Faculty Bibliography

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.

OBJECTIVES/OBJECTIVE:Cognitive biases can result in clinical reasoning failures that can lead to diagnostic errors. Autobrewery syndrome is a rare, but likely underdiagnosed, condition in which gut flora ferment glucose, producing ethanol. It most frequently presents with unexplained episodes of inebriation, though more case studies are necessary to better characterize the syndrome. CASE PRESENTATION/METHODS:This is a case of a 41-year old male with a past medical history notable only for frequent sinus infections, who presented with recurrent episodes of acute pancreatitis. In the week prior to his first episode of pancreatitis, he consumed four beers, an increase from his baseline of 1-2 drinks per month. At home, he had several episodes of confusion, which he attributed to fatigue. He underwent laparoscopic cholecystectomy and testing for genetic and autoimmune causes of pancreatitis, which were non-revealing. He was hospitalized 10 more times during that 9-month period for acute pancreatitis with elevated transaminases. During these admissions, he had elevated triglycerides requiring an insulin drip and elevated alcohol level despite abstaining from alcohol for the prior eight months. His alcohol level increased after consumption of complex carbohydrates, confirming the diagnosis of autobrewery syndrome. CONCLUSIONS:Through integrated commentary on the diagnostic reasoning process, this case underscores how overconfidence can lead to premature closure and anchoring resulting in diagnostic error. Using a metacognitive overview, case discussants describe the importance of structured reflection and a standardized approach to early hypothesis generation to navigate these cognitive biases.

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: 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

The initial phase of the SARS-CoV-2 pandemic in the United States saw rapidly-rising patient volumes along with shortages in personnel, equipment, and intensive care unit (ICU) beds across many New York City hospitals. As our hospital wards quickly filled with unstable, hypoxemic patients, our hospitalist group was forced to fundamentally rethink the way we triaged and managed cases of hypoxemic respiratory failure. Here, we describe the oxygenation protocol we developed and implemented in response to changing norms for acuity on inpatient wards. By reflecting on lessons learned, we re-evaluate the applicability of these oxygenation strategies in the evolving pandemic. We hope to impart to other providers the insights we gained with the challenges of management reasoning in COVID-19.

BACKGROUND:Despite evidence of socio-demographic disparities in outcomes of COVID-19, little is known about characteristics and clinical outcomes of patients admitted to public hospitals during the COVID-19 outbreak. OBJECTIVE:To assess demographics, comorbid conditions, and clinical factors associated with critical illness and mortality among patients diagnosed with COVID-19 at a public hospital in New York City (NYC) during the first month of the COVID-19 outbreak. DESIGN/METHODS:Retrospective chart review of patients diagnosed with COVID-19 admitted to NYC Health + Hospitals / Bellevue Hospital from March 9th to April 8th, 2020. RESULTS:A total of 337 patients were diagnosed with COVID-19 during the study period. Primary analyses were conducted among those requiring supplemental oxygen (n = 270); half of these patients (135) were admitted to the intensive care unit (ICU). A majority were male (67.4%) and the median age was 58 years. Approximately one-third (32.6%) of hypoxic patients managed outside the ICU required non-rebreather or non-invasive ventilation. Requirement of renal replacement therapy occurred in 42.3% of ICU patients without baseline end-stage renal disease. Overall, 30-day mortality among hypoxic patients was 28.9% (53.3% in the ICU, 4.4% outside the ICU). In adjusted analyses, risk factors associated with mortality included dementia (adjusted risk ratio (aRR) 2.11 95%CI 1.50-2.96), age 65 or older (aRR 1.97, 95%CI 1.31-2.95), obesity (aRR 1.37, 95%CI 1.07-1.74), and male sex (aRR 1.32, 95%CI 1.04-1.70). CONCLUSION/CONCLUSIONS:COVID-19 demonstrated severe morbidity and mortality in critically ill patients. Modifications in care delivery outside the ICU allowed the hospital to effectively care for a surge of critically ill and severely hypoxic patients.

Introduction: Educators have theorized that interventions grounded in dual process theory (DPT) and script theory (ST) may improve the diagnostic reasoning process of physicians but little empirical evidence exists. Methods: In this quasi-experimental study, we assessed the impact of a clinical reasoning (CR) curriculum grounded in DPT and ST on medicine residents participating in one of three groups during a 6-month period: no, partial, or full intervention. Residents completed the diagnostic thinking inventory (DTI) at baseline and 6 months. At 6 months, participants also completed a post-survey assessing application of concepts to cases. Results: There was a significant difference between groups in application of concepts (no intervention 1.6 (0.65) compared to partial 2.3 (0.81) and full 2.2 (0.91), p = 0.05), as well as describing cases in problem representation format (no intervention 1.2 (0.38) and partial 1.5 (0.55) compared to full 2.1 (0.93), p = 0.004). There was no significant difference in change in DTI scores (no intervention 7.0 (16.3), partial 8.8 (9.8), full 7.8 (12.0)). Conclusions: Residents who participated in a CR curriculum grounded in DPT and ST were effective in applying principles of CR in cases from their practice. To our knowledge, this is the first workplace-based CR educational intervention study showing differences in the reasoning process residents apply to patients.

Background Clinical reasoning (CR) is a core competency in medical education. Few studies have examined efforts to train faculty to teach CR and lead CR curricula in medical schools and residencies. In this report, we describe the development and preliminary evaluation of a faculty development workshop to teach CR grounded in CR theory. Methods Twenty-six medicine faculty (nine hospitalists and 17 subspecialists) participated in a workshop that introduced a framework to teach CR using an interactive, case-based didactic followed by role-play exercises. Faculty participated in pre- and post-Group Observed Structured Teaching Exercises (GOSTE), completed retrospective pre-post assessments (RPPs), and made commitment to change statements (CTCs). Results In the post-GOSTE, participants significantly improved in their use of problem representation and illness scripts to teach CR. RPPs revealed that faculty were more confident in their ability and more likely to teach CR using educational strategies grounded in CR educational theory. At 2-month follow-up, 81% of participants reported partially implementing these teaching techniques. Conclusions After participating in this 3-h workshop, faculty demonstrated increased ability to use these teaching techniques and expressed greater confidence and an increased likelihood to teach CR. The majority of faculty reported implementing these newly learned educational strategies into practice.