Beyond thinking fast and slow: Implications of a transtheoretical model of clinical reasoning and error on teaching, assessment, and research.

From dual process to a family of theories known collectively as situativity, both micro and macro theories of cognition inform our current understanding of clinical reasoning (CR) and error. CR is a complex process that occurs in a complex environment, and a nuanced, expansive, integrated model of these theories is necessary to fully understand how CR is performed in the present day and in the future. In this perspective, we present these individual theories along with figures and descriptive cases for purposes of comparison before exploring the implications of a transtheoretical model of these theories for teaching, assessment, and research in CR and error.

Decisions to Practice in Rural Areas Among Mental Health Care Professionals.

This survey study assesses factors associated with mental health care professionals choosing to practice in rural locations.

Homophily and social mixing in a small community: Implications for infectious disease transmission.

Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study. We constructed egocentric networks of the participants and their contacts and assessed age-mixing, ethnic/racial homophily, and gender homophily. The total size of the egocentric networks was 2,544 people (n = 384 index cases + n = 2,160 recruited peers or other contacts). We observed high rates of in-group mixing among ethnic/racial groups compared to the ethnic/racial proportions of the background population. Black or African-American respondents interacted with a wider range of ages than other ethnic/racial groups, largely due to familial relationships. The egocentric networks of non-binary contacts had little age diversity. Black or African-American respondents in particular reported mixing with older or younger family members, which could increase the risk of transmission to vulnerable age groups. Understanding community mixing patterns can inform infectious disease risk, support analyses to predict epidemic size, or be used to design campaigns such as vaccination strategies so that community members who have vulnerable contacts are prioritized.

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study.

Importance: Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning. Objective: To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources. Design: Multi-center, randomized clinical vignette study. Setting: The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions. Participants: Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine. Interventions: Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams. Main Outcomes and Measures: The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis. Results: 50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group. Conclusions and Relevance: In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

Characteristics differentiating problem representation synthesis between novices and experts.

BACKGROUND: Formulating a thoughtful problem representation (PR) is fundamental to sound clinical reasoning and an essential component of medical education. Aside from basic structural recommendations, little consensus exists on what characterizes high-quality PRs. OBJECTIVES: To elucidate characteristics that distinguish PRs created by experts and novices. METHODS: Early internal medicine residents (novices) and inpatient teaching faculty (experts) from two academic medical centers were given two written clinical vignettes and were instructed to write a PR and three-item differential diagnosis for each. Deductive content analysis described the characteristics comprising PRs. An initial codebook of characteristics was refined iteratively. The primary outcome was differences in characteristic frequencies between groups. The secondary outcome was characteristics correlating with diagnostic accuracy. Mixed-effects regression with random effects modeling compared case-level outcomes by group. RESULTS: Overall, 167 PRs were analyzed from 30 novices and 54 experts. Experts included 0.8 fewer comorbidities (p < .01) and 0.6 more examination findings (p = .01) than novices on average. Experts were less likely to include irrelevant comorbidities (odds ratio [OR] = 0.4, 95% confidence interval [CI] = 0.2-0.8) or a diagnosis (OR = 0.3, 95% CI = 0.1-0.8) compared with novices. Experts encapsulated clinical data into higher-order terms (e.g., sepsis) than novices (p < .01) while including similar numbers of semantic qualifiers (SQs). Regardless of expertise level, PRs following a three-part structure (e.g., demographics, temporal course, and clinical syndrome) and including temporal SQs were associated with diagnostic accuracy (p < .01). CONCLUSIONS: Compared with novices, expert PRs include less irrelevant data and synthesize information into higher-order concepts. Future studies should determine whether targeted educational interventions for PRs improve diagnostic accuracy.

Consensus-Derived Recommended Skills for Transition to Residency Courses.

PURPOSE: Transition to residency (TTR) courses facilitate the medical student-residency transition and are an integral part of senior medical student training. The authors established a common set of skills for TTR courses, and an expected level of entrustment students should demonstrate in each skill on TTR course completion. METHOD: A modified Delphi approach was used with 3 survey iterations between 2020 and 2022 to establish skills to be included in a TTR course. Nine TTR experts suggested general candidate skills and conducted a literature search to ensure no vital skills were missed. A stakeholder panel was solicited from email lists of TTR educators, residency program directors, and residents at the panelists' institutions. Consensus was defined as more than 75% of participants selecting a positive inclusion response. An entrustment questionnaire asked panelists to assign a level of expected entrustment to each skill, with 1 indicating observation only and 6 indicating perform independently. RESULTS: The stakeholder panel initially consisted of 118 respondents with representation across educational contexts and clinical specialties. Response rates were 54% in iteration 2, 42% in iteration 3, and 33% on the entrustment questionnaire. After 3 iterations, 54 skills met consensus and were consolidated into 37 final skills categorized into 18 clinical skills (e.g., assessment and management of inpatient concerns), 14 communication skills (e.g., delivering serious news or having difficult conversations), 4 personal and professional skills (e.g., prioritization of clinical tasks), and 1 procedural skill (mask ventilation). Median entrustment levels were reported for all skills, with 19 skills having a level of expected entrustment of 4 (perform independently and have all findings double-checked). CONCLUSIONS: These consensus skills can serve as the foundation of a standardized national TTR curriculum framework. Entrustment guidance may help educational leaders optimize training and allocation of resources for TTR curriculum development and implementation.

The inseparability of context and clinical reasoning.

Early descriptions of clinical reasoning have described a dual process model that relies on analytical or nonanalytical approaches to develop a working diagnosis. In this classic research, clinical reasoning is portrayed as an individual-driven cognitive process based on gathering information from the patient encounter, forming mental representations that rely on previous experience and engaging developed patterns to drive working diagnoses and management plans. Indeed, approaches to patient safety, as well as teaching and assessing clinical reasoning focus on the individual clinician, often ignoring the complexity of the system surrounding the diagnostic process. More recent theories and evidence portray clinical reasoning as a dynamic collection of processes that takes place among and between persons across clinical settings. Yet, clinical reasoning, taken as both an individual and a system process, is insufficiently supported by theories of cognition based on individual clinicals and lacks the specificity needed to describe the phenomenology of clinical reasoning. In this review, we reinforce that the modern healthcare ecosystem - with its people, processes and technology - is the context in which health care encounters and clinical reasoning take place.

Phonological impairments in Hindi aphasics: Error analyses and cross-linguistic comparisons.

We assessed phonological and apraxic impairments in Hindi persons with aphasia (PwA) and compared them to Italian PwA reported in previous studies. Overall, we found strong similarities. Phonological errors were present across production tasks (repetition, reading and naming), most errors were non-lexical and, among those, a majority involved individual phonemes. There were significant effects of length, but not frequency. Hindi PwA, like the Italian PwA, showed strong effects of syllabic structure, with most errors occurring on consonants and weak syllabic positions, preserving syllable structure and simplifying phonemes or syllabic templates. These similarities were modulated by some language-specific patterns. Vowel insertions were more common in Hindi, possibly due to the presence of a central vowel, and segmental simplifications concentrated on marked aspiration and retroflection features. We hope our study will encourage further research in Hindi and other Indian languages. This will improve clinical diagnosis and our understanding of cross-linguistic differences.

Exploring Competency-Based Medical Education Through the Lens of the UME-GME Transition: A Qualitative Study.

PURPOSE: Competency-based medical education (CBME) represents a shift to a paradigm with shared definitions, explicit outcomes, and assessments of competence. The groundwork has been laid to ensure all learners achieve the desired outcomes along the medical education continuum using the principles of CBME. However, this continuum spans the major transition from undergraduate medical education (UME) to graduate medical education (GME) that is also evolving. This study explores the experiences of medical educators working to use CBME assessments in the context of the UME-GME transition and their perspectives on the existing challenges. METHOD: This study used a constructivist-oriented qualitative methodology. In-depth, semistructured interviews of UME and GME leaders in CBME were performed between February 2019 and January 2020 via Zoom. When possible, each interviewee was interviewed by 2 team members, one with UME and one with GME experience, which allowed follow-up questions to be pursued that reflected the perspectives of both UME and GME educators more fully. A multistep iterative process of thematic analysis was used to analyze the transcripts and identify patterns across interviews. RESULTS: The 9 interviewees represented a broad swath of UME and GME leadership positions, though most had an internal medicine training background. Analysis identified 4 overarching themes: mistrust (a trust chasm exists between UME and GME); misaligned goals (the residency selection process is antithetical to CBME); inadequate communication (communication regarding competence is infrequent, often unidirectional, and lacks a shared language); and inflexible timeframes (current training timeframes do not account for individual learners' competency trajectories). CONCLUSIONS: Despite the mutual desire and commitment to move to CBME across the continuum, mistrust, misaligned goals, inadequate communication, and inflexible timeframes confound such efforts of individual schools and programs. If current efforts to improve the UME-GME transition address the themes identified, educators may be more successful implementing CBME along the continuum.

Characterization of antibiotic overuse for common infectious disease states at hospital discharge.

Objective: To evaluate rates of and outcomes associated with antibiotic overuse at hospital discharge for patients with common infectious diseases states. Design: Single-center, respective cohort study. Setting: A large, academic medical center in the Midwest United States. Patients: Adult patients who received antibiotics for community-acquired pneumonia (CAP), uncomplicated cystitis, or mild, non-purulent cellulitis. Patients were excluded if they did not receive antibiotic(s) upon hospital discharge, were pregnant, severely immunocompromised, had concomitant infections, died during hospitalization, or were transferred to another hospital or to an intensive care unit. Methods: Data were abstracted from the electronic medical record of ambulatory antibiotic orders for included patients based on inpatient encounters from August 1, 2021 through July 31, 2022. Results: Of the 182 patients included in the study, antibiotic overuse was common for all three infectious disease states: CAP (n = 87/125, 69.6%), uncomplicated cystitis (n = 21/28, 75.0%), mild, non-purulent cellulitis (n = 28/29, 96.6%). The prevailing reason for overuse was excessive antibiotic duration (n = 127/182, 69.8%; mean antibiotic duration 5.39 vs. 8.32 days, p = 0.001). Antibiotic overuse was associated with approximately one additional day in the hospital (2.48 vs. 3.32 days, p = 0.001), and an increase in emergency department visits within 30 days after discharge (1 vs. 31, p = 0.001) compared to patients without antibiotic overuse at discharge. Conclusion: Antibiotic overuse was prevalent upon hospital discharge for these three common infectious disease states. Transitions of care should be prioritized as an area for antimicrobial stewardship intervention.

The Effect of Pass/Fail Exam Grading on Exam Performance in a Pediatric Clerkship.

OBJECTIVE: Clerkship grades are a component of determining a residency candidate's competitiveness. In 2017, the University of Minnesota Medical School's pediatric clerkship transitioned its standardized multiple-choice exam, the Aquifer Pediatrics Examination, to pass/fail with eligibility for honors being determined by clinical performance, not exam performance. We assessed the effect this change had on Aquifer exam performance and evaluated for correlation between Aquifer exam performance and clinical evaluation scores in order to gather insight into the validity of each type of assessment with respect to one another. METHODS: We analyzed de-identified data from 750 medical students between the academic years of 2016 to 2017 and 2019 to 2020. Individual Aquifer exam scores were compared to individual clinical performance scores. Differences in exam performance before and after the transition to pass/fail were investigated with a two-sample t-test and Cohen's d for effect size. RESULTS: No correlation was found between Aquifer exam scores and clinical performance scores. The mean Aquifer exam score prior to the transition to pass/fail was 80.02 ± 7.51 while the mean after the exam was made pass/fail was 77.8 ± 7.42. This difference was statistically significant (P < .001) with a Cohen's d (effect size) of 0.297. CONCLUSIONS: A lack of correlation between the Aquifer exam scores and clinical performance scores was found. There was a small yet statistically significant decrease in Aquifer exam scores after the change to pass/fail; it is not clear if this represents a meaningful decrease in learning by students.

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

Tissue-specific DNA methylation variability and its potential clinical value.

Complex diseases have multifactorial etiologies making actionable diagnostic biomarkers difficult to identify. Diagnostic research must expand beyond single or a handful of genetic or epigenetic targets for complex disease and explore a broader system of biological pathways. With the objective to develop a diagnostic tool designed to analyze a comprehensive network of epigenetic profiles in complex diseases, we used publicly available DNA methylation data from over 2,400 samples representing 20 cell types and various diseases. This tool, rather than detecting differentially methylated regions at specific genes, measures the intra-individual methylation variability within gene promoters to identify global shifts away from healthy regulatory states. To assess this new approach, we explored three distinct questions: 1) Are profiles of epigenetic variability tissue-specific? 2) Do diseased tissues exhibit altered epigenetic variability compared to normal tissue? 3) Can epigenetic variability be detected in complex disease? Unsupervised clustering established that global epigenetic variability in promoter regions is tissue-specific and promoter regions that are the most epigenetically stable in a specific tissue are associated with genes known to be essential for its function. Furthermore, analysis of epigenetic variability in these most stable regions distinguishes between diseased and normal tissue in multiple complex diseases. Finally, we demonstrate the clinical utility of this new tool in the assessment of a multifactorial condition, male infertility. We show that epigenetic variability in purified sperm is correlated with live birth outcomes in couples undergoing intrauterine insemination (IUI), a common fertility procedure. Men with the least epigenetically variable promoters were almost twice as likely to father a child than men with the greatest number of epigenetically variable promoters. Interestingly, no such difference was identified in men undergoing in vitro fertilization (IVF), another common fertility procedure, suggesting this as a treatment to overcome higher levels of epigenetic variability when trying to conceive.

Framework and Schema are False Synonyms: Defining Terms to Improve Learning.

Clinical reasoning is an essential expertise of health care professionals that includes the complex cognitive processes that lead to diagnosis and management decisions. In order to optimally teach, learn, and assess clinical reasoning, it is imperative for teachers and learners to have a shared understanding of the language. Currently, educators use the terms schema and framework interchangeably but they are distinct concepts. In this paper, we offer definitions for schema and framework and use the high-stakes field of aviation to demonstrate the interplay of these concepts. We offer examples of framework and schema in the medical education field and discuss how a clear understanding of these concepts allows for greater intentionality when teaching and assessing clinical reasoning.