Lecture 8 clinical decision support systems

1. Lecture 8 clinical decision support systems Dr Carl Thompson, University of York

2. What are they and how do I know if they are any good? • To introduce the major dimensions of computerised clinical decision support systems (CDSSs) • Suggested appraisal criteria for CDSS

3. A scenario • Chief nurse in a PCT. • audit results reveal significant variability, exception reports and low satisfaction around guidelines for monitoring and managing warfarin therapy. • Whether specialist clinics and CDSS might be a better approach? • Find a paper (Fitzmaurice 2000)

4. Some assumptions (humans and decisions) • Decisions are choices • Types of decisions merit types of research evidence • All require the combination of information and injection of context to make knowledge • Combining information is difficult and error prone.

5. Some assumptions (CDSS) • Designed around the correct kind of knowledge for the problem faced • Must take into account natural variations in patients and so must work with individual profiles and data • Must offer tailored advice and actually inform decision making

6. Kinds of decision support • AUDIENCE: Public, professional and embedded • Functionalist • Information management • Focussing attention • Patient specific consultations • Clinical role (Dx, Tx) • Architectural approach

7. CDSS architecture Decision models Quantitative qualitative Neural networks Bayesian, Fuzzy sets Truth tables, Boolean logic Decision trees Expert systems, reasoning models

8. CDSS quantitative

9. ROC

10. hypothyroidism

11. Decision thresholds… 1: T4 of 5 or less 2: T4 of 7 or less Impact 3: T4 of 9 or less

12. roc

13. interpretation • .90-1 = excellent (A) • .80-.90 = good (B) • .70-.80 = fair (C) • .60-.70 = poor (D) • .50-.60 = fail (F)

14. Qualitative approaches • Symbolic rule based reasoning (?Boolean logic) • Truth tables • Flowcharts or algorythms • Expert systems • Forward driven reasoning • Backwards reasoning

15. Truth tables T = true F = false D = don’t care E1 all RR intervals are regular E2 All QRS complexes are identical E3 QRS complexes longer than 120 msec E4 P waves present … E10 PR intervals regular

16. algorithmic start E1 F T E2 F VT F F T F E3 E4 T E10 T T

17. Knowledge* based systems Practice guidelines Secondary or primary Evidence base Knowledge Base Inference Methods Patient Database Knowledge Acquisition explication Electronic Health Records *NB may be called expert systems in Older literature

25. http://www.isabel.org.uk/private/diagnostic/drsnav.htm#

26. How might CDSS help innovation?

27. ENABLING

28. reinforcing

29. Critical appraisal of CDSS - validity • Were study participants randomised • If not, did the investigators demonstrate similarity in all known determinants of prognosis – or adjust for differences in analysis? • Was the control group uninfluenced by the CDSS? • Were interventions similar in the two groups ? • Was outcome assessed uniformly in the experimental and control groups?

30. Critical appraisal of CDSS – results and application • What is the effect of the CDSS? • What elements of the CDSS are required • Is the CDSS exportable to a new site? • Is the CDSS decision support system likely to be accepted in your setting? • Do the benefits of the CDSS justify the risks and costs?