research ethics in medical education

• Open access
• Published: 01 December 2022

Building connections between biomedical sciences and ethics for medical students

• Oluwaseun Olaiya 1 ,
• Travis Hyatt 2 ,
• Alwyn Mathew 2 ,
• Shawn Staudaher 2 ,
• Zachary Bachman 3 &
• Yuan Zhao 4  

BMC Medical Education volume  22 , Article number:  829 ( 2022 ) Cite this article

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Medical ethics education is crucial for preparing medical students to face ethical situations that can arise in patient care. Instances of ethics being integrated into biomedical science education to build the connection between human science and ethics is limited. The specific aim of this study was to measure student attitudes towards an innovative curriculum design that integrates ethics education directly into a biomedical science course in pre-clinical medical curriculum.

In this cross-sectional study, three ethics learning modules were designed and built in a biomedical science course in the pre-clinical curriculum. All students of Class of 2024 who were enrolled in the course in 2021 were included in the study. Each module integrated ethics with basic science topics and was delivered with different teaching modalities. The first module used a documentary about a well-known patient with severe combined immunodeficiency disease. The second module was delivered through a clinical scenario on HIV infection. The third module used small group discussion and debate on the topic of blood transfusion. For evaluation, students were asked to self-identify the ethical challenges associated with each module and complete reflective writing to assess their knowledge and attitude. Quantitative and qualitative analyses were conducted on student perceptions of each module.

Likert scale ratings on the usefulness of each module revealed significantly higher ratings for the small group discussion/debate module, seconded by the documentary and lastly the case scenario only modules. Narrative analysis on student feedback revealed three themes: General favorable impression , Perceived learning outcomes , and Critiques and suggestion . Common and unique codes were identified to measure the strengths and weaknesses of each module. Overall, students’ perception of the curriculum design was extremely positive.

Conclusions

This curriculum design enabled us to highlight foundational biomedical sciences and clinical conditions with ethical dilemmas that physicians are likely to face in practice. Students found value in the modules, with a preference for the most active learning method. This study provides insight on a novel approach for integrating medical ethics into biomedical science courses that can be tailored to any institution. Strategies learned include utilizing active learning modalities and discussion.

Peer Review reports

Medical ethics is the study of the moral issues inherent in the practice of medicine, including, among many other topics, the moral choices physicians face in their day-to-day interactions with patients, colleagues, and the broader society in which they practice [ 1 , 2 , 3 , 4 ]. Knowledge of medical ethics is crucial for training morally competent healthcare professionals to manage ethical considerations that arise in patient care [ 5 ]. Evolving health care systems, expanding involvement of allied health professionals, and advances in technologies and treatment regimens have given rise to increasingly complex moral dilemmas faced by medical professionals in everyday practice. There is thus a compelling argument to continuously improve the incorporation of medical ethics into both pre-clinical and clinical medical education.

In the Association of American Medical Colleges published curriculum report, 143 out of 145 allopathic medical schools covered medical ethics in either a required or an elective course in 2016-2017 academic year [ 6 ]. The curriculum topics reported by the American Association of College of Osteopathic Medicine shows all osteopathic medical schools checked off medical ethics in a required course or rotation and 21 out of 38 schools had it covered in a selective/elective course or rotation in academic year of 2017-2018 [ 7 ]. Not surprisingly, reports on medical student perspectives of ethics education have revealed strong recognition of the importance of ethics as part of their medical training and a perceived need and desire for more formal bioethical education [ 8 , 9 ]. Although there is consensus from both faculty and students that medical ethics is an important part of medical training, literature suggests notable heterogeneity across medical schools regarding the best practice of teaching medical ethics [ 10 , 11 , 12 ].

Various pedagogical approaches have been employed to teach this subject, including the content, method, and timing of ethics education [ 10 , 13 , 14 ]. In the aspect of curriculum design, ethics inclusion in pre-clinical medical education has been done through various strategies. In addition to the most common traditional stand-alone ethics course, other approaches have also been explored, such as elective courses, students’ medical ethics rounds, a scholarly concentration program, etc [ 15 , 16 , 17 , 18 ]. Various formats of delivery have been reported as well, including small group session, case-based teaching, narrative approach, peer-based teaching, team-based learning, etc [ 15 , 19 , 20 , 21 ]. A commonality among these various pedagogical approaches is that the ethics content is delivered in a way that tends to treat ethics as a distinct subject matter that students are required to learn.

A core component of medical education is, of course, also learning the sciences related to understanding the human body. Many of the ethical challenges that doctors face – such as recruiting patients for clinical trials or securing informed consent for an invasive procedure – are directly related to the science that students learn in pre-clinical biomedical education. When an ethics education is cleaved off from the underlying context that gives rise to the ethical issues being studied, it is natural to treat ethics and the sciences core to medicine as inhabiting separate realms: after all, ethics studies how the world ought to be while science studies how the world is . Ethical norms often become viewed as a set of norms externally imposed on scientists and doctors, rather than norms internal to their practice [ 22 ]. But since medicine is fundamentally about using science to treat disease and illness in the context of a doctor-patient relationship, it stands to reason that the aim of the practice of medicine is to use science in a way consistent with the moral norms that govern the doctor-patient relationship. A good doctor, in other words, is one who uses science in an ethical manner to promote healing. Given the way in which ethics and science are interwoven in medical practice, we asked the question whether ethics could be integrated in biomedical science curriculum of pre-clinical medical training. While a review of literature has revealed recent efforts to implement ethics education into science education [ 23 , 24 , 25 , 26 ], we couldn’t find any discussion of efforts to embed ethics curriculum within the biomedical science curriculum in particular, except for anatomy [ 27 ].

Given the rationale above, we initiated a project to develop strategies for medical educators to integrate ethics modules into biomedical science courses, with the aim of promoting student awareness of how scientific practice and ethics are interrelated. Our first step in this project, which this paper analyzes, was to assess student attitudes towards the inclusion of ethics modules in pre-clinical biomedical science courses - how will students respond to this new course design? Future objectives, not undertaken here, will be to measure student learning as a result of our interventions, assess the effectiveness of different inclusion strategies, and create a framework that other medical educators can use in their courses.

Our study concerns a curriculum design we implemented that incorporates ethics threads in a pre-clinical biomedical science course using various teaching modalities. Our model enabled us to highlight the pathophysiology and clinical presentations of the disorders, along with ethical dilemmas that physicians are likely to face in clinical practice. By learning biomedical science side-by-side with medical ethics, students could make meaningful connections between the two domains. We believe this pedagogical approach of teaching medical ethics can help students better understand the relationship between science and ethics in medical practice as well as build richer “organizational structures” of knowledge that will aid in the retention and application of information [ 28 , 29 ]. This curriculum design can also shed light on how to incorporate ethics education creatively and effectively in the pre-clinical medical curriculum.

This study was conducted at Sam Houston State University College of Osteopathic Medicine in 2021. Three ethics learning modules were designed and built in a six-week system course “Immune System and HEENT” (HEENT: Head, Eyes, Ears, Nose, Throat) which was offered in the spring semester of the first year of pre-clinical curriculum. In this course, students were introduced to the principles of trauma, inflammatory disorders, infections and cancers associated with HEENT as it relates to the immune system. Students learned to apply the basic concepts of immunology in normal and disease states and to diagnose, prevent, and treat infections, cancers and immunological diseases. All students from our institute who were enrolled in this course in 2021 were included in the study. These students were in their first year of a four-year Doctor of Osteopathic Medicine program. A total of 74 first-year medical students in the Class of 2024 were enrolled in the course and completed all three modules and assessments. Forty were males and thirty-four were females. The average age of the cohort was 26 years ranging from 23 to 45 years.

The learning objectives of the ethics modules were identified and standardized based on the Romanell Report [ 28 ] which reviewed medical ethics education in the United States and offers suggestions for objectives, teaching methods, and assessment strategies.

The design of the three modules is presented in Table  1 .

The first module used a documentary about David Vetter, a well-known pediatric patient with severe combined immunodeficiency disease. After students completed the session “Introduction of the Immune System”, they were provided an asynchronous ethics module in a learning management software and assigned a one-hour long documentary named The Boy in the Bubble released in 2006 by PBS [ 29 ], and then completed the assessments at their own time. The second module used a clinical case on human immunodeficiency virus (HIV) that was introduced in team-based learning (TBL), a form of peer collaboration. This case concerned a patient diagnosed with HIV and the dual roles of physician as mandatory reporter of communicable disease and protector of patient confidentiality. Immediately following the two-hour TBL, the students were provided assessments to be completed on their own. The third module was a one-hour mandatory live session offered 4 days after students completed the session “Blood Transfusion”. The students were given an ethics case about a young Jehovah’s Witness in need of a blood transfusion and asked to complete the assessments in class. They were then sorted into small groups for discussion and subsequently assigned a position to debate on whether the patient should receive the blood transfusion. For all three module assessments, students were provided a list of twenty ethical challenges cited from the Romanell report and were asked to select the challenges that they recognized in the learning module and provide supporting explanations (Additional file  1 : Appendix 1). Reflective writing prompts were included for students to complete on their own for thinking critically about the ethical challenges associated with the module. Module #1 reflective questions were tied to surrogate decision making and informed consent. An example of the reflective writing prompt from Module #1 includes “Would the case have been handled any differently were David a competent adult? At what point should David be considered autonomous and capable of making healthcare decisions? Explain your reasoning.” Module #2 reflective questions were tied to patient confidentiality and the reporting of communicable diseases. Module #3 reflective questions were tied to the impact of religion on clinical decisions. Students were also asked to voluntarily respond to the perception question “How useful did you consider this module in ethics training?” to rate the usefulness of the module on a 1 to 5 Likert scale (1-not useful at all, 5-very useful) and provide feedback. We expected students took 30 min to 1 h to complete all assessments. General feedback were provided by YZ and OO in person or in writing for each module.

Analytical procedure

The analytical procedure was aligned with the study’s aim to measure student attitudes about the ethics modules. The first analysis measured differences between perceived usefulness of the modules to determine if students found one teaching modality more useful than the others. The second was a qualitative study on written student feedback.

The statistical analysis of perceived usefulness was performed with the python programming language using the pandas, statsmodels, scipy, and scikit_posthocs packages. Descriptive statistics were calculated for each analysis with reported averages following the format of the mean ± one standard deviation. Group differences between the Likert-based usefulness ratings were initially analyzed with an ANOVA and normalcy of the standardized residuals were computed with a Shapiro-Wilk test. The final analysis used a Kruskal-Wallis test and post-hoc Dunn test with a Bonferroni correction to determine differences between groups (corrected- α for all tests was set to 0.05).

Student feedback was analyzed using two different qualitative approaches: constant comparison analysis [ 30 ] and classical content analysis [ 31 ]. Using more than one approach in qualitative data analysis, as recommended by Leech and Onwuegbuzie [ 32 ], can increase interpretive validity, or the degree to which the perspectives of students are accurately rendered by the researcher [ 33 ]. Two of the investigators (YZ and KO) double coded the de-identified student feedback with Dedoose 8.3.47b to independently assign codes to the text for each module. The investigators then reviewed the accuracy and relevance of these codes according to their interpretation of the students’ meaning and used the software to merge similar codes and remove other codes that were no longer pertinent. Next, the investigators used printouts from the software to complete axial coding, which involves comparing text segments and codes to create categories made up of similar codes, and to combine categories into broad themes. Last, the investigators used printouts from the software to conduct classical content analysis, calculating percentages of codes associated with each theme to determine their relative significance to the participants. The premise underlying classical content analysis is that the frequency of occurrence is connected to the meaning of the content [ 31 ]. This analysis allowed the investigators to discover the relative importance that each theme held for students (i.e., based upon the frequency of the codes associated with each theme), which gave more insight into students’ responses. The data were entered into Microsoft Excel for data management.

Ethical considerations

Exempt status for the research project was granted by the IRB committee of SHSU.

The majority of students (73/74) completed the Likert-based usefulness ratings. In general, students found each module useful, with an average across all modules of 4.37 ± 0.99. Descriptive statistics for each module are reported in Table  2 and the distribution of answers are shown in Fig.  1 .

Usefulness ratings for each ethics learning module

To test differences between the Likert-based usefulness ratings between modules, a one-way ANOVA was performed with modules as groups and Likert-results as the dependent variable. However, it was found that the standardized residuals of the ANOVA did not follow a normal distribution after testing with a Shapiro-Wilk test ( W  = 0.84, p  < 0.001). Due to non-normal standardized residuals, a Kruskal-Wallis test was employed and found a statistically significant difference in rank-order between treatments ( H  = 16.2, p  < 0.001). A post-hoc Dunn test with a Bonferroni correction found that the only treatment pair with a statistically significant difference (corrected- α  < 0.05) was between Module #3 and Module #2 (corrected p  < 0.001). Complete results from the post-hoc Dunn test are reported in Table  3 .

The number of narrative responses to the perception question was consistently high, but not complete, with 82% of students who completed Module #1 providing feedback, with 85% for Module #2 and 80% for Module #3. Constant comparison analysis of student perception of the learning modules reveals three themes. These include general favorable impression for the learning modules , perceived learning outcomes for the learning module , and suggestions and critiques from students  (Table  4 ).

General favorable impression of students for the learning modules

Students’ overall impression of the ethics learning modules integrated in a biomedical science course was positive. Based on classical content analysis (Table 4 ), the student’s general impression theme contains the highest percentage of codes, suggesting it is the most relevant theme from students’ perceptive responses. A detailed breakdown of common and unique codes for this theme is presented in Table  5 .

Engaging and enjoyable is the most dominant code in this theme with more comments from Module #1 (documentary) and Module #3 (SGD/Debate). In addition, several students described participating in Module #3 as “ fun ”.

“It was very interesting to learn about ethics this way and certainly something that I will not forget for a very long time.” (Module #1)

“Everyone in my group was excited to participate and contribute thought. I loved this.” (Module #3)

According to students, all the modules were considered effective and useful, thought provoking, and provided opportunities for them to examine ethical challenges and different perspectives which promoted their critical thinking. Most of the relevant comments associated with these codes were from Module #3, seconded by Module #2 and then Module #1.

“If I had watched the documentary on my own, I probably would not have thought about it as deeply as I did for this activity” (Module #1)

“ … the questions challenge me to think from different perspectives and consider multiples factors.” (Module #2)

“The debate made me think of the case on a deeper level and truly analyze each argument.” (Module #3)

Unique codes were also identified for Modules #1 and #3. For Module #1, students commented that watching the documentary helped them to see different viewpoints and it is more effective than traditional teaching styles such as reading text. For Module #3, the students described the debate as stress free but challenging and highlighted that it provided the opportunity to present and view different perspectives which ultimately allowed them to learn from each other. It was well perceived by students as a favorable format of teaching ethics.

“Thus, having these discussions are still very important, and sharing unique perspectives is great for that in two regards. One, these discussions teach us who others are and what others think about the world around them, and we must try our best to respect and understand these perspectives of others. Two, these discussions could reveal more about ourselves and even help us understand ourselves better, which allows us to develop our sense of uniqueness.” (Module #3)

Perceived learning outcome

Our analysis also revealed students’ perceived learning outcomes for each module. Several common and overlapping codes were identified as well as unique codes. (Table  6 ).

Many students felt that all the modules provided real world preparation and increased awareness of their roles as future physician. This code was the most dominant one compared to the other common codes. They felt that the modules helped them recognize the impact of ethical issues in clinical situations and made them think ahead as to how they might and should proceed in real-life circumstances.

“ … this is a very ethically engaging case and an issue that we will likely come across in our careers.” (Module #1)

“Really challenging situations like this do happen in real life and we need to have the skills to navigate through these situations and do what is best for the patient and their life.” (Module #3)

Students also described that the modules helped them raise awareness of the complexity of ethics by seeing the difficulty of ethics and how sometimes there is not a clear-cut answer as to what to do in a situation . In addition, the integration of ethics learning in biomedical science course helped them build connection of ethics with science. The classroom activities encouraged the application of biomedical knowledge learned in the course.

“I found this module to be useful in terms of utilizing all that we have learned so far to understand HIV from a different lens than previously thought.” (Module #2)

“I really like seeing the ethical side of the science that we are learning. It is easy to get so focused in the science and technology that it is nice to take a step back and think of the human perspective of it.” (Module #3)

The unique codes for the perceived learning outcomes were consistent with the distinct ethical challenges that were highlighted in each module. In Module #1, several students felt it improved awareness of the connection of ethics and research as well as recognizing the importance of a research compliance body oversight. Students felt this module helped them understand the role of ethics within the larger health care system. One student commented:

“My value of the scientific community and of institutional review boards has now increased as I believe that they could have helped improve the situation David and his family were facing if they intervened appropriately. ” (Module #1)

In Module #2, many students felt it raised awareness of the interplay between ethics and law, made them consider the legal rights versus the patients’ rights when it comes down to certain situations as physician.In Module #3, students identified increased awareness of the complexity of patient care as well and of the role of religion in health care. They also felt this highlighted the importance of patient-centered care.

“ … physicians must not just deal with symptoms but also the social aspects and ethical principles when addressing a patient’s care. Education, personal experience, stress, and religious beliefs are a few of the variables that differ amongst individuals and increase the complexity of a patient case. ” (Module #3)

Student critiques and suggestions

Some students commented on the fact that addition of group discussion would have been preferred and more effective in both Module #1 and #2. In Module #1, some wished for more structured instruction along with concrete objectives and didactic information . In Module #2, some students felt that the case was hypothetical and lacked background information. As one student commented “ It would be more useful knowing more about the state laws and regulations surrounding this kind of diagnosis.” Adding more context to the case and providing relevant learning materials would allow for more insightful discussion to the suggested way to approach difficult scenarios for us as future physicians . In Module #3, one student felt they needed more time to consider the ethical challenges as it was a harder ethics choice .

Although the goal is for students to explore and identify ethical challenges on their own, one student commented the Module #1 is not instructional in pointing out ethical issues/errors in the video as they happen. A detailed breakdown of common and unique codes for this theme is presented in Table  7 .

Given the importance of ethics in medical education, we created an innovative curriculum design for ethics learning made up of three unique modules that were integrated into a biomedical science course in the first-year pre-clinical curriculum. We started this project with the overall aim to increase student awareness and understanding of the ethical dimensions of the biomedical sciences. The literature on interleaving would suggest that students who learn medical ethics within a biomedical science context will improve their learning of both the foundational science content and the medical ethics content [ 34 ], for by exercising different forms of reasoning – scientific reasoning and ethical reasoning – within the same course, students may increase their ability to retain and apply the content learned, at least as compared to massed learning [ 35 ]. Literature is limited regarding strategies to integrate ethics in biomedical science courses [ 35 ]. Ultimately, we believe that a curricular design like the one that we developed can help medical students build connections between science, human disease and ethics, but our first step for this project was to see how students would react to this novel course design by evaluating their attitudes.

The design of our ethics modules was heavily influenced by the mounting evidence suggesting that students learn better and retain information longer when they learn through multiple modalities [ 35 ]. Several educational modalities have been shown to be effective in the teaching and learning of ethics in medical education. Examples include the use of ethical dilemmas in integrated small group sessions, standardized patients, team-based approaches, case-based discussion, problem-based methods, student-driven curriculum, peer-based teaching and ethics guest lectures [ 4 , 10 , 13 , 20 , 36 , 37 ]. These teaching modalities additionally provide opportunities for active learning which can increase student engagement and retention of information [ 35 ].

With this in mind, our modules were created utilizing different modalities to allow for maximal engagement and connection with the content. The particular choice of active learning strategy for each module was made by considering the content and the availability of course schedule along with the instructors’ content expertise. All three modules generated a consensus regarding the effectiveness and benefits of this curriculum design of ethics education in improving understanding and future preparation for encountering real dilemmas in medical practice. While all modules were considered to be engaging and thought-provoking, student responses highlighted various perceived strengths and weaknesses of each unique module and pedagogical modality. Module #1 was delivered through an asynchronous module using a commercially available documentary without formalized discussion. While the design of the documentary module did not allow for collaboration between the students or didactic instruction, choosing media with an existing reputation for engaging audiences made it more likely that the students would have at least a base-level interest in the module. Interactive learning strategies such as using the documentary as a basis for an interrupted case study could be utilized in the future to enhance the engagement. Module #2 was presented in a case-based fashion and without group discussion. A perceived weakness of this module was the lack of detailed background information in comparison to Module #1 which is a well-publicized case with robust details. Since the module was embedded in a TBL case that was focused on the scientific foundations of HIV, students felt it helped them strengthen their understanding of the ethical dimensions of the science they learned. Module #3 allowed for both small and large group discussion while incorporating a debate format which prompted rich discussion.

Although all modules were considered useful, student responses indicated a strong preference for Module #3, with a statistical significance when compared to Module #2, but not Module #1. There were more unique codes and comments generated related to its complexity and challenging format. This could be because the debate allowed students the uninterrupted opportunity to voice an opinion regarding the many ethical dilemmas central to the case being examined. Further, students enjoyed learning about their classmates and hearing new viewpoints from colleagues. Students were assigned a side to defend which compelled some students to make arguments different from their own perspective. Our finding resonates with existing literature which has suggested that the use of debates can be an effective tool for teaching medical ethics because it increases students’ critical thinking expression and tolerance toward ambiguity [ 38 ]. In addition, the reflective writing time was integrated into the session module which encouraged more valuable, thorough, and accurate feedback. Another reason students may have reported a preference for Module #3 could be that it was the last module of the course and close to the completion of the course. Overall, these elements highlighted the benefit of a debate format to encourage discussion of difficult topics emphasized in ethics courses, which contributed to the preference of Module #3. Interestingly, only one participant mentioned the link between ethics and science for Module #3, this might be due to the timing and method of the science session delivery. The session “Blood Transfusion” was offered asynchronously at the beginning of the week, while Module #3 was delivered at the end of the week due to scheduling conflict. This suggests the importance of purposeful design, delivery, and sequencing of both science and ethics sessions to help students better recognize the connection between the two subjects.

Our study has several limitations that affect the reliability and validity of the study. Although students were provided opportunities to practice ethical reasoning and decision making through providing explanation for self-identified ethical challenges and reflective writing, the direct learning outcome was not assessed. The lack of baseline data has hindered the analysis on the gain of students’ knowledge and attitude, although as a whole they perceived the modules as valuable and beneficial. Future studies should include pre- and post-assessment and longitudinal evaluation of the growth of the knowledge and moral attitudes of students. We also do not know whether students’ usefulness ratings were based on their preference for learning modalities or their specific interest in the topic of the module. For future studies the usefulness question should be revised to remove this ambiguity and improve content validity. The students were also not asked to directly compare the modules. Instead, they gave their responses at the time they completed each module, which was weeks apart from one another. Their general opinion may have changed over time and the order in which the modules were delivered may have influenced their responses. The modules could also be expanded to include multiple classes and to incorporate the modules in multiple courses. Furthermore, backward design strategy could be incorporated to ensure achievement of ethics learning objectives. The long-term impact of the modules may be evaluated by using preceptors survey in clerkship.

Expanding the study, and ethics education in general, faces several obstacles. Perhaps the most challenging obstacles are mundane: the lack of time within curriculum, lack of time in faculty schedules, and the lack of teachers qualified to teach ethics in the context of medical education [ 10 ]. Our study shows that ethics may be integrated in non-traditional places in curriculum and that student-directed learning can be used to alleviate the burden of curriculum load, although more student interaction should be encouraged. We plan to develop pre- and post-testing along with additional modules in order to measure longitudinal learning and to further integrate ethics into our biomedical science curriculum. To address the lack of standardized ethics training or certification for the instructors some institutions may face, collaborating with ethicists through interdepartmental or interinstitutional effort may be helpful. Together, the team can develop the modules as well as provide narrative feedback to students, which may enhance the delivery and assessment of the ethics modules.

Our study demonstrates that ethics education can be integrated with biomedical sciences. As is universal in education, the pedagogical design of the curriculum and relevant activities is the key to gaining students’ interest in learning. Strategies for ethics learning that we noted include the importance of purposeful design and sequence as well as the use of active learning modalities that involve discussion such as debate. Our model can shed light on an innovative way of integrating ethics education into medical education.

Availability of data and materials

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

Veatch RM. Medical Ethics: Jones and Bartlett Publishers; 1997. (Jones and Bartlett series in philosophy). Available from: https://books.google.com/books?id=UCOT4sj-DwUC

Google Scholar  

Beauchamp TL, Childress JF. Principles of biomedical ethics. 7th ed: Oxford University Press; 2013.

Vaugh L. Bioethics: principles, issues, and cases. 4th ed. New York, NY: Oxford University Press; 2020.

Miles SH, Lane LW, Bickel J, Walker RM, Cassel CK. Medical ethics education: coming of age. Acad Med. 1989;64(12):705–14.

Article   Google Scholar  

Savulescu J, Crisp R, Fulford KWM, Hope T. Evaluating ethics competence in medical education. J Med Ethics. 1999;25(5):367–74.

Curriculum Topics in Required and Elective Courses at Medical School Programs. AAMC. https://www.aamc.org/data-reports/curriculum-reports/interactive-data/curriculum-topics-required-and-elective-courses-medical-school-programs . Accessed 29 Nov 2022.

2017-18 Osteopathic Medical College Curriculum Topics. AACOM. https://www.aacom.org/reports-programs-initiatives/aacom-reports/curriculum . Accessed 29 Nov 2022.

DeFoor MT, Chung Y, Zadinsky JK, Dowling J, Sams RW. An interprofessional cohort analysis of student interest in medical ethics education: a survey-based quantitative study. BMC Med Ethics. 2020;21:26.

AlMahmoud T, Hashim MJ, Elzubeir MA, Branicki F. Ethics teaching in a medical education environment: preferences for diversity of learning and assessment methods. Med Educ Online. 2017;22(1):1328257.

Soleymani Lehmann L, Kasoff WS, Koch P, Federman DD. A survey of medical ethics education at U.S. and Canadian medical schools. Acad Med. 2004;79(7):682–9.

DuBois JM, Burkemper J. Ethics education in U.S. medical schools: a study of syllabi. Acad Med. 2002;77(5):432–7.

Shamim M, Baig L, Zubairi N, Torda A. Review of ethics teaching in undergraduate medical education. J Pak Med Assoc. 2019;70(6):1056–62.

de la Garza S, Phuoc V, Throneberry S, Blumenthal-Barby J, McCullough L, Coverdale J. Teaching medical ethics in graduate and undergraduate medical education: a systematic review of effectiveness. Acad Psychiatry. 2017;41(4):520–5.

Giubilini A, Milnes S, Savulescu J. The medical ethics curriculum in medical schools: present and future. J Clin Ethics. 2016;27(2):129–45.

Goldie J. Review of ethics curricula in undergraduate medical education. Med Educ. 2000;34:108–19.

Aguilera ML, Martínez Siekavizza S, Barchi F. A practical approach to clinical ethics education for undergraduate medical students: a case study from Guatemala. J Med Educ Curric Dev. 2019;6:238212051986920.

Beigy M, Pishgahi G, Moghaddas F, Maghbouli N, Shirbache K, Asghari F, et al. Students’ medical ethics rounds: a combinatorial program for medical ethics education. J Med Ethics Hist Med. 2016;9:3.

Liu EY, Batten JN, Merrell SB, Shafer A. The long-term impact of a comprehensive scholarly concentration program in biomedical ethics and medical humanities. BMC Med Educ. 2018;18(1):204.

Chung EK, Rhee JA, Baik YH, A OS. The effect of team-based learning in medical ethics education. Med Teach. 2009;31(11):1013–7.

Hindmarch T, Allikmets S, Knights F. A narrative review of undergraduate peer-based healthcare ethics teaching. Int J Med Educ. 2015;6:184–90.

Donaldson TM, Fistein E, Dunn M. Case-based seminars in medical ethics education: how medical students define and discuss moral problems. J Med Ethics. 2010;36(12):816–20.

Wolpe PR. Reasons scientists avoid thinking about ethics. Cell. 2006;125(6):1023–5.

Mcgowan A. Teaching science and ethics to undergraduates: a multidisciplinary approach. Sci Eng Ethics. 2013;19(2):535–43.

Reese AJ. An undergraduate elective course that introduces topics of diversity, equity, and inclusion into discussions of science. J Microbiol Biol Educ. 2020;21(1):21.1.10.

Mann MK. The right place and the right time: incorporating ethics into the undergraduate biochemistry curriculum. In: Kloepper KD, Crawford GL, editors. ACS symposium series. Washington, DC: American Chemical Society; 2017. p. 45–70. Available from: https://pubs.acs.org/doi/abs/10.1021/bk-2017-1266.ch004 . [Cited 2022 Jul 19].

Smith K, Wueste D, Frugoli J. Using “ethics labs” to set a framework for ethical discussion in an undergraduate science course. Biochem Mol Biol Educ. 2007;35(5):332–6.

Cornwall J, Hildebrandt S. Anatomy, education, and ethics in a changing world. Anat Sci Educ. 2019;12(4):329–31.

Carrese JA, Malek J, Watson K, Lehmann LS, Green MJ, McCullough LB, et al. The essential role of medical ethics education in achieving professionalism: the Romanell report. Acad Med. 2015;90(6):744–52.

The boy in the bubble. Public Broadcasting Service (PBS); 2006.

Glaser B. The constant comparative method of qualitative analysis. Soc Probl. 1965;12(4):436–45.

Berelson B. Content analysis in communication research. Ann Am Acad Pol Soc Sci. 1952;283(1):197–8.

Leech NL, Onwuegbuzie AJ. An array of qualitative data analysis tools: a call for data analysis triangulation. Sch Psychol Q. 2007;22(4):557–84.

Maxwell JA. Understanding and validity in qualitative research. In: The qualitative Researcher’s companion. Thousand Oaks: SAGE Publications, Inc.; 2022. Available from: https://methods.sagepub.com/book/the-qualitative-researchers-companion .

Safuan S, Ali S, Kuan G, Long I, Nik N. The challenges of bioethics teaching to mixed-ability classes of health sciences students. Educ Med J. 2017;9:41–9.

Brown PC. Make it stick : the science of successful learning. Cambridge: The Belknap Press of Harvard University Press, [2014]; 2014. Available from: https://search.library.wisc.edu/catalog/9910195454802121

Book   Google Scholar  

Mattick K. Teaching and assessing medical ethics: where are we now? J Med Ethics. 2006;32(3):181–5.

Sullivan BT, DeFoor MT, Hwang B, Flowers WJ, Strong W. A novel peer-directed curriculum to enhance medical ethics training for medical students: a single-institution experience. J Med Educ Curric Dev. 2020;7:2382120519899148.

Amar-Gavrilman N, Bentwich ME. To debate or not to debate? Examining the contribution of debating when studying medical ethics in small groups. BMC Med Educ. 2022;22(1):114.

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Travis Hyatt, Alwyn Mathew & Shawn Staudaher

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YZ and OO designed the study and delivered all learning modules. They collected the data and conducted the qualitative data analysis and were major contributors in writing the manuscript. TH and AM conducted the literature review and contributed to the writing of the manuscript. SS conducted the quantitative data analysis and contributed to the editing of the manuscript. ZB contributed to the editing of the manuscript. All authors read and approved the final manuscript.

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Appendix 1. List of Ethical Challenges Cited from the Romanell Report.

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Home > Books > Advances in Medical Education and Training [Working Title]

Professional Values and Ethics in Medical Education

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Medical ethics, professionalism and values have been commonly attached to medicine since its ancient era. These terms, although they differ, have been used in medical literature to refer to the right way to do the right thing by medical professionals. Professionalism was and still is one of the major aims of medical education. It refers to a complex process by which medical professionals acquire and apply the essential knowledge and skills together with acceptable values and ethics to serve society. However, teaching and assessing professionalism is not a straightforward mission. The complexity of it comes from its real nature being affected by many factors in multiple dimensions, including individual, interindividual and the larger societal or institutional levels. In this chapter, I describe the interaction between these factors after shedding light on the different interrelated terms. I also highlighted the advances and recommendations regarding teaching and assessment of professional values in the field of medicine.

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Ebtihaj t. nafea *.

• Taibah University, Madinah, Saudi Arabia

*Address all correspondence to: [email protected]

1. Introduction

Ethical principles have long framed medicine. The Hippocratic Oath, 400 BC, reflected the constant goal of medicine ‘ I will use treatment to help the sick according to my ability and judgment; I will keep them from harm and injustice’ . This oath is based on beneficence, non-maleficence, justice and respect for the patient’s autonomy with its two rules of confidentiality and veracity. Looking at this, medicine is commonly perceived as a respectable profession that best serves society. Doctors are expected to perform procedures and take decisions based on their professional education and training. They are usually assumed to do all that is right in the right manner. Therefore, certain values characterise medical professionals and even medical students as they collectively form a special society with specific characteristics. When we look at this society one would assume that they attain sophisticated knowledge and they have special skills that an ordinary individual would not easily acquire or apply. The white coats usually symbolise highly respected professionals. These professionals besides their knowledge and skills are commonly perceived as being wise and strive to do the right things. It is not uncommon for patients seeking treatment to trust their physicians and let them decide on and carry out the best options in the right manner based on their experience. The physicians’ presented behaviour and ethical values are affected by multiple factors. Understanding the dynamic nature of professionalism would aid its teaching and assessment and therefore, its acquisition by medical professionals.

In this chapter, I describe ethics, morality and professionalism based on medical literature. In depth contemporary categorisation of medical values is discussed. I also highlighted the advances in teaching and assessing professional values.

2. What are the differences between ethics, morality and professionalism?

Ethics, morality and professionalism are frequently encountered terms when discussing what is right and wrong. Although sometimes these terms were used synonymously, there are some differences between them. I will describe these differences in a simple way. Members of a particular profession are governed by a set of values or principles, duties and obligations based upon standards of right and wrong. These sets of standards are known as ethics , and they are formulated by the governing council of a professional group. All professions have a known Code of Ethics that characterises professions and reflects a high level of trust between professionals and their clients [ 1 ]. Morality on the other hand reflects the individual values and conduct that people believe in when deciding good and bad behaviours. Morality is deeply rooted within the individuals themselves rather than being proposed by governing bodies [ 1 ]. The last term proposed here is professionalism . This term has a wider aspect when compared to ethics and morality. It includes both mastering biomedical aspects (knowledge and its proper application), psychometric aspects (mastering of specific skills) and humanistic aspects (attitude, behaviour, virtues and characteristics), desirable among professionals. The usual definition of a profession is that it is a vocational occupation characterised by specific knowledge and skills specially devoted to this specific field, which comprises autonomy and self-regulated bodies. All this knowledge and skills are serving the good of the public [ 2 ].

Medicine is a speciality which has long been known to focus on professionalism, representing a trustworthy relationship between physicians and their patients. Acceptable conduct or traits constituting professionalism were imperative to health professions. However, professionalism, per se, was not the focus of medical education 30 years ago. This state remained until the American Board of Internal Medicine (ABIM) started its humanism project in the early 1980s, which led to Project Professionalism in the mid-1990s [ 3 ]. Although professionalism is actually one of the 11 values encountered in healthcare professions literature [ 4 ], the term professionalism was also used synonymously in medical literature to refer to sets of values that are collectively acceptable by medical professionals. I suggest that using the term professionalism to refer to multiple professional values might reflect that medicine is an autonomous profession that is characterised by its ethics and values.

There is a large amount of literature on professionalism in medicine as old as 1890. However, these were mainly common advice to doctors and medical students towards doing what was socially acceptable and what was the right thing to do such as being honest and maintaining the patient’s dignity as Cathell reported over a 100 years ago [ 5 ]. Professionalism was, and still is, generally perceived as a developmental value that medical students informally and passively catch from their educators [ 6 ]. Despite the increasing interest in professionalism, a consensus about the proper definition is still lacking in medical literature [ 3 ]. There are more than 20 different definitions for professionalism in medical literature reflecting the different aspects of it [ 3 , 7 ]. Some of these definitions simply refer to the constitution of the contract between the profession and the society [ 8 , 9 ]. Burak et al. [ 6 ] found 90 constituent elements of professionalism in their review of medical literature. Altruism, accountability, respect and integrity were among the most common elements. They identified three types of professionalism based on the nature of interaction. Interpersonal professionalism reflects meeting the demands for adequate contact with patients and other healthcare professionals. Elements such as altruism, respect, integrity, service, honour, honesty and compassion were related to this type. The other type is the public professionalism , which is expressed by meeting the demands society places on the medical profession. Elements related to this type include accountability, submission to an ethical code/moral commitment, excellence and self-regulation. The last type is intrapersonal professionalism perceived as meeting the demands to function in the medical profession as an individual including lifelong learning, maturity, morality, value of medical work intrinsically, humility and critique as common elements. The discipline that deals with the practical application of professional ethics is known as clinical medical ethics. Its main aim is always to ensure that the right and good decisions are taken for individual patients’ cases, which in turn improve the quality of the healthcare provided [ 10 ].

3. What values are encountered in medical professions?

It is a common practice to refer to lists of values, traits and behaviours when discussing professionalism in medical education. This practice has its advantages and limitations. On one hand, analysis of the data having these lists of measurable behaviours that reflect certain values would formulate the basis for teaching and assessing medical professionalism for certification. However, there is a need to implement the internationally accepted and agreed-upon list of values to aid in teaching professionalism in medical curricula [ 11 , 12 ]. On the other hand, it would be unwise to limit the definition of professionalism in such a manner. Focusing on these lists would mask the interactive and developmental process of professionalism.

Is attaining the listed behaviours enough to reflect the level of professionalism? To answer this question, we must immerse ourselves in professionalism and its sociological perspectives. Martimianakis [ 5 ] argued that there are competing and unstable factors affecting professionalism. These factors are related to the individual traits of the professional, the available facilities of the healthcare organisation and the power of assessing individuals related to race, gender and class.

As discussed earlier, a long time ago central values were attached to doctors such as beneficence, benevolence, respect and concern for patients, truthfulness, friendliness and justice when treating their patients [ 13 ]. Technical, interpersonal and humanistic values are all required to ensure the best care delivery to society [ 7 ]. To start discussing the professional values related to healthcare, we should highlight the definition of values. Values usually refer to the right and acceptable thing to do with a consensus in society. Values are prominent in everyday activities. They guide the evaluation of people, choices and actions and influence their behaviours [ 14 ].

The nature of the doctor-patient relationship was categorised by Drane [ 13 ] into 6 dimensions: medical, which represents diagnostic and therapeutic acts; spiritual , the verbal communication between the two; volitional , the decision of the doctor and the patient; affective , reflecting the feeling associated; social , the broader aspect of medicine towards the society; and lastly a religious role that sometimes played by doctors in some cases. We should also differentiate between personal values, which guide an individual’s behaviour and personal choices [ 14 ], from professional values, which govern their behaviour as members of a profession [ 15 ]. Both types of values affect the decision-making process taken by clinicians. Therefore, personal and professional values are equally important to be clearly understood and assessed in medical education. Healthcare specialties share relatively similar professional values with some differences in prioritising them and focusing on specific profession-related ones. Common principles such as putting the patients’ interests above self-interests, avoiding harm to patients and equitable access to healthcare, are shared and applied to all healthcare professions [ 16 ].

Values that serve individual interests, which are known as self-enhancement conflict with values that serve collective interests referred to as self-transcendence values.

Values that highlight independent thoughts and flexibility to change, which are known as openness to change values oppose values that emphasise self-restriction, order and resistance to change representing conservation.

The healthcare practitioner values framework. Adapted from Moyo et al. [ 4 ]. Motivational goals are present in frames at each quadrant of the sphere. Each goal comprises number of values. Values between brackets are special for the healthcare practitioner. They are written below their corresponding values commonly found in individuals derived from Schwartz’s structure of value relations [ 19 ]. Adjacent values show greater compatibility. However, competing values are located opposite to each other. The projected behaviour of an individual is a result of the interaction between these values.

Within these groups, there are 11 values derived from values presented by healthcare practitioners. These value types are namely: authority, capability, pleasure, intellectual stimulation, critical-thinking, equality, altruism, morality, professionalism, safety and spirituality [ 4 ].

Authority, capability and pleasure into the group of self-enhancement values as they emphasise advancing self-interests

Equality and altruism into self-transcendence values as they emphasise concern for the welfare and interests of others

Critical-thinking, intellectual stimulation and pleasure are grouped into openness-to-change values as they emphasise independent action, thought

New experiences; and spirituality, morality, professionalism and safety are grouped into conservation values as they emphasise order and preservation of traditions.

Pleasure is placed in both self-enhancement and openness-to change groups, as it shares emphases with both groups

From the previous discussion of human values, we understand that the nature of the presented behaviour or action is based on the conflict or enhancement interaction between the different values. Therefore, applying clinical ethics is not a direct process. Rather, the interaction between many factors plays important roles in this process. Clinicians try to make their decisions based on medical and scientific facts. Moreover, the preferences, values and their nature discussed earlier, and the goals of both the physician and the patient are important. Their decision is also affected by external constraints, such as cost, limited resources and legal duties, that may shape or limit choices [ 10 , 21 ].

4. Teaching medical ethics

Nowadays, the focus has been given to the humanistic values in medical literature and little to the technical ones, because the latter were kind of normally expected by patients when they seek medical treatments by healthcare services [ 22 , 23 ]. With the changing goals of medicine towards a more patient-based approach, professional ethics should be applied besides clinical competency and dictate the physician-patient relationship [ 24 ]. As old as the 1970s medical educators reinforced the importance of teaching ethics. In the late 1980s and early 1990s, there were repeated calls for the formal education of medical ethics in medical schools [ 25 ]. Gazibara [ 26 ] described the need for a holistic approach in contemporary education. In this approach, certain important qualities were compared to the active components of human presence: the heart (values), head (knowledge) and hands (skills). All of these contribute to the values necessary for the development of professionalism.

Nevertheless, dilemmas and conflicting views are present as to whether ethics can be effectively taught or not. Can we anticipate that when medical ethics are taught, medical students will have ethical values when practising medicine? Is there a difference between good doctors and doctors who perform well? Is it a skill that can be taught and applied later in training or values that characterise the identity of medical professionals? These questions will lead us to think about the nature of ethical value education. The focus here would be the effect of individual values and personality. It was found that medical students caught for unprofessional behaviours during their undergraduate study were more inclined to do the same after they graduated [ 27 ]. This argument further stresses the effect of individual values that tend to be stable. Another issue surrounding the teaching of medical ethics is, if we can formally teach and assess ethics as tools present in the curriculum, are we neglecting socialisation in the development of these values? The complex and multidimensional nature of professionalism together with the present lack of consensus about what constitutes professionalism adds more difficulties to teaching it. In addition to all these factors, it would be unwise to underestimate the effect of hidden curriculum. A hidden curriculum can adversely affect the acquiring of ethical values in medical students when the teaching faculties do not appreciate the role of the hidden curriculum, which should be aligned with the formal instructions [ 25 ].

It is recommended that teaching medical ethics should start early in medical schools and continue throughout undergraduate and postgraduate study and training [ 1 ]. Instead of having lists of theoretical medical values, teaching ethical values should focus on applying them in clinical situations where they are practically applied in the real world [ 28 ]. Another recommendation in teaching medical ethics is giving full attention to hidden curriculum, by playing not only role models by the faculty but also by engaging all educators and students in making up a culture of medicine representing ethical values. Teaching ethical values is an integrative and comprehensive process. It should be directed towards raising sensitivity, providing knowledge, facilitating reflection, decision-making and improving action and behaviours [ 29 ]. This teaching still needs support from educational organisations. It could be facilitated by directing the learning towards relevant ethical problems regularly encountered in clinical settings and including modalities that encourage knowledge and skill development [ 30 ]. However, studies have indicated that there is low consensus among doctors in solving a wide range of ethical dilemmas encountered daily. This further indicates the need to address more ethical dilemmas, other than the classical dilemmas dealing with death and life, when teaching medical ethics [ 31 ].

There are different designs and course contents applied in medical schools regarding teaching medical ethics [ 30 ]. The different modalities involved, regular instructions and knowledge of the ethical values by lecturing and seminars, whether in person or online, the use of simulation and the presenting of real-life scenarios that involve students’ discussions and reflection, with the latter proved to be more effective in developing the students’ ethical behaviours [ 29 , 30 ]. Problem-based learning and narrative reflections were also effective modalities that could be used to teach medical ethics [ 30 , 32 , 33 ]. Programs for teaching professional medical ethics have involved teams of faculty, residents and students in a longitudinal curriculum, focusing on the shared real experience of the members and the associated reflection by the students. This was thought to be effective in creating a medical school environment around professionalism [ 34 ]. Small group discussion is another modality that is thought to facilitate the discussion and reflection by students, especially for sensitive ethical issues that one would avoid discussing on a large scale [ 35 ]. Some innovative strategies, such as team-based learning were used to teach medical professionalism and found to be effective [ 36 ].

Searching the literature on medical education over the last five years led to some insights into how to develop ethical values in medical students. Some teaching experiences were found to enhance the appreciation of medical ethics by students, such as patient-led educational sessions. It also was found to aid the development of professional identity by role-playing [ 37 ]. Interventional workshops designed for fostering the development of professional values in medical students were also found to be effective in raising the students’ positive attitudes, subjective norms, perceived behavioural control and intentions [ 38 ]. Measures to teach professional values may require adjustment of the medical curriculum to integrate more professional values of social accountability such as relevance, quality, equity and cost-effectiveness in the regular curriculum. These measures were found to be effective when they were applied to a problem based learning (PBL) curriculum [ 39 ]. In including the socialisation aspect of professionalism, I found a single study in medical education literature. This study used an innovative especially designed co-curriculum program with experiential learning opportunities in social settings during summer time. This was found to be effective in enhancing medical students’ socialisation skills and teamwork, all of which contributed to their professional development and aided hominisation of medical curricula [ 40 ].

In conclusion, despite the serious attempts regarding the teaching of medical ethics, this practice is still not precise and not officially included in medical curricula [ 1 ]. Lastly, although one can assume that ethical education increases ethical sensitivity and the ability to detect an ethical problem, it is not obvious that education influences the development of ethical behaviour in medical students [ 41 ]. Therefore, the International Ottawa Conference Working Group on the Assessment of Professionalism recommended that research should be directed towards distinguishing what values are amenable and therefore can be taught from those which are deeply rooted in individuals and unable to amend. In this case, they should be included in the selection criteria during admission [ 42 ].

5. Assessing professionalism

As Arnold [ 3 ] stated ‘ Without solid assessment tools, questions about the efficacy of approaches to educating learners about professional behavior will not be effectively answered’ . Assessment of professional values is important to guide medical educators during the teaching of these values. It also helps to discover the level of professionalism in practitioners and facilitates the development of problem-solving skills [ 4 ]. Furthermore, research in the assessment of professionalism highlighted that high levels of professionalism were associated with better clinical performance by medical students [ 3 , 4 ]. It is also highly recommended to implement formative assessment because medical students can benefit from the feedback that they receive during this process in managing their behaviours [ 42 ].

However, it is very difficult to agree upon the best practices to assess professionalism. The difficulty reflects the complex nature of this phenomenon. Professionalism encompasses a wide range of aspects, starting from the individual level, which includes attributes, capacities and behaviours. It extends to the interpersonal domain, involving interactions with other individuals and various contexts. Finally, it reaches the macro-societal level, where concepts like social responsibility, morality, political agendas and economic imperatives come into play. Additionally, these domains interact with each other. For instance, an individual’s professional behaviour can be influenced by the context they are in, and similarly, the individuals within an institution can impact its collective professional values [ 42 ]. In order to assess this phenomenon, it would be beneficial to have lists of measurable behavioural expectations that are derived from each proposed value [ 43 ].

Regarding the available assessment tools for professionalism, a variety of them are developed and used for three specific intentions. Some of these tools were directed towards measuring professional behaviour as a component of clinical performance. The other type focuses on assessing professional behaviour, as a comprehensive entity in itself. The last type aimed to assess only single components of professionalism [ 3 ].

Professionalism was used to be assessed as a stable characteristic of an individual rather than assessing the professional behaviour of the individual [ 44 ]. Educators who support this view focus on assessing professionalism as a stable trait that is inherently present or sometimes developed in individuals. Therefore, they stress the importance of assessing professionalism for applicants in medical schools before their admission. For this group, who believe in the individualist approach, many tools are used, up to 88 scales and ratings [ 45 ]. Recent research showed that very short answer questions can also be used to assess ethical reasoning in medical students [ 36 ]. Most of the available assessment tools are criticised for their validity, reliability and their theoretical basis [ 46 ]. We now understand that professionalism is a complex construct that involves knowledge, values, attitudes and the ability to employ professional behaviours in real practice settings. Assessing professionalism as an individual construct by focusing on behaviour alone misses the complexity and multidimensional nature of it. In reality, the expressed behaviour resulted from conflicts between values, and for this specific reason, the assessment of professionalism should focus on the interaction and the conflicts between values and emotions that lead to specific professional or unprofessional behaviour, by medical students [ 47 ].

In contrast to the rigid assumption of individualist approach, a more fluid approach considers the interactions between individuals as an important factor responsible for the projected behaviours. In this view, professionalism is seen as an interpersonal process. Context is given more attention. Moreover, for the assessment tool to be reliable it also should consider the task-dependence nature of professionalism and its environmental factors [ 3 ]. Professionalism is perceived as being entirely created in interpersonal interactions. Assessment should take into account assessing students’ cognitive problem-solving processes, monitoring learning environments as well as teacher-student relationships for interpersonal characteristics that could lead to unprofessional behaviours [ 45 ]. Therefore, educators who support this view argue that professionalism should not be assessed by scales at all [ 48 ]. This view suggests assessing professionalism by using data from multiple methods such as observation, conversations about behaviour and behavioural explanations, through narrative and text assessment besides using multi-source feedback [ 49 ].

The last view in the assessment of professionalism perceives it as a societal/institutional phenomenon. In this view assessment of professionalism should be directed towards fulfilling the expectations of the society or organisation. For that reason, values that constitute professionalism and their targeted assessment process may vary from one culture to another reflecting the different beliefs and interests [ 50 ]. In this view, professionalism should be assessed at a macro-level in terms of the function of groups, settings and institutions more than the micro-level of the individuals [ 5 ]. In 2011, the International Ottawa Conference Working Group on Professionalism concluded that assessing professionalism should be directed towards a multi-dimensional, multi-paradigmatic approach at different levels: individual, interpersonal and societal/institutional [ 42 ].

6. Conclusion

Medical professional values have been commonly discussed in medical literature. Medicine is well known for its ethics and values. However, we lack a consensus regarding the definition of professionalism and how it would ideally be taught and assessed. Listing medical professional values without deeply understanding their interaction and the multiple factors involved would mask the richness of professionalism phenomenon. Implementing the different perceptions of medical professionalism reflects its multidimensional nature and facilitates its teaching and assessment. In this chapter, I shed light on the contemporary view of medical values and the advances in teaching and assessing professionalism.

Conflict of interest

The authors declare no conflict of interest.

• 1. Desai MK, Kapadia JD. Medical professionalism and ethics. Journal of Pharmacology and Pharmacotherapeutics. 2022; 13 :113-118. DOI: 10.1177/0976500X221111448
• 2. Evetts J. The concept of professionalism: Professional work, professional practice and learning. International Handbook of Research in Professional and Practice-Based Learning. 2014; 1 :29-56
• 3. Arnold L. Assessing professional behavior: Yesterday, today, and tomorrow. Academic Medicine. 2002; 77 :502-515
• 4. Moyo M, Goodyear-Smith FA, Weller J, Robb G, Shulruf B. Healthcare practitioners’ personal and professional values. Advances in Health Sciences Education. 2016; 21 :257-286
• 5. Martimianakis MA, Maniate JM, Hodges BD. Sociological interpretations of professionalism. Medical Education. 2009; 43 :829-837
• 6. Burack JH, Irby DM, Carline JD, Root RK, Larson EB. Teaching compassion and respect: Attending physicians’ responses to problematic behaviors. Journal of General Internal Medicine. 1999; 14 :49-55
• 7. Wynia MK, Papadakis MA, Sullivan WM, Hafferty FW. More than a list of values and desired behaviors: A foundational understanding of medical professionalism. Academic Medicine. 2014; 89 :712-714
• 8. Cohen JJ. Professionalism in medical education, an American perspective: From evidence to accountability. Medical Education. 2006; 40 :607-617
• 9. Pavlica P, Barozzi L. Medical professionalism in the new Millenium: A physicians charter. La Radiologia Medica. 2003; 105 :263-265
• 10. Siegler MA. Professional values in modern clinical practice. The Hastings Center Report. 2000; 30 :S19-S22
• 11. Atienza MI. A comparison of four models of professionalism in medical education. The Asia Pacific Scholar. 2021; 6 :24
• 12. Jegan R, Dierickx K. Ethics without borders: An analysis of national and international guidelines on ethics in basic medical education. Advances in Health Sciences Education. 2023; 28 :1-22
• 13. Drane JF. Becoming a Good Doctor: The Place of Virtue and Character in Medical Ethics. Kansas City: Rowman & Littlefield; 1988. ISBN: 1556122098
• 14. Rokeach M. The Nature of Human Values. Washington, DC: Free Press; 1973. ISBN: 0029267501
• 15. Eddy DK, Elfrink V, Weis D, Schank MJ. Importance of professional nursing values: A National Study of baccalaureate programs. Journal of Nursing Education. 1994; 33 :257-262
• 16. Beauchamp TL. The ‘four principles’ approach to health care ethics. Principles of Health Care Ethics. 2007; 29 :3-10
• 17. Martin P, Yarbrough S, Alfred D. Professional values held by baccalaureate and associate degree nursing students. Journal of Nursing Scholarship. 2003; 35 :291-296
• 18. Braithwaite VA, Law HG. Structure of human values: Testing the adequacy of the Rokeach value survey. Journal of Personality and Social Psychology. 1985; 49 :250
• 19. Schwartz SH. Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. In: Advances in Experimental Social Psychology. Vol. 25. San Diego: Elsevier; 1992. pp. 1-65. ISBN: 0065-2601
• 20. McNair RP. The case for educating health care students in professionalism as the Core content of Interprofessional education. Medical Education. 2005; 39 :456-464
• 21. Nafea E. Clinical Reasoning in Dental Students: A Comparative Cross-Curricula Study. Nottingham, UK: The University of Nottingham; 2015
• 22. Dossetor JB. Bioethics--Human values in health care: Trying to get it right. CMAJ. 1997; 157 :1689-1690
• 23. Markakis KM, Beckman HB, Suchman AL, Frankel RM. The path of professionalism: Cultivating humanistic values and attitudes in residency training. Academic Medicine. 2000; 75 :141-149
• 24. Pellegrino ED. The metamorphosis of medical ethics: A 30-year retrospective. JAMA. 1993; 269 :1158-1162
• 25. Hafferty FW, Franks R. The hidden curriculum, ethics teaching, and the structure of medical education. Academic Medicine. 1994; 69 :861-871
• 26. Gazibara S. “Head, heart and hands learning”-A challenge for contemporary education. The Journal of Education, Culture, and Society. 2013; 4 :71-82
• 27. van Mook WNKA, Gorter SL, De Grave WS, van Luijk SJ, Wass V, Zwaveling JH, et al. Bad apples spoil the barrel: Addressing unprofessional behaviour. Medical Teacher. 2010; 32 :891-898
• 28. Siegler M. A legacy of Osler: Teaching clinical ethics at the bedside. JAMA. 1978; 239 :951-956
• 29. Lechasseur K, Hegg Mme S, Mbemba GIC, Caux C, Maltais C. Educational strategies supporting the development of ethical competence among nursing students: An Integrative Review. Quality Advancement in Nursing Education-Avancées en Formation Infirmière. 2023; 9 :4
• 30. Andersson H, Svensson A, Frank C, Rantala A, Holmberg M, Bremer A. Ethics education to support ethical competence learning in healthcare: An integrative systematic review. BMC Medical Ethics. 2022; 23 :1-26
• 31. Bringedal B, Rø KI, Magelssen M, Førde R, Aasland OG. Between professional values, social regulations and patient preferences: Medical doctors’ perceptions of ethical dilemmas. Journal of Medical Ethics. 2018; 44 :239-243
• 32. Pandya R, Shukla R, GoR AP, Ganguly B. Personal experience narratives by students: A teaching-learning tool in bioethics. Indian Journal of Medical Ethics [Internet]. 2016; 1 :144-147
• 33. Buxton M, Phillippi JC, Collins MR. Simulation: A new approach to teaching ethics. Journal of Midwifery & Women's Health. 2015; 60 :70-74
• 34. Lazarus CJ, Chauvin SW, Rodenhauser P, Whitlock R. The program for professional values and ethics in medical education. Teaching and Learning in Medicine. 2000; 12 :208-211
• 35. Lee W, Choi S, Kim S, Min A. A case-centered approach to nursing ethics education: A qualitative study. International Journal of Environmental Research and Public Health. 2020; 17 :7748
• 36. Guraya SS, Guraya SY, Doubell F-R, Mathew B, Clarke E, Ryan Á, et al. Understanding medical professionalism using express team-based learning; a qualitative case-based study. Medical Education Online. 2023; 28 :2235793
• 37. Butani L, Sweeney C, Plant J. Effect of a patient-led educational session on pre-clerkship students’ learning of professional values and on their professional development. Medical Education Online. 2020; 25 :1-6
• 38. Guraya SS, Rashid-Doubell F, Fredericks S, Halabi MOO, Mallah SIY, Sefen JAN, et al. Changing professional behaviors in the digital world using the medical education E-professionalism (MEeP) framework—A mixed methods multicentre study. Frontiers in Medicine (Lausanne). 2022; 9 :1-14. DOI: 10.3389/fmed.2022.846971
• 39. Dash NR, Taha MH, Shorbagi S, Abdalla ME. Evaluation of the integration of social accountability values into medical education using a problem-based learning curriculum. BMC Medical Education. 2022; 22 :1-7. DOI: 10.1186/s12909-022-03245-6
• 40. Senok A, John-Baptiste A-M, Al Heialy S, Naidoo N, Otaki F, Davis D. Leveraging the added value of experiential Co-curricular programs to humanize medical education. The Journal of Experimental Education. 2022; 45 :172-190
• 41. Cannaerts N, Gastmans C, de Casterlé BD. Contribution of ethics education to the ethical competence of nursing students: Educators’ and students’ perceptions. Nursing Ethics. 2014; 21 :861-878
• 42. Hodges BD, Ginsburg S, Cruess R, Cruess S, Delport R, Hafferty F, et al. Assessment of professionalism: Recommendations from the Ottawa 2010 conference. Medical Teacher. 2011; 33 :354-363
• 43. Lesser CS, Lucey CR, Egener B, Braddock CH, Linas SL, Levinson W. A behavioral and systems view of professionalism. JAMA. 2010; 304 :2732-2737
• 44. Ginsburg S, Regehr G, Hatala R, McNaughton N, Frohna A, Hodges B, et al. Context, conflict, and resolution: A new conceptual framework for evaluating professionalism. Academic Medicine. 2000; 75 :S6-S11
• 45. Lynch DC, Surdyk PM, Eiser AR. Assessing professionalism: A review of the literature. Medical Teacher. 2004; 26 :366-373. DOI: 10.1080/01421590410001696434
• 46. Li H, Ding N, Zhang Y, Liu Y, Wen D. Assessing medical professionalism: A systematic review of instruments and their measurement properties. PLoS One. 2017; 12 :e0177321
• 47. Gillam L, Delany C, Guillemin M, Warmington S. The role of emotions in health professional ethics teaching. Journal of Medical Ethics. 2013; 40 :331-335
• 48. Ginsburg S, Regehr G, Mylopoulos M. From Behaviours to attributions: Further concerns regarding the evaluation of professionalism. Medical Education. 2009; 43 :414-425
• 49. Rees CE, Knight L. V the trouble with assessing students’ professionalism: Theoretical insights from Sociocognitive psychology. Academic Medicine. 2007; 82 :46-50
• 50. Ho M-J, Yu K-H, Hirsh D, Huang T-S, Yang P-C. Does one size fit all? Building a framework for medical professionalism. Academic Medicine. 2011; 86 :1407-1414

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Ethical Issues in the Planning and the Conduct of Escape Rooms in Medical Education

Affiliations.

• 1 Deputy Director (Research and Development), Off Campus, Datta Meghe Institute of Higher Education and Research, Department of Community Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Wanadongri, Nagpur, Maharashtra, India.
• 2 Department of Research and Development, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Sawangi, Wardha, Maharashtra, India.
• PMID: 38707669
• PMCID: PMC11068247
• DOI: 10.4103/ccd.ccd_571_23

Escape rooms in medical education are relatively a novel approach to facilitate critical thinking and decision-making in simulated realistic clinical scenarios among the medical students. The success of escape rooms in the attainment of specified competencies depends on several factors that must be given due consideration for optimizing the overall outcome. Further, there might be multiple ethical concerns that must be given due attention before, during, and after the conduct of such sessions. There is an immense need to integrate ethical considerations while designing and implementing escape rooms in medical schools, as it will aid in the creation of a respectful and encouraging learning atmosphere for the students. In conclusion, escape rooms provide a great learning opportunity for medical students to critically think, engage in teamwork, and learn the art of adaptation depending on the given clinical scenario. However, the successful conduct of such sessions is determined by a wide range of factors, including ethical considerations, and all of them need to be systematically analyzed and measures should be taken to reduce their impact on students.

Keywords: Communication skills; escape room; ethics; medical education; medical students.

Copyright: © 2024 Contemporary Clinical Dentistry.

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Institutional Ethics Committee Members:

Maria Basile, MD, MBA Phyllis Migdal, MD, MA Stephen G. Post, PhD

The antiquity of the Hippocratic Oath—which enjoins the practitioners of medicine to “never do harm” and to act “for the good of the patient”—makes clear that the professional practice of medicine has always been an ethically motivated endeavor. The great strides in both science and technology that have come since this oath was first sworn, along with the ever-changing legal environment in which medicine is practiced, have only increased the ethical complexity of the medical situations our patients (and their families), physicians, and other health care staff face on a daily basis. In response to the increasing complexity of situations (for example, surrounding the end of life) as well as clear abuses of medical authority in the past (such as the experiments performed by Dr. Mengele on behalf of the Nazi regime), bioethicists, philosophers, theologians and others have developed a number of ethical principles (such as respect for patient autonomy, non-¬ maleficence, beneficence, and justice) while lawyers, advocates, and legislatures (at both the state and national level) have developed a number of laws to ensure appropriate adherence to these principles. The ethical and legal complexity of even the most routine medical care can often be staggering for a patient and his family confronting these situations for the first time, as well as the health care provider who wants nothing more than to care for her patient. The Institutional Ethics Committee (IEC) at Stony Brook University Hospital aims to help both health care providers and their patients (and family and friends) work through these complexities and ensure that both legal and ethical conflicts are addressed proficiently and to everyone’s satisfaction.

The Institutional Ethics Committee has a tripartite mission in serving Stony Brook University Hospital and the community through Education, Policy advisement and Consultative Service. The IEC provides prospective study and education as well as timely ethics consultation to ensure that the Stony Brook University Hospital is able to meet all of the ethical and legal questions that its staff faces each day with compassion and beneficence in order to ensure the best experience possible for all of our patients.

The IEC is on call 24 hours a day, 7 days a week for consultation. A consultation may be requested by anyone involved in the case—patient, family member, friend, a health care provider, physician, nurse, or any staff member. Ethics consultations are handled by a team comprised of multiple disciplines, including, physicians, nurses, legal advisors, social workers, clergy, religious advisors, ethicists, and others. The Consultation service both helps advise those involved in the case and also helps build consensus among the affected parties so that all feel satisfied and comfortable with the outcome. The IEC keeps careful records of consultations and offers followup on a regular basis.

The IEC also maintains an Ethics Review Committee that regularly reviews the current laws, attends to changes as they are made, and revisits hospital policies and forms to ensure they are up to date and comply with all new laws. Educational opportunities are then provided to all hospital staff including orientations, ethics sessions, conferences, and Ethics Rounds.

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Guest Essay

In Medicine, the Morally Unthinkable Too Easily Comes to Seem Normal

By Carl Elliott

Dr. Elliott teaches medical ethics at the University of Minnesota. He is the author of the forthcoming book “The Occasional Human Sacrifice: Medical Experimentation and the Price of Saying No,” from which this essay is adapted.

Here is the way I remember it: The year is 1985, and a few medical students are gathered around an operating table where an anesthetized woman has been prepared for surgery. The attending physician, a gynecologist, asks the group: “Has everyone felt a cervix? Here’s your chance.” One after another, we take turns inserting two gloved fingers into the unconscious woman’s vagina.

Had the woman consented to a pelvic exam? Did she understand that when the lights went dim she would be treated like a clinical practice dummy, her genitalia palpated by a succession of untrained hands? I don’t know. Like most medical students, I just did as I was told.

Last month the Department of Health and Human Services issued new guidance requiring written informed consent for pelvic exams and other intimate procedures performed under anesthesia. Much of the force behind the new requirement came from distressed medical students who saw these pelvic exams as wrong and summoned the courage to speak out.

Whether the guidance will actually change clinical practice I don’t know. Medical traditions are notoriously difficult to uproot, and academic medicine does not easily tolerate ethical dissent. I doubt the medical profession can be trusted to reform itself.

What is it that leads a rare individual to say no to practices that are deceptive, exploitative or harmful when everyone else thinks they are fine? For a long time I assumed that saying no was mainly an issue of moral courage. The relevant question was: If you are a witness to wrongdoing, will you be brave enough to speak out?

But then I started talking to insiders who had blown the whistle on abusive medical research. Soon I realized that I had overlooked the importance of moral perception. Before you decide to speak out about wrongdoing, you have to recognize it for what it is.

This is not as simple as it seems. Part of what makes medical training so unsettling is how often you are thrust into situations in which you don’t really know how to behave. Nothing in your life up to that point has prepared you to dissect a cadaver, perform a rectal exam or deliver a baby. Never before have you seen a psychotic patient involuntarily sedated and strapped to a bed or a brain-dead body wheeled out of a hospital room to have its organs harvested for transplantation. Your initial reaction is often a combination of revulsion, anxiety and self-consciousness.

To embark on a career in medicine is like moving to a foreign country where you do not understand the customs, rituals, manners or language. Your main concern on arrival is how to fit in and avoid causing offense. This is true even if the local customs seem backward or cruel. What’s more, this particular country has an authoritarian government and a rigid status hierarchy where dissent is not just discouraged but also punished. Living happily in this country requires convincing yourself that whatever discomfort you feel comes from your own ignorance and lack of experience. Over time, you learn how to assimilate. You may even come to laugh at how naïve you were when you first arrived.

A rare few people hang onto that discomfort and learn from it. When Michael Wilkins and William Bronston started working at the Willowbrook State School in Staten Island as young doctors in the early 1970s, they found thousands of mentally disabled children condemned to the most horrific conditions imaginable: naked children rocking and moaning on concrete floors in puddles of their own urine; an overpowering stench of illness and filth; a research unit where children were deliberately infected with hepatitis A and B.

“It was truly an American concentration camp,” Dr. Bronston told me. Yet when he and Dr. Wilkins tried to enlist Willowbrook doctors and nurses to reform the institution, they were met with indifference or hostility. It seemed as if no one else on the medical staff could see what they saw. It was only when Dr. Wilkins went to a reporter and showed the world what was happening behind the Willowbrook walls that anything began to change.

When I asked Dr. Bronston how it was possible for doctors and nurses to work at Willowbrook without seeing it as a crime scene, he told me it began with the way the institution was structured and organized. “Medically secured, medically managed, doctor-validated,” he said. Medical professionals just accommodated themselves to the status quo. “You get with the program because that’s what you’re being hired to do,” he said.

One of the great mysteries of human behavior is how institutions create social worlds where unthinkable practices come to seem normal. This is as true of academic medical centers as it is of prisons and military units. When we are told about a horrific medical research scandal, we assume that we would see it just as the whistle-blower Peter Buxtun saw the Tuskegee syphilis study : an abuse so shocking that only a sociopath could fail to perceive it.

Yet it rarely happens this way. It took Mr. Buxtun seven years to convince others to see the abuses for what they were. It has taken other whistle-blowers even longer. Even when the outside world condemns a practice, medical institutions typically insist that the outsiders don’t really understand.

According to Irving Janis, a Yale psychologist who popularized the notion of groupthink, the forces of social conformity are especially powerful in organizations that are driven by a deep sense of moral purpose. If the aims of the organization are righteous, its members feel, it is wrong to put barriers in the way.

This observation helps explain why academic medicine not only defends researchers accused of wrongdoing but also sometimes rewards them. Many of the researchers responsible for the most notorious abuses in recent medical history — the Tuskegee syphilis study, the Willowbrook hepatitis studies, the Cincinnati radiation studies , the Holmesburg prison studies — were celebrated with professional accolades even after the abuses were first called out.

The culture of medicine is notoriously resistant to change. During the 1970s, it was thought that the solution to medical misconduct was formal education in ethics. Major academic medical centers began establishing bioethics centers and programs throughout the 1980s and ’90s, and today virtually every medical school in the country requires ethics training.

Yet it is debatable whether that training has had any effect. Many of the most egregious ethical abuses in recent decades have taken place in medical centers with prominent bioethics programs, such as the University of Pennsylvania , Duke University , Columbia University and Johns Hopkins University , as well as my own institution, the University of Minnesota .

One could be forgiven for concluding that the only way the culture of medicine will change is if changes are forced on it from the outside — by oversight bodies, legislators or litigators. For example, many states have responded to the controversy over pelvic exams by passing laws banning the practice unless the patient has explicitly given consent.

You may find it hard to understand how pelvic exams on unconscious women without their consent could seem like anything but a terrible invasion. Yet a central aim of medical training is to transform your sensibility. You are taught to steel yourself against your natural emotional reactions to death and disfigurement; to set aside your customary views about privacy and shame; to see the human body as a thing to be examined, tested and studied.

One danger of this transformation is that you will see your colleagues and superiors do horrible things and be afraid to speak up. But the more subtle danger is that you will no longer see what they are doing as horrible. You will just think: This is the way it is done.

Carl Elliott ( @FearLoathingBTX ) teaches medical ethics at the University of Minnesota. He is the author of the forthcoming book “The Occasional Human Sacrifice: Medical Experimentation and the Price of Saying No,” from which this essay is adapted.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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JIPMER Invites Application For Research Proposals To IEC Observational Studies June 2024

Puducherry- The Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, has issued a circular inviting online applications for the submission of proposals to the Institutional Ethics Committee Observational Studies for the June 2024 session.

The last date for submission of proposals to reach the office of the undersigned for consideration of approval by the Institutional Ethics Committee for June 2024 is on or before Monday, May 27 2024. Proposals submitted before the last date will be taken up for discussion in the IEC Observational Committee meeting scheduled to be held on Thursday, June 20 2024.

Candidates must submit one hard copy of certain documents to the Member-Secretary at the Institute Ethics Committee (IEC) office, Room no. 106, First floor, Administrative block, JIPMER.

1 Covering letter.

2 Download of the online IEC application.

3 Signed Declaration form.

4 Consent forms / Waiver of consent forms.

5 Research proposal JSAC (ver. July 2020) / UGRMC/PGRMC.

6 JSAC/ UGRMC/PGRMC certificate.

7 Data collection proformas.

8 Curriculum vitae of PI & Co-Investigator (s) / guide & co-guide.

As the notice, states, “ Faculty, PhD scholar, P.G. and U.G. Students are invited to submit research proposals approved by JSAC/PGRMC/SCTRC/GJ-STRAUS for approval by Institutional Ethics Committee – Observational studies in the prescribed proforma ”.

PIs are instructed to send a soft copy of their signed declaration form, consent forms and scientific proposal along with a cover letter (both as PDF & Word copy) at the email ID, which is mentioned in the notice.

The Jawaharlal Institute of Postgraduate Medical Education & Research (JIPMER) is a medical school in Pondicherry. It is an institute of national importance (INI) and a tertiary care referral hospital. It is under the direct administrative control of the Ministry of Health and Family Welfare and the Indian Government, with autonomy to run its internal administration.

To view the notice, click the link below

I am a student of Journalism and Mass Communication and also a passionate writer and explorer. With a keen interest in medicine, I have joined Medical Dialogues as a Content Writer. Within this role, I curate various healthcare-related news including the latest updates on health, hospitals, and regulatory updates from NMC/DCI. For any query or information, feel free to reach out to me at [email protected]

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

Published on 10.5.2024 in Vol 12 (2024)

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

Authors of this article:

• Carl Preiksaitis, MD   ; 
• Nicholas Ashenburg, MD   ; 
• Gabrielle Bunney, MBA, MD   ; 
• Andrew Chu, MD   ; 
• Rana Kabeer, MPH, MD   ; 
• Fran Riley, MSE, MD   ; 
• Ryan Ribeira, MPH, MD   ; 
• Christian Rose, MD  

Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States

Corresponding Author:

Carl Preiksaitis, MD

Department of Emergency Medicine

Stanford University School of Medicine

900 Welch Road

Palo Alto, CA, 94304

United States

Phone: 1 650 723 6576

Email: [email protected]

Background: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.

Objective: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs’ potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.

Methods: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs’ use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.

Results: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs’ outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs’ capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.

Conclusions: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians’ AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Introduction

Emergency medicine (EM) is at an inflection point. With increasing patient volumes, decreasing staff availability, and rapidly evolving clinical guidelines, emergency providers are overburdened and burnout is significant [ 1 ]. While the role of artificial intelligence (AI) in enhancing emergency care is increasingly recognized, the emergence of large language models (LLMs) offers a novel perspective. Previous reviews have systematically categorized AI applications in EM, focusing on diagnostic-specific and triage-specific branches, emphasizing diagnostic prediction and decision support [ 2 - 5 ]. This review aims to build upon these foundations by exploring the unique potential of LLMs in EM, particularly in areas requiring complex data processing and decision-making under time constraints.

An LLM is a deep learning–based artificial neural network, distinguished from traditional machine learning models by its training on vast amounts of textual data. This enables LLMs to recognize, translate, predict, or generate text or other content [ 6 ]. Characterized by transformer architecture and the ability to encode contextual information using several parameters, LLMs allow for nuanced understanding and application across a diverse range of topics. Unlike traditional AI models, which often rely on structured data and predefined algorithms, LLMs are adept at interpreting unstructured text data. This feature makes them particularly useful in tasks such as real-time data interpretation, augmenting clinical decision-making, and enhancing patient engagement in clinical settings. For instance, LLMs can efficiently sift through electronic health records (EHRs) to identify critical patient histories and assist clinicians in interpreting multimodal diagnostic data. In addition, they can serve as advanced decision support tools in differential diagnosis, enhancing the quality of care while reducing the cognitive load and decision fatigue for emergency providers. Furthermore, the content generation ability of LLMs, ranging from technical computer code to essays and poetry, demonstrates their versatility and exceeds the functional scope of traditional machine learning models in terms of content creation and natural language processing.

While interest in applying LLMs to EM is gaining momentum, the existing body of literature remains a patchwork of isolated studies, theoretical discussions, and small-scale implementations. Moreover, existing research often focuses on specific use cases, such as diagnostic assistance or triage prioritization, rather than providing a holistic view of how LLMs can be integrated into the EM workflow. Conclusions based on other forms of machine learning are not readily translatable to LLMs. This fragmented landscape makes it challenging for emergency clinicians, who are already burdened by the complexities and pace of their practice, to discern actionable insights or formulate a coherent strategy for adopting these technologies. Despite the promise shown by several models, such as ChatGPT-4 (OpenAI) or Med-PaLM 2 (Google AI), the absence of standardized metrics for evaluating their clinical efficacy, ethical use, and long-term sustainability leaves researchers and clinicians navigating an uncharted territory. Consequently, the potential for LLMs to enhance emergency medical care remains largely untapped and poorly understood.

Goals of This Review

In light of these complexities and informational disparities, our study undertakes a crucial step to consolidate, assess, and contextualize the fragmented knowledge base surrounding LLMs in EM. Through a scoping review, we aim to establish a foundational understanding of the field’s current standing, from technological capabilities to clinical applications and ethical considerations. This synthesis serves a dual purpose: first, to equip emergency providers with a navigable map of existing research and, second, to identify critical gaps and avenues for future inquiry. As EM increasingly embraces technological solutions for its unique challenges, our goal is to provide clarity to the responsible and effective incorporation of LLMs into clinical practice.

We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [ 7 ] and used the scoping review methodology proposed by Arksey and O’Malley [ 8 ] and furthered by Levac et al [ 9 ]. This included the following steps: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consultation. Our full review protocol is published elsewhere [ 10 ].

Identifying the Research Question

The overall purpose of this review was to map the current literature describing the potential uses of LLMs in EM and to identify directions for future research. To achieve this goal, we aimed to answer the primary research question: “What are the current and potential uses of LLMs in EM described in the literature?” We chose to explicitly focus on LLMs as this subset of AI is rapidly developing and generating significant interest for potential applications.

Identifying Relevant Studies

In August 2023, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for potential citations of interest. We limited our search to papers published after January 2018 as the Bidirectional Encoder Representations from Transformers (BERT; Google) model was introduced that year and considered by many to be the first in the contemporary class of LLMs [ 11 ]. Our search strategy ( Multimedia Appendix 1 ), created in consultation with a medical librarian, combined keywords and MeSH (Medical Subject Headings) terms related to LLMs and EM. We reviewed the bibliographies of identified studies for potential missed papers.

Study Selection

Citations were managed using Covidence web-based software (Veritas Health Innovation). Manuscripts were included if they discussed the use of an LLM in EM, including applications in the emergency department (ED) and prehospital and periadmission settings. Furthermore, we included use cases related to public health, disease monitoring, or disaster preparedness as these are relevant to EDs. We excluded studies that used other forms of machine learning or natural language processing that were not LLMs and studies that did not clearly relate to EM. We also excluded cases where the only use of an LLM was in generating the manuscript without any additional commentary.

Two investigators (CP and CR) independently screened 100 abstracts, and the interrater reliability showed substantial agreement (κ=0.75). The remaining abstracts were screened by 1 author (CP), who consulted with a second author as needed for clarification regarding inclusion and exclusion criteria. All papers meeting the initial criteria were independently reviewed in full by 2 authors (CP and CR). Studies determined to meet the eligibility criteria by both reviewers were included in the analysis. Discrepancies were resolved by consensus and with the addition of a third reviewer (NA) if needed. Our initial search strategy identified 2065 papers, of which 73 (3.54%) were duplicates, resulting in 1992 (96.46%) papers for screening ( Figure 1 ). Of the 1992 papers, 1891 (94.93%) were excluded based on the title or abstract. In total, 5.07% (101/1992) of the papers were reviewed in full, and 2.11% (42/1992) of the papers were found to meet the study inclusion criteria. During manuscript review, 2 additional papers were brought to our attention by experts, and 1 of these met the inclusion criteria, bringing the total number of included papers to 43.

Charting the Data

Data abstraction was independently conducted using a structured form to capture paper details, including the author, year of publication, study type, specific study population, study or paper location, purpose, and main findings. Data to address our primary research question was iteratively abstracted from the papers as our themes emerged, as explained in the subsequent sections.

Collating, Summarizing, and Reporting the Results

We synthesized and collated the data, performing both a quantitative and qualitative analysis. A descriptive summary of the included studies was created. Then, we used the methodology proposed by Braun and Clarke [ 12 ] to conduct a thematic analysis to address our primary research question. Five authors (CP, CR, AC, NA, and RR) independently familiarized themselves with and generated codes for a purposively diverse selection of 10 papers, focusing on content that suggested possible uses for LLMs in EM. The group met to discuss preliminary findings and refine the group’s approach. Individuals then independently aggregated codes into themes. These themes were reviewed and refined as a group. Then, 2 authors (CP and CR) reviewed the remaining manuscripts for any additional themes and data that supported or contradicted our existing themes. These data were used to refine themes through group discussion. Our analysis included a discussion and emphasis on the implications and future research directions for the field, based on the guidance from Levac et al [ 9 ].

Consultation

To ensure our review accurately characterized the available knowledge and that our interpretations of it were correct, we consulted with external emergency physicians with topic expertise in AI. We incorporated feedback as appropriate. For example, we more completely defined LLMs for clarity and included a table describing common models ( Table 1 ). Our findings and recommendations were endorsed by our consultants.

a AI: artificial intelligence.

Most identified studies (29/43, 67%) were published in 2023. Of the 43 studies, 14 (33%) were conducted in the United States, followed by 6 (14%) in China, 4 (9%) in Australia, 3 (7%) each in Taiwan and France, and 2 (5%) each in Singapore and Korea. Several other individual studies (5/43, 12%) were from various countries ( Table 2 ).

In terms of study type, 40% (17/43) of the papers were methodology studies; 40% (17/43) were case studies; 16% (7/43) were commentaries; and 2% (1/43) each of a case report, qualitative investigation, and retrospective cross-sectional study. In total, 58% (25/43) of these studies addressed the ED setting specifically, followed by 14% (6/43) addressing the prehospital setting and 14% (6/43) addressing other non-ED hospital settings. In total, 7% (3/43) of the studies focused on using LLMs for the public, 5% (2/43) focused on using them for social media analysis, and 2% (1/43) focused on using them for research applications. LLMs used in the reviewed papers ( Table 1 ) included versions of GPT (OpenAI; eg, ChatGPT, GPT-4, and GPT-2), Pathways Language Model (Bard; Google AI), Embeddings from Language Model, XLNet, and BERT (Google; eg, BioBERT, ClinicalBERT, and decoding-enhanced BERT with disentangled information).

We identified four major themes in our analysis: (1) clinical decision-making and support; (2) efficiency, workflow, and information management; (3) risks, ethics, and transparency; and (4) education and communication. Major themes, subthemes, and representative quotations are presented in Table 3 .

a ED: emergency department.

b CDMS: clinical decision-making and support.

c EWIM: efficiency, workflow, and information management.

d BERT: Bidirectional Encoder Representations from Transformers.

e EMS: emergency medical service.

f DeBERTa: decoding-enhanced Bidirectional Encoder Representations from Transformers with disentangled information.

g NHAMCS: National Hospital Ambulatory Medical Care Survey.

h CXR: chest x-ray.

i AI: artificial intelligence.

j N/A: not applicable.

k RET: risks, ethics, and transparency.

l EM: emergency medicine.

m PaLM: Pathways Language Model.

n EC: education and communication.

Theme 1: Clinical Decision-Making and Support

The first theme we identified is clinical decision-making and support. LLMs have been used or proposed for applications such as providing advice to the public before arrival; aiding in triage as patients arrive at the ED; or augmenting the activities of physicians as they provide care, either through supporting diagnostics or predicting patient resource use.

Several applications focused on advising the public and aiding in symptom checking, self-triage, and occasionally advising first-aid before the arrival of emergency medical services. These included counseling parents during potential pediatric emergencies, recognizing stroke, or providing advice during potential cardiac arrests [ 40 - 42 ]. Wang et al [ 27 ] proposed a model that could potentially help patients navigate the complexities of the health care system in China and present to the correct medical setting for the care they need.

Furthermore, LLMs have the potential to efficiently screen patients for important outcomes, such as pediatric patients at risk for nonaccidental trauma, suicide risk, or COVID-19 infection [ 30 , 32 , 34 ]. These can be implemented based on data in the medical record or as clinical data are obtained in real time.

Early identification of patient risks could help physicians more rapidly identify important diagnoses. Several studies discussed implementations of LLMs that work in conjunction with physicians while caring for patients in the ED [ 50 , 51 ]. Brown et al [ 52 ] discuss the potential role of these models in overcoming cognitive biases and reducing errors. These models could be used in developing a differential diagnosis, recommending imaging studies, providing treatment recommendations, or interpreting clinical guidelines [ 37 , 44 , 55 , 56 ].

Several studies centered on predicting outcomes such as presentation to the ED, hospitalization, intensive care unit admission, or in-hospital cardiac arrest [ 25 , 33 , 35 , 57 ]. Applications of LLMs in the triage process could potentially identify patients who require immediate attention or patients at a high risk of certain diagnoses, such as gastrointestinal bleeding [ 24 , 26 , 53 , 58 , 60 ].

Theme 2: Efficiency, Workflow, and Information Management

The second theme identified is information management, workflow, and efficiency. LLMs show great promise in increasing the usability of data available in the EHR. Interactions with the EHR take up a substantial amount of physician time, and it is often difficult to identify crucial information during critical times [ 43 ]. LLMs could serve a variety of information management functions. They could be used to perform audits for quality improvement purposes, identify potential adverse events such as drug interactions, anticipate and monitor public health emergencies, and assist with information entry during the clinical encounter [ 19 , 20 , 22 , 23 , 28 , 31 , 39 , 43 , 49 ]. LLMs developed and trained on data from the ED could quickly identify similar patient presentations, recognize patterns, and extract important information from unstructured text [ 18 , 20 , 21 , 60 ].

Some authors suggest that LLMs can enhance care throughout the entire EM encounter [ 30 , 50 - 52 ]. LLMs could potentially be used as digital adjuncts for clinical decision-making because they could generate differentials, predict final diagnoses, offer interpretations of imaging studies, and suggest treatment plans [ 30 , 51 , 52 , 61 ]. They may mitigate human cognitive biases and address human factors (eg, time constraints, frequent task switching, high cognitive load, constant interruptions, and decision fatigue) that predispose emergency physicians to error [ 52 ].

The flexibility and versatility of the LLMs offer particular benefits to EM practice. The diverse ways in which these models can aid throughout the entire clinical workflow could help physicians process large quantities of complex clinical data, mitigate cognitive biases, and deliver relevant information in a comprehensible format [ 30 , 51 , 52 , 61 ]. By streamlining these burdensome tasks, LLMs could help improve the efficiency of care for the high volume of patients the physicians routinely see in the ED.

Theme 3: Risks, Transparency, and Ethics

Despite the potential for advancement and improvement in the care that EM physicians can provide through the inclusion of LLMs in practice, several issues limit their implementation into practice at this time.

The most often discussed risk, mentioned in 11 (26%) of the 43 papers, is the reliability of model responses and the potential for erroneous results [ 20 , 21 , 28 - 30 , 44 , 51 , 53 , 55 , 56 , 59 ]. These output errors often result from inaccuracies in the training data, which are most commonly gathered from the internet and unvetted for reliability. Sources of inaccurate responses may be identified by examining the training material, but other errors due to data noise, mislabeling, or outdated information may be harder to detect [ 21 , 28 , 30 , 56 ]. Similarly, biases in training data can be propagated to the model, leading to inaccurate or discriminatory results [ 51 , 53 , 57 , 60 , 62 ]. In medical applications, the consequences of the errors can be significant, and even small errors could lead to adverse outcomes [ 51 ].

Understanding and mitigating errors in LLMs is challenging due to issues with transparency and reproducibility of model outputs [ 52 - 54 , 59 , 62 ]. Better understanding among clinicians of the algorithms and statistical methods used by LLMs is a suggested method to ensure cautious use [ 52 ]. Concentrating on making models more explainable or transparent is another potential approach [ 62 ]. However, the degree to which this will be feasible, given the complexity of these models, remains to be determined.

Patient and data privacy is another clearly articulated risk of using these models in the clinical environment [ 35 , 52 , 53 ]. There are some proposed methodologies using unsupervised methods that can train the models with limited access to sensitive information; however, these require further exploration [ 35 ]. Patient attitudes and willingness to allow models access to their health information for training and how to address disclosure of this use have not been extensively discussed. Finally, the legal and ethical implications of using LLM output to guide patient care is an often-mentioned concern [ 52 , 53 , 59 ]. How the responsibility for patient care decisions is distributed if LLMs are used to guide clinical decisions is yet to be determined.

Theme 4: Education and Communication

LLMs offer several opportunities for education and communication. First, several papers noted that the successful integration of LLMs into clinical practice will require physicians to understand the underlying algorithms and statistical methods used by these models [ 52 , 59 ]. There is a need for dedicated educational programs on AI in medicine at all levels of medical education to ensure that the solutions developed align with the clinical environment and address the unique challenges of working with clinical data [ 34 , 51 , 63 ].

In terms of clinical education, several studies have demonstrated reasonable performance of LLMs on standardized tests in medicine, which could indicate the potential for these models to develop study materials [ 36 ]. In addition, these models may be able to help physicians communicate with and educate the patients. Dahdah et al [ 45 ] used ChatGPT to answer several common medical questions in easy-to-understand language, suggesting the ability to enhance physician responses to patient queries. Webb [ 54 ] demonstrated the use of ChatGPT to simulate patient conversation and provide feedback to a physician learning how to break bad news.

Patient education may be facilitated via these models without physician input as well. As discussed in the previous sections, several authors described applications designed to educate patients during emergencies before they arrived in the ED [ 27 , 40 - 42 ]. Finally, LLMs could be used to aid in knowledge dissemination. Gottleib et al [ 46 ] and Babl and Babl [ 47 ] describe potential applications for LLMs in research and scientific writing. They highlight potential benefits to individuals who struggle with English or have challenges with writing or knowledge synthesis. In addition, models may be used to translate scientific papers more rapidly. However, the use of these models to generate scientific papers raises concerns regarding the potential for academic dishonesty [ 46 , 47 ].

Principal Findings

Our review aligns with the growing body of literature emphasizing the great potential for AI in EM, particularly in areas such as time-sensitive decision-making and managing high-volume data [ 2 - 5 , 60 ]. However, our focus on LLMs and their unique capabilities extends the current understanding of AI applications in EM. Although several specific applications and limitations have been reported and suggested in the literature, our analysis identified 4 major areas of focus for LLMs in EM: clinical decision support, workflow efficiency, risks, ethics, and education. We propose these topics as a framework for understanding emerging implementations of LLMs and as a guide to inform future areas of investigation.

At their core, LLMs and their associated natural language processing techniques offer a way to organize and engage with vast amounts of unstructured text data. Depending on how they are trained and used, they can be operationalized to make predictions or identify patterns, which gives rise to most of our identified applications. Most commercially available LLMs, such as ChatGPT, are trained on massive volumes of text gathered from the internet and then optimized for conversational interaction [ 64 ]. This ability to access a breadth of general knowledge and the resulting wide applicability have contributed to the increased use of LLMs by professionals and the public across a variety of fields [ 65 ]. As these models become more ubiquitous, there is potential for their use across the care continuum. They could not only support clinical care but also provide an opportunity to offer advice to the public regarding medical concerns. Several papers (3/34, 9%) in our review identified the feasibility of using LLMs to provide first-aid instructions and offer decision support to potential patients seeking care [ 40 - 42 ].

Preliminary work suggests that dedicated training can enhance the ability of these models to make triage recommendations, but prospective implementation has not been tested [ 27 ]. LLMs could certainly aid patients in self-triage or with basic medical questions; nevertheless, how this can be effectively and safely implemented needs further exploration, especially with concerns regarding the accuracy of outputs. Possibilities to improve outputs include additional dedicated training of the models to align with the medical and emergency settings to improve their reliability and accuracy. These context-specific models could be equipped with information on the local health care system to help patients identify available resources, schedule appointments, or activate emergency medical services.

In the ED, LLMs could increase workflow efficiency by rapidly synthesizing relevant information from a patient’s medical record, structuring and categorizing chief complaint data, and assigning an emergency severity index level [ 18 , 21 , 26 , 45 , 53 , 58 ]. In addition, quickly accessing data from the medical record could improve the efficiency and thoroughness of chart review. A model’s ability to identify subtle patterns in data could offer additional diagnostic support by recommending or interpreting laboratory and imaging studies [ 30 , 51 , 52 , 61 ]. By facilitating tasks such as information retrieval and synthesis, LLMs could reduce this burden for clinicians and minimize errors due to buried or disorganized data, potentially contributing to workflow efficiency. Furthermore, they may counteract human cognitive biases and fatigue when used to support clinical decisions [ 52 ]. Although some studies have demonstrated reasonable accuracy on focused use cases, further validation of any of these applications across diverse settings and patient populations is required. Thoughtful integration of LLMs has the potential to revolutionize EM by providing clinical decision support, improving situational awareness, and increasing productivity.

However, barriers to seamless implementation exist. As noted by several authors, erroneous outputs remain a concern, given the dependence on training data [ 28 - 30 , 35 , 51 , 53 , 55 , 56 , 59 ]. Information surrounding the most publicly available LLMs today is obscured across three important layers: (1) the underlying training data used—commonly reported to be publicly available data on the internet and from third-party licensed data sets, (2) the underlying architecture of the model—whose exact mechanisms are not always easy to discern, and (3) the intricacies of human-led fine-tuning—often done at the end of development to provide guardrails for output. These layers of obscurity make it difficult to troubleshoot the cause of any single erroneous output.

Regarding privacy and data rights, it is imperative to discuss and implement privacy-preserving methods for patient data. The use of techniques such as data anonymization, differential privacy, and federated learning are instrumental in safeguarding patient information. Data anonymization involves removing or modifying personal identifiers to prevent the association of data with individual patients. Differential privacy introduces randomness into the data or queries to ensure individual data points cannot be isolated [ 66 ]. Federated learning enables models to be trained against multiple decentralized devices or servers holding local data samples without exchanging them, thus enhancing privacy [ 67 ]. The specific ways in which LLMs will interface with other hospital information systems, such as the EHR, need further exploration, and careful integration is critical to address privacy concerns, especially given the sensitive nature of health care data.

Moreover, the ongoing discussions about the information used in these models underscore the need for continuous scrutiny [ 52 , 53 , 59 ]. In addition to privacy, the legal and ethical implications of AI-assisted health care require further exploration to establish robust oversight and accountability structures. Without a commitment to explainability and transparency, the use of black box LLMs may encounter resistance from clinicians.

Our review reveals several opportunities for future exploration and research. Perhaps the most important is effectively identifying problems that are best solved using LLMs in EM. Our review outlines several immediate areas of potential exploration, including improved communication, translation, and summarization of highly detailed and domain-specific knowledge for providers and patients, but further exploration and prospective validation of specific use cases is required. We expect the potential use cases in EM to grow as LLMs become increasingly complex and develop emergent properties–actions that are not explicitly programmed or anticipated. To bridge the AI chasm between innovations in the research realm and widespread adoption, these applications should be identified with significant input from providers in the clinical space who can uniquely identify areas of potential benefit. To accomplish this, a better understanding of the abilities and limitations of LLMs among physicians is needed to optimize their best use and ensure they are effectively implemented, and AI literacy is increasingly described as an essential competency for physicians [ 68 ]. We encourage the development of curricula and training programs designed for emergency physicians.

Given the black-box nature of LLMs, standardized frameworks and metrics for evaluation that are specific to health care use cases are needed to evaluate their performance and implementation effectively. These frameworks should encompass an understanding of both the technical capabilities and constraints of a model, along with the human interaction aspects that affect its use. A crucial part of this assessment involves comparing the performance of LLMs to human proficiency, determining whether the objective is to replace or enhance tasks currently carried out by health care professionals. Thorough testing of models in real time, real-world scenarios is imperative before their deployment. The selection of patient- or provider-focused outcomes is essential, and the effectiveness of models should not be evaluated in isolation. Instead, it is crucial to assess the combined performance of the provider and AI system to ensure that models are effective and practical in real-world settings. Implementing and validating solutions should occur across diverse populations and care environments, with particular focus on cohorts underrepresented in the training data to mitigate potential harm from model biases [ 69 ]. Provider perspectives are essential, but equally important are patient perspectives about the use of LLMs in medicine. Impacts on physician-patient communication, patient concerns surrounding privacy, and attitudes toward AI-generated recommendations must be further explored. Collaboration between all relevant stakeholders who develop or will be impacted by LLMs for clinical medicine is essential for developing models that can be used effectively, equitably, and safely.

Limitations

This scoping review has some limitations worth noting. First, we restricted our search to papers published after 2018, when LLMs first emerged. While this captures the current era of LLMs, earlier works relevant to natural language processing in EM may have been overlooked. In addition, despite searching 4 databases and consulting a medical librarian on the search strategy, some pertinent studies may have been missed, and given the rapidly evolving nature of this research area, there are certainly more studies that have emerged since our literature search [ 70 ]. However, our review establishes an initial foundation that can be built upon as the field continues to grow. Finally, in an effort to be maximally inclusive in our review, we did not include or exclude papers based on the quality of their evidence. Similarly, we did not make any quality determinations of our included studies. High-quality studies are required to make any determination regarding the efficacy of LLMs for the applications we described, and our review hopefully provides a framework to design these investigations.

Conclusions

This review underscores the transformative potential of LLMs in enhancing the delivery of emergency care. By leveraging their ability to process vast amounts of data rapidly, LLMs offer unprecedented opportunities to improve decision-making speed and accuracy, a critical component in the high-stakes, fast-paced EM environment. From the identified themes, it is evident that LLMs have the potential to revolutionize various aspects of emergency care, highlighting their versatility and the breadth of their applicability.

From the theme of clinical decision-making and support, LLMs can augment the diagnostic process, support differential diagnosis, and aid in the efficient allocation of resources. In the domain of efficiency, workflow, and information management, LLMs have shown promise in enhancing operational efficiencies, reducing the cognitive load on clinicians, and streamlining patient care processes. Regarding risks, ethics, and transparency, the review illuminates the need for meticulous attention to the accuracy, bias, and ethical considerations inherent in deploying LLMs in a clinical setting. Finally, in the realm of education and communication, LLMs’ potential to facilitate learning and improve patient and provider communication signifies a paradigm shift in medical education and engagement.

The most urgent research need identified in this review is the development of robust, evidence-based frameworks for evaluating the clinical efficacy of LLMs in EM; addressing ethical concerns; ensuring data privacy; and mitigating potential biases in model outputs. There is a critical need for prospective studies that validate the utility of LLMs in real-world emergency care settings and explore the optimization of these models for specific clinical tasks. Furthermore, research should focus on understanding the best practices for integrating LLMs into the existing health care workflows without disrupting the clinician-patient relationship.

The successful integration of LLMs into EM necessitates a multidisciplinary approach involving clinicians, computer scientists, ethicists, patients, and policy makers. Collaborative efforts are essential to navigate the challenges of implementing AI technologies in health care, ensuring LLMs complement the clinical judgment of EM professionals and align with the overarching goal of improving patient care. The judicious application of LLMs has the potential to fundamentally redefine much of EM practice, ushering in a future where care is more accurate, efficient, and responsive to the needs of patients. Furthermore, by reducing the many burdens that currently encumber clinicians, these technologies hold the promise of restoring and deepening the invaluable human connections between physicians and their patients.

Conflicts of Interest

None declared.

Literature review search strategy.

PRISMA-ScR checklist.

• Petrino R, Riesgo LG, Yilmaz B. Burnout in emergency medicine professionals after 2 years of the COVID-19 pandemic: a threat to the healthcare system? Eur J Emerg Med. Aug 01, 2022;29(4):279-284. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform. Dec 2023;180:105274. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kirubarajan A, Taher A, Khan S, Masood S. Artificial intelligence in emergency medicine: a scoping review. J Am Coll Emerg Physicians Open. Nov 07, 2020;1(6):1691-1702. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The aspects of running artificial intelligence in emergency care; a scoping review. Arch Acad Emerg Med. May 11, 2023;11(1):e38. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: a narrative review. Acute Med Surg. Mar 1, 2022;9(1):e740. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Thirunavukarasu AJ, Ting DS, Elangovan K, Gutierrez L, Tan TF, Ting DS. Large language models in medicine. Nat Med. Aug 2023;29(8):1930-1940. [ CrossRef ] [ Medline ]
• Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. Feb 23, 2005;8(1):19-32. [ CrossRef ]
• Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. Sep 20, 2010;5:69. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Preiksaitis C. Protocol for a scoping review of the application of large language models in emergency medicine. OSF Home. Oct 19, 2023. URL: https://osf.io/tdghu/ [accessed 2024-04-28]
• Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv. Preprint posted online October 11, 2018. 2024;(https://arxiv.org/abs/1810.04805). [ FREE Full text ] [ CrossRef ]
• Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101. [ CrossRef ]
• Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language models are few-shot learners. arXiv. Preprint posted online May 28, 2020. 2024. [ CrossRef ]
• Schreiner M. GPT-4 architecture, datasets, costs and more leaked. The Decoder. Jul 11, 2023. URL: https://the-decoder.com/gpt-4-architecture-datasets-costs-and-more-leaked/ [accessed 2023-10-12]
• Narang S, Chowdhery A. Pathways language model (PaLM): scaling to 540 billion parameters for breakthrough performance. Google Research. Apr 04, 2022. URL: https://blog.research.google/2022/04/pathways-language-model-palm-scaling-to.html [accessed 2023-10-12]
• AllenNLP - ELMo. Allen Institute for Artificial Intelligence. URL: https://allenai.org/allennlp/software/elmo [accessed 2023-10-12]
• Devlin J, Chang MW. Open sourcing BERT: state-of-the-art pre-training for natural language processing. Google Research. URL: https://blog.research.google/2018/11/open-sourcing-bert-state-of-art-pre.html [accessed 2023-10-12]
• Xu B, Gil-Jardiné C, Thiessard F, Tellier E, Avalos M, Lagarde E. Pre-training a neural language model improves the sample efficiency of an emergency room classification model. arXiv. Preprint posted online August 30, 2019. 2024.
• Wang T, Lu K, Chow KP, Zhu Q. COVID-19 sensing: negative sentiment analysis on social media in China via BERT model. IEEE Access. Jul 28, 2020;8:138162-138169. [ CrossRef ]
• Chen YP, Chen YY, Lin JJ, Huang CH, Lai F. Modified bidirectional encoder representations from transformers extractive summarization model for hospital information systems based on character-level tokens (AlphaBERT): development and performance evaluation. JMIR Med Inform. Apr 29, 2020;8(4):e17787. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chang D, Hong WS, Taylor RA. Generating contextual embeddings for emergency department chief complaints. JAMIA Open. Jul 15, 2020;3(2):160-166. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang H, Yeung WL, Ng QX, Tung A, Tay JA, Ryanputra D, et al. A weakly-supervised named entity recognition machine learning approach for emergency medical services clinical audit. Int J Environ Res Public Health. Jul 22, 2021;18(15):7776. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, et al. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scand J Trauma Resusc Emerg Med. Mar 31, 2021;29(1):55. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shung D, Tsay C, Laine L, Chang D, Li F, Thomas P, et al. Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules. J Gastroenterol Hepatol. Jun 2021;36(6):1590-1597. [ CrossRef ] [ Medline ]
• Tahayori B, Chini-Foroush N, Akhlaghi H. Advanced natural language processing technique to predict patient disposition based on emergency triage notes. Emerg Med Australas. Jun 2021;33(3):480-484. [ CrossRef ] [ Medline ]
• Kim D, Oh J, Im H, Yoon M, Park J, Lee J. Automatic classification of the Korean triage acuity scale in simulated emergency rooms using speech recognition and natural language processing: a proof of concept study. J Korean Med Sci. Jul 12, 2021;36(27):e175. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wang J, Zhang G, Wang W, Zhang K, Sheng Y. Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT. J Cloud Comput. Jan 15, 2021;10:4. [ CrossRef ]
• McMaster C, Chan J, Liew DF, Su E, Frauman AG, Chapman WW, et al. Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions. J Biomed Inform. Jan 2023;137:104265. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chen YP, Lo YH, Lai F, Huang CH. Disease concept-embedding based on the self-supervised method for medical information extraction from electronic health records and disease retrieval: algorithm development and validation study. J Med Internet Res. Jan 27, 2021;23(1):e25113. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Drozdov I, Szubert B, Reda E, Makary P, Forbes D, Chang SL, et al. Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments. Sci Rep. Oct 14, 2021;11(1):20384. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhang X, Zhang H, Sheng L, Tian F. DL-PER: deep learning model for Chinese prehospital emergency record classification. IEEE Access. Jun 03, 2022;10:64638-64649. [ CrossRef ]
• Pease JL, Thompson D, Wright-Berryman J, Campbell M. User feedback on the use of a natural language processing application to screen for suicide risk in the emergency department. J Behav Health Serv Res. Oct 03, 2023;50(4):548-554. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chae S, Davoudi A, Song J, Evans L, Hobensack M, Bowles KH, et al. Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model. J Am Med Inform Assoc. Sep 25, 2023;30(10):1622-1633. [ CrossRef ] [ Medline ]
• Huang D, Cogill S, Hsia RY, Yang S, Kim D. Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma. NPJ Digit Med. Jul 19, 2023;6(1):131. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chen MC, Huang TY, Chen TY, Boonyarat P, Chang YC. Clinical narrative-aware deep neural network for emergency department critical outcome prediction. J Biomed Inform. Feb 2023;138:104284. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Smith J, Choi PM, Buntine P. Will code one day run a code? Performance of language models on ACEM primary examinations and implications. Emerg Med Australas. Oct 2023;35(5):876-878. [ CrossRef ] [ Medline ]
• Gupta P, Nayak R, Alazzeh M. The accuracy of medical diagnoses in emergency medicine by modern artificial intelligence. Acad Emerg Med. 2023;30(Suppl 1):395. [ FREE Full text ] [ CrossRef ]
• Abavisani M, Dadgar F, Keikha M. A commentary on emergency surgery in the era of artificial intelligence: ChatGPT could be the doctor's right-hand man. Int J Surg. Oct 01, 2023;109(10):3195-3196. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rahman MA, Preum SM, Williams RD, Alemzadeh H, Stankovic J. EMS-BERT: a pre-trained language representation model for the emergency medical services (EMS) domain. In: Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies. 2023. Presented at: CHASE '23; June 21-23, 2023; Orlando, FL. [ CrossRef ]
• Lam WY, Au SC. Stroke care in the ChatGPT era: potential use in early symptom recognition. J Acute Dis. Jun 2023;12(3):129-130. [ CrossRef ]
• Bushuven S, Bentele M, Bentele S, Gerber B, Bansbach J, Ganter J, et al. “ChatGPT, can you help me save my child’s life?” - diagnostic accuracy and supportive capabilities to lay rescuers by ChatGPT in prehospital Basic Life Support and Paediatric Advanced Life Support cases – an in-silico analysis. Research Square. Preprint posted online May 12, 2023. 2024. [ FREE Full text ] [ CrossRef ]
• Ahn C. Exploring ChatGPT for information of cardiopulmonary resuscitation. Resuscitation. Apr 2023;185:109729. [ CrossRef ] [ Medline ]
• Preiksaitis C, Sinsky CA, Rose C. ChatGPT is not the solution to physicians' documentation burden. Nat Med. Jun 2023;29(6):1296-1297. [ CrossRef ] [ Medline ]
• Barash Y, Klang E, Konen E, Sorin V. ChatGPT-4 assistance in optimizing emergency department radiology referrals and imaging selection. J Am Coll Radiol. Oct 2023;20(10):998-1003. [ CrossRef ] [ Medline ]
• Dahdah JE, Kassab J, Helou MC, Gaballa A, Sayles S3, Phelan MP. ChatGPT: a valuable tool for emergency medical assistance. Ann Emerg Med. Sep 2023;82(3):411-413. [ CrossRef ] [ Medline ]
• Gottlieb M, Kline JA, Schneider AJ, Coates WC. ChatGPT and conversational artificial intelligence: friend, foe, or future of research? Am J Emerg Med. Aug 2023;70:81-83. [ CrossRef ] [ Medline ]
• Babl FE, Babl MP. Generative artificial intelligence: can ChatGPT write a quality abstract? Emerg Med Australas. Oct 2023;35(5):809-811. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chen J, Liu Q, Liu X, Wang Y, Nie H, Xie X. Exploring the functioning of online self-organizations during public health emergencies: patterns and mechanism. Int J Environ Res Public Health. Feb 23, 2023;20(5):4012. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bradshaw JC. The ChatGPT era: artificial intelligence in emergency medicine. Ann Emerg Med. Jun 2023;81(6):764-765. [ CrossRef ] [ Medline ]
• Cheng K, Li Z, Guo Q, Sun Z, Wu H, Li C. Emergency surgery in the era of artificial intelligence: ChatGPT could be the doctor's right-hand man. Int J Surg. Jun 01, 2023;109(6):1816-1818. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rao A, Pang M, Kim J, Kamineni M, Lie W, Prasad AK, et al. Assessing the utility of ChatGPT throughout the entire clinical workflow. medRxiv. Preprint posted online February 26, 2023. Feb 26, 2023. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brown C, Nazeer R, Gibbs A, Le Page P, Mitchell AR. Breaking bias: the role of artificial intelligence in improving clinical decision-making. Cureus. Mar 20, 2023;15(3):e36415. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bhattaram S, Shinde VS, Khumujam PP. ChatGPT: the next-gen tool for triaging? Am J Emerg Med. Jul 2023;69:215-217. [ CrossRef ] [ Medline ]
• Webb JJ. Proof of concept: using ChatGPT to teach emergency physicians how to break bad news. Cureus. May 09, 2023;15(5):e38755. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hamed E, Eid A, Alberry M. Exploring ChatGPT's potential in facilitating adaptation of clinical guidelines: a case study of diabetic ketoacidosis guidelines. Cureus. May 09, 2023;15(5):e38784. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Altamimi I, Altamimi A, Alhumimidi AS, Altamimi A, Temsah MH. Snakebite advice and counseling from artificial intelligence: an acute venomous snakebite consultation with ChatGPT. Cureus. Jun 13, 2023;15(6):e40351. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gebrael G, Sahu KK, Chigarira B, Tripathi N, Mathew Thomas V, Sayegh N, et al. Enhancing triage efficiency and accuracy in emergency rooms for patients with metastatic prostate cancer: a retrospective analysis of artificial intelligence-assisted triage using ChatGPT 4.0. Cancers (Basel). Jul 22, 2023;15(14):3717. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sarbay İ, Berikol G, Özturan İ. Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): a preliminary, scenario-based cross-sectional study. Turk J Emerg Med. Jun 26, 2023;23(3):156-161. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Okada Y, Mertens M, Liu N, Lam SS, Ong ME. AI and machine learning in resuscitation: ongoing research, new concepts, and key challenges. Resusc Plus. Jul 28, 2023;15:100435. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chenais G, Lagarde E, Gil-Jardiné C. Artificial intelligence in emergency medicine: viewpoint of current applications and foreseeable opportunities and challenges. J Med Internet Res. May 23, 2023;25:e40031. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chen HL, Chen HH. Have you chatted today? - medical education surfing with artificial intelligence. J Med Educ. Mar 2023;27(1):1-4. [ FREE Full text ]
• Fanconi C, van Buchem M, Hernandez-Boussard T. Natural language processing methods to identify oncology patients at high risk for acute care with clinical notes. AMIA Jt Summits Transl Sci Proc. Jun 16, 2023;2023:138-147. [ FREE Full text ] [ Medline ]
• Preiksaitis C, Rose C. Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review. JMIR Med Educ. Oct 20, 2023;9:e48785. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Introducing ChatGPT. OpenAI. URL: https://openai.com/blog/chatgpt [accessed 2023-10-06]
• Hu K. ChatGPT sets record for fastest-growing user base - analyst note. Reuters. Feb 02, 2023. URL: https:/​/www.​reuters.com/​technology/​chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/​ [accessed 2023-10-06]
• Ziller A, Usynin D, Braren R, Makowski M, Rueckert D, Kaissis G. Medical imaging deep learning with differential privacy. Sci Rep. Jun 29, 2021;11(1):13524. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. Sep 14, 2020;3:119. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Acad Med. Jan 01, 2024;99(1):22-27. [ CrossRef ] [ Medline ]
• Rose C, Barber R, Preiksaitis C, Kim I, Mishra N, Kayser K, et al. A conference (missingness in action) to address missingness in data and AI in health care: qualitative thematic analysis. J Med Internet Res. Nov 23, 2023;25:e49314. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chenais G, Gil-Jardiné C, Touchais H, Avalos Fernandez M, Contrand B, Tellier E, et al. Deep learning transformer models for building a comprehensive and real-time trauma observatory: development and validation study. JMIR AI. Jan 12, 2023;2:e40843. [ CrossRef ]

Abbreviations

Edited by A Castonguay; submitted 19.10.23; peer-reviewed by L Zhu, C Gil-Jardiné, MO Khursheed; comments to author 13.12.23; revised version received 20.12.23; accepted 05.04.24; published 10.05.24.

©Carl Preiksaitis, Nicholas Ashenburg, Gabrielle Bunney, Andrew Chu, Rana Kabeer, Fran Riley, Ryan Ribeira, Christian Rose. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 10.05.2024.

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