Faculty Bibliography

BACKGROUND/UNASSIGNED:A lack of educational continuity creates disorienting friction at the onset of residency. Few programs have harnessed the benefits of coaching, which can facilitate self-directed learning, competency development, and professional identity formation, to help ease this transition. OBJECTIVE/UNASSIGNED:To describe the process of training faculty Bridge Coaches for the Transition to Residency Advantage (TRA) program for interns. METHODS/UNASSIGNED:Nineteen graduate faculty educators participated in a coaching training course with formative skills assessment as part of a faculty development program starting in January 2020. Surveys (n = 15; 79%) and a focus group (n = 7; 37%) were conducted to explore the perceived impact of the training course on coaching skills, perceptions of coaching, and further program needs during the pilot year of the TRA program. RESULTS/UNASSIGNED:Faculty had strong skills around establishing trust, authentic listening, and supporting goal-setting. They required more practice around guiding self-discovery and following a coachee-led agenda. Faculty found the training course to be helpful for developing coaching skills. Faculty embraced their new roles as coaches and appreciated having a community of practice with other coaches. Suggestions for improvement included more opportunities to practice and receive feedback on skills and additional structures to further support TRA program encounters with coaches. CONCLUSIONS/UNASSIGNED:The faculty development program was feasible and had good acceptance among participants. Faculty were well-suited to serve as coaches and valued the coaching mindset. Adequate skills reinforcement and program structure were identified as needs to facilitate a coaching program in graduate medical education.

BACKGROUND:Experiential learning through patient care is fundamental to graduate medical education. Despite this, the actual content to which trainees are exposed in clinical practice is difficult to quantify and is poorly characterized. There remains an unmet need to define precisely how residents' patient care activities inform their educational experience.  METHODS: Using a recently-described crosswalk tool, we mapped principal ICD-10 discharge diagnosis codes to American Board of Internal Medicine (ABIM) content at four training hospitals of a single Internal Medicine (IM) Residency Program over one academic year to characterize and compare residents' clinical educational experiences. Frequencies of broad content categories and more specific condition categories were compared across sites to profile residents' aggregate inpatient clinical experiences and drive curricular change. RESULTS:There were 18,604 discharges from inpatient resident teams during the study period. The crosswalk captured > 95% of discharges at each site. Infectious Disease (ranging 17.4 to 39.5% of total discharges) and Cardiovascular Disease (15.8 to 38.2%) represented the most common content categories at each site. Several content areas (Allergy/Immunology, Dermatology, Obstetrics/Gynecology, Ophthalmology, Otolaryngology/Dental Medicine) were notably underrepresented (≤ 1% at each site). There were significant differences in the frequencies of conditions within most content categories, suggesting that residents experience distinct site-specific clinical content during their inpatient training. CONCLUSIONS:There were substantial differences in the clinical content experienced by our residents across hospital sites, prompting several important programmatic and curricular changes to enrich our residents' hospital-based educational experiences.

BACKGROUND:Residents and fellows receive little feedback on their clinical reasoning documentation. Barriers include lack of a shared mental model and variability in the reliability and validity of existing assessment tools. Of the existing tools, the IDEA assessment tool includes a robust assessment of clinical reasoning documentation focusing on four elements (interpretive summary, differential diagnosis, explanation of reasoning for lead and alternative diagnoses) but lacks descriptive anchors threatening its reliability. OBJECTIVE:Our goal was to develop a valid and reliable assessment tool for clinical reasoning documentation building off the IDEA assessment tool. DESIGN, PARTICIPANTS, AND MAIN MEASURES/UNASSIGNED:The Revised-IDEA assessment tool was developed by four clinician educators through iterative review of admission notes written by medicine residents and fellows and subsequently piloted with additional faculty to ensure response process validity. A random sample of 252 notes from July 2014 to June 2017 written by 30 trainees across several chief complaints was rated. Three raters rated 20% of the notes to demonstrate internal structure validity. A quality cut-off score was determined using Hofstee standard setting. KEY RESULTS/RESULTS:The Revised-IDEA assessment tool includes the same four domains as the IDEA assessment tool with more detailed descriptive prompts, new Likert scale anchors, and a score range of 0-10. Intraclass correlation was high for the notes rated by three raters, 0.84 (95% CI 0.74-0.90). Scores ≥6 were determined to demonstrate high-quality clinical reasoning documentation. Only 53% of notes (134/252) were high-quality. CONCLUSIONS:The Revised-IDEA assessment tool is reliable and easy to use for feedback on clinical reasoning documentation in resident and fellow admission notes with descriptive anchors that facilitate a shared mental model for feedback.

PURPOSE:Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. METHOD:Categorical applicant data from the 2018, 2019, and 2020 residency application cycles (n = 8,243 applicants) at one large internal medicine residency program were downloaded from the Electronic Residency Application Service and linked to the outcome measure: interview invitation by human reviewers (n = 1,235 invites). An ML model using gradient boosting was designed using training data (80% of applicants) with over 60 applicant features (e.g., demographics, experiences, academic metrics). Model performance was validated on held-out data (20% of applicants). Sensitivity analysis was conducted without United States Medical Licensing Examination (USMLE) scores. An interactive DST incorporating the ML model was designed and deployed that provided applicant- and cohort-level visualizations. RESULTS:The ML model areas under the receiver operating characteristic and precision recall curves were 0.95 and 0.76, respectively; these changed to 0.94 and 0.72, respectively, with removal of USMLE scores. Applicants' medical school information was an important driver of predictions-which had face validity based on the local selection process-but numerous predictors contributed. Program directors used the DST in the 2021 application cycle to select 20 applicants for interview that had been initially screened out during human review. CONCLUSIONS:The authors developed and validated an ML algorithm for predicting residency interview offers from numerous application elements with high performance-even when USMLE scores were removed. Model deployment in a DST highlighted its potential for screening candidates and helped quantify and mitigate biases existing in the selection process. Further work will incorporate unstructured textual data through natural language processing methods.

OBJECTIVES/OBJECTIVE:Our discussant's thoughtful consideration of the patient's case allows for review of three maxims of medicine: Occam's razor (the simplest diagnosis is the most likely to be correct), Hickam's dictum (multiple disease entities are more likely than one), and Crabtree's bludgeon (the tendency to make data fit to an explanation we hold dear). CASE PRESENTATION/METHODS:A 66-year-old woman with a history of hypertension presented to our hospital one day after arrival to the United States from Guinea with chronic daily vomiting, unintentional weight loss and progressive shoulder pain. Her labs are notable for renal failure, nephrotic range proteinuria and normocytic anemia while her shoulder X-ray shows osseous resorption in the lateral right clavicle. Multiple myeloma became the team's working diagnosis; however, a subsequent shoulder biopsy was consistent with follicular thyroid carcinoma. Imaging suggested the patient's renal failure was more likely a result of a chronic, unrelated process. CONCLUSIONS:It is tempting to bludgeon diagnostic possibilities into Occam's razor. Presumption that a patient's signs and symptoms are connected by one disease process often puts us at a cognitive advantage. However, atypical presentations, multiple disease processes, and unique populations often lend themselves more to Hickam's dictum than to Occam's razor. Diagnostic aids include performing a metacognitive checklist, engaging analytic thinking, and acknowledging the imperfections of these axioms.

BACKGROUND: The transition from medical school to residency is characterized by an abrupt transition of learning needs and goals. Coaching is a promising intervention to support individual learning and growth trajectories of learners. It is uncommon for medical school faculty to have undergone training as coaches. We explored our faculty's perceptions and skills after instituting a new coaching program.
METHOD(S): Faculty advisors (N=12) and GME (N=16) participated in a coaching development program and in community of practice meetings where challenging coaching scenarios were shared. GME faculty also participated in a Group Objective Structured Clinical Exam (GOSCE) to practice and receive feedback on their skills. Peer-faculty observers and resident raters used behaviorally grounded checklists to assess faculty performance. We conducted 2 focus groups: 1) UME advisors engaged in longitudinal coaching (n=9) and 2) GME faculty participating in the coaching development program (n=8) to better understand how faculty make sense of and put into practice these new coaching roles and skills.
RESULT(S): Simple thematic coding showed that both groups emphasized the blurring of the many roles they serve when interacting with trainees and struggled with recognizing both which hat to wear (role to adopt) and which skills to call upon in specific situations. UME advisors who have dedicated advising/coaching roles reported assuming multiple roles at different times with their same students. Many of the GME coaches serve as Associate Program Directors, and described adopting a coaching frame of reference (mentality) and requiring external reinforcement for coaching skills. Some reported realizing after the fact that coaching would have been a valuable approach. Faculty newer to their role felt more successful in engaging in coaching mindset and coaching. Faculty were curious about how trainees would feel about this approach and anticipated that some would appreciate this more than others. 12 faculty participated in a three station Coaching GOSCE. Both resident raters and faculty peer raters suggested faculty coaches were able to establish trust and engage in authentic listening. Coaches negotiated the tension between empathetic listening with supporting goal-setting. Residents provided slightly lower ratings than peer observers on coaches' ability to ask questions and assume a coachee- focused agenda.
CONCLUSION(S): Medical educators may benefit from obtaining coaching skills, but deliberate training in how these skills complement, and differ, from existing skills requires both didactic and experiential learning. Cultivating a community of practice and offering opportunities for deliberate practice, observation and feedback is essential for medical educators to achieve mastery as coaches. LEARNING OBJECTIVE #1: Identify and perform appropriate learning activities to guide personal and professional development (PBL) LEARNING OBJECTIVE #2: Understand and apply core longitudinal coaching skills (Professionalism)

BACKGROUND: Clinical reasoning (CR) is a core component of medical training, yet residents often receive little feedback on their CR documentation. Here we describe the process of developing a machine learning (ML) algorithm for feedback on CR documentation to increase the frequency and quality of feedback in this domain.
METHOD(S): To create this algorithm, note quality first had to be rated by gold standard human rating. We selected the IDEA Assessment Tool-a note rating instrument across four domains (I=Interpretive summary, D=Differential diagnosis, E=Explanation of reasoning, A=Alternative diagnoses explained) that uses a 3-point Likert scale without descriptive anchors. To develop descriptive anchors we conducted an iterative process reviewing notes from the EHR written by medicine residents and validated the Revised-IDEA Assessment Tool using Messick's framework- content validity, response process, relation to other variables, internal structure, and consequences. Using the Hofstee standard setting method, cutoffs for high quality clinical reasoning for the IDEA and DEA scores were set. We then created a dataset of expertrated notes to create the ML algorithm. First, a natural language processing software was applied to the set of notes that enabled recognition and automatic encoding of clinical information as a diagnosis or disease (D's), a sign or symptom (E or A), or semantic qualifier (e.g. most likely). Input variables to the ML algorithm included counts of D's, E/A's, semantic qualifiers, and proximity of semantic qualifiers to disease/ diagnosis. ML output focused on DEA quality and was binarized to low or high quality CR. Finally, 200 notes were randomly selected for human validation review comparing ML output to human rated DEA score.
RESULT(S): The IDEA and DEA scores ranged from 0-10 and 0-6, respectively. IDEA score of >= 6.5 and a DEA score of >= 3 was deemed high quality. 252 notes were rated to create the dataset and 20% were rated by 3 raters with high intraclass correlation 0.84 (95% CI 0.74-0.90). 120 of these notes comprised the testing set for ML model development. The logistic regression model was the best performing model with an AUC 0.87 and a positive predictive value (PPV) of 0.65. 48 (40%) of the notes were high quality. There was substantial interrater reliability between ML output and human rating on the 200 note validation set with a Cohen's Kappa 0.64.
CONCLUSION(S): We have developed a ML algorithm for feedback on CR documentation that we hypothesize will increase the frequency and quality of feedback in this domain. We have subsequently developed a dashboard that will display the output of the ML model. Next steps will be to provide internal medicine residents' feedback on their CR documentation using this dashboard and assess the impact this has on their documentation quality. LEARNING OBJECTIVE #1: Describe the importance of high quality documentation of clinical reasoning. LEARNING OBJECTIVE #2: Identify machine learning as a novel assessment tool for feedback on clinical reasoning documentation

BACKGROUND:Few programs train residents in recognizing and responding to distressed colleagues at risk for suicide. AIM/OBJECTIVE:To assess interns' ability to identify a struggling colleague, describe resources, and recognize that physicians can and should help colleagues in trouble. SETTING/METHODS:Residency programs at an academic medical center. PARTICIPANTS/METHODS:One hundred forty-five interns. PROGRAM DESIGN/UNASSIGNED:An OSCE case was designed to give interns practice and feedback on their skills in recognizing a colleague in distress and recommending the appropriate course of action. Embedded in a patient "sign-out" case, standardized health professionals (SHP) portrayed a resident with depressed mood and an underlying drinking problem. The SHP assessed intern skills in assessing symptoms and directing the resident to seek help. PROGRAM EVALUATION/RESULTS:Interns appreciated the opportunity to practice addressing this situation. Debriefing the case led to productive conversations between faculty and residents on available resources. Interns' skills require further development: while 60% of interns asked about their colleague's emotional state, only one-third screened for depression and just under half explored suicidal ideation. Only 32% directed the colleague to specific resources for his depression (higher among those that checked his emotional state, 54%, or screened for depression, 80%). DISCUSSION/CONCLUSIONS:This OSCE case identified varying intern skill levels for identifying and assessing a struggling colleague while also providing experiential learning and supporting a culture of addressing peer wellness.

Case Summary: A 66-year-old woman with hypertension presented to the hospital one day after arrival to New York City from Guinea with chronic daily vomiting, unintentional weight loss, progressive shoulder pain, and a subacute pruritic rash. On exam, patient was hypertensive, had limited range of motion in her right shoulder, scaling plaques over legs and trunk, and asterixis. Labs were notable for a creatinine of 15 with normocytic anemia. Calcium was normal. The patient was admitted to hospital for acute renal failure and further workup. Diagnoses: Primary end stage renal disease of unknown etiology; and follicular thyroid carcinoma with metastases to the clavicle and lungs with paraneoplastic rash.
Discussion(s): This case highlights the diagnostic principles of Occam's razor, Hickam's dictum, and Crabtree's bludgeon. The initial differential diagnosis and workup proceeded with the expectation of a unifying diagnosis to explain the wide constellation of presenting symptoms. But after common systemic unifying diagnoses, including multiple myeloma and other infiltrative processes, were ruled out, it became evident that two processes were at play. The first was end stage renal failure, likely long-standing given imaging findings suggestive of chronicity. The second, a common malignancy with uncommon metastases. Perhaps an absence of regular interactions with the healthcare system prior to presentation increases like the positive predictive value of Hickam's dictum. Additionally, common diseases with uncommon presentations are still more common than zebras. Lessons from this case allows us to expand our thinking beyond the dogma of any one diagnostic principle, to avoid type I thinking and help us to direct our diagnostic reasoning