Chapter 4: Decision-Making in Clinical Medicine

INTRODUCTION

Medical practice requires making decisions under uncertainty, combining scientific knowledge with the art of probability. The modern physician must master clinical reasoning, rational test use, and evidence-based medicine (EBM) to provide excellent care.

BRIEF INTRODUCTION TO CLINICAL REASONING

Clinical Expertise

Clinical expertise is difficult to define and measure objectively. Expert clinicians often cannot explicitly articulate their reasoning processes. Expertise includes cognitive skills for diagnosis and test interpretation as well as procedural and communication skills. Research focuses mainly on cognitive diagnostic reasoning.

Intuitive Versus Analytic Reasoning

Intuition (System 1): Fast, pattern-based, effortless judgments via heuristics.
Analysis (System 2): Slow, methodical, deliberate reasoning.

Pattern recognition allows quick diagnosis based on stored disease patterns but risks errors like premature closure without analytic verification.

Heuristics and Cognitive Shortcuts

Common heuristics include:

Representativeness: Judging disease likelihood by symptom similarity, possibly ignoring prevalence.
Availability: Judging based on memorable cases, risking recall bias.
Anchoring: Sticking to initial impressions despite new evidence.
Simplicity (Occam’s Razor): Preferring simplest explanation, risking missed diagnoses.

The hypothetico-deductive model involves generating, refining, and verifying diagnostic hypotheses based on clinical data.

Experts develop illness scripts and prototypes to connect findings and refine diagnoses.

DIAGNOSTIC VERSUS THERAPEUTIC DECISION-MAKING

Personalized treatment combines best evidence with patient characteristics and preferences. Shared decision-making involves clinicians and patients collaborating on treatment choices. Therapeutic decisions rely on large trial data rather than individual experience.

NONCLINICAL INFLUENCES ON CLINICAL DECISION-MAKING

Factors influencing decisions include:

Physician practice style shaped by training, experience, and beliefs.
Defensive medicine driven by malpractice fears.
Practice environment: workflow, technology, organizational culture.
Payment systems: fee-for-service encourages volume, capitation and salary have different incentives.

INTERPRETATION OF DIAGNOSTIC TESTS

Diagnostic tests reduce uncertainty but require careful interpretation. Sensitivity and specificity measure test accuracy against a gold standard. Trade-offs exist between sensitivity and specificity, illustrated by ROC curves.

MEASURES OF DISEASE PROBABILITY AND BAYES’ RULE

Bayes’ rule updates disease probability after testing, incorporating:

Pretest probability (based on prevalence and clinical context)
Test sensitivity and specificity

Likelihood ratios express how much a test result changes probability of disease. Nomograms help apply Bayes’ rule practically.

APPLICATIONS TO DIAGNOSTIC TESTING IN CAD

Examples of exercise treadmill and myocardial perfusion imaging tests illustrate applying Bayes’ rule and likelihood ratios to clinical decision-making. Test accuracy and pretest probability determine clinical usefulness.

STATISTICAL PREDICTION MODELS

Multivariable models improve risk prediction by weighting multiple patient factors. Widely used models include the ACC/AHA risk calculator and CHA₂DS₂-VASc score. Validation in diverse populations is essential.

FORMAL DECISION SUPPORT TOOLS

Computer-based decision support has advanced from information retrieval to AI and machine learning, but clinical impact remains limited. Checklists and reminders embedded in EHRs show promise.

Decision analysis offers prescriptive frameworks for complex decisions, such as breast cancer screening strategies, by weighing benefits, harms, and uncertainties.

DIAGNOSIS AS AN ELEMENT OF QUALITY OF CARE

Diagnostic errors are common and largely due to system failures. Solutions include decision support and checklists, though their impact on diagnostic error reduction is not yet proven.

EVIDENCE-BASED MEDICINE

EBM combines medical knowledge, intuition, and judgment with systematic appraisal of clinical research. It involves:

Formulating clinical questions
Searching for evidence
Critically appraising research
Integrating evidence with patient context

Systematic reviews and meta-analyses summarize evidence but vary in quality. Randomized clinical trials remain the gold standard for reliable data.

Clinical practice guidelines synthesize evidence to assist decisions but require interpretation and individualized application.

CONCLUSIONS

EBM is an ideal guiding tool, but clinical reasoning and individualized patient care remain essential. Many decisions lack high-quality trial data, making expertise and patient preferences paramount.