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Diagnostic Decision-Making: How do we do it and how can we (and our learners) improve? META Scholars September 5, 2013 Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve? Objectives • Be able to describe the basic process of making a diagnosis • Acknowledge we struggle with making diagnoses • List several ways we can improve our diagnostic skills Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice How do Doctors Think? Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Data collection • • • • History Physical examination Laboratory studies Imaging studies Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Problem Representation • • • • • Making sense of the data obtained Identification of the key elements Categorization Semantic qualifiers Frame things (context is everything) Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Illness Scripts • Mental representations of the key elements of specific diagnoses – History – Physical – Labs – Imaging – Response to therapy Acute Coronary Syndrome Pericarditis Pulmonary embolism Aortic Dissection (AD) Epidemiology Older age, risk factors include diabetes, hypertension, dyslipidemia, family history, tobacco use Uremia, auto-immune disease, prior URI, recent MI or heart surgery, malignancy Risk factors of endothelial injury, hypercoaguability, and stasis: recent surgery, active cancer (e.g. adenocarcinoma), medications (e.g. OCP); immobility Older patient, HTN the primary risk. Younger patients also at risk (cocaine, collagen vascular, bicuspic aortic valve…) Time Course Acute onset, not necessarily preceded by exertional angina Acute, but may occur in setting of sub-acute or chronic disease Acute onset usually without progression, unless second PE Acute onset, usually constant Clinical Features (1) History (2) Exam (3) Labs (4)Imaging Advanced Studies 1) Chest pain, with crescendo to maximal pain; often dull and substernal, radiating to arms/shoulders; diaphoresis; dyspnea; nausea/vomiting, diaphoresis. 2) Tachycardia 3) Elevated cardiac biomarkers (troponin/CK), abnormal ECG (ST elevation/ depression, T wave changes) 4) Regional wall motion abnormality on echocardiogram 1) Sharp, stabbing chest pain radiating to back and trapezius ridge; improved with sitting forward 2) Pericardial friction rub (may be ephemeral, more pronounced with sitting forward) 3) Abnormal ECG (diffuse ST elevation, PR depression); elevated inflammatory markers (ESR, CRP) 4) Common: Pericardial effusion on echo or CT 1) Shortness of breath, pleuritic chest pain 2) Tachycardia; tachypnea; normal lung exam, 3. Common: positive Ddimer 4. Xray with minimal abnormalities; CT chest with pulmonary angiogram demonstrates a clot; V/Q scan with unmatched perfusion defect 1) Common: Sudden onset, severe ripping and tearing CP radiating to back Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Illness Script Selection • Match the problem formulation to the illness script Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice How do doctors think? • We’re not really sure, but we do have a general idea • A couple of key points: – Experience really matters – Lots of complexity Question 1: Image from Wikimedia Commons Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Question 2: Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice How it plays out…. • Bedside Clinical Reasoning – Hypothesis generation – Hypotheses refinement – Diagnostic testing – Causal reasoning – Diagnostic verification A Case • 69 year-old man with a history of CAD presents with chest pain – Acute coronary syndrome! Hypothesis Generation • Unlike prior MI • Pain is sharp and stabbing – Less likely ACS, maybe PE? – Pericarditis? • No associated dyspnea • Radiates through to the back – ?Aortic Dissection Hypothesis Refinement and Generation • Exam – Differential pulses in upper extremities – Aortic insufficiency murmur Causal Reasoning Hypothesis Refinement • CXR – Widened mediastinum • CT scan – Aortic dissection Diagnostic Testing and Verification • Bedside Clinical Reasoning – Hypothesis generation – Hypotheses refinement – Diagnostic testing – Causal reasoning – Diagnostic verification Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve? Definition of a Diagnostic Error: • A diagnosis that, on the basis of the eventual appreciation of more definitive information, was – Unintentionally delayed, or – Wrong, or – Missed altogether Question 3 What is your personal rate of diagnostic error? A) B) C) D) E) <1% 2-3% 5% 10-15% >20% Question 4 What is the overall rate of diagnostic error in medicine? A) B) C) D) E) <1% 2-3% 5% 10-15% >20% Rate of Diagnostic Error • Overall, likely rate of diagnostic error is about 10% • Error rate varies by specialty and study – Anatomic pathology 2-5% – ED up to 12% – Medical inpatient diagnosis ~6-8% Do these errors matter? • Account for up to 17% of adverse events • 40,000-80,000 US hospital deaths per year attributable to diagnostic error • 5% of all autopsies show a lethal diagnosis that could have been treated ante-mortem • Tort claims data (really expensive) JAMA 2002; 288:2405 What do these errors look like? Diagnosis Stroke Sub-arachnoid hemorrhage Pulmonary Tb Missed on initial evaluation 9% 5% 45% Acute Coronary Syndrome 2-3% Appendicitis 19% What causes these errors? • Three general categories of diagnostic error – “No Fault” (7%) • Very unusual presentations, patient-related error – Systems-related (19%) 46% • Technical failure, organizational issues – Cognitive errors (28%) • Faults in knowledge, data gathering, information processing or metacognition Arch Intern Med 2005;165:1493-1499. Basis of Cognitive Errors • Cognitive Errors – Faulty knowledge – Faulty data gathering – Faulty synthesis – Affective error Basis of Cognitive Errors • Cognitive Errors – Faulty knowledge – Faulty data gathering • Failure to ask or look • EMRs – Faulty synthesis – Affective error Red Flag Medicine • We often embrace “Red Flag Medicine” – Overly trusting of technology – Doubt the utility of the clinical exam – Lack confidence in clinical skills ! Basis of Cognitive Errors • Cognitive Errors – Faulty knowledge – Faulty data gathering • Failure to ask or look • EMRs – Faulty synthesis – Affective error Basis of Cognitive Errors • Cognitive Errors – Faulty knowledge – Faulty data gathering • Failure to ask or look • EMRs – Faulty synthesis/metacognition • Premature closure • Misjudging the importance of a finding • Faulty context generation Question 5: • List two things that annoy you about people • List three of your favorite people Basis of Cognitive Errors • Cognitive Errors – Faulty knowledge – Faulty data gathering – Faulty synthesis – Affective error Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve? Potential Solutions • Monitoring and feedback systems • Reframe root cause analysis • Provide improved clinical decision support • Mandate/encourage appropriate use of EMRs • Data visualization tools • Cognitive awareness and techniques Expert Performance Experienced Non Expert Time Slide from Gurpreet Dhaliwal Making Experts • • • • Progressive Problem Solving Feedback Simulation Deliberate Practice Progressive Problem Solving • Avoid the routinization of work – Go past where you have to • Reformulate problems – Add challenging, nuance and difficulty Diagnostic Feedback • Diagnostic Closure • Are we really as good as we think we are? Croskerry P. The feedback sanction. Academic Emergency Med 2000. Simulation • Practice, practice, practice • We can’t see as many patients as we need to • We don’t see all the presentations and diseases we need to High-Fidelity Sim Fox MC et al. N Engl J Med 2013;369:966-972 Deliberate Practice • • • • What do I stink at? Focus on it Work on it repeatedly Assess performance Fox MC et al. N Engl J Med 2013;369:966-972 Habits for Good to Great Experienced Expert As needed Progressive Problem Solving Feedback on my patient outcomes Random Sought out Case Reading Spectator Simulator As it happens Deliberate Practice On The Job Learning Skill Development Dhaliwal G. Clinical Excellence: Make It A Habit. Academic Medicine 2012 Action Steps 1. Mindset Continuous learning/pushing ourselves 2. Feedback Set up a system 3. Simulation One case per week 4. What is lacking? Get deliberate Slide from Gurpreet Dhaliwal Question 6: • List the two most important things you learned in the past hour • List the two things you wish we had covered but didn’t Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve? Objectives • Be able to describe the basic process of making a diagnosis • Acknowledge we struggle with making diagnoses • List several ways we can improve our diagnostic skills More Information http://www.improvediagnosis.org/?Clinical Reasoning Diagnostic Decision-Making: How do we do it and how can we (and our learners) improve? META Scholars September 5, 2013