases, predicting the changes in the number of new infections can serve as a valuable reference for healthcare institutions in forecasting the demand for ICU healthcare resources. The accurate forecasting of the demand for ICU healthcare resources can facilitate the rational resource allocation of hospitals under changes in demand patterns, which is crucial for improving the provision of critical care and rescue efficiency. Therefore, in this study, we combined a support vector regression (SVR) prediction model optimized by a genetic algorithm (GA) with bidirectional long-short-term memory (BILSTM), with the aim of enhancing the dynamic and accurate prediction of the number of current confirmed cases. Based on this, we forecasted the demand for ICU healthcare resources, which in turn may enable more efficient resource deployment during severe epidemic outbreaks and improve the precise supply of ICU healthcare resources.

Research on the demand forecasting of emergency materials generally employs quantitative methods, and traditional approaches mainly include linear regression and GM (1,1). Linear regression involves the use of regression equations to make predictions based on data. Sui et al. proposed a method based on multiple regression that aimed to predict the demand for emergency supplies in the power grid system following natural disasters [ 3 ]. Historical data was used to obtain the impact coefficient of each factor on emergency resource forecasting, enabling the quick calculation of the demand for each emergency resource during a given type of disaster. However, to ensure prediction accuracy, regression analysis needs to be supported by data from a large sample size. Other researchers have carried out demand forecasting for emergency supplies from the perspective of grey prediction models. Li et al. calculated the development coefficient and grey action of the grey GM (1,1) model using the particle swarm optimization algorithm to minimize the relative errors between the real and predicted values [ 4 ]. Although these studies have improved the prediction accuracy of grey models, they mainly involve pre-processing the initial data series without considering the issue of the excessively fast increase in predicted values by traditional grey GM (1,1) models. In emergency situations, the excessively fast increase in predicted values compared to real values will result in the consumption of a large number of unnecessary resources, thereby decreasing efficiency and increasing costs. As traditional demand forecasting models for emergency supplies have relatively poor perfect order rates in demand analysis, which result in low prediction accuracy, they are not mainstream.

At present, dynamic models of infectious diseases and demand forecasting models based on machine learning are at the cutting edge of research. With regard to the dynamic models of infectious diseases, susceptible infected recovered model (SIR) is a classic mathematical model employed by researchers [ 5 , 6 , 7 ]. After many years of development, the SIR model has been expanded into various forms within the field of disease transmission, including susceptible exposed infected recovered model (SEIR) and susceptible exposed infected recovered dead model (SEIRD) [ 8 , 9 ]. Nevertheless, with the outbreak of COVID-19, dynamic models of infectious diseases have once again come under the spotlight, with researchers combining individual and group variables and accounting for different factors to improve the initial models and reflect the state of COVID-19 [ 10 , 11 , 12 , 13 ]. Based on the first round of epidemic data from Wuhan, Li et al. predicted the time-delay distributions, epidemic doubling time, and basic reproductive number [ 14 ]. Upon discovering the presence of asymptomatic COVID-19 infections, researchers began constructing different SEIR models that considered the infectivity of various viral incubation periods, yielding their respective predictions of the inflection point. Based on this, Anggriani et al. further considered the impact of the status of infected individuals and established a transmission model with seven compartments [ 15 ]. Efimov et al. set the model parameters for separating the recovered and the dead as uncertain and applied the improved SEIR model to analyze the transmission trend of the pandemic [ 16 ]. In addition to analyzing the transmission characteristics of normal COVID-19 infection to predict the status of the epidemic, many researchers have also used infectious disease models to evaluate the effects of various epidemic preventive measures. Lin et al. applied an SEIR model that considered individual behavioral responses, government restrictions on public gatherings, pet-related transmission, and short-term population movements [ 17 ]. Cao et al. considered the containment effect of isolation measures on the pandemic and solved the model using Euler’s numerical method [ 18 ]. Reiner et al. employed an improved SEIR model to study the impact of non-pharmaceutical interventions implemented by the government (e.g., restricting population movement, enhancing disease testing, and increasing mask use) on disease transmission and evaluated the effectiveness of social distancing and the closure of public spaces [ 19 ]. These studies have mainly focused on modeling the COVID-19 pandemic to perform dynamic forecasting and analyze the effectiveness of control measures during the epidemic. Infectious disease dynamics offer good predictions for the early transmission trends of epidemics. However, this approach is unable to accurately estimate the spread of the virus in open-flow environments. Furthermore, it is also impossible to set hypothetical parameters, such as disease transmissibility and the recovery probability constant, that are consistent with the conditions in reality. Hence, with the increase in COVID-19 data, this approach has become inadequate for the accurate long-term analysis of epidemic trends.

Machine learning has shown significant advantages in this regard [ 20 , 21 ]. Some researchers have adopted the classic case-based reasoning approach in machine learning to make predictions. However, it is not feasible to find historical cases that fully match the current emergency event, so this approach has limited operability. Other researchers have also employed neural network training in machine learning to make predictions. For example, Hamou et al. predicted the number of injuries and deaths, which in turn were used to forecast the demand for emergency supplies [ 22 ]. However, this approach requires a large initial dataset and a high number of training epochs, while uncertainty due to large changes in intelligence information can lead to significant errors in data prediction [ 23 , 24 , 25 ]. To address these problems, researchers have conducted investigations that account (to varying degrees) for data characterized by time-series and non-linearity and have employed time-series models with good non-linear fitting [ 26 , 27 , 28 ]. The use of LSTM to explore relationships within the data can improve the accuracy of predicting COVID-19 to some extent. However, there are two problems with this approach. First, LSTM neural networks require extremely large datasets, and each wave of the epidemic development cycle would be insufficient to support a dataset suitable for LSTM. Second, neural networks involve a large number of parameters and highly complex models and, hence, are susceptible to overfitting, which can prevent them from achieving their true and expected advantages in prediction.

Overall, Our study differs from other papers in the following three ways. First, the research object of this paper focuses on the specific point of ICU healthcare resource demand prediction, aiming to improve the rate of critical care patient treatment. However, past research on public health emergencies has focused more on resource prediction , such as N95 masks, vaccines, and generalized medical supplies during the epidemic , to mitigate the impact of rapid transmission and high morbidity rates. This has led to less attention being paid to the reality of the surge in critically ill patients due to their high rates of severe illness and mortality.

Second, the idea of this paper is to further forecast resource needs based on the projected number of people with confirmed diagnoses, which is more applicable to healthcare organizations than most other papers that only predict the number of people involved. However, in terms of the methodology for projecting the number of people, this paper adopts a combined prediction method that combines regression algorithms and recurrent neural networks to propose a BILSTM-GASVR prediction model for the number of confirmed diagnoses. It capitalizes on both the suitability of SVR for small samples and non-linear prediction as well as the learning and memory abilities of BILSTM in processing time-series data. On the basis of the prediction model for the number of infected cases, by considering the characteristics of ICU healthcare resources, we constructed a demand forecasting model of emergency healthcare supplies. Past public health emergencies are more likely to use infectious disease models or a single prediction model in deep learning. some of the articles, although using a combination of prediction, but also more for the same method domain combination, such as CNN-LSTM, GRU-LSTM, etc., which are all recurrent neural networks.

Third, in terms of specific categorization of resources to be forecasted, considering the specificity of ICU medical resources, we introduce human resource prediction on the basis of previous studies focusing on material security, and classified ICU medical resources into three categories: ICU human resources, drugs and medical equipment. The purpose of this classification is to match the real-life prediction scenarios of public health emergencies and improve the demand forecasting performance for local ICU healthcare resources. Thus, it is easy for healthcare institutions to grasp the overall development of events, optimizing decision-making, and reducing the risk of healthcare systems collapsing during the outbreak stage.

In this section, we accomplish the following two tasks. Firstly, we introduce the idea of predicting the number of infected cases and show the principle of the relevant models. Secondly, based on the number of infected cases, ICU healthcare resources are divided into two categories (healthcare workers and healthcare supplies), and their respective demand forecasting models are constructed.

Prediction model for the number of infected cases

Gasvr model.

Support vector machine (SVM) is a machine-learning language for classification developed by Vapnik [ 29 ]. Suppose there are two categories of samples: H1 and H2. If hyperplane H is able to correctly classify the samples into these two categories and maximize the margin between the two categories, it is known as the optimal separating hyperplane (OSH). The sample vectors closest to the OSH in H1 and H2 are known as the support vectors. To apply SVM to prediction, it is essential to perform regression fitting. By introducing the \(\varepsilon\) -insensitive loss function, SVM can be converted to a support vector regression machine, where the role of the OSH is to minimize the error of all samples from this plane. SVR has a theoretical basis in statistical learning and relatively high learning performance, making it suitable for performing predictions in small-sample, non-linear, and multi-dimensional fields [ 30 , 31 ].

Assume the training sample set containing \(l\) training samples is given by \(\{({x}_{i},{y}_{i}),i=\mathrm{1,2},...,l\}\) , where \({x}_{i}=[{x}_{i}^{1},{x}_{i}^{2},...,{x}_{i}^{d}{]}^{\rm T}\) and \({y}_{i}\in R\) are the corresponding output values.

Let the regression function be \(f(x)=w\Phi (x)+b\) , where \(\phi (x)\) is the non-linear mapping function. The linear \(\varepsilon\) -insensitive loss function is defined as shown in formula ( 1 ).

Among the rest, \(f(x)\) is the predicted value returned by the regression function, and \(y\) is the corresponding real value. If the error between \(f(x)\) and \(y\) is ≤ \(\varepsilon\) , the loss is 0; otherwise, the loss is \(\left|y-f(x)\right|-\varepsilon\) .

The slack variables \({\xi }_{i}\) and \({\xi }_{i}^{*}\) are introduced, and \(w\) , \(b\) are solved using the following equation as shown in formula ( 2 ).

Among the rest, \(C\) is the penalty factor, with larger values indicating a greater penalty for errors > \(\varepsilon\) ; \(\varepsilon\) is defined as the error requirement, with smaller values indicating a smaller error of the regression function.

The Lagrange function is introduced to solve the above function and transformed into the dual form to give the formula ( 3 ).

Among the rest, \(K({x}_{i},{x}_{j})=\Phi ({x}_{i})\Phi ({x}_{j})\) is the kernel function. The kernel function determines the structure of high-dimensional feature space and the complexity of the final solution. The Gaussian kernel is selected for this study with the function \(K({x}_{i},{x}_{j})=\mathit{exp}(-\frac{\Vert {x}_{i}-{x}_{j}\Vert }{2{\sigma }^{2}})\) .

Let the optimal solution be \(a=[{a}_{1},{a}_{2},...,{a}_{l}]\) and \({a}^{*}=[{a}_{1}^{*},{a}_{2}^{*},...,{a}_{l}]\) to give the formula ( 4 ) and formula ( 5 ).

Among the rest, \({N}_{nsv}\) is the number of support vectors.

In sum, the regression function is as shown in formula ( 6 ).

when some of the parameters are not 0, the corresponding samples are the support vectors in the problem. This is the principle of SVR. The values of the three unknown parameters (penalty factor C, ε -insensitive loss function, and kernel function coefficient \(\sigma )\) , can directly impact the model effect. The penalty factor C affects the degree of function fitting through the selection of outliers in the sample by the function. Thus, excessively large values lead to better fit but poorer generalization, and vice versa. The ε value in the ε-insensitive loss function determines the accuracy of the model by affecting the width of support vector selection. Thus, excessively large values lead to lower accuracy that does not meet the requirements and excessively small values are overly complex and increase the difficulty. The kernel function coefficient \(\sigma\) determines the distribution and range of the training sample by controlling the size of inner product scaling in high-dimensional space, which can affect overfitting.

Therefore, we introduce other algorithms for optimization of the three parameters in SVR. Currently the commonly used algorithms are 32and some heuristic algorithms. Although the grid search method is able to find the highest classification accuracy, which is the global optimal solution. However, sometimes it can be time-consuming to find the optimal parameters for larger scales. If a heuristic algorithm is used, we could find the global optimal solution without having to trace over all the parameter points in the grid. And GA is one of the most commonly used heuristic algorithms, compared to other heuristic algorithms, it has the advantages of strong global search, generalizability, and broader blending with other algorithms.

Given these factors, we employ a GA to encode and optimize the relevant parameters of the model. The inputs are the experimental training dataset, the Gaussian kernel function expression, the maximum number of generations taken by the GA, the accuracy range of the optimized parameters, the GA population size, the fitness function, the probability of crossover, and the probability of mutation. The outputs are the optimal penalty factor C, ε-insensitive loss function parameter \(\varepsilon ,\) and optimal Gaussian kernel parameter \(\sigma\) of SVR, thus achieving the optimization of SVR. The basic steps involved in GA optimization are described in detail below, and the model prediction process is shown in Fig. 1 .

Prediction process of the GASVR model

Population initialization

The three parameters are encoded using binary arrays composed of 0–1 bit-strings. Each parameter consisted of six bits, and the initial population is randomly generated. The population size is set at 60, and the number of iterations is 200.

Fitness calculation

In the same dataset, the K-fold cross-validation technique is used to test each individual in the population, with K = 5. K-fold cross validation effectively avoids the occurrence of model over-learning and under-learning. For the judgment of the individual, this paper evaluates it in terms of fitness calculations. Therefore, combining the two enables the effective optimization of the model’s selected parameters and improves the accuracy of regression prediction.

Fitness is calculated using the mean error method, with smaller mean errors indicating better fitness. The fitness function is shown in formula ( 7 ) [ 32 ].

The individual’s genotype is decoded and mapped to the corresponding parameter value, which is substituted into the SVR model for training. The parameter optimization range is 0.01 ≤ C ≤ 100, 0.1 ≤ \(\sigma\) ≤ 20, and 0.001 ≤ ε ≤ 1.

Selection: The selection operator is performed using the roulette wheel method.

Crossover: The multi-point crossover operator, in which two chromosomes are selected and multiple crossover points are randomly chosen for swapping, is employed. The crossover probability is set at 0.9.

Mutation: The inversion mutation operator, in which two points are randomly selected and the gene values between them are reinserted to the original position in reverse order, is employed. The mutation probability is set at 0.09.

Decoding: The bit strings are converted to parameter sets.

The parameter settings of the GASVR model built in this paper are shown in Table 1 .

BILSTM model

The LSTM model is a special recurrent neural network algorithm that can remember the long-term dependencies of data series and has an excellent capacity for self-learning and non-linear fitting. LSTM automatically connects hidden layers across time points, such that the output of one time point can arbitrarily enter the output terminal or the hidden layer of the next time point. Therefore, it is suitable for the sample prediction of time-series data and can predict future data based on stored data. Details of the model are shown in Fig. 2 .

Schematic diagram of the LSTM model

LSTM consists of a forget gate, an input gate, and an output gate.

The forget gate combines the previous and current time steps to give the output of the sigmoid activation function. Its role is to screen the information from the previous state and identify useful information that truly impacts the subsequent time step. The equation for the forget gate is shown in formula ( 8 ).

Among the number, \(W_{f}\) is the weight of the forget gate, \({b}_{f}\) is the bias, \(\sigma\) is the sigmoid activation function, \({f}_{t}\) is the output of the sigmoid activation function, \(t-1\) is the previous time step, \(t\) is the current time step, and \({x}_{t}\) is the input time-series data at time step \(t\) .

The input gate is composed of the output of the sigmoid and tanh activation functions, and its role is to control the ratio of input information entering the information of a given time step. The equation for the input gate is shown in formula ( 9 ).

Among the number, \({W}_{i}\) is the output weight of the input gate, \({i}_{t}\) is the output of the sigmoid activation function, \({b}_{i}\) and \({b}_{C}\) are the biases of the input gate, and \({W}_{C}\) is the output of the tanh activation function.

The role of the output gate is to control the amount of information output at the current state, and its equation is shown in formula ( 10 ).

Among the number, \({W}_{o}\) is the weight of \({o}_{t}\) , and \({b}_{o}\) is the bias of the output gate.

The values of the above activation functions \(\sigma\) and tanh are generally shown in formulas ( 11 ) and ( 12 ).

\({C}_{t}\) is the data state of the current time step, and its value is determined by the input information of the current state and the information of the previous state. It is shown in formula ( 13 ).

Among the number, \(\widetilde{{C}_{t}}=\mathit{tan}h({W}_{c}[{h}_{t-1},{x}_{t}]+{b}_{c})\) .

\({h}_{t}\) is the state information of the hidden layer at the current time step, \({h}_{t}={o}_{t}\times \mathit{tan}h({c}_{t})\) .Each time step \({T}_{n}\) has a corresponding state \({C}_{t}\) . By undergoing the training process, the model can learn how to modify state \({C}_{t}\) through the forget, output, and input gates. Therefore, this state is consistently passed on, implying that important distant information will neither be forgotten nor significantly affected by unimportant information.

The above describes the principle of LSTM, which involves forward processing when applied. BILSTM consists of two LSTM networks, one of which processes the input sequence in the forward direction (i.e., the original order), while the other inputs the time series in the backward direction into the LSTM model. After processing both LSTM networks, the outputs are combined, which eventually gives the output results of the BILSTM model. Details of the model are presented in Fig. 3 .

Schematic diagram of the BILSTM model

Compared to LSTM, BILSTM can achieve bidirectional information extraction of the time-series and connect the two LSTM layers onto the same output layer. Therefore, in theory, its predictive performance should be superior to that of LSTM. In BILSTM, the equations of the forward hidden layer( \(\overrightarrow{{h}_{t}}\) ) , backward hidden layer( \(\overleftarrow{{h}_{t}}\) ) , and output layer( \({o}_{t}\) ) are shown in formulas ( 14 ) , ( 15 ) and ( 16 ).

The parameter settings of the BILSTM model built in this paper are shown in Table 2 .

Informer model

The Informer model follows the compiler-interpreter architecture in the Transformer model, and based on this, structural optimizations have been made to reduce the computational time complexity of the algorithm and to optimize the output form of the interpreter. The two optimization methods are described in detail next.

With large amounts of input data, neural network models can have difficulty capturing long-term interdependencies in sequences, which can produce gradient explosions or gradient vanishing and affect the model's prediction accuracy. Informer model solves the existential gradient problem by using a ProbSparse Self-attention mechanism to make more efficient than conventional self-attention.

The value of Transformer self-attention is shown in formula ( 17 ).

Among them, \(Q\in {R}^{{L}_{Q}\times d}\) is the query matrix, \(K\in {R}^{{L}_{K}\times d}\) is the key matrix, and \(V\in {R}^{{L}_{V}\times d}\) is the value matrix, which are obtained by multiplying the input matrix X with the corresponding weight matrices \({W}^{Q}\) , \({W}^{K}\) , \({W}^{V}\) respectively, and d is the dimensionality of Q, K, and V. Let \({q}_{i}\) , \({k}_{i}\) , \(v_{i}\) represent the ith row in the Q, K, V matrices respectively, then the ith attention coefficient is shown in formula ( 18 ) as follows.

Therein, \(p({k}_{j}|{q}_{i})\) denotes the traditional Transformer's probability distribution formula, and \(k({q}_{i},{K}_{l})\) denotes the asymmetric exponential sum function. Firstly, q=1 is assumed, which implies that the value of each moment is equally important; secondly, the difference between the observed distribution and the assumed one is evaluated by the KL scatter, if the value of KL is bigger, the bigger the difference with the assumed distribution, which represents the more important this moment is. Then through inequality \(ln{L}_{k}\le M({q}_{i},K)\le {\mathit{max}}_{j}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}-\frac{1}{{L}_{k}}{\sum }_{j=1}^{{L}_{k}}\left\{\frac{{q}_{i}{k}_{j}^{\rm T}}{\sqrt{d}}\right\}+ln{L}_{k}\) , \(M({q}_{i},K)\) is transformed into \(\overline{M}({q}_{i},K)\) . According to the above steps, the ith sparsity evaluation formula is obtained as shown in formula ( 19 ) [ 33 ].

One of them, \(M({q}_{i},K)\) denotes the ith sparsity measure; \(\overline{M}({q}_{i},K)\) denotes the ith approximate sparsity measure; \({L}_{k}\) is the length of query vector. \(TOP-u\) quantities of \(\overline{M}\) are selected to form \(\overline{Q}\) , \(\overline{Q}\) is the first u sparse matrices, and the final sparse self-attention is shown in Formula ( 20 ). At this point, the time complexity is still \(O({n}^{2})\) , and to solve this problem, only l moments of M2 are computed to reduce the time complexity to \(O(L\cdot \mathit{ln}(L))\) .

Informer uses a generative decoder to obtain long sequence outputs.Informer uses the standard decoder architecture shown in Fig. 4 , in long time prediction, the input given to the decoder is shown in formula ( 21 ).

Informer uses a generative decoder to obtain long sequence outputs

Therein, \({X}_{de}^{t}\) denotes the input to the decoder; \({X}_{token}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}\) is the dimension of the encoder output, which is the starting token without using all the output dimensions; \({X}_{0}^{t}\in {R}^{({L}_{token}+{L}_{y})\times {d}_{\mathit{mod}el}}\) is the dimension of the target sequence, which is uniformly set to 0; and finally the splicing input is performed to the encoder for prediction.

The parameter settings of Informer model created in this paper are shown in Table 3 .

BILSTM-GASVR combined prediction model

SVR has demonstrated good performance in solving problems like finite samples and non-linearity. Compared to deep learning methods, it offers faster predictions and smaller empirical risks. BILSTM has the capacity for long-term memory, can effectively identify data periodicity and trends, and is suitable for the processing of time-series data. Hence, it can be used to identify the effect of time-series on the number of confirmed cases. Given the advantages of these two methods in different scenarios, we combined them to perform predictions using GASVR, followed by error repair using BILSTM. The basic steps for prediction based on the BILSTM-GASVR model are as follows:

Normalization is performed on the initial data.

The GASVR model is applied to perform training and parameter optimization of the data to obtain the predicted value \(\widehat{{y}_{i}}\) .

After outputting the predicted value of GASVR, the residual sequence between the predicted value and real data is extracted to obtain the error \({\gamma }_{i}\) (i.e., \({\gamma }_{i}={y}_{i}-\widehat{{y}_{i}}\) ).

The BILSTM model is applied to perform training of the error to improve prediction accuracy. The BILSTM model in this paper is a multiple input single output model. Its inputs are the true and predicted error values \({\gamma }_{i}\) and its output is the new error value \(\widehat{{\gamma }_{i}}\) predicted by BILSTM.

The final predicted value is the sum of the GASVR predicted value and the BILSTM residual predicted value (i.e., \({Y}_{i}=\widehat{{y}_{i}}+\widehat{{\gamma }_{i}}\) ).

The parameter settings of the BILSTM-GASVR model built in this paper are shown in Table 4 .

Model testing criteria

To test the effect of the model, the prediction results of the BILSTM-GASVR model are compared to those of GASVR, LSTM, BILSTM and Informer. The prediction error is mainly quantified using three indicators: mean squared error (MSE), root mean squared error (RMSE), and correlation coefficient ( \(R^{2}\) ). Their respective equations are shown in formulas ( 22 ), ( 23 ) and ( 24 ).

Demand forecasting model of ICU healthcare resources

ICU healthcare resources can be divided into human and material resources. Human resources refer specifically to the professional healthcare workers in the ICU. Material resources, which are combined with the actual consumption of medical supplies, can be divided into consumables and non-consumables. Consumables refer to the commonly used drugs in the ICU, which include drugs for treating cardiac insufficiency, vasodilators, anti-shock vasoactive drugs, analgesics, sedatives, muscle relaxants, anti-asthmatic drugs, and anticholinergics. Given that public health emergencies have a relatively high probability of affecting the respiratory system, we compiled a list of commonly used drugs for respiratory diseases in the ICU (Table 5 ).

Non-consumables refer to therapeutic medical equipment, including electrocardiogram machines, blood gas analyzers, electrolyte analyzers, bedside diagnostic ultrasound machines, central infusion workstations, non-invasive ventilators, invasive ventilators, airway clearance devices, defibrillators, monitoring devices, cardiopulmonary resuscitation devices, and bedside hemofiltration devices.

The demand forecasting model of ICU healthcare resources constructed in this study, as well as its relevant parameters and definitions, are described below. \({R}_{ij}^{n}\) is the forecasted demand for the \(i\) th category of resources on the \(n\) th day in region \(j\) . \({Y}_{j}^{n}\) is the predicted number of current confirmed cases on the \(n\) th day in region \(j\) . \({M}_{j}^{n}\) is the number of ICU healthcare workers on the \(n\) th day in region \(j\) , which is given by the following formula: number of healthcare workers the previous day + number of new recruits − reduction in number the previous day, where the reduction in number refers to the number of healthcare workers who are unable to work due to infection or overwork. In general, the number of ICU healthcare workers should not exceed 5% of the number of current confirmed cases (i.e., it takes the value range [0, \(Y_{j}^{n}\) ×5%]). \(U_{i}\) is the maximum working hours or duration of action of the \(i\) th resource category within one day. \({A}_{j}\) is the number of resources in the \(i\) th category allocated to patients (i.e., how many units of resources in the \(i\) th category is needed for a patient who need the \(i\) th unit of the given resource). \({\varphi }_{i}\) is the demand conversion coefficient (i.e., the proportion of the current number of confirmed cases who need to use the \(i\) th resource category). \({C}_{ij}^{n}\) is the available quantity of material resources of the \(i\) th category on the \(n\) th day in region \(j\) . At the start, this quantity is the initial reserve, and once the initial reserve is exhausted, it is the surplus from the previous day. The formula for this parameter is given as follows: available quantity from the previous day + replenishment on the previous day − quantity consumed on the previous day, where if \({C}_{ij}^{n}\) is a negative number, it indicates the amount of shortage for the given category of resources on the previous day.

In summary, the demand forecast for emergency medical supplies constructed in this study is shown in formula ( 25 ).

The number of confirmed cases based on data-driven prediction is introduced into the demand forecasting model for ICU resources to forecast the demand for the various categories of resources. In addition to the number of current confirmed cases, the main variables of the first demand forecasting model for human resources are the available quantity and maximum working hours. The main variable of the second demand forecasting model for consumable resources is the number of units consumed by the available quantity. The main variable of the third model for non-consumable resources is the allocated quantity. These three resource types can be predicted using the demand forecasting model constructed in this study.

Prediction of the number of current infected cases

The COVID-19 situation in Shanghai is selected for our experiment. A total of 978 entries of epidemic-related data in Shanghai between January 20, 2020, and September 24, 2022, are collected from the epidemic reporting platform. This dataset is distributed over a large range and belongs to a right-skewed leptokurtic distribution. The specific statistical description of data is shown in Table 6 . Part of the data is shown in Table 7 .

And we divided the data training set and test set in an approximate 8:2 ratio, namely, 798 days for training (January 20, 2020 to March 27, 2022) and 180 days for prediction (March 28, 2022 to September 24, 2022).

Due to the large difference in order of magnitude between the various input features, directly implementing training and model construction would lead to suboptimal model performance. Such effects are usually eliminated through normalization. In terms of interval selection, [0, 1] reflects the probability distribution of the sample, whereas [-1, 1] mostly reflects the state distribution or coordinate distribution of the sample. Therefore, [-1, 1] is selected for the normalization interval in this study, and the processing method is shown in formula ( 26 ).

Among the rest, \(X\) is the input sample, \({X}_{min}\) and \({X}_{max}\) are the minimum and maximum values of the input sample, and \({X}_{new}\) is the input feature after normalization.

In addition, we divide the data normalization into two parts, considering that the amount of data in the training set is much more than the test set in the real operating environment. In the first step, we normalize the training set data directly according to the above formula; in the second step, we normalize the test data set using the maximum and minimum values of the training data set.

The values of the preprocessed data are inserted into the GASVR, LSTM, Informer, BILSTM models and the BILSTM-GASVR model is constructed. Figures 5 , 6 , 7 , 8 and 9 show the prediction results. From Figs. 5 , 6 , and 7 , it can be seen that in terms of data accuracy, GASVR more closely matches the real number of infected people relative to BILSTM and LSTM. Especially in the most serious period of the epidemic in Shanghai (April 17, 2022 to April 30, 2022), the advantage of the accuracy of the predicted data of GASVR is even more obvious, which is due to the characteristics of GASVR for small samples and nonlinear prediction. However, in the overall trend of the epidemic, BILSTM and LSTM, which have the ability to learn and memorize to process time series data, are superior. It is clearly seen that in April 1, 2022-April 7, 2022 and May 10, 2022-May 15, 2022, there is a sudden and substantial increase in GASVR in these two time phases, and a sudden and substantial decrease in April 10, 2022-April 14, 2022. These errors also emphasize the stability of BILSTM and LSTM, which are more closely matched to the real epidemic development situation in the whole process of prediction, and the difference between BILSTM and LSTM prediction is that the former predicts data more accurately than the latter, which is focused on the early stage of prediction as well as the peak period of the epidemic. Informer is currently an advanced time series forecasting method. From Fig. 8 , it can be seen that the prediction data accuracy and the overall trend of the epidemic are better than the single prediction models of GASVR, LSTM and BILSTM. However, Informer is more suitable for long time series and more complex and large prediction problems, so the total sample size of less than one thousand cases is not in the comfort zone of Informer model. Figure 9 shows that the BILSTM-GASVR model constructed in this paper is more suitable for this smaller scale prediction problem, with the best prediction results, closest to the actual parameter (number of current confirmed cases), demonstrating small sample and time series advantages. In Short, the prediction effect of models is ranked as follows: BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM.

The prediction result of the GASVR model

The prediction result of the LSTM model

The prediction result of the BILSTM model

The prediction result of the Informer model

The prediction result of the BILSTM-GASVR model

The values of the three indicators (MSE, RMSE, and correlation coefficient \({R}^{2}\) ) for the five models are shown in Table 8 . MSE squares the error so that the larger the model error, the larger the value, which help capture the model's prediction error more sensitively. RMSE is MSE with a root sign added to it, which allows for a more intuitive representation of the order of magnitude difference from the true value. \({R}^{2}\) is a statistical indicator used to assess the overall goodness of fit of the model, which reflects the overall consistency of the predicted trend and does not specifically reflect the degree of data. The results in the Table 8 are consistent with the prediction results in the figure above, while the ranking of MSE, RMSE, and \({R}^{2}\) are also the same (i.e., BILSTM-GASVR> Informer> GASVR> BILSTM> LSTM).

In addition, we analyze the five model prediction data using significance tests as a way of demonstrating whether the model used is truly superior to the other baseline models. The test dataset with kurtosis higher than 4 does not belong to the approximate normal distribution, so parametric tests are not used in this paper. Given that the datasets predicted by each of the five models are continuous and independent datasets, this paper uses the Kruskal-Wallis test, which is a nonparametric test. The test steps are as follows.

Determine hypotheses (H0, H1) and significance level ( \(\alpha\) ).

For each data set, all its sample data are combined and ranked from smallest to largest. Then find the number of data items ( \({n}_{i}\) ), rank sum ( \({R}_{i}\) ) and mean rank of each group of data respectively.

Based on the rank sum, the test statistic (H) is calculated for each data set in the Kruskal-Wallis test. The specific calculation is shown in formula ( 27 ).

According to the test statistic and degrees of freedom, find the corresponding p-value in the Kruskal-Wallis distribution table. Based on the P-value, determine whether the original hypothesis is valid.

In the significance test, we set the significance setting original hypothesis (H0) as there is no significant difference between the five data sets obtained from the five predictive models. We set the alternative hypothesis (H1) as there is a significant difference between the five data sets obtained from the five predictive models. At the same time, we choose the most commonly used significance level taken in the significance test, namely 0.05. In this paper, multiple comparisons and two-by-two comparisons of the five data sets obtained from the five predictive models are performed through the SPSS software. The results of the test show that in the multiple comparison session, P=0.001<0.05, so H0 is rejected, which means that the difference between the five groups of data is significant. In the two-by-two comparison session, BILSTM-GASVR is less than 0.05 from the other four prediction models. The specific order of differences is Informer < GASVR < BILSTM < LSTM, which means that the BILSTM-GASVR prediction model does get a statistically significant difference between the dataset and the other models.

In summary, combined prediction using the BILSTM-GASVR model is superior to the other four single models in various aspects in the case study analysis of Shanghai epidemic with a sample size of 978.

Demand forecasting of ICU healthcare resources

Combined with the predicted number of current infected cases, representatives are selected from the three categories of resources for forecasting. The demand for nurses is selected as the representative for the first category of resources.

In view of the fact that there are currently no specific medications that are especially effective for this public health emergency, many ICU treatment measures involved helping patients survive as their own immune systems eliminated the virus. This involved, for example, administering antibiotics when patients developed a secondary bacterial infection. glucocorticoids are used to temporarily suppress the immune system when their immune system attacked and damaged lung tissues causing patients to have difficulty breathing. extracorporeal membrane oxygenation (ECMO) is used for performing cardiopulmonary resuscitation when patients are suffering from cardiac arrest. In this study, we take dexamethasone injection (5 mg), a typical glucocorticoid drug, as the second category of ICU resources (i.e., drugs); and invasive ventilators as the third category of ICU resources (i.e., medical equipment).

During the actual epidemic in Shanghai, the municipal government organized nine critical care teams, which are stationed in eight municipally designated hospitals and are dedicated to the treatment of critically ill patients. In this study, the ICU nurses, dexamethasone injections, and invasive ventilators in Shanghai are selected as the prediction targets and introduced into their respective demand forecasting models. Forecasting of ICU healthcare resources is then performed for the period from March 28, 2022, to April 28, 2022, as an example. Part of the parameter settings for the three types of resources are shown in Tables 9 , 10 , and 11 , respectively.

Table 12 shows the forecasting results of the demand for ICU nurses, dexamethasone injections, and invasive ventilators during the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022.

For the first category (i.e., ICU nurses), human resource support is only needed near the peak period, but the supply could not be replenished immediately. In the early stages, Shanghai could only rely on the nurses’ perseverance, alleviating the shortage of human resources by reducing the number of shifts and increasing working hours. This situation persisted until about April 10 and is only resolved when nurses from other provinces and regions successively arrived in Shanghai.

The second category of ICU resources is drugs, which are rapidly consumed. The pre-event reserve of 30,000 dexamethasone injections could only be maintained for a short period and is fully consumed during the outbreak. Furthermore, daily replenishment is still needed, even when the epidemic has passed its peak and begun its decline.

The third category is invasive ventilators, which are non-consumables. Thus, the reserve lasted for a relatively long period of time in the early stages and did not require replenishment after its maximum usage during the peak period.

Demand forecasting models are constructed based on the classification of healthcare resources according to their respective features. We choose ICU nurses, dexamethasone injections, and invasive ventilators as examples, and then forecast demand for the epidemic wave in Shanghai between March 28, 2022, and April 28, 2022. The main conclusions are as follows:

A long period of time is needed to train ICU healthcare workers who can independently be on duty, taking at least one year from graduation to entering the hospital, in addition to their requiring continuous learning, regular theoretical training, and the accumulation of clinical experience during this process. Therefore, for the first category of ICU healthcare resources, in the long term, healthcare institutions should place a greater emphasis on their talent reserves. Using China as an example, according to the third ICU census, the ratio of the number of ICU physicians to the number of beds is 0.62:1 and the ratio of the number of nurses to the number of beds is 1.96:1, which are far lower than those stipulated by China itself and those of developed countries. Therefore, a fundamental solution is to undertake proactive and systematic planning and construction to ensure the more effective deployment of human resources in the event of a severe outbreak. In the short term, healthcare institutions should focus on the emergency expansion capacity of their human resources. In case there are healthcare worker shortages during emergencies, the situation can be alleviated by summoning retired workers back to work and asking senior medical students from various universities to help in the hospitals to prevent the passive scenario of severely compressing the rest time of existing staff or waiting for external aid. However, it is worth noting that to ensure the effectiveness of such a strategy of using retired healthcare workers or senior students of university medical faculties, it is necessary for healthcare organizations to provide them with regular training in the norm, such as organizing 2-3 drills a year, to ensure the professionalism and proficiency of healthcare workers who are temporarily and suddenly put on the job. At the same time, it is also necessary to fully mobilize the will of individuals. Medical institutions can provide certain subsidies to retired health-care workers and award them with honorable titles. For senior university medical students, volunteer certificates are issued and priority is given to their internships, so that health-care workers can be motivated to self-realization through spiritual and material rewards.

Regarding the second category of ICU resources (i.e., drugs), healthcare institutions perform the subdivision of drug types and carry out dynamic physical preparations based on 15–20% of the service recipient population for clinically essential drugs. This will enable a combination of good preparedness during normal times and emergency situations. In addition, in-depth collaboration with corporations is needed to fully capitalize on their production capacity reserves. This helps medical institutions to be able to scientifically and rationally optimize the structure and quantity of their drug stockpiles to prevent themselves from being over-stressed. Yet the lower demand for medicines at the end of the epidemic led to the problem of excess inventory of enterprises at a certain point in time must be taken into account. So, the medical institutions should sign a strategic agreement on stockpiling with enterprises, take the initiative to bear the guaranteed acquisition measures, and consider the production costs of the cooperative enterprises. These measures are used to truly safeguard the enthusiasm of the cooperative enterprises to invest in the production capacity.

Regarding the third category of ICU resources (i.e., medical equipment), large-scale medical equipment cannot be rapidly mass-produced due to limitations in the capacity for emergency production and conversion of materials. In addition, the bulk procurement of high-end medical equipment is also relatively difficult in the short term. Therefore, it is more feasible for healthcare institutions to have physical reserves of medical equipment, such as invasive ventilators. However, the investment costs of medical equipment are relatively high. Ventilators, for example, cost up to USD $50,000, and subsequent maintenance costs are also relatively high. After all, according to the depreciable life of specialized hospital equipment, the ventilator, as a surgical emergency equipment, is depreciated over five years. And its depreciation rate is calculated at 20% annually for the first five years, which means a monthly depreciation of $835. Thus, the excessively low utilization rate of such equipment will also impact the hospital. Healthcare institutions should, therefore, conduct further investigations on the number of beds and the reserves of ancillary large-scale medical equipment to find a balance between capital investment and patient needs.

The limitations of this paper are reflected in the following three points. Firstly, in the prediction of the number of infections, the specific research object in this paper is COVID-19, and other public health events such as SARS, H1N1, and Ebola are not comparatively analyzed. The main reason for this is the issue of data accessibility, and it is easier for us to analyze events that have occurred in recent years. In addition, using the Shanghai epidemic as a specific case may be more representative of the epidemic situation in an international metropolis with high population density and mobility. Hence, it has certain regional limitations, and subsequent studies should expand the scope of the case study to reflect the characteristics of epidemic transmission in different types of urban areas and enhance the generalizability.

Secondly, the main emphasis of this study is on forecasting the demand for ICU healthcare resources across the entire region of the epidemic, with a greater focus on patient demand during public emergencies. Our aims are to help all local healthcare institutions more accurately identify changes in ICU healthcare resource demand during this local epidemic wave, gain a more accurate understanding of the treatment demands of critically ill patients, and carry out comprehensive, scientifically based decision-making. Therefore, future studies can examine individual healthcare institutions instead and incorporate the actual conditions of individual units to construct multi-objective models. In this way, medical institutions can further grasp the relationship between different resource inputs and the recovery rate of critically ill patients, and achieve the balance between economic and social benefits.

Finally, for the BILSTM-GASVR prediction method, in addition to the number of confirmed diagnoses predicted for an outbreak in a given region, other potential applications beyond this type of medium-sized dataset still require further experimentation. For example, whether the method is suitable for procurement planning of a certain supply in production management, forecasting of goods sales volume in marketing management, and other long-period, large-scale and other situations.

Within the context of major public health events, the fluctuations and uncertainties in the demand for ICU resources can lead to large errors between the healthcare supply and actual demand. Therefore, this study focuses on the question of forecasting the demand for ICU healthcare resources. Based on the number of current confirmed cases, we construct the BILSTM-GASVR model for predicting the number of patients. By comparing the three indicators (MSE, MAPE, and correlation coefficient \(R^{2}\) ) and the results of the BILSTM, LSTM, and GASVR models, we demonstrate that our model have a higher accuracy. Our findings can improve the timeliness and accuracy of predicting ICU healthcare resources and enhance the dynamics of demand forecasting. Hence, this study may serve as a reference for the scientific deployment of ICU resources in healthcare institutions during major public events.

Given the difficulty in data acquisition, only the Shanghai epidemic dataset is selected in this paper, which is one of the limitations mentioned in Part 4. Although the current experimental cases of papers in the same field do not fully conform to this paper, the results of the study cannot be directly compared. However, after studying the relevant reviews and the results of the latest papers, we realize that there is consistency in the prediction ideas and prediction methods [ 34 , 35 ]. Therefore, we summarize the similarities and differences between the results of the study and other research papers in epidemic forecasting as shown below.

Similarities: on the one hand, we all characterize trends in the spread of the epidemic and predict the number of infections over 14 days. On the other hand, we all select the current mainstream predictive models as the basis and combine or improve them. Moreover, we all use the same evaluation method (comparison of metrics such as MSE and realistic values) to evaluate the improvements against other popular predictive models.

Differences: on the one hand, other papers focus more on predictions at the point of the number of patients, such as hospitalization rate, number of infections, etc. This paper extends the prediction from the number of patients to the specific healthcare resources. This paper extends the prediction from the number of patients to specific healthcare resources. We have divided the medical resources and summarized the demand regularities of the three types of information in the epidemic, which provides the basis for decision-making on epidemic prevention to the government or medical institutions. On the other hand, in addition to the two assessment methods mentioned in the same point, this paper assesses the performance of the prediction methods with the help of significance tests, which is a statistical approach to data. This can make the practicality of the forecasting methodology more convincing.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.

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Zhang, W., Li, X. A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies. BMC Health Serv Res 24 , 477 (2024). https://doi.org/10.1186/s12913-024-10955-8

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Supported by

New Study Bolsters Idea of Athletic Differences Between Men and Trans Women

Research financed by the International Olympic Committee introduced new data to the unsettled and fractious debate about bans on transgender athletes.

By Jeré Longman

A new study financed by the International Olympic Committee found that transgender female athletes showed greater handgrip strength — an indicator of overall muscle strength — but lower jumping ability, lung function and relative cardiovascular fitness compared with women whose gender was assigned female at birth.

That data, which also compared trans women with men, contradicted a broad claim often made by proponents of rules that bar transgender women from competing in women’s sports. It also led the study’s authors to caution against a rush to expand such policies, which already bar transgender athletes from a handful of Olympic sports.

The study’s most important finding, according to one of its authors, Yannis Pitsiladis, a member of the I.O.C.’s medical and scientific commission, was that, given physiological differences, “Trans women are not biological men.”

Alternately praised and criticized, the study added an intriguing data set to an unsettled and often politicized debate that may only grow louder with the Paris Olympics and a U.S. presidential election approaching.

The authors cautioned against the presumption of immutable and disproportionate advantages for transgender female athletes who compete in women’s sports, and they advised against “precautionary bans and sport eligibility exclusions” that were not based on sport-specific research.

Outright bans, though, continue to proliferate. Twenty-five U.S. states now have laws or regulations barring transgender athletes from competing in girls and women’s sports, according to the Movement Advancement Project , a nonprofit that focuses on gay, lesbian, bisexual and transgender parity. And the National Association of Intercollegiate Athletics , the governing body for smaller colleges, this month barred transgender athletes from competing in women’s sports unless their sex was assigned female at birth and they had not undergone hormone therapy.

Two of the most visible sports at this summer’s Paris Games — swimming and track and field — along with cycling have effectively barred transgender female athletes who went through puberty as males. Rugby has instituted a total ban on trans female athletes, citing safety concerns, and those permitted to participate in other sports often face stricter requirements in suppressing their levels of testosterone.

The International Olympic Committee has left eligibility rules for transgender female athletes up to the global federations that govern individual sports. And while the Olympic committee provided financing for the study — as it does on a variety of topics through a research fund — Olympic officials had no input or influence on the results, Dr. Pitsiladis said.

In general, the argument for the bans has been that profound advantages gained from testosterone-fueled male puberty — broader shoulders, bigger hands, longer torsos, and greater muscle mass, strength, bone density and heart and lung capacity — give transgender female athletes an inequitable and largely irreversible competitive edge.

The new laboratory-based, peer-reviewed and I.O.C.-funded study at the University of Brighton, published this month in the British Journal of Sports Medicine , tested 19 cisgender men (those whose gender identity matches the sex they were assigned at birth) and 12 trans men, along with 23 trans women and 21 cisgender women.

All of the participants played competitive sports or underwent physical training at least three times a week. And all of the trans female athletes had undergone at least a year of treatment suppressing their testosterone levels and taking estrogen supplementation, the researchers said. None of the participants were athletes competing at the national or international level.

The study found that transgender female participants showed greater handgrip strength than cisgender female participants but lower lung function and relative VO2 max, the amount of oxygen used when exercising. Transgender female athletes also scored below cisgender women and men on a jumping test that measured lower-body power.

The study acknowledged some limitations, including its small sample size and the fact that the athletes were not followed over the long term as they transitioned. And, as previous research has indicated, it found that transgender female athletes did retain at least one advantage over cisgender female athletes — a measurement of handgrip strength .

But it is a combination of factors, not a single parameter, that determines athletic performance, said Dr. Pitsiladis, a professor of sport and exercise science.

Athletes who grow taller and heavier after going through puberty as males must “carry this big skeleton with a smaller engine” after transitioning, he said. He cited volleyball as an example, saying that, for transgender female athletes, “the jumping and blocking will not be to the same height as they were doing before. And they may find that, overall, their performance is less good.”

But Michael J. Joyner, a doctor at the Mayo Clinic who studies the physiology of male and female athletes, said that, based on his research and the research of others, science supports the bans in elite sports, where events can be decided by the smallest of margins.

“We know testosterone is performance enhancing,” Dr. Joyner said. “And we know testosterone has residual effects.” Additionally, he added, declines in performance by trans women after taking drugs to suppress their testosterone levels do not fully reduce the typical differences in athletic performance between men and women.

Supporters of transgender athletes, and some scientists who disagree with bans, have accused governing bodies and lawmakers of enacting solutions for a problem that doesn’t exist. There are few elite trans female athletes, they have noted. And there has been limited scientific study of presumed unalterable advantages in strength, power and aerobic capacity gained by experiencing puberty as a male.

For those who have competed in the Olympics, results have varied widely. At the 2021 Tokyo Games, Quinn , a soccer player who is trans nonbinary and was assigned female at birth, helped Canada’s team win a gold medal. But Laurel Hubbard , a transgender weight lifter from New Zealand, failed to complete a lift in her event.

“The idea that trans women are going to take over women’s sports is ludicrous,” said Joanna Harper, a leading researcher of trans athletes and a postdoctoral scholar at Oregon Health & Science University.

Dr. Harper, who is transgender, said it was important for sports to consider physiological differences between transgender women and cisgender women and that she supported certain restrictions, such as requiring the suppression of testosterone levels. But she called blanket bans “unnecessary and unjustified” and said she welcomed the I.O.C.-funded study.

“This fear that trans women aren’t really women, that they’re men who are invading women’s sports, and that trans women will carry all of their male athleticism, their athletic capabilities, into women’s sports — neither of those things are true,” Dr. Harper said.

Sebastian Coe, the president of World Athletics, which governs global track and field, acknowledged that the science remains unresolved. But the organization decided to bar transgender female athletes from international track and field, he said, because “I’m not going to take a risk on this.”

“We think this is in the best interest of preserving the female category,” Mr. Coe said.

In at least two prominent cases, the fight over transgender bans has moved to the courts. The former University of Pennsylvania swimmer Lia Thomas is challenging a ban imposed by World Aquatics, swimming’s global governing body, after she won the 500-yard freestyle race at the 2022 N.C.A.A. championships. That victory made Thomas, who had been among the best men’s swimmers in the Ivy League, the first known trans athlete to win a women’s championship event in college sports’ top division.

Thomas did not dominate all of her races, though, finishing tied for fifth in a second race and eighth in a third. Her winning time in the 500 was more than nine seconds slower than the N.C.A.A. record. Her case, filed at the Swiss-based Court of Arbitration for Sport, is not expected to be resolved before the Paris Olympics begin in July.

Meanwhile, more than a dozen current and former U.S. college athletes, including at least one who competed against Thomas, sued the N.C.A.A. last month . They claimed that, by letting Thomas participate in the national championships, the organization had violated their rights under Title IX, the law that prohibits sex discrimination at institutions that receive federal funding. (Title IX has also been relied upon to argue in favor of transgender female athletes.)

Outsports , a website that reports on L.G.B.T.Q. issues, hailed the I.O.C.-funded study as a “landmark” that concluded that “blanket sports bans are a mistake.” But some scientists and athletes called the study deeply flawed in an article in The Telegraph , which labeled the suggestion that transgender women are at a disadvantage in sports a “new low” for the I.O.C.

So heated is the debate that Dr. Pitsiladis said he and his research team have received threats. That, he warned, could lead other scientists to shy away from pursuing research on the topic.

“Why would any scientist do this if you’re going to get totally slammed and character-assassinated?” he said. “This is no longer a science matter. Unfortunately, it’s become a political matter.”

Jeré Longman covers international sports, focusing on competitive, social, cultural and political issues around the world. More about Jeré Longman

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Technology as a Tool for Improving Patient Safety

Introduction .

In the past several decades, technological advances have opened new possibilities for improving patient safety. Using technology to digitize healthcare processes has the potential to increase standardization and efficiency of clinical workflows and to reduce errors and cost across all healthcare settings. 1 However, if technological approaches are designed or implemented poorly, the burden on clinicians can increase. For example, overburdened clinicians can experience alert fatigue and fail to respond to notifications. This can lead to more medical errors. As a testament to the significance of this topic in recent years, several government agencies [(e.g. the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare and Medicaid services (CMS)] have developed resources to help healthcare organizations integrate technology, such as the Safety Assurance Factors for EHR Resilience (SAFER) guides developed by the Office of the National Coordinator for Health Information Technology (ONC). 2,3,4  However, there is some evidence that these resources have not been widely used.5 Recently, the Centers for Medicare & Medicaid Services (CMS) started requiring hospitals to use the SAFER guides as part of the FY 2022 Hospital Inpatient Prospective Payment Systems (IPPS), which should raise awareness and uptake of the guides. 6

During 2022, research into technological approaches was a major theme of articles on PSNet. Researchers reviewed all relevant articles on PSNet and consulted with Dr. A Jay Holmgren, PhD, and Dr. Susan McBride, PhD, subject matter experts in health IT and its role in patient safety. Key topics and themes are highlighted below.  

Clinical Decision Support  

The most prominent focus in the 2022 research on technology, based on the number of articles published on PSNet, was related to clinical decision support (CDS) tools. CDS provides clinicians, patients, and other individuals with relevant data (e.g. patient-specific information), purposefully filtered and delivered through a variety of formats and channels, to improve and enhance care. 7   

Computerized Patient Order Entry  

One of the main applications of CDS is in computerized patient order entry (CPOE), which is the process used by clinicians to enter and send treatment instructions via a computer application. 8 While the change from paper to electronic order entry itself can reduce errors (e.g., due to unclear handwriting or manual copy errors), research in 2022 showed that there is room for improvement in order entry systems, as well as some promising novel approaches. 

Two studies looked at the frequency of and reasons for medication errors in the absence of CDS and CPOE and demonstrated that there was a clear patient safety need. One study found that most medication errors occurred during the ordering or prescribing stage, and both this study and the other study found that the most common medication error was incorrect dose. Ongoing research, such as the AHRQ Medication Safety Measure Development project, aims to develop and validate measure specifications for wrong-patient, wrong-dose, wrong-medication, wrong-route, and wrong-frequency medication orders within EHR systems, in order to better understand and capture health IT safety events.9 Errors of this type could be avoided or at least reduced through the use of effective CPOE and CDS systems. However, even when CPOE and CDS are in place, errors can still occur and even be caused by the systems themselves. One study reviewed duplicate medication orders and found that 20% of duplicate orders resulted from technological issues, including alerts being overridden, alerts not firing, and automation issues (e.g., prefilled fields). A case study last year Illustrated one of the technological issues, in this case a manual keystroke error, that can lead to a safety event. A pharmacist mistakenly set the start date for a medication to the following year rather than the following day , which the CPOE system failed to flag. The authors recommended various alerts and coding changes in the system to prevent this particular error in the future.  

There were also studies in 2022 that showed successful outcomes of well-implemented CPOE systems. One in-depth pre-post, mixed-methods study showed that a fully implemented CPOE system significantly reduced specific serious and commonly occurring prescribing and procedural errors. The authors also presented evidence that it was cost-effective and detailed implementation lessons learned drawn from the qualitative data collected for the study. A specific CPOE function that demonstrated statistically significant improvement in 2022 was automatic deprescribing of medication orders and communication of the relevant information to pharmacies. Deprescribing is the planned and supervised process of dose reduction or stopping of a medication that is no longer beneficial or could be causing harm. That study showed an immediate and sustained 78% increase in successful discontinuations after implementation of the software. A second study on the same functionality determined that currently only one third to one half of medications are e-prescribed, and the study proposed that e-prescribing should be expanded to increase the impact of the deprescribing software. It should be noted, however, that the systems were not perfect and that a small percentage of medications were unintentionally cancelled. Finally, an algorithm to detect patients in need of follow-up after test results was developed and implemented in another study . The algorithm showed some process improvements, but outcome measures were not reported. 

Usability  

Usability of CDS systems was a large focus of research in 2022. Poorly designed systems that do not fit into existing workflows lead to frustrated users and increase the potential for errors. For example, if users are required to enter data in multiple places or prompted to enter data that are not available to them, they could find ways to work around the system or even cease to use it, increasing the potential for patient safety errors. The documentation burden is already very high on U.S. clinicians, 10 so it is important that novel technological approaches do not add to this burden but, if possible, alleviate it by offering a high level of usability and interoperability.  

One study used human-factored design in creating a CDS to diagnose pulmonary embolism in the Emergency Department and then surveyed clinician users about their experiences using the tool. Despite respondents giving the tool high usability ratings and reporting that the CDS was valuable, actual use of the tool was low. Based on the feedback from users, the authors proposed some changes to increase uptake, but both users and authors mentioned the challenges that arise when trying to change the existing workflow of clinicians without increasing their burden. Another study gathered qualitative feedback from clinicians on a theoretical CDS system for diagnosing neurological issues in the Emergency Department. In this study too, many clinicians saw the potential value in the CDS tool but had concerns about workflow integration and whether it would impact their ability to make clinical decisions. Finally, one study developed a dashboard to display various risk factors for multiple hospital-acquired infections and gathered feedback from users. The users generally found the dashboard useful and easy to learn, and they also provided valuable feedback on color scales, location, and types of data displayed. All of these studies show that attention to end user needs and preferences is necessary for successful implementation of CDS.  However, the recent market consolidation in Electronic Health Record vendors may have an impact on the amount of user feedback gathered and integrated into CDS systems. Larger vendors may have more resources to devote to improving the usability and design of CDS, or their near monopolies in the market may not provide an incentive to innovate further. 11 More research is needed as this trend continues.  

Alerts and Alarms 

Alerts and alarms are an important part of most CDS systems, as they can prompt clinicians with important and timely information during the treatment process. However, these alerts and alarms must be accurate and useful to elicit an appropriate response. The tradeoff between increased safety due to alerts and clinician alert fatigue is an important balance to strike. 12

Many studies in 2022 looked at clinician responses to medication-related alerts, including override and modification rates. Several of the studies found a high alert override rate but questioned the validity of using override rates alone as a marker of CDS effectiveness and usability. For example, one study looked at drug allergy alerts and found that although 44.8% of alerts were overridden, only 9.3% of those were inappropriately overridden, and very few overrides led to an adverse allergic reaction. A study on “do not give” alerts found that clinicians modified their orders to comply with alert recommendations after 78% of alerts but only cancelled orders after 26% of alerts. A scoping review looked at drug-drug interaction alerts and found similar results, including high override rates and the need for more data on why alerts are overridden. These findings are supported by another study that found that the underlying drug value sets triggering drug-drug interaction alerts are often inconsistent, leading to many inappropriate alerts that are then appropriately overridden by clinicians. These studies suggest that while a certain number of overrides should be expected, the underlying criteria for alert systems should be designed and regularly reviewed with specificity and sensitivity in mind. This will increase the frequency of appropriate alerts that foster indicated clinical action and reduce alert fatigue. 

There also seems to be variability in the effectiveness of alert systems across sites. One study looked at an alert to add an item to the problem list if a clinician placed an order for a medication that was not indicated based on the patient’s chart. The study found about 90% accuracy in alerts across two sites but a wide difference in the frequency of appropriate action between the sites (83% and 47%). This suggests that contextual factors at each site, such as culture and organizational processes, may impact success as much as the technology itself.  

A different study looked at the psychology of dismissing alerts using log data and found that dismissing alerts becomes habitual and that the habit is self-reinforcing over time. Furthermore, nearly three quarters of alerts were dismissed within 3 seconds. This indicates how challenging it can be to change or disrupt alert habits once they are formed. 

Artificial Intelligence and Machine Learning  

In recent years, one of the largest areas of burgeoning technology in healthcare has been artificial intelligence (AI) and machine learning. AI and machine learning use algorithms to absorb large amounts of historical and real-time data and then predict outcomes and recommend treatment options as new data are entered by clinicians. Research in 2022 showed that these techniques are starting to be integrated into EHR and CDS systems, but challenges remain. A full discussion of this topic is beyond the scope of this review. Here we limit the discussion to several patient-safety-focused resources posted on PSNet in 2022.  

One of the promising aspects of AI is its ability to improve CDS processes and clinician workflow overall. For example, one study last year looked at using machine learning to improve and filter CDS alerts. They found that the software could reduce alert volume by 54% while maintaining high precision. Reducing alert volume has the potential to alleviate alert fatigue and habitual overriding. Another topic explored in a scoping review was the use of AI to reduce adverse drug events. While only a few studies reviewed implementation in a clinical setting (most evaluated algorithm technical performance), several promising uses were found for AI systems that predict risk of an adverse drug event, which would facilitate early detection and mitigate negative effects.  

Despite enthusiasm for and promising applications of AI, implementation is slow. One of the challenges facing implementation is the variable quality of the systems. For example, a commonly used sepsis detection model was recently found to have very low sensitivity. 13 Algorithms also drift over time as new data are integrated, and this can affect performance, particularly during and after large disturbances like the COVID-19 pandemic. 14 There is also emerging research about the impact of AI algorithms on racial and ethnic biases in healthcare; at the time of publication of this essay, an AHRQ EPC was conducting a review of evidence on the topic. 15  These examples highlight the fact that AI is not a “set it and forget it” application; it requires monitoring and customization from a dedicated resource to ensure that the algorithms perform well over time. A related challenge is the lack of a strong business case for using high-quality AI. Because of this, many health systems choose to use out-of-the-box AI algorithms, which may be of poor quality overall (or are unsuited to particular settings) and may also be “black box” algorithms (i.e., not customizable by the health system because the vendor will not allow access to the underlying code). 16 The variable quality and the lack of transparency may cause mistrust by clinicians and overall aversion to AI interventions.  

In an attempt to address these concerns, one article in 2022 detailed best practices for AI implementation in health systems, focusing on the business case. Best practices include using AI to address a priority problem for the health system rather than treating it as an end itself. Additionally, testing the AI using the health system’s patients and data to demonstrate applicability and accuracy for that setting, confirming that the AI can provide a return on investment, and ensuring that the AI can be implemented easily and efficiently are also important. Another white paper described a human-factors and ergonomics framework for developing AI in order to improve the implementation within healthcare systems, teams, and workflows. The federal government and international organizations have also published AI guidelines, focusing on increasing trustworthiness (National Artificial Intelligence Initiative) 17 and ensuring ethical governance (World Health Organization). 18   

Conclusion and Next Steps 

As highlighted in this review, the scope and complexity of technology and its application in healthcare can be intimidating for healthcare systems to approach and implement. Researchers last year thus created a framework that health systems can use to assess their digital maturity and guide their plans for further integration.  

The field would benefit from more research in several areas in upcoming years. First and foremost, high-quality prospective outcome studies are needed to validate the effectiveness of the new technologies. Second, more work is needed on system usability, how the systems are integrated into workflows, and how they affect the documentation burden placed on clinicians. For CDS specifically, more focus is needed on patient-centered CDS (PC CDS), which supports patient-centered care by helping clinicians and patients make the best decisions given each individual’s circumstances and preferences. 19 AHRQ is already leading efforts in this field with their CDS Innovation Collaborative project. 20 Finally, as it becomes more common to incorporate EHR scribes to ease the documentation burden, research on their impact on patient safety will be needed, especially in relation to new technological approaches. For example, when a scribe encounters a CDS alert, do they alert the clinician in all cases? 

In addition to the approaches mentioned in this article, other emerging technologies in early stages of development hold theoretical promise for improving patient safety. One prominent example is “computer vision,” which uses cameras and AI to gather and process data on what physically happens in healthcare settings beyond what is captured in EHR data, 21 including being able to detect immediately that a patient fell in their room. 22  

As technology continues to expand and improve, researchers, clinicians, and health systems must be mindful of potential stumbling blocks that could impede progress and threaten patient safety. However, technology presents a wide array of opportunities to make healthcare more integrated, efficient, and safe.  

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• https://www.healthit.gov/topic/safety/safer-guides
• https://cds.ahrq.gov/cdsconnect/repository
• https://www.cms.gov/about-cms/obrhi
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• Sittig DF, Sengstack P, Singh H. Guidelines for US hospitals and clinicians on assessment of electronic health record safety using SAFER guides. J ama . 2022;327:719-720.
• https://library.ahima.org/doc?oid=300027#.Y-6RhXbMKHt
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This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers

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Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. August 21, 2019

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

A growing share of Americans say reducing crime should be a top priority for the president and Congress to address this year. Around six-in-ten U.S. adults (58%) hold that view today, up from 47% at the beginning of Joe Biden’s presidency in 2021.

We conducted this analysis to learn more about U.S. crime patterns and how those patterns have changed over time.

The analysis relies on statistics published by the FBI, which we accessed through the Crime Data Explorer , and the Bureau of Justice Statistics (BJS), which we accessed through the  National Crime Victimization Survey data analysis tool .

To measure public attitudes about crime in the U.S., we relied on survey data from Pew Research Center and Gallup.

Additional details about each data source, including survey methodologies, are available by following the links in the text of this analysis.

With the issue likely to come up in this year’s presidential election, here’s what we know about crime in the United States, based on the latest available data from the federal government and other sources.

How much crime is there in the U.S.?

It’s difficult to say for certain. The  two primary sources of government crime statistics  – the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS) – paint an incomplete picture.

The FBI publishes  annual data  on crimes that have been reported to law enforcement, but not crimes that haven’t been reported. Historically, the FBI has also only published statistics about a handful of specific violent and property crimes, but not many other types of crime, such as drug crime. And while the FBI’s data is based on information from thousands of federal, state, county, city and other police departments, not all law enforcement agencies participate every year. In 2022, the most recent full year with available statistics, the FBI received data from 83% of participating agencies .

BJS, for its part, tracks crime by fielding a  large annual survey of Americans ages 12 and older and asking them whether they were the victim of certain types of crime in the past six months. One advantage of this approach is that it captures both reported and unreported crimes. But the BJS survey has limitations of its own. Like the FBI, it focuses mainly on a handful of violent and property crimes. And since the BJS data is based on after-the-fact interviews with crime victims, it cannot provide information about one especially high-profile type of offense: murder.

All those caveats aside, looking at the FBI and BJS statistics side-by-side  does  give researchers a good picture of U.S. violent and property crime rates and how they have changed over time. In addition, the FBI is transitioning to a new data collection system – known as the National Incident-Based Reporting System – that eventually will provide national information on a much larger set of crimes , as well as details such as the time and place they occur and the types of weapons involved, if applicable.

Which kinds of crime are most and least common?

Property crime in the U.S. is much more common than violent crime. In 2022, the FBI reported a total of 1,954.4 property crimes per 100,000 people, compared with 380.7 violent crimes per 100,000 people.  

By far the most common form of property crime in 2022 was larceny/theft, followed by motor vehicle theft and burglary. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/nonnegligent manslaughter.

BJS tracks a slightly different set of offenses from the FBI, but it finds the same overall patterns, with theft the most common form of property crime in 2022 and assault the most common form of violent crime.

How have crime rates in the U.S. changed over time?

Both the FBI and BJS data show dramatic declines in U.S. violent and property crime rates since the early 1990s, when crime spiked across much of the nation.

Using the FBI data, the violent crime rate fell 49% between 1993 and 2022, with large decreases in the rates of robbery (-74%), aggravated assault (-39%) and murder/nonnegligent manslaughter (-34%). It’s not possible to calculate the change in the rape rate during this period because the FBI  revised its definition of the offense in 2013 .

The FBI data also shows a 59% reduction in the U.S. property crime rate between 1993 and 2022, with big declines in the rates of burglary (-75%), larceny/theft (-54%) and motor vehicle theft (-53%).

Using the BJS statistics, the declines in the violent and property crime rates are even steeper than those captured in the FBI data. Per BJS, the U.S. violent and property crime rates each fell 71% between 1993 and 2022.

While crime rates have fallen sharply over the long term, the decline hasn’t always been steady. There have been notable increases in certain kinds of crime in some years, including recently.

In 2020, for example, the U.S. murder rate saw its largest single-year increase on record – and by 2022, it remained considerably higher than before the coronavirus pandemic. Preliminary data for 2023, however, suggests that the murder rate fell substantially last year .

How do Americans perceive crime in their country?

Americans tend to believe crime is up, even when official data shows it is down.

In 23 of 27 Gallup surveys conducted since 1993 , at least 60% of U.S. adults have said there is more crime nationally than there was the year before, despite the downward trend in crime rates during most of that period.

While perceptions of rising crime at the national level are common, fewer Americans believe crime is up in their own communities. In every Gallup crime survey since the 1990s, Americans have been much less likely to say crime is up in their area than to say the same about crime nationally.

Public attitudes about crime differ widely by Americans’ party affiliation, race and ethnicity, and other factors . For example, Republicans and Republican-leaning independents are much more likely than Democrats and Democratic leaners to say reducing crime should be a top priority for the president and Congress this year (68% vs. 47%), according to a recent Pew Research Center survey.

How does crime in the U.S. differ by demographic characteristics?

Some groups of Americans are more likely than others to be victims of crime. In the  2022 BJS survey , for example, younger people and those with lower incomes were far more likely to report being the victim of a violent crime than older and higher-income people.

There were no major differences in violent crime victimization rates between male and female respondents or between those who identified as White, Black or Hispanic. But the victimization rate among Asian Americans (a category that includes Native Hawaiians and other Pacific Islanders) was substantially lower than among other racial and ethnic groups.

The same BJS survey asks victims about the demographic characteristics of the offenders in the incidents they experienced.

In 2022, those who are male, younger people and those who are Black accounted for considerably larger shares of perceived offenders in violent incidents than their respective shares of the U.S. population. Men, for instance, accounted for 79% of perceived offenders in violent incidents, compared with 49% of the nation’s 12-and-older population that year. Black Americans accounted for 25% of perceived offenders in violent incidents, about twice their share of the 12-and-older population (12%).

As with all surveys, however, there are several potential sources of error, including the possibility that crime victims’ perceptions about offenders are incorrect.

How does crime in the U.S. differ geographically?

There are big geographic differences in violent and property crime rates.

For example, in 2022, there were more than 700 violent crimes per 100,000 residents in New Mexico and Alaska. That compares with fewer than 200 per 100,000 people in Rhode Island, Connecticut, New Hampshire and Maine, according to the FBI.

The FBI notes that various factors might influence an area’s crime rate, including its population density and economic conditions.

What percentage of crimes are reported to police? What percentage are solved?

Most violent and property crimes in the U.S. are not reported to police, and most of the crimes that  are  reported are not solved.

In its annual survey, BJS asks crime victims whether they reported their crime to police. It found that in 2022, only 41.5% of violent crimes and 31.8% of household property crimes were reported to authorities. BJS notes that there are many reasons why crime might not be reported, including fear of reprisal or of “getting the offender in trouble,” a feeling that police “would not or could not do anything to help,” or a belief that the crime is “a personal issue or too trivial to report.”

Most of the crimes that are reported to police, meanwhile,  are not solved , at least based on an FBI measure known as the clearance rate . That’s the share of cases each year that are closed, or “cleared,” through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. In 2022, police nationwide cleared 36.7% of violent crimes that were reported to them and 12.1% of the property crimes that came to their attention.

Which crimes are most likely to be reported to police? Which are most likely to be solved?

Around eight-in-ten motor vehicle thefts (80.9%) were reported to police in 2022, making them by far the most commonly reported property crime tracked by BJS. Household burglaries and trespassing offenses were reported to police at much lower rates (44.9% and 41.2%, respectively), while personal theft/larceny and other types of theft were only reported around a quarter of the time.

Among violent crimes – excluding homicide, which BJS doesn’t track – robbery was the most likely to be reported to law enforcement in 2022 (64.0%). It was followed by aggravated assault (49.9%), simple assault (36.8%) and rape/sexual assault (21.4%).

The list of crimes  cleared  by police in 2022 looks different from the list of crimes reported. Law enforcement officers were generally much more likely to solve violent crimes than property crimes, according to the FBI.

The most frequently solved violent crime tends to be homicide. Police cleared around half of murders and nonnegligent manslaughters (52.3%) in 2022. The clearance rates were lower for aggravated assault (41.4%), rape (26.1%) and robbery (23.2%).

When it comes to property crime, law enforcement agencies cleared 13.0% of burglaries, 12.4% of larcenies/thefts and 9.3% of motor vehicle thefts in 2022.

Are police solving more or fewer crimes than they used to?

Nationwide clearance rates for both violent and property crime are at their lowest levels since at least 1993, the FBI data shows.

Police cleared a little over a third (36.7%) of the violent crimes that came to their attention in 2022, down from nearly half (48.1%) as recently as 2013. During the same period, there were decreases for each of the four types of violent crime the FBI tracks:

• Police cleared 52.3% of reported murders and nonnegligent homicides in 2022, down from 64.1% in 2013.
• They cleared 41.4% of aggravated assaults, down from 57.7%.
• They cleared 26.1% of rapes, down from 40.6%.
• They cleared 23.2% of robberies, down from 29.4%.

The pattern is less pronounced for property crime. Overall, law enforcement agencies cleared 12.1% of reported property crimes in 2022, down from 19.7% in 2013. The clearance rate for burglary didn’t change much, but it fell for larceny/theft (to 12.4% in 2022 from 22.4% in 2013) and motor vehicle theft (to 9.3% from 14.2%).

Note: This is an update of a post originally published on Nov. 20, 2020.

• Criminal Justice

John Gramlich is an associate director at Pew Research Center

8 facts about Black Lives Matter

#blacklivesmatter turns 10, support for the black lives matter movement has dropped considerably from its peak in 2020, fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, most popular.

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