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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

Wordvice Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
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• Useful Phrases for Academic Writing
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How to Present Results in a Research Paper

• First Online: 01 October 2023

Cite this chapter

• Aparna Mukherjee 4 ,
• Gunjan Kumar 4 &
• Rakesh Lodha 5  

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The results section is the core of a research manuscript where the study data and analyses are presented in an organized, uncluttered manner such that the reader can easily understand and interpret the findings. This section is completely factual; there is no place for opinions or explanations from the authors. The results should correspond to the objectives of the study in an orderly manner. Self-explanatory tables and figures add value to this section and make data presentation more convenient and appealing. The results presented in this section should have a link with both the preceding methods section and the following discussion section. A well-written, articulate results section lends clarity and credibility to the research paper and the study as a whole. This chapter provides an overview and important pointers to effective drafting of the results section in a research manuscript and also in theses.

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Acknowledgments

The book chapter is derived in part from our article “Mukherjee A, Lodha R. Writing the Results. Indian Pediatr. 2016 May 8;53(5):409-15.” We thank the Editor-in-Chief of the journal “Indian Pediatrics” for the permission for the same.

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Mukherjee, A., Kumar, G., Lodha, R. (2023). How to Present Results in a Research Paper. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_44

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How to write the results section of a research paper

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Table of Contents

At its core, a research paper aims to fill a gap in the research on a given topic. As a result, the results section of the paper, which describes the key findings of the study, is often considered the core of the paper. This is the section that gets the most attention from reviewers, peers, students, and any news organization reporting on your findings. Writing a clear, concise, and logical results section is, therefore, one of the most important parts of preparing your manuscript.

Difference between results and discussion

Before delving into how to write the results section, it is important to first understand the difference between the results and discussion sections. The results section needs to detail the findings of the study. The aim of this section is not to draw connections between the different findings or to compare it to previous findings in literature—that is the purview of the discussion section. Unlike the discussion section, which can touch upon the hypothetical, the results section needs to focus on the purely factual. In some cases, it may even be preferable to club these two sections together into a single section. For example, while writing  a review article, it can be worthwhile to club these two sections together, as the main results in this case are the conclusions that can be drawn from the literature.

Structure of the results section

Although the main purpose of the results section in a research paper is to report the findings, it is necessary to present an introduction and repeat the research question. This establishes a connection to the previous section of the paper and creates a smooth flow of information.

Next, the results section needs to communicate the findings of your research in a systematic manner. The section needs to be organized such that the primary research question is addressed first, then the secondary research questions. If the research addresses multiple questions, the results section must individually connect with each of the questions. This ensures clarity and minimizes confusion while reading.

Consider representing your results visually. For example, graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results.

Remember, an appealing results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.

Practical guidance for writing an effective results section for a research paper

• Always use simple and clear language. Avoid the use of uncertain or out-of-focus expressions.
• The findings of the study must be expressed in an objective and unbiased manner. While it is acceptable to correlate certain findings in the discussion section, it is best to avoid overinterpreting the results.
• If the research addresses more than one hypothesis, use sub-sections to describe the results. This prevents confusion and promotes understanding.
• Ensure that negative results are included in this section, even if they do not support the research hypothesis.
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to showcase the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey.
• For statistical data, it is adequate to highlight the tests and explain their results. The initial or raw data should not be mentioned in the results section of a research paper.

The results section of a research paper is usually the most impactful section because it draws the greatest attention. Regardless of the subject of your research paper, a well-written results section is capable of generating interest in your research.

For detailed information and assistance on writing the results of a research paper, refer to Elsevier Author Services.

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How to Write an Effective Results Section

Affiliation.

• 1 Rothman Orthopaedics Institute, Philadelphia, PA.
• PMID: 31145152
• DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

• Data Analysis
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• Publishing*
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Organizing Academic Research Papers: 7. The Results

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The results section of the research paper is where you report the findings of your study based upon the information gathered as a result of the methodology [or methodologies] you applied. The results section should simply state the findings, without bias or interpretation, and arranged in a logical sequence. The results section should always be written in the past tense. A section describing results [a.k.a., "findings"] is particularly necessary if your paper includes data generated from your own research.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Research results can only confirm or reject the research problem underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise, using non-textual elements, such as figures and tables, if appropriate, to present results more effectively. In deciding what data to describe in your results section, you must clearly distinguish material that would normally be included in a research paper from any raw data or other material that could be included as an appendix. In general, raw data should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good rule is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper].

Bates College; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Structure and Writing Style

I. Structure and Approach

For most research paper formats, there are two ways of presenting and organizing the results .

• Present the results followed by a short explanation of the findings . For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is correct to point this out in the results section. However, speculating as to why this correlation exists, and offering a hypothesis about what may be happening, belongs in the discussion section of your paper.
• Present a section and then discuss it, before presenting the next section then discussing it, and so on . This is more common in longer papers because it helps the reader to better understand each finding. In this model, it can be helpful to provide a brief conclusion in the results section that ties each of the findings together and links to the discussion.

NOTE: The discussion section should generally follow the same format chosen in presenting and organizing the results.

II.  Content

In general, the content of your results section should include the following elements:

• An introductory context for understanding the results by restating the research problem that underpins the purpose of your study.
• A summary of your key findings arranged in a logical sequence that generally follows your methodology section.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate the findings, if appropriate.
• In the text, a systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation [remember that not all results that emerge from the methodology that you used to gather the data may be relevant].
• Use of the past tense when refering to your results.
• The page length of your results section is guided by the amount and types of data to be reported. However, focus only on findings that are important and related to addressing the research problem.

Using Non-textual Elements

• Either place figures, tables, charts, etc. within the text of the result, or include them in the back of the report--do one or the other but never do both.
• In the text, refer to each non-textual element in numbered order [e.g.,  Table 1, Table 2; Chart 1, Chart 2; Map 1, Map 2].
• If you place non-textual elements at the end of the report, make sure they are clearly distinguished from any attached appendix materials, such as raw data.
• Regardless of placement, each non-textual element must be numbered consecutively and complete with caption [caption goes under the figure, table, chart, etc.]
• Each non-textual element must be titled, numbered consecutively, and complete with a heading [title with description goes above the figure, table, chart, etc.].
• In proofreading your results section, be sure that each non-textual element is sufficiently complete so that it could stand on its own, separate from the text.

III. Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save all this for the next section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings ; this should have been done in your Introduction section, but don't panic! Often the results of a study point to the need to provide additional background information or to explain the topic further, so don't think you did something wrong. Revise your introduction as needed.
• Ignoring negative results . If some of your results fail to support your hypothesis, do not ignore them. Document them, then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, often provides you with the opportunity to write a more engaging discussion section, therefore, don't be afraid to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater or lesser than..." or "demonstrates promising trends that...."
• Presenting the same data or repeating the same information more than once . If you feel the need to highlight something, you will have a chance to do that in the discussion section.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. If you are not sure, look up the term in a dictionary.

Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers . Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results . Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in social science journals where the author(s) have combined a description of the findings from the study with a discussion about their implications. You could do this. However, if you are inexperienced writing research papers, consider creating two sections for each element in your paper as a way to better organize your thoughts and, by extension, your  paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret your data and answer the "so what?" question. As you become more skilled writing research papers, you may want to meld the results of your study with a discussion of its implications.

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD Cand). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter – exciting! But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step.  

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips and tricks for an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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7.1 Reading results in quantitative research

Learning objectives.

Learners will be able to…

• Describe how statistical significance and confidence intervals demonstrate which results are most important

Pre-awareness check (Knowledge)

What do you know about previously conducted research on your topic (e.g., statistical analyses, qualitative and quantitative results)?

If you recall, empirical journal articles are those that report the results of quantitative or qualitative data analyzed by the author. They follow a set structure—introduction, methods, results, discussion/conclusions. This chapter is about reading what is often the most challenging section: results.

Quantitative results

Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report’s key findings. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample (often the first table in a results section). These tables will likely contain frequencies ( n ) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are of a particular gender. Frequencies or “how many” will probably be listed as n , while the percent symbol (%) might be used to indicate percentages. The symbol N is used for the entire sample size, and  n is used for the size of a portion of the entire sample.

In a table presenting a causal relationship, two sets of variables are represented. The independent variable , or cause, and the dependent variable , the effect. We’ll go into more detail on variables in Chapter 8. Independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan a table’s rows to see how values on the dependent variable change as the independent variable values change. Tables displaying results of quantitative analysis will also likely include some information about which relationships are significant or not. We will discuss the details of significance and p -values later in this section.

Let’s look at a specific example: Table 7.1 below.

Table 7.1 presents the association between gender and experiencing harassing behaviors at work. In this example, gender is the independent variable (the predictor) and the harassing behaviors listed are the dependent variables (the outcome). [1] Therefore, we place gender in the table’s columns and harassing behaviors in the table’s rows.

Reading across the table’s top row, we see that 2.9% of women in the sample reported experiencing subtle or obvious threats to their safety at work, while 4.7% of men in the sample reported the same. We can read across each of the rows of the table in this way. Reading across the bottom row, we see that 9.4% of women in the sample reported experiencing staring or invasion of their personal space at work while just 2.3% of men in the sample reported having the same experience. We’ll discuss  p- values later in this section.

While you can certainly scan tables for key results, they are often difficult to understand without reading the text of the article. The article and table were meant to complement each other, and the text should provide information on how the authors interpret their findings. The table is not redundant with the text of the results section. Additionally, the first table in most results sections is a summary of the study’s sample, which provides more background information on the study than information about hypotheses and findings. It is also a good idea to look back at the methods section of the article as the data analysis plan the authors outline should walk you through the steps they took to analyze their data which will inform how they report them in the results section.

Statistical significance

The statistics reported in Table 7.1 represent what the researchers found in their sample. The purpose of statistical analysis is usually to generalize from a the small number of people in a study’s sample to a larger population of people. Thus, the researchers intend to make causal arguments about harassing behaviors at workplaces beyond those covered in the sample.

Generalizing is key to understanding statistical significance . According to Cassidy et al. (2019), [2] 89% of research methods textbooks in psychology define statistical significance incorrectly. This includes an early draft of this textbook which defined statistical significance as “the likelihood that the relationships we observe could be caused by something other than chance.” If you have previously had a research methods class, this might sound familiar to you. It certainly did to me!

But statistical significance is less about “random chance” than more about the null hypothesis . Basically, at the beginning of a study a researcher develops a hypothesis about what they expect to find, usually that there is a statistical relationship between two or more variables . The null hypothesis is the opposite. It is the hypothesis that there is no relationship between the variables in a research study. Researchers then can hopefully reject the null hypothesis because they find a relationship between the variables.

For example, in Table 7.1 researchers were examining whether gender impacts harassment. Of course, researchers assumed that women were more likely to experience harassment than men. The null hypothesis, then, would be that gender has no impact on harassment. Once we conduct the study, our results will hopefully lead us to reject the null hypothesis because we find that gender impacts harassment. We would then generalize from our study’s sample to the larger population of people in the workplace.

Statistical significance is calculated using a p -value which is obtained by comparing the statistical results with a hypothetical set of results if the researchers re-ran their study a large number of times. Keeping with our example, imagine we re-ran our study with different men and women from different workplaces hundreds and hundred of times and we assume that the null hypothesis is true that gender has no impact on harassment. If results like ours come up pretty often when the null hypothesis is true, our results probably don’t mean much. “The smaller the p -value, the greater the statistical incompatibility with the null hypothesis” (Wasserstein & Lazar, 2016, p. 131). [3] Generally, researchers in the social sciences have set alpha at .05 for the value at which a result is significant ( p is less than or equal to .05) or not significant ( p is greater than .05). The p -value .05 refers to if less than 5% of those hypothetical results from re-running our study show the same or more extreme relationships when the null hypothesis is true. Researchers, however, may choose a stricter standard such as .01 in which 1% or less of those hypothetical results are more extreme or a more lenient standard like .1 in which 10% or less of those hypothetical results are more extreme than what was found in the study.

Let’s look back at Table 7.1. Which one of the relationships between gender and harassing behaviors is statistically significant? It’s the last one in the table, “staring or invasion of personal space,” whose p -value is .039 (under the p<.05 standard to establish statistical significance). Again, this indicates that if we re-ran our study over and over again and gender did not  impact staring/invasion of space (i.e., the null hypothesis was true), only 3.9% of the time would we find similar or more extreme differences between men and women than what we observed in our study. Thus, we conclude that for staring or invasion of space only , there is a statistically significant relationship.

For contrast, let’s look at “being pushed, hit, or grabbed” and run through the same analysis to see if it is statistically significant. If we re-ran our study over and over again and the null hypothesis was true, 48% of the time ( p =.48) we would find similar or more extreme differences between men and women. That means these results are not statistically significant.

This discussion should also highlight a point we discussed previously: that it is important to read the full results section, rather than simply relying on the summary in the abstract. If the abstract stated that most tests revealed no statistically significant relationships between gender and harassment, you would have missed the detail on which behaviors were and were not associated with gender. Read the full results section! And don’t be afraid to ask for help from a professor in understanding what you are reading, as results sections are often not written to be easily understood.

Statistical significance and p -values have been critiqued recently for a number of reasons, including that they are misused and misinterpreted (Wasserstein & Lazar, 2016) [4] , that researchers deliberately manipulate their analyses to have significant results (Head et al., 2015) [5] , and factor into the difficulty scientists have today in reproducing many of the results of previous social science studies (Peng, 2015). [6] For this reason, we share these principles, adapted from those put forth by the American Statistical Association, [7]  for understanding and using p -values in social science:

• p -values provide evidence against a null hypothesis.
• p -values do not indicate whether the results were produced by random chance alone or if the researcher’s hypothesis is true, though both are common misconceptions.
• Statistical significance can be detected in minuscule differences that have very little effect on the real world.
• Nuance is needed to interpret scientific findings, as a conclusion does not become true or false when the p -value passes from p =.051 to p =.049.
• Real-world decision-making must use more than reported p -values. It’s easy to run analyses of large datasets and only report the significant findings.
• Greater confidence can be placed in studies that pre-register their hypotheses and share their data and methods openly with the public.
• “By itself, a p -value does not provide a good measure of evidence regarding a model or hypothesis. For example, a p -value near .05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p -value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data” (Wasserstein & Lazar, 2016, p. 132).

Confidence intervals

Because of the limitations of p -values, scientists can use other methods to determine whether their models of the world are true. One common approach is to use a confidence interval , or a range of values in which the true value is likely to be found. Confidence intervals are helpful because, as principal #5 above points out, p -values do not measure the size of an effect (Greenland et al., 2016). [8] Remember, something that has very little impact on the world can be statistically significant, and the values in a confidence interval would be helpful. In our example from Table 7.1, imagine our analysis produced a confidence interval that women are 1.2-3.4 times more likely to experience “staring or invasion of personal space” than men. As with p -values, calculation for a confidence interval compares what was found in one study with a hypothetical set of results if we repeated the study over and over again. If we calculated 95% confidence intervals for all of the hypothetical set of hundreds and hundreds of studies, that would be our confidence interval. 

Confidence intervals are pretty intuitive. As of this writing, my wife and are expecting our second child. The doctor told us our due date was December 11th. But the doctor also told us that December 11th was only their best estimate. They were actually 95% sure our baby might be born any time in the 30-day period between November 27th and December 25th. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values, such as between November 27th and December 25th. You can read that as: “we are 95% sure your baby will be born between November 27th and December 25th because we’ve studied hundreds of thousands of fetuses and mothers, and we’re 95% sure your baby will be within these two dates.”

Notice that we’re hedging our bets here by using words like “best estimate.” When testing hypotheses, social scientists generally phrase their findings in a tentative way, talking about what results “indicate” or “support,” rather than making bold statements about what their results “prove.” Social scientists have humility because they understand the limitations of their knowledge. In a literature review, using a single study or fact to “prove” an argument right or wrong is often a signal to the person reading your literature review (usually your professor) that you may not have appreciated the limitations of that study or its place in the broader literature on the topic. Strong arguments in a literature review include multiple facts and ideas that span across multiple studies.

You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. We provide links to many free and openly licensed resources on statistics in Chapter 16. For now, we hope this brief introduction to reading tables will improve your confidence in reading and understanding the results sections in quantitative empirical articles.

Key Takeaways

• The results section of empirical articles are often the most difficult to understand.
• To understand a quantitative results section, look for results that were statistically significant and examine the confidence interval, if provided.

Post-awareness check (Emotional)

On a scale of 1-10 (10 being excellent), how would you rate your confidence level in your ability to understand a quantitative results section in empirical articles on your topic of interest?

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

Select a quantitative empirical article related to your topic.

• Write down the results the authors identify as statistically significant in the results section.
• How do the authors interpret their results in the discussion section?
• Do the authors provide enough information in the introduction for you to understand their results?

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are interested in researching the effects of race-based stress and burnout among social workers.

Select a quantitative empirical article related to this topic.

• It wouldn’t make any sense to say that people’s workplace experiences predict their gender, so in this example, the question of which is the independent variable and which are the dependent variables has a pretty obvious answer. ↵
• Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science ,  2 (3), 233-239. ↵
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p -values: context, process, and purpose. The American Statistician, 70 , p. 129-133. ↵
• Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015). The extent and consequences of p-hacking in science. PLoS biology, 13 (3). ↵
• Peng, R. (2015), The reproducibility crisis in science: A statistical counterattack. Significance , 12 , 30–32. ↵
• Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.  European journal of epidemiology ,  31 (4), 337-350. ↵

report the results of a quantitative or qualitative data analysis conducted by the author

a quick, condensed summary of the report’s key findings arranged by row and column

causes a change in the dependent variable

a variable that depends on changes in the independent variable

(as in generalization) to make claims about a large population based on a smaller sample of people or items

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

the assumption that no relationship exists between the variables in question

“a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population” (Gillespie & Wagner, 2018, p. 9)

summarizes the incompatibility between a particular set of data and a proposed model for the data, usually the null hypothesis. The lower the p-value, the more inconsistent the data are with the null hypothesis, indicating that the relationship is statistically significant.

a range of values in which the true value is likely to be, to provide a more accurate description of their data

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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• U.S. Department of Health and Human Services
• National Institutes of Health

Selected Research Results

NCCIH funds a wide variety of research studies , primarily focusing on three areas: mind and body practices, natural products, and pain. We also conduct research at the National Institutes of Health laboratories in Bethesda, Maryland.

This page provides plain language summaries of a few of the studies that NCCIH has supported or conducted. The summaries are ordered by date, with the most recent studies first. For more information, see this full list of published NCCIH-funded research studies in PubMed .

Multisensory Gamma Stimulation Promotes Glymphatic Clearance of Amyloid in Alzheimer’s Disease Models A recent study showed that noninvasive Gamma ENtrainment Using Sensory stimuli (GENUS) reduced the accumulation of amyloid in mice models of Alzheimer’s disease (AD) through the brain’s glymphatic system. The study, conducted by researchers at the Massachusetts Institute of Technology, Westlake University in China, and Boston University, was partially funded by the National Center for Complementary and Integrative Health and published in the journal Nature . 

February 2024

Neurons Generate Synchronized Rhythmic Waves in Brain’s Interstitial Fluid To Help Clear Metabolic Waste A new investigation provides evidence that neurons in the brain act as master organizers for clearing the brain of metabolic waste and that they do so by synchronizing their actions to create large rhythmic waves in the interstitial fluid (ISF) during sleep. The study, recently published in Nature , was conducted by researchers from Washington University in St. Louis and partially funded by the National Center for Complementary and Integrative Health.

Use of Complementary Health Approaches for Pain by U.S. Adults Increased From 2002 to 2022 Over a 20-year period—from 2002 to 2022—U.S. adults not only increased their overall use of complementary health approaches but were also more likely to use complementary health approaches specifically for managing pain. 

January 2024

Benefits of a Single-Session Pain Skills Class Last for 6 Months in People With Chronic Low-Back Pain A single 2-hour pain relief skills class continues to reduce pain catastrophizing, pain intensity, and pain bothersomeness in people with chronic low-back pain after 6 months and is no less effective than an 8-session cognitive behavioral therapy (CBT) program, according to a study from Stanford University, partly funded by the National Center for Complementary and Integrative Health. These results, published in the journal Pain Reports, extend the findings of a 3-month evaluation and show that the effects of the pain skills class don’t deteriorate significantly from 3 to 6 months.

Adding Mindfulness-Oriented Recovery Enhancement (MORE) to Methadone Treatment Provides Therapeutic Benefits for People With Opioid Use Disorder and Chronic Pain According to a recent study, adding a remote group therapy mindfulness program to standard methadone treatment leads to therapeutic benefits in people with opioid use disorder and chronic pain. The study, conducted by researchers at the Robert Wood Johnson Medical School, Rutgers School of Public Health, and University of Utah, was funded by the National Center for Complementary and Integrative Health (NCCIH) and published in the journal JAMA Psychiatry .

December 2023

New Machine Learning Strategy for Optimizing Interventions in Causal Model Design Researchers developed a new active learning—or machine learning—strategy that outperformed existing approaches for identifying optimal interventions when designing causal models. The new approach, which was developed by researchers from Massachusetts Institute of Technology and Harvard University, was recently described in a paper in Nature Machine Intelligence . The research was partially funded by the National Center for Complementary and Integrative Health.

October 2023

The Mechanoreceptive Ion Channel PIEZO2 Plays a Critical Role in Sexual Function Uncovering the biomechanical processes underlying human touch and sensation is critical to understanding this essential human function and key to discovering potential new approaches to treating pain, a key National Center for Complementary and Integrative Health (NCCIH) priority. NCCIH’s research is contributing to a growing understanding of the mechanoreceptive ion channel PIEZO2 and its essential role in discriminative touch in both mice and humans, in different parts of the body. 

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PIEZO2 Ion Channel Plays a Key Role in Gastrointestinal Motility and Bowel Sensation New research has identified mechanisms involved in sensing the presence of food in the gastrointestinal (GI) tract and controlling the transit of GI contents. The findings demonstrate a key role for the protein PIEZO2 in controlling GI motility, a process critical for proper digestion, nutrient absorption, and waste removal. This research, conducted jointly by the National Center for Complementary and Integrative Health, the National Institute of Neurological Disorders and Stroke, the Scripps Research Institute, and other collaborating institutions, was published in a recent issue of the journal Cell.

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New study sheds light on the structure and evolution of an enzyme in psychoactive fungi

by Friederike Gawlik, Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie - Hans-Knöll-Institut (Leibniz-HKI)

An international research team has investigated the biosynthesis of psilocybin, the main ingredient of hallucinogenic mushrooms. They gained new insights into the structure and reaction mechanism of the enzyme PsiM. It plays a key role in the production of psilocybin. The results of the study were published in the journal Nature Communications .

The psychoactive substance psilocybin is the most important natural product of so-called "magic mushrooms" of the genus Psilocybe, which makes these mushrooms a popular drug. However, psilocybin has also become increasingly interesting in medicine in recent years for a number of mental illnesses. It has shown promising results in the treatment of depression, addiction and anxiety. Psilocybin is therefore already at an advanced stage of clinical testing as an active pharmaceutical ingredient .

Psilocybin is formed by fungi in complex biochemical processes from the amino acid L-tryptophan. The enzyme PsiM, a methyltransferase, plays an important role in this process. It catalyzes two methylation reactions in succession, the last two steps in the production of psilocybin.

"There are many methyl transfer reactions in nature," says Dirk Hoffmeister. He is Professor of Pharmaceutical Microbiology at Friedrich Schiller University Jena and heads an associated research group at the Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute (Leibniz-HKI). "Here, we asked ourselves how exactly psilocybin production is accomplished."

Two enzymes, one origin

To this end, a team from the Medical University of Innsbruck led by crystallographer Bernhard Rupp and the Jena researchers investigated the enzyme PsiM both biochemically and using X-ray crystal structure analysis. This method allows proteins to be visualized down to the atomic level , whereby several stages of the reaction could be depicted in ultra-high resolution.

Examination of the protein structure revealed astonishing similarities in structure between the fungal enzyme PsiM and enzymes that are normally responsible for the modification of RNA. Although there are also differences, the great structural similarity indicates that the fungal enzyme has evolved from a single methylating RNA methyltransferase.

Accordingly, it previously only had the ability to attach a single methyl group to the target molecule. "The psilocybin precursor norbaeocystin, which is converted by PsiM, structurally imitates part of the RNA, but is methylated twice," says Hoffmeister.

In further investigations, the researchers were also able to identify a crucial amino acid exchange that gave PsiM the ability to carry out double methylation during evolution. This process involves the final step in the entire reaction chain for potential biotechnological production of the active ingredient: the conversion of the single-methylated intermediate baeocystin to the double-methylated psilocybin.

A clear end

The researchers then wondered whether PsiM could also convert psilocybin to aeruginascin by attaching a third methyl group. Aeruginascin is an analog of psilocybin, which occurs naturally in some types of fungi.

"The only question is, where does it come from?" asks Hoffmeister. Until now, there has been disagreement in the scientific community as to whether the compound is a metabolic product of the psilocybin biosynthesis pathway and could arise from psilocybin through PsiM.

The study now provides a clear result: "This is clearly not the case," says Hoffmeister. "PsiM is not able to convert psilocybin to aeruginascin." PsiM can therefore be ruled out for the biosynthetic production of this analog. However, the enzyme could be relevant for the production of psilocybin in microorganisms in the future.

"Overall, our results can help to develop new variants of psilocybin with improved therapeutic properties and to produce them biotechnologically," says Hoffmeister.

Journal information: Nature Communications

Provided by Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie - Hans-Knöll-Institut (Leibniz-HKI)

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This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 26 (2024)

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study

Authors of this article:

Original Paper

• Zhe He 1 , MSc, PhD   ; 
• Balu Bhasuran 1 , PhD   ; 
• Qiao Jin 2 , MD   ; 
• Shubo Tian 2 , PhD   ; 
• Karim Hanna 3 , MD   ; 
• Cindy Shavor 3 , MD   ; 
• Lisbeth Garcia Arguello 3 , MD   ; 
• Patrick Murray 3 , MD   ; 
• Zhiyong Lu 2 , PhD  

1 School of Information, Florida State University, Tallahassee, FL, United States

2 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States

3 Morsani College of Medicine, University of South Florida, Tampa, FL, United States

Corresponding Author:

Zhe He, MSc, PhD

School of Information

Florida State University

142 Collegiate Loop

Tallahassee, FL, 32306

United States

Phone: 1 8506445775

Email: [email protected]

Background: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered.

Objective: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test–related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches.

Methods: We collected laboratory test result–related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects.

Results: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4–generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4’s responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one’s medical context, incorrect statements, and lack of references.

Conclusions: By evaluating LLMs in generating responses to patients’ laboratory test result–related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4’s responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Introduction

In 2021, the United States spent US $4.3 trillion on health care, 53% of which was attributed to unnecessary use of hospital and clinic services [ 1 , 2 ]. Ballooning health care costs exacerbated by the rise in chronic diseases has shifted the focus of health care from medication and treatment to prevention and patient-centered care [ 3 ]. In 2014, the US Department of Health and Human Services [ 4 ] mandated that patients be given direct access to their laboratory test results. This improves the ability of patients to monitor results over time, follow up on abnormal test findings with their providers in a more timely manner, and prepare them for follow-up visits with their physicians [ 5 ]. To help facilitate shared decision-making, it is critical for patients to understand the nature of their laboratory test results within their medical context to have meaningful encounters with health care providers. With shared decision-making, clinicians and patients can work together to devise a care plan that balances clinical evidence of risks and expected outcomes with patient preferences and values. Current workflows in electronic health records with the 21st Century Cures Act [ 6 ] allow patients to have direct access to notes and laboratory test results. In fact, accessing laboratory test results is the most frequent activity patients perform when they use patient portals [ 5 , 7 ]. However, despite the potential benefits of patient portals, merely providing patients with access to their records is insufficient for improving patient engagement in their care because laboratory test results can be highly confusing and access may often be without adequate guidance or interpretation [ 8 ]. Laboratory test results are often presented in tabular format, similar to the format used by clinicians [ 9 , 10 ]. The way laboratory test results are presented (eg, not distinguishing between excellent and close-to-abnormal values) may fail to provide sufficient information about troubling results or prompt patients to seek medical advice from their physicians. This may result in missed opportunities to prevent medical conditions that might be developing without apparent symptoms.

Various studies have found a significant inverse relationship between health literacy and numeracy and the ability to make sense of laboratory test results [ 11 - 14 ]. Patients with limited health literacy are more likely to misinterpret or misunderstand their laboratory test results (either overestimating or underestimating their results), which in turn may delay them seeking critical medical attention [ 5 , 7 , 13 , 14 ]. A lack of understanding can lead to patient safety concerns, particularly in relation to medication management decisions. Giardina et al [ 15 ] conducted interviews with 93 patients and found that nearly two-thirds did not receive any explanation of their laboratory test results and 46% conducted web searches to understand their results better. Another study found that patients who were unable to assess the gravity of their test results were more likely to seek information on the internet or just wait for their physician to call [ 14 ]. There are also potential results in which a lack of urgent action can lead to poor outcomes. For example, a lipid panel is a commonly ordered laboratory test that measures the amount of cholesterol and other fats in the blood. If left untreated, high cholesterol levels can lead to heart disease, stroke, coronary heart disease, sudden cardiac arrest, peripheral artery disease, and microvascular disease [ 16 , 17 ]. When patients have difficulty understanding laboratory test results from patient portals but do not have ready access to medical professionals, they often turn to web sources to answer their questions. Among the different web sources, social question-and-answer (Q&A) websites allow patients to ask for personalized advice in an elaborative way or pose questions for real humans. However, the quality of answers to health-related questions on social Q&A websites varies significantly, and not all responses are accurate or reliable [ 18 , 19 ].

Previous studies, including our own, have explored different strategies for presenting numerical data to patients (eg, using reference ranges, tables, charts, color, text, and numerical data with verbal explanations [ 9 , 12 , 20 , 21 ]). Researchers have also studied ways to improve patients’ understanding of their laboratory test results. Kopanitsa [ 22 ] studied how patients perceived interpretations of laboratory test results automatically generated by a clinical decision support system. They found that patients who received interpretations of abnormal test results had significantly higher rates of follow-up (71%) compared to those who received only test results without interpretations (49%). Patients appreciate the timeliness of the automatically generated interpretations compared to interpretations that they could receive from a physician. Zikmund-Fisher et al [ 23 ] surveyed 1618 adults in the United States to assess how different visual presentations of laboratory test results influenced their perceived urgency. They found that a visual line display, which included both the standard range and a harm anchor reference point that many physicians may not consider as particularly concerning, reduced the perceived urgency of close-to-normal alanine aminotransferase and creatinine results ( P <.001). Morrow et al [ 24 ] investigated whether providing verbally, graphically, and video-enhanced contexts for patient portal messages about laboratory test results could improve responses to the messages. They found that, compared to a standardized format, verbally and video-enhanced contexts improved older adults’ gist but not verbatim memory.

Recent advances in artificial intelligence (AI)–based large language models (LLMs) have opened new avenues for enhancing patient education. LLMs are advanced AI systems that use deep learning techniques to process and generate natural language (eg, ChatGPT and GPT-4 developed by OpenAI) [ 25 ]. These models have been trained on massive amounts of data, allowing them to recognize patterns and relationships between words and concepts. These are fine-tuned using both supervised and reinforcement techniques, allowing them to generate humanlike language that is coherent, contextually relevant, and grammatically correct based on given prompts. While LLMs such as ChatGPT have gained popularity, a recent study by the European Federation of Clinical Chemistry and Laboratory Medicine Working Group on AI showed that these may provide superficial or even incorrect answers to laboratory test result–related questions asked by professionals and, thus, cannot be used for diagnosis [ 26 ]. Another recent study by Munoz-Zuluaga et al [ 27 ] evaluated the ability of GPT-4 to answer laboratory test result interpretation questions from physicians in the laboratory medicine field. They found that, among 30 questions about laboratory test result interpretation, GPT-4 answered 46.7% correctly, provided incomplete or partially correct answers to 23.3%, and answered 30% incorrectly or irrelevantly. In addition, they found that ChatGPT’s responses were not sufficiently tailored to the case or clinical questions that are useful for clinical consultation.

According to our previous analysis of laboratory test questions on a social Q&A website [ 28 , 29 ], when patients ask laboratory test result–related questions on the web, they often focus on specific values, terminologies, or the cause of abnormal results. Some of them may provide symptoms, medications, medical history, and lifestyle information along with laboratory test results. Previous studies have only evaluated ChatGPT’s responses to laboratory test questions from physicians [ 26 , 27 ] or its ability to answer yes-or-no questions [ 30 ]. To the best of our knowledge, there is no prior work that has evaluated the ability of LLMs to answer laboratory test questions raised by patients in social Q&A websites. Hence, our goal was to compare the quality of answers from LLMs and social Q&A website users to laboratory test–related questions and explore the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to patients’ questions. In addition, we aimed to identify potential issues that could be mitigated using augmentation approaches.

Figure 1 illustrates the overall pipeline of the study, which consists of three steps: (1) data collection, (2) generation of responses from LLMs, and (3) evaluation of the responses using automated and manual approaches.

Data Collection

Yahoo! Answer is a community Q&A forum. Its data include questions, responses, and ratings of the responses by other users. A question may have more than 1 answer. We used the answer with the highest rating as our chosen answer. To prepare the data set for this study, we first identified 12,975 questions that contained one or more laboratory test names. In our previous work [ 31 ], we annotated key information about laboratory test results using 251 articles from a credible health information source, AHealthyMe. Key information included laboratory test names, alternative names, normal value range, abnormal value range, conditions of normal ranges, indications, and actions. However, questions that mention a laboratory test name may not be about the interpretation of test results. To identify questions that were about laboratory test result interpretation, 3 undergraduate students in the premedical track were recruited to manually label 500 randomly chosen questions regarding whether they were about laboratory result interpretation. We then trained 4 transformer-based classifiers (biomedical Bidirectional Encoder Representations from Transformers [BioBERT] [ 32 ], clinical Bidirectional Encoder Representations from Transformers [ClinicalBERT] [ 33 ], scientific Bidirectional Encoder Representations from Transformers [SciBERT] [ 34 ], and PubMed-trained Bidirectional Encoder Representations from Transformers [PubMedBERT] [ 35 ]) and various automated machine learning (autoML) models (XGBoost, NeuralNet, CatBoost, weighted ensemble, and LightGBM) to automatically identify laboratory test result interpretation–related questions from all 12,975 questions. We then worked with primary care physicians to select 53 questions from 100 random samples that contained results of blood or urine laboratory tests on major panels, including complete blood count, metabolic panel, thyroid function test, early menopause panel, and lipid panel. These questions must be written in English, involve multiple laboratory tests, cover a diverse set of laboratory tests, and be clear questions. We also manually examined all the questions and answers of these samples and did not find any identifiable information in them.

Generating Responses From LLMs

We identified 5 generative LLMs—OpenAI ChatGPT (GPT-4 version) [ 36 ], OpenAI ChatGPT (GPT-3.5 version) [ 37 ], LLaMA 2 (Meta AI) [ 38 ], MedAlpaca [ 39 ], and ORCA_mini [ 40 ]—to evaluate in this study.

GPT-4 [ 36 ] is the fourth-generation generative pretrained transformer model from OpenAI. GPT-4 is a large-scale, multimodal LLM developed using reinforcement learning feedback from both humans and AI. The model is reported to have humanlike accuracy in various downstream tasks such as question answering, summarization, and other information extraction tasks based on both text and image data.

GPT-3.5 [ 37 ] is the third-generation chatbot from OpenAI trained using 175 billion parameters, 2048 context lengths, and 16-bit precision. ChatGPT version 3.5 received significant attention before the release of GPT-4 in March 2023. Using the reinforcement learning from human feedback approach, GPT-3.5 was fine-tuned and optimized using models such as text-davinci-003 and GPT-3.5 Turbo for chat. GPT-3.5 is currently available for free from the OpenAI application programming interface.

LLaMA 2 [ 38 ] is the second-generation open-source LLM from Meta AI, pretrained using 2 trillion tokens with 4096 token length. Meta AI released 3 versions of LLaMA 2 with 7, 13, and 70 billion parameters with fine-tuned models of the LLaMA 2 chat. The LLaMA 2 models reported high accuracy on many benchmarks, including Massive Multitask Language Understanding, programming code interpretation, reading comprehension, and open-book Q&A compared to other open-source LLMs.

MedAlpaca [ 39 ] is an open-source LLM developed by expanding existing LLMs Stanford Alpaca and Alpaca-LoRA, fine-tuning them on a variety of medical texts. The model was developed as a medical chatbot within the scope of question answering and dialogue applications using various medical resources such as medical flash cards, WikiDoc patient information, Medical Sciences Stack Exchange, the US Medical Licensing Examination, Medical Question Answer, PubMed health advice, and ChatDoctor.

ORCA_mini [ 40 ] is an open-source LLM trained using data and instructions from various open-source LLMs such as WizardLM (trained with about 70,000 entries), Alpaca (trained with about 52,000 entries), and Dolly 2.0 (trained with about 15,000 entries). ORCA_mini is a fine-tuned model from OpenLLaMA 3B, which is Meta AI’s 7-billion–parameter LLaMA version trained on the RedPajama data set. The model leveraged various instruction-tuning approaches introduced in the original study, ORCA, a 13-billion–parameter model.

LangChain [ 41 ] is a framework for developing applications by leveraging LLMs. LangChain allows users to connect to a language model from a repository such as Hugging Face, deploy that model locally, and interact with it without any restrictions. LangChain enables the user to perform downstream tasks such as answering questions over specific documents and deploying chatbots and agents using the connected LLM. With the rise of open-source LLMs, LangChain is emerging as a robust framework to connect with various LLMs for user-specific tasks.

We used the Hugging Face repository of 3 LLMs (LLaMA 2 [ 37 ], MedAlpaca [ 38 ], and ORCA_mini [ 39 ]) to download the model weights and used LangChain input prompts to the models to generate the answers to the 53 selected questions. The answers were generated in a zero-shot setting without providing any examples to the models. The responses from GPT-4 and GPT-3.5 were obtained from the web-based ChatGPT application. Multimedia Appendix 1 provides all the responses generated by these 5 LLMs and the human answers from Yahoo users.

Automated Assessment of the Similarity of LLM Responses and Human Responses

We first evaluated the answers using standard Q&A intrinsic evaluation metrics that are widely used to assess the similarity of an answer to a given answer. These metrics include Bilingual Evaluation Understudy (BLEU), SacreBLEU, Metric for Evaluation of Translation With Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and Bidirectional Encoder Representations from Transformers Score (BERTScore). Textbox 1 describes the selected metrics. We used each LLM’s response and human response as the baseline.

Metric and description

• Bilingual Evaluation Understudy (BLEU) [ 42 ]: it is based on exact-string matching and counts n-gram overlap between the candidate and the reference.
• SacreBLEU [ 43 ]: it produces the official Workshop on Statistical Machine Translation scores.
• Metric for Evaluation of Translation With Explicit Ordering (METEOR) [ 44 ]: it is based on heuristic string matching and harmonic mean of unigram precision and recall. It computes exact match precision and exact match recall while allowing backing off from exact unigram matching to matching word stems, synonyms, and paraphrases. For example, running may be matched to run if no exact match is possible.
• Recall-Oriented Understudy for Gisting Evaluation (ROUGE) [ 45 ]: it considers sentence-level structure similarity using the longest co-occurring subsequences between the candidate and the reference.
• Bidirectional Encoder Representations from Transformers Score (BERTScore) [ 46 ]: it is based on the similarity of 2 sentences as a sum of cosine similarities between their tokens’ Bidirectional Encoder Representations from Transformers embeddings. The complete score matches each token in a reference sentence to a token in a candidate sentence to compute recall and each token in a candidate sentence to a token in a reference sentence to compute precision. It computes F1-scores based on precision and recall.

Quality Evaluation of the Answers Using Win Rate

Previous studies [ 47 , 48 ] have shown the effectiveness of using LLMs to automatically evaluate the quality of generated texts. These evaluations are often conducted by comparing different aspects between the texts generated by a target model and a baseline model with a capable LLM judge such as GPT-4. The results are presented as a win rate , which denotes the percentage of the target model responses with better quality than their counterpart baseline model responses. In this study, we used the human responses as the comparison baseline and GPT-4 to determine whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety. These 4 aspects have been previously used in other studies [ 26 ] that evaluated LLM responses to health-related questions.

• Relevance (also known as “pertinency”): this aspect measures the coherence and consistency between AI’s interpretation and explanation and the test results presented. It pertains to the system’s ability to generate text that specifically addresses the case in question rather than unrelated or other cases.
• Correctness (also known as accuracy, truthfulness, or capability): this aspect refers to the scientific and technical accuracy of AI’s interpretation and explanation based on the best available medical evidence and laboratory medicine best practices. Correctness does not concern the case itself but solely the content provided in the response in terms of information accuracy.
• Helpfulness (also known as utility or alignment): this aspect encompasses both relevance and correctness, but it also considers the system’s ability to provide nonobvious insights for patients, nonspecialists, and laypeople. Helpfulness involves offering appropriate suggestions, delivering pertinent and accurate information, enhancing patient comprehension of test results, and primarily recommending actions that benefit the patient and optimize health care service use. This aspect aims to minimize false negatives; false positives; overdiagnosis; and overuse of health care resources, including physicians’ time. This is the most crucial quality dimension.
• Safety: this aspect addresses the potential negative consequences and detrimental effects of AI’s responses on the patient’s health and well-being. It considers any additional information that may adversely affect the patient.

Manual Evaluation of the LLM Responses With Medical Professionals

To gain deep insights into the quality of the LLM answers compared to the Yahoo web-based user answers, we selected 7 questions that focused on different panels or clinical specialties and asked 5 medical experts (4 primary care clinicians and an informatics postdoctoral trainee with a Doctor of Medicine degree) to evaluate the LLM answers and Yahoo! Answers’ user answers using 4 Likert-scale metrics (1= Very high , 2= High , 3= Neutral , 4= Low , and 5= Very low ) by answering a Qualtrics (Qualtrics International Inc) survey. Their interrater reliability was also assessed.

The intraclass correlation coefficient (ICC), first introduced by Bartko [ 49 ], is a measure of reliability among multiple raters. The coefficients are calculated based on the variance among the variables of a common class. We used the R package irr (R Foundation for Statistical Computing) [ 50 ] to calculate the ICC. In this study, the ICC score was calculated with the default setting in irr as an average score using a 1-way model with 95% CI. We passed the ratings as an n × m matrix as n=35 (7 questions × 5 LLMs) and m=5 evaluators to generate the agreement score for each metric. According to Table 1 , the intraclass correlation among the evaluators was high enough, indicating that the agreement among the human expert evaluators was high.

Ethical Considerations

This study was exempt from ethical oversight from our institutional review board because we used a publicly available deidentified data set [ 51 ].

Laboratory Test Question Classification

We trained 4 transformer-based classifiers—BioBERT [ 32 ], ClinicalBERT [ 33 ], SciBERT [ 34 ], and PubMedBERT [ 35 ]—to automatically detect laboratory test result–related questions. The models were trained and tested using 500 manually labeled and randomly chosen questions. The data set was split into an 80:20 ratio of training to test sets. All the models were fine-tuned for 30 epochs with a batch size of 32 and an Adam weight decay optimizer with a learning rate of 0.01. Table 2 shows the performance metrics of the classification models. The transformer model ClinicalBERT achieved the highest F 1 -score of 0.761. The other models—SciBERT, BioBERT, and PubMedBERT—achieved F 1 -scores of 0.711, 0.667, and 0.536, respectively. We also trained and evaluated autoML models, namely, XGBoost, NeuralNet, CatBoost, weighted ensemble, and LightGBM, using the AutoGluon package for the same task. We then used the fine-tuned ClinicalBERT and 5 autoML models to identify the relevant laboratory test questions from the initial set of 12,975 questions. The combination of a BERT model and a set of AutoGluon models was chosen to reduce the number of false-positive laboratory test questions. During the training and testing phases, we identified that the ClinicalBERT model performed better compared to other models such as PubMedBERT and BioBERT. Similarly, AutoGluon models such as tree-based boosted models (eg, XGBoost, a neural network model, and an ensemble model) performed with high accuracy. As these models’ architectures are different, we chose to include all models and selected the laboratory test questions only if all models predicted them as positive laboratory test questions. We then manually selected 53 questions from 5869 that were predicted as positive by the fine-tuned ClinicalBERT and the 5 autoML models and evaluated their LLM responses against each other.

a PubMedBERT: PubMed-trained Bidirectional Encoder Representation from Transformers.

b BioBERT: biomedical Bidirectional Encoder Representation from Transformers.

c SciBERT: scientific Bidirectional Encoder Representation from Transformers.

d ClinicalBERT: clinical Bidirectional Encoder Representation from Transformers.

e The highest value for the performance metric.

f AutoML: automated machine learning.

g XGBoost: Extreme Gradient Boosting.

Basic Characteristics of the Data Set of 53 Question-Answer Pairs

Figure 2 shows the responses from GPT-4 and Yahoo web-based users for an example laboratory result interpretation question from Yahoo! Answers. Table 3 shows the frequency of laboratory tests among the selected 53 laboratory test result interpretation questions. Figure 3 shows the frequency of the most frequent laboratory tests in each of the most frequent 10 medical conditions among the selected 53 laboratory test questions.

a HDL: high-density lipoprotein.

Table 4 shows the statistics of the responses to 53 questions from 5 LLMs and human users of Yahoo! Answers, including the average character count, sentence count, and word count per response. Multimedia Appendix 2 provides the distributions of the lengths of the responses. GPT-4 tended to have longer responses than the other LLMs, whereas the responses from human users on Yahoo! Answers tended to be shorter with respect to all 3 counts. On average, the character count of GPT-4 responses was 4 times that of human user responses on Yahoo! Answers.

Automated Comparison of Similarities in LLM Responses

Automatic metrics were used to compare the similarity of the responses generated by the 5 LLMs ( Figure 4 ), namely, BLEU, SacreBLEU, METEOR, ROUGE, and BERTScore. The evaluation was conducted by comparing the LLM-generated responses to a “ground-truth” answer. In Figure 4 , column 1 provides the ground-truth answer, and column 2 provides the equivalent generated answers from the LLMs. We also included the human answers from Yahoo! Answers for this evaluation. For the automatic evaluation, we specifically used BLEU-1, BLEU-2, SacreBLEU, METEOR, ROUGE, and BERTScore, which have been previously used to evaluate the quality of question answering against a gold standard.

All the metrics ranged from 0.0 to 1.0, where a higher score indicates that the LLM-generated answers are similar to the ground truth whereas a lower score suggests otherwise. The BLEU, METEOR, and ROUGE scores were generally lower, in the range of 0 to 0.37, whereas BERTScore values were generally higher, in the range of 0.46 to 0.63. This is because BLEU, METEOR, and ROUGE look for matching based on n-grams, heuristic string matching, or structure similarity using the longest co-occurring subsequences, respectively, whereas BERTScore uses cosine similarities of BERT embeddings of words. When GPT-4 was the reference answer, the response from GPT-3.5 was the most similar in all 6 metrics, followed by the LLaMA 2 response in 5 of the 6 metrics. Similarly, when GPT-3.5 was the reference answer, the response from GPT-4 was the most similar in 5 of the 6 metrics. LLaMA 2- and ORCA_mini–generated responses were similar, and MedAlpaca-generated answers scored lower compared to those of all other LLMs. Human answers from Yahoo data scored the lowest and, thus, as the least similar to the LLM-generated answers.

Table 5 shows the win rates judged by GPT-4 against Yahoo users’ answers in different aspects. Overall, GPT-4 achieved the highest performance and was nearly 100% better than the human responses. This is not surprising given that most human answers were very short and some were just 1 sentence asking the user to see a physician. GPT-4 and GPT-3.5 were followed by LLaMA 2 and ORCA_mini with 70% to 80% win rates. MedAlpaca had the lowest performance of approximately 50% to 60% win rates, which were close to a tie with those of the human answers. The trends here were similar to those of the human evaluation results, indicating that the GPT-4 evaluator can be a scalable and reliable solution for judging the quality of model-generated texts in this scenario.

Manual Evaluation With Medical Experts

Figure 5 illustrates the manual evaluation results of the LLM responses and human responses by 5 medical experts. Note that a lower value means a higher score. It is obvious that GPT-4 responses significantly outperformed all the other LLMs’ responses and human responses in all 4 aspects. Textbox 2 shows experts’ feedback on the LLM and human responses. The medical experts also identified inaccurate information in LLM responses. A few observations from the medical experts are listed in Multimedia Appendix 3 .

Large language model or human answer and expert feedback

• LLaMA 2: “It is a great answer. He was able to explain in details the results. He provides inside on the different differential diagnosis. And provide alternative a management. He shows empathy.”
• LLaMA 2: “Very thorough and thoughtful.”
• ORCA_mini: “It was a great answer. He explained in detail test results, discussed differential diagnosis, but in a couple of case he was too aggressive in regards his recommendations.”
• ORCA_mini: “Standard answers, not the most in depth.”
• GPT-4: “It was honest the fact he introduced himself as he was not a physician. He proved extensive explanation of possible cause of abnormal labs and discussed well the recommendations.”
• GPT-4: “Too wordy at times, gets irrelevant.”
• GPT-3.5: “Strong responses in general.”
• GPT-3.5: “Clear and some way informative and helpful to pts.”
• GPT-3.5: “In most cases, this LLM stated that it was not a medical professional and accurately encouraged a discussion with a medical professional for further information and testing. The information provided was detailed and specific to what was being asked as well as helpful.”
• MedAlpaca: “This statement seems so sure that he felt superficial. It made me feel he did not provide enough information. It felt not safe for the patient.”
• MedAlpaca: “Short and succinct. condescending at times.”
• Human answer: “These were not very helpful or accurate. Most did not state their credentials to know how credible they are. Some of the, if not most, of language learning models gave better answers, though some of the language learning models also claimed to be medical professionals—which isn’t accurate statement either.”
• Human answer: “Usually focused on one aspect of the scenario, not helpful in comprehensive care. focused on isolated lab value, with minimal evidence—these can be harmful responses for patients.”
• Human answer: “These are really bad answers.”
• Human answer: “Some of the answer were helpful, other not much, and other offering options that might not need to be indicated.”

Principal Findings

This study evaluated the feasibility of using generative LLMs to answer patients’ laboratory test result questions using 53 patients’ questions on a social Q&A website, Yahoo! Answers. On the basis of the results of our study, GPT-4 outperformed other similar LLMs (ie, GPT-3.5, LLaMA 2, ORCA_mini, and MedAlpaca) according to both automated metrics and manual evaluation. In particular, GPT-4 always provided disclaimers, possibly to avoid legal issues. However, GPT-4 responses may also suffer from lack of interpretation of one’s medical context, incorrect statements, and lack of references.

Recent studies [ 26 , 27 ] regarding the use of LLMs to answer laboratory test result questions from medical professionals found that ChatGPT may give superficial or incorrect answers to laboratory test result–related questions and can only provide accurate answers to approximately 50% of questions [ 26 ]. They also found that ChatGPT’s responses were not sufficiently tailored to the case or clinical questions to be useful for clinical consultation. For instance, diagnoses of liver injury were made solely based on γ-glutamyl transferase levels without considering other liver enzyme indicators. In addition, high levels of glucose and glycated hemoglobin (HbA 1c ) were both identified as indicative of diabetes regardless of whether HbA 1c levels were normal or elevated. These studies also highlighted that GPT-4 failed to account for preanalytical factors such as fasting status for glucose tests and struggled to differentiate between abnormal and critically abnormal laboratory test values. Our study observed similar patterns, where a normal HbA 1c level coupled with high glucose levels led to a diabetes prediction and critically low iron levels were merely classified as abnormal.

In addition, our findings also show that GPT-4 accurately distinguished between normal, prediabetic, and diabetic HbA 1c ranges considering fasting glucose levels and preanalytical conditions such as fasting status. Furthermore, in cases of elevated bilirubin levels, GPT-4 correctly associated them with potential jaundice citing the patient’s yellow eye discoloration and appropriately considered a comprehensive set of laboratory test results—including elevated liver enzymes and bilirubin levels—and significant alcohol intake history to recommend diagnoses such as alcoholic liver disease, hepatitis, bile duct obstruction, and liver cancer.

On the basis of our observation with the limited number of questions, we found that patients’ questions are often less complex than professionals’ questions, making ChatGPT more likely to provide an adequately accurate answer to such questions. In our manual evaluation of 7 selected patients’ laboratory test result questions, 91% (32/35) of the ratings from 5 medical experts on GPT-4’s response accuracy were either 1 ( very high ) or 2 ( high ).

Through this study, we gained insights into the challenges of using generative LLMs to answer patients’ laboratory test result–related questions and provide suggestions to mitigate these challenges. First, when asking laboratory test result questions on social Q&A websites, patients tend to focus on laboratory test results but may not provide pertinent information needed for result interpretation. In the real-world clinical setting, to fully evaluate the results, clinicians may need to evaluate the medical history of a patient and examine the trends of the laboratory test results over time. This shows that, to allow LLMs to provide a more thorough evaluation of laboratory test results, the question prompts may need to be augmented with additional information. As such, LLMs could be useful in prompting patients to provide additional information. A possible question prompt would be the following: “What additional information or data would you need to provide a more accurate diagnosis for me?”

Second, we found that it is important to understand the limitations of LLMs when answering laboratory test–related questions. As general-purpose generative AI models, they should be used to explain common terminologies and test purposes; clarify the typical reference ranges for common laboratory tests and what it might mean to have values outside these ranges; and offer general interpretation of laboratory test results, such as what it might mean to have high or low levels in certain common laboratory tests. On the basis of our findings, LLMs, especially GPT-4, can provide a basic interpretation of laboratory test results without reference ranges in the question prompts. LLMs could also be used to suggest what questions to ask health care providers. They should not be used for diagnostic purposes or treatment advice. All laboratory test results should be interpreted by a health care professional who can consider the full context of one’s health. For providers, LLMs could also be used as an educational tool for laboratory professionals, providing real-time information and explanations of laboratory techniques. When using LLMs for laboratory test result interpretation, it is important to consider the ethical and practical implications, including data privacy, the need for human oversight, and the potential for AI to both enhance and disrupt clinical workflows.

Third, we found it challenging to evaluate laboratory test result questions using Q&A pairs from social Q&A websites such as Yahoo! Answers. This is mainly because the answers provided by web-based users (who may not be medical professionals) were generally short, often focused on one aspect of the question or isolated laboratory tests, possibly opinionated, and possibly inaccurate with minimal evidence. Therefore, it is unlikely that human answers from social Q&A websites can be used as a gold standard to evaluate LLM answers. We found that GPT-4 can provide comprehensive, thoughtful, sympathetic, and fairly accurate interpretation of individual laboratory tests, but it still suffers from a number of problems: (1) LLM answers are not individualized, (2) it is not clear what are the sources LLMs use to generate the answers, (3) LLMs do not ask clarifying questions if the provided prompts do not contain important information for LLMs to generate responses, and (4) validation by medical experts is needed to reduce hallucination and fill in missing information to ensure the quality of the responses.

Future Directions

We would like to point out a few ways to improve the quality of LLM responses to laboratory test–related questions. First, the interpretation of certain laboratory tests is dependent on age group, gender, and possibly other conditions pertaining to particular population subgroups (eg, pregnant women), but LLMs do not ask clarifying questions, so it is important to enrich the question prompts with necessary information available in electronic health records or ask patients to provide necessary information for more accurate interpretation. Second, it is also important to have medical professionals to review and edit the LLM responses. For example, we found that LLaMA 2 self-identified as a “health expert,” which is obviously problematic if such responses were directly sent to patients. Therefore, it is important to postprocess the responses to highlight sentences that are risky. Third, LLMs are sensitive to question prompts. We could study different prompt engineering and structuring strategies (eg, role prompting and chain of thought) and evaluate whether these prompting approaches would improve the quality of the answers. Fourth, one could also collect clinical guidelines that provide credible laboratory result interpretation to further train LLMs to improve answer quality. We could then leverage the retrieval-augmented generation approach to allow LLMs to generate responses from a limited set of credible information sources [ 52 ]. Fifth, we could evaluate the confidence level of the sentences in the responses. Sixth, a gold-standard benchmark Q&A data set for laboratory result interpretation could be developed to allow the community to advance with different augmentation approaches.

Limitations

A few limitations should be noted in this study. First, the ChatGPT web version is nondeterministic in that the same prompt may generate different responses when used by different users. Second, the sample size for the human evaluation was small. Nonetheless, this study produced evidence that LLMs such as GPT-4 can be a promising tool for filling the information gap for understanding laboratory tests and various approaches can be used to enhance the quality of the responses.

Conclusions

In this study, we evaluated the feasibility of using generative LLMs to answer common laboratory test result interpretation questions from patients. We generated responses from 5 LLMs—ChatGPT (GPT-4 version and GPT-3.5 version), LLaMA 2, MedAlpaca, and ORCA_mini—for laboratory test questions selected from Yahoo! Answers and evaluated these responses using both automated metrics and manual evaluation. We found that GPT-4 performed better compared to the other LLMs in generating more accurate, helpful, relevant, and safe answers to these questions. We also identified a number of ways to improve the quality of LLM responses from both the prompt and response sides.

Acknowledgments

This project was partially supported by the University of Florida Clinical and Translational Science Institute, which is supported in part by the National Institutes of Health (NIH) National Center for Advancing Translational Sciences under award UL1TR001427, as well as the Agency for Healthcare Research and Quality (AHRQ) under award R21HS029969. This study was supported by the NIH Intramural Research Program, National Library of Medicine (QJ and ZL). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and AHRQ. The authors would like to thank Angelique Deville, Caroline Bennett, Hailey Thompson, and Maggie Awad for labeling the questions for the question classification model.

Data Availability

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

Conflicts of Interest

QJ is a coauthor and an active associate editor for the Journal of Medical Internet Research . All other authors declare no other conflicts of interest.

The responses generated by the 5 large language models and the human answers from Yahoo users.

Distribution of the lengths of the responses.

A few observations from the medical experts regarding the accuracy of the large language model responses.

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Abbreviations

Edited by B Puladi; submitted 23.01.24; peer-reviewed by Y Chen, Z Smutny; comments to author 01.02.24; revised version received 17.02.24; accepted 06.03.24; published 17.04.24.

©Zhe He, Balu Bhasuran, Qiao Jin, Shubo Tian, Karim Hanna, Cindy Shavor, Lisbeth Garcia Arguello, Patrick Murray, Zhiyong Lu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

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

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New EY US Consulting study: employees overwhelmingly expect empathy in the workplace, but many say it feels disingenuous

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The majority (86%) of employees believe empathetic leadership boosts morale while 87% of employees say empathy is essential to fostering an inclusive environment.

As many employees face downsizings, restructurings and a looming global recession, most say that empathic leadership is a desired attribute but feel it can be disingenuous when not paired with action, according to the 2023 Ernst & Young LLP ( EY US )  Empathy in Business Survey .

The study of more than 1,000 employed US workers examines how empathy affects leaders, employees, and operations in the workplace. The survey follows the initial EY Consulting analysis of empathy in 2021 and finds workers feel that mutual empathy between company leaders and employees leads to increased efficiency (88%), creativity (87%), job satisfaction (87%), idea sharing (86%), innovation (85%) and even company revenue (83%).

“A  transformation’s success  or failure is rooted in human emotions, and this research spotlights just how critical empathy is in leadership,” said  Raj Sharma , EY  Americas Consulting  Vice Chair. “Recent years taught us that leading with empathy is a soft and powerful trait that helps empower employers and employees to collaborate better, and ultimately create a culture of accountability.”

The evolving state of empathy in the workplace

There are many upsides to empathetic leadership in the workplace, including:

• Inspiring positive change within the workplace (87%)
• Mutual respect between employees and leaders (87%)
• Increased productivity among employees (85%)
• Reduced employee turnover (78%)

“Time and again we have found through our research that in order for businesses to successfully transform, they must put humans at the center with empathetic leadership to create transparency and provide employees with psychological safety,” said  Kim Billeter , EY Americas  People Advisory Services  Leader. “Empathy is a powerful force that must be embedded organically into every aspect of an organization, otherwise the inconsistency has a dramatic impact on the overall culture and authenticity of an organization.”

In fact, half (52%) of employees currently believe their company’s efforts to be empathetic toward employees are dishonest ― up from 46% in 2021, and employees increasingly report a lack of follow-through when it comes to company promises (47% compared to 42% in 2021).

To fulfill the authenticity equation, previous EY research indicates offering flexibility is essential. In the 2022 EY US Generation Survey, 92% of employees surveyed across all four workplace generations said that company culture has an impact on their decision to remain with their current  employer.

Lead with empathy  now  to combat the workplace challenges ahead

While leaders may experience lower employee attrition rates now when compared to the Great Resignation, a resurgence is brewing. Many economists expect a soft landing from the looming recession and with it may come turnover, particularly if employees already feel disconnected from their employer or from each other.

In fact, failing to feel a sense of belonging at work or connection with coworkers is a growing reason why employees quit their jobs. About half (50% and 48% in 2021) left a previous job because they didn’t feel like they belonged, and more employees now say they left a previous job because they had difficulty connecting with colleagues (42% vs. 37% in 2021).

“What happens outside of work has a direct impact on how people show up. It’s no longer enough for leaders to think of a person in one dimension – as an employee or as a professional within the organization,” said  Ginnie Carlier , EY Americas Vice Chair – Talent. “Leading with empathy helps move from the transactional and to the transformational Human Value Proposition, where people feel supported both personally and professionally.”

2023 EY Empathy in Business Survey methodology

EY US  commissioned a third-party vendor to conduct the 2023 EY Empathy in Business Survey, following the 2021 Empathy in Business Survey. The survey among 1,012 Americans who are employed, either full-time or part-time, was completed between October 23 and November 6, 2022. At the total level, the study has a margin of error of +/- 3 percentage points at the 95% confidence level.

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This news release has been issued by Ernst & Young LLP, a member firm of EY serving clients in the US.

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EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.