Guideline implementation Types of CDSS

1. Guideline implementationTypes of CDSS A.Hasman

2. Do physicians need support? • In 2.3% of the 1.3 million patients (30.000 patients) preventable errors were made during their stay in a hospital in the Netherlands. • About 10.000 patients suffered a permanent injury. This could have been prevented in 6.000 patients. • For 4.1% of the 42.000 patients who died in the hospital death could have been prevented.

3. Conclusion • Doctors are not infallible • Support physicians and nurses both in repeating and difficult tasks

4. How to support physicians and nurses? • Easy access to the scientific literature • Guidelines • Computer-based diagnostic systems

5. Medical decision support • Already available for years • ECG/EEG analysis (signal analysis and parameter interpretation) • Diagnostic systems (cardiology (congenital heart disease diagnosis), radiology (bone tumor classification)) • Radiotherapy planning • Medication selection, dosing • Clinical algorithms (flowcharts on paper, for nurses and ancillary personnel) • Guidelines

10. Methodology used by CDSS • Decision trees • Statistical approaches • Bayes’ rule • Discriminant analysis • Logistic regression • Inference techniques • Rules • Logic • Semantic networks • Etc.

11. Decision trees BP lower than 140/90 No Yes Send patient home First visit? Yes No

12. x x O x Var 1 x x x O x O x O O O O x O Var 2

13. Bayes’ rule P(Dj |Si ) = P(Dj) * P(Si|Dj)/P(Si) P(Dj) prior probability of disease j (prevalence) P(Si) probability of symptom i in population P(Si|Dj) conditional probability (sensitivity or specificity)) P(Dj|Si) posterior probability (predictive value)

14. Types of decision support • Passive • Physician actively searches in the knowledge base for relevant information. Information indexed • Active • System pro-actively provides physician with relevant information • System re-actively provides physician with relevant information

15. Guidelines • Systematically developed statements to assist practitioner and patient decisions about appropriate healthcare for specific clinical circumstances • They provide information for various types of patients having some common problem • Provide a common standard of care both within a healthcare organization and among different organizations • Based on consensus or evidence-based

16. Use of guidelines • May lead to a reduction of errors, practice variability and patient care costs, while improving patient care • Narrative guidelines usually population-based, not patient specific • Healthcare organizations pay more attention to guideline development than to guideline implementation, evidently hoping • That clinicians will simply familiarize themselves with published guidelines and then apply them appropriately during the care of patients

17. Background: paper-based guidelines

18. Problems with guidelines • Guidelines often contain ambiguities, vague sentences and ‘open ends’ • Leads to different interpretations • This limits the use of guidelines

19. Guideline Dissemination • Assumption: Practitioners will read the guidelines • Assumption: Practitioners will internalize and there after follow guidelines • Reality: Physicians do not use the guidelines or do not use them correctly

21. Why was decision support not accepted? • Decision support systems were only applied in the institution where they were developed, if at all • Computer systems were stand-alone systems: no integration, so double data entry • Computer systems were slow and expensive • Initially physicians did not accept guidelines or clinical algorithms (Cookbook medicine, patients differ, useful for ancillary personnel) • Because of current emphasis on quality of care (evidence based medicine) guidelines are becoming relevant

22. Myth-1 • Diagnosis is the dominant decision-making issue in medicine • Typical questions are not “What does this patient have?” but, rather, “What should I do for this patient? Ted Shortliffe

24. Myth-2 • Clinicians will use knowledge-based systems if the programs can be shown to function at the level of experts • What do we know about “expertise” and the associated cognitive factors?

25. The nature of clinical expertise • Tremendous variation in practice, even among “experts” • Need to understand better how experts meld personal heuristics and experience with data, and knowledge from the literature, in order to arrive at decisions (medical cognition) • Can we better teach such skills? • How could improved understanding affect the way decision-support systems offer their advice or information? • How will such insights affect our under-standing of clinicians as computer users?

26. Myth-3 • Clinicians will use stand-alone decision-support tools. • The death of the “Greek Oracle” model →Integrated decision support in the context of routine workflow

27. Systematic review of Garg et al • Systems that warn physicians have more effect on the physicians (success in 44/60 studies) than systems that have to be inititiated by physicians (17/36 studies) • In the case of diagnostic systems 4 out 10 trials indicated that the use of a DSS leads to better results (an improvement for at least 50% of the the measured outcomes)

28. Reminder systems effective? • For 16/21 trials concerning reminder systems for prevention using a DSS led to a better performance of the physicians (screening, test requests, drug prescription, etc.) • Studies did not show a significant improvement in patient outcome

29. Computer interpretable guidelines

30. Computer-interpretable guidelines • Guideline implementations best affect clinician behaviour if they deliver patient specific advice during patient encounters • Computer-interpretable guidelines could provide such advice efficiently • Computerized guideline systems are crucial elements in long-term strategies for promoting the use of guidelines (IOM)

31. Possibilities of ICT • Computersystems can not only show guideline texts but can also reason with the information from the guideline. To do that information about an individual patient is necessary • The combination of a formalized guideline and an EPR can lead to advice (pro-actively or reactively) concerning an individual patient

32. Medical Protocols

33. 20 Methods and techniques

34. System description Guideline Base EPR Physician Guideline / Knowledge Base editor Execution engine Knowledge Base

35. CIG ingredients • Guideline model • Guideline expression language for expressing decision criteria and eligibility criteria • Mapping of terminology in guideline to the terminology used in EPR • Scheduling constraint specification language for scheduling multiple steps • Guideline execution engine

36. Guideline modeling and representation • System editor should provide • A domain ontology • A (visual) language for expressing the steps in a guideline

37. Example: simple guideline • Primitive: If … then • Domain ontology: Digoxin, Potassium, … If Digoxin and Potassium>3 mmol/l then “warning”

38. Phases in development process • Select guideline to be formalized • Formalize guideline • Enter guideline into guideline system • Guideline verification and testing • Guideline execution

39. Acquiring guidelines

40. Acquiring guidelines

41. Acquiring guidelines

42. Acquiring guidelines

43. Acquiring guidelines5

44. Acquiring guidelines6

45. Complex guideline • Primitives: Branch step, Synchronization step, Decision step, …. • Domain ontology: Digoxin, ….

46. Guideline representation in GastonMode structure

47. Guideline representation in GastonMode structure

48. Guideline representation in GastonMode contents

50. ICPC- module IS ~ GP Reminder Re-active decision support Request module Evaluation module No reminder KB