Introduction to Risk Analysis in Healthcare

1. Introduction to Risk Analysis in Healthcare Farrokh Alemi Ph.D.Professor of Health Administration and PolicyCollege of Health and Human Services, George Mason University4400 University Drive, Fairfax, Virginia 22030703 993 1929 falemi@gmu.edu

2. Scope of Topic • From introduction to probability to probabilistic risk analysis • Subjective probability assessment • Conditional likelihood ratios • Causal modeling • Bayesian networks • Time to the event assessment • Tools you can use at work • Logical reasoning

3. Not More of the Usual • Teach by application • Continuous distributions not covered • Focus on contingency tables

4. Focus on Modeling • Rules of probability • Causes and effects • Model complex tasks • Estimated probabilities • Check assumptions • Ask “what if” questions

5. Other Books • Feldman RM, Valdez-Flores C. Applied Probability and Stochastic Processes • Hallenbeck W. Quantitative Risk Analysis for Environmental and Occupational Health • Bedford T, Cooke R. Probabilistic Risk Analysis: Foundations and Methods • Cox LA. Risk Analysis: Foundations, Models and Methods

6. Why Do Risk Analysis? • Allocate resources • Reduce • Manage • Insure • Provides an organization with insight

7. Why Probabilistic Analysis? • To compare one risk against another • Consistent method of aggregating risk of a sequence of events

8. Why Probabilistic Analysis? • To compare one risk against another • Consistent method of aggregating risk of a sequence of events

9. Criticism of PRA • It cannot be done because catastrophic events are rare • It is not practical as it takes too much time • It should not be done because it misses creative new threats

10. Criticism of PRA • It cannot be done because catastrophic events are rare • It is not practical as it takes too much time • It should not be done because it misses creative new threats

11. Criticism of PRA • It cannot be done because catastrophic events are rare • It is not practical as it takes too much time • It should not be done because it misses creative new threats

12. History in Aerospace Industry • 1967 Apollo flight loss • 1969 Probability of loss 1% • 1983 probabilistic risk analysis of shuttle flights • NASA administrators abandon PRA • Later events proved accuracy of analysis • Common practice

13. History in Nuclear Safety • Reactor Safety study • Three Mile Island accident • Probability of reactor melt down • NRC manual for risk analysis • Assessment of other catastrophic events

14. History in Natural Disasters • Earthquake predictions • Floods and coastal designs • Environmental pollution • Waste disposal and environmental health

15. History in Healthcare • Root causes of sentinel adverse events • Typically not quantified risk • Failure mode analysis of near catastrophic events • Typically based on rank order of risks • New drug development Relatively New

16. History in Terrorism • Cyber terrorism risks • Department of Homeland Security Critical Infrastructure Protection Relatively New

17. Requirements Prior to Start • Algebra, not calculus • Facile with numbers and counting • Access to a software for Bayesian analysis • Software to make contingency tables • No prior course in probability • No course in statistics • No computer programming • Some knowledge of health care systems • Access to an organization to try out the ideas

18. Expectations • Probabilistic analysis • Apply to a realistic problem • No exams • No other assignments

19. Pedagogical Features • Learn one, do one, teach one assignments in class • Question and answer • Take home message • Peer to peer comments

20. Primary Audience • Graduate course on risk analysis • Professionals in the field

21. Secondary Audience • These lectures may also be of use to students of probability and causal modeling.

22. Lecture Outline • What is probability? • Probability distributions • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

23. Lecture Outline • What is probability? • Probability distributions • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

24. Lecture Outline • What is probability? • Probability distributions • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

25. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

26. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

27. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

28. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

29. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

30. Lecture Outline • What is probability? • Assessment of rare probabilities • Calculus of probability • Conditional independence • Causal modeling • Case based learning • Validation of risk models • Examples

31. Take Home Lesson Tools that are better than comprehensive list of risks

32. What Do You Know? • Why is probabilistic risk analysis preferred to comprehensive lists of vulnerabilities?

33. What Do You Know? • How is our approach to risk analysis different from other books on probability or on risk analysis?

34. What Do You Know? • What is assumed and required prior to start of these lectures?

35. What Do You Know? • What is expected from you prior to end of this course?

36. Minute Evaluations • Please use the course web site to ask a question and rate this lecture