Lessons Learned from Remediation of Clinical Reasoning: Identifying Deficits and Teaching in Real Time Andrew S Parsons, MD, MPH Karen M Warburton, MD
Outline • 3 case vignettes • Clinical reasoning as a concept • Clinical reasoning deficits in graduate medical education • Our approach to remediating clinical reasoning at UVA • Applying the lessons learned from remediation to every day teaching
Case 1 – PGY2 • Trouble managing a patient list • Handoffs are ineffective • Seems to lack “the big picture” with patients • As an intern, presentations were “a bit all over the place,” struggled to communicate effectively when calling consults • As a medical student, comments suggest that “presentations improved over the course of the rotation”
Case 2 – PGY2 • Easily overwhelmed and seems disorganized • Not responding appropriately to urgent situations • MET call – needs a lot of supervision • MICU attending concerned that prioritization of issues during presentations does not reflect recognition of urgency, sick versus not sick • Can’t triage her “to do” list • Seems to “miss the forest for the trees”
Case 3 – PGY3 • Extremely detail oriented • Presents expansive differential diagnosis lists even after diagnosis is relatively certain • Orders expensive/invasive imaging and procedures, labs multiple times per day on all patients, long antibiotics courses • Uncomfortable with discharge • Focused on “worst case scenario” • As a medical student, Honors core clerkships
What do all of these learners have in common?
KNOWLEDGE (Content and Process) “A complex patient-centered cyclical process of information gathering, information integration and interpretation, and forming a working diagnosis. This process involves hypothesis generation and updating prior probabilities as new information is learned. As the diagnostic process proceeds, a broad list of potential diagnoses may be narrowed into fewer KNOWLEDGE potential options. Throughout the process, there is an ongoing assessment of whether sufficient information has been collected. The end goal is not certainty, but a reduction in diagnostic uncertainty sufficient enough to make optimal decisions about management/treatment.” Bowen JL. N Engl J Med. 2006;355:2217-25; Balogh et al., Improving Diagnosis in Health Care.
Knowledge is necessary but… Disease Physiology Pathophysiology (signs and symptoms) Goal: Causal Understanding (Content) Organize and Disparate Manipulate Diagnosis Biomedical Biomedical Facts Facts Goal: Categorization (Process) Schmidt HG. Med Educ 2015; Custers EJ. Med Teach 2015
Foundational Pillars of Teaching Decision-Making • Dual Process Theory • Problem Representation • Illness Script
Dual Process Theory Fast stimulus to switch Pattern Recognition (illness scripts, heuristics) toggle Clinical Diagnosis Problem Analytic (logical thinking) Information Processing Slow Croskerry P. Acad Med. 2009; 84:1022-28.
Problem Representation • A fluid concise summary that highlights the defining features of a case, helping clinicians generate a focused differential diagnosis. • Develops context (framing) (FRAMING) • Prompts illness scripts Evolves during a clinical encounter to answer 3 Q’s: a) Who is the patient? (demographics and risk factors) b) What is the temporal pattern of illness? (use semantic qualifiers) c) What is the key signs and symptoms? (combine key features) PRIORITIZED DIFFERENTIAL DIAGNOSIS Bowen JL. N Engl J Med. 2006
Illness Script •Demographics, risk •Anatomy, physiology, A mental representation factors, exposures biochemistry, pathology, immunology, of a disease, stored in pharmacology, etc. LTM. Repeated exposures to different presentations of the Epidemiology Mechanisms same disease (or CC) allow clinicians to link distinguishing features and recognize patterns, Clinical elaborating on prior Time Course Presentation scripts or creating new ones. Scripts are handled in working memory as a •Key differentiating •Duration and Pattern of features Sx (acute, chronic, chunk. constant, waxing and waning…) K Gavinski, et al. Acad Med. 2019
Developing Expertise Working Memory Capacity 7 + 2 Novice Expert Items or “chunks” Scripts • The # and variability of illness scripts stored in long term memory • The richness of the coding and retrieval networks to access scripts when they are needed Cutrer WB, Acad Med 2017
Putting it all Together Working Diagnosis Refine Hypotheses Problem Representation Data Gathering Hypothesis Generation Parsons A, Clancy C, Rencic J, Warburton KM. Under review for publication
Clinical reasoning deficits among medical trainees • GME remediation data reported in surveys and single-center reviews • Not an ACGME competency, so data not captured in surveys • Probably often “misdiagnosed” • Medical knowledge, organization, communication, professionalism • University of Colorado • 25-30% of residents, and 40-45% of medical students, referred to a remediation program • University of Pennsylvania • 40-45% of learners referred to a combined UME-GME remediation program
UVA COACH Warburton KM, Shahane, AS. ACGME 2019, NEGEA 2019
UVA COACH Data, 2016-current • 65 referrals • 49 residents • 16 fellows • 15 departments • 1/3 self referrals Unpublished data
CR Coaching Program at UVA • Lead Coach – Andrew Parsons, MD, MPH Coachi hing C Clini nical al R Reas asoni ning: A Remediati tion on P Program • Clinical Reasoning Subcommittee of COACH COACH • 11 faculty members from 8 departments Clinical R Reasoning S Subcommittee • Mission 1. Train members in a standardized approach to the diagnosis and coaching of clinical reasoning deficits February 13, 13, 201 2019 among graduate medical learners 2. Develop durable materials that a variety of academic departments at UVA can use for coaching
Diagnose the deficit Working Diagnosis Refine Hypotheses Problem Representation Data Gathering Hypothesis Generation
The Eyes Have It: Structured Reflection: Practice visual diagnosis to List aspects of patient Then treat… enhance pattern recognition presentation that are concordant, discordant, and expected but missing for leading diagnoses Persuade the MD: Assuming the role of a patient, learner describes how they would convince a physician of a specific diagnosis Articulated Problem Representation: Working Use key features and Diagnosis semantic qualifiers to Findings that craft a 1-2 sentence Matter: case summary Identify findings that have the biggest Refine impact on increasing Hypotheses or decreasing the Problem probability of Reverse Presentation: Representation diagnoses Start presentation with assessment to prime for feedback on selection and organization of subjective Highlighter: Data and objective data Identify key clinical Gathering features in a written H&P based on Hypothesis possible diagnoses Generation Think Base Rate: Categorize initial differential in terms of common, atypical, rare, and “can’t miss” diagnoses Priming for Co-Selection: Outline differential based on chief complaint alone; propose three Scaffolding: diagnoses followed by five questions Start with chief complaint and and five exam findings to promote demographics, use an analytic approach hypothesis-driven reasoning (anatomic, pathophysiology, or systems- based schema or mnemonics) to craft broad differential Parsons A, Clancy C, Rencic J, Warburton KM. Under review for publication
Remember Case 1 Working • PGY2 Diagnosis • Trouble managing a patient list Refine Hypotheses • Handoffs are ineffective Problem Representation • Seems to lack “the big picture” with patients Data Gathering • Intern presentations were “a bit all over the place” Hypothesis Generation • Struggles calling consults
Diagnosis the deficit Clue: multiple forms of communication (presentation, handoffs/sign-out, calling consults) lack clear, concise information Dx: Data gathering and/or problem representation Tx: Highlighter, priming for co-selection, articulated problem representation, reverse presentation
Remember Case 2 • PGY2 Working Diagnosis • Easily overwhelmed and seems disorganized Refine Hypotheses Problem • Not responding appropriately to urgent situations Representation • MET call – needs a lot of supervision • MICU concerned cannot identify “sick versus not sick” Data Gathering Hypothesis • Can’t triage her “to do” list Generation • Seems to “miss the forest for the trees”
Diagnosis the deficit Clue: Struggles with urgency/triage Dx: Could be any/all. Hypothesis generation? Tx: Standardized framework, prioritized to-do list, forest checklist
Remember Case 3 • PGY3 Working Diagnosis • Extremely detail oriented, presents expansive Ddx lists even after diagnosis is relatively certain Refine Hypotheses • Known for ordering expensive/invasive imaging and Problem Representation procedures, labs multiple times per day on all patients, long abx courses Data Gathering • Uncomfortable with discharge Hypothesis Generation • Focused on “worst case scenario” • As a medical student, honors core clerkships
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