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Medical Decision Making

Medical Decision Making. Abra Jeffers VA Palo Alto Dept. of Mgmt Science & Engineering Stanford University. Decision Analysis. Quantitative, systematic Identifies alternatives, outcomes, utility of outcomes Uses models Represent structural relationships Deterministic Probabilistic

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Medical Decision Making

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  1. Medical Decision Making Abra Jeffers VA Palo Alto Dept. of Mgmt Science & Engineering Stanford University

  2. Decision Analysis • Quantitative, systematic • Identifies alternatives, outcomes, utility of outcomes • Uses models • Represent structural relationships • Deterministic • Probabilistic • Compute expected outcomes of each alternative • Balance feasibility v. reality / simplicity v. complexity • Examine robustness

  3. Identify Alternatives • Mutually exclusive • A, B mutually exclusive ⇒ P(A∩B) = 0 • One and only one of the events must occur • Collectively exhaustive • A, B collectively exhaustive ⇒ P(A) + P(B) = 1 • Events represent entire outcome space • At least one event must occur

  4. Identifying Outcomes • Multi-dimensional • Examples: • Costs • Infections averted • Life years (LY) saved • Quality adjusted life years (QALY) saved • “Effectiveness” • Ultimately utils – Howard school • Cost-effectiveness • Incremental cost to effectiveness ratio (ICER)

  5. Cost-Effectiveness Plane ΔC Increased Costs Decreased Effectiveness Use status quo Increased Costs Increased Effectiveness Is ICER < WTP? ΔE Decreased Costs Decreased Effectiveness Probably use status quo Decreased Costs Increased Effectiveness Use new strategy

  6. Steps • Determine problem • Determine status quo intervention • Determine alternative interventions • Analysis • Sensitivity Analysis

  7. Example • Hypothetical cohort with prevalent disorder • Treatment • Reduces probability of serious complications • Long term side effects • There is an imperfect test for disorder • Alternatives: • Treat no one • Test all and treat positives • Treat all

  8. Decision Diagram Terminal node Probabilistic node Decision node

  9. Example cont. • Outcomes • No disorder, no treatment • No disorder, treatment • Disorder, treatment, complications • Disorder, treatment, no complications • Disorder, no treatment, complications • Disorder, no treatment, no complications • For each outcome assume known costs and LE (clinical trial) • Intermediate events • Tests • Side effects

  10. Create Decision Node

  11. Create Decision Node

  12. Create Decision Node

  13. Create Decision Node

  14. Strategy 1: Treat None

  15. Strategy 1: Treat None

  16. Strategy 1: Treat None

  17. Strategy 1: Treat None

  18. Strategy 1: Treat None

  19. Select Subtree

  20. Collapse Subtree

  21. Strategy 2 - Test

  22. Strategy 2 - Test

  23. Strategy 2 - Test

  24. Now What? • Now we have our tree structure • Lets start filling in probabilities

  25. # = 1- prev

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