1. Guideline implementation Types 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 Send patient home First visit? Yes No Yes No

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

13. Bayes’ rule P(D j |S i ) = P(D j ) * P(S i |D j )/P(S i ) P(D j ) prior probability of disease j (prevalence) P(S i ) probability of symptom i in population P(S i |D j ) conditional probability (sensitivity or specificity)) P(D j |S i ) 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. Methods and techniques 20

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

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

43. Acquiring guidelines5

44. Acquiring guidelines6

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

46. Guideline representation in Gaston Mode structure

48. Guideline representation in Gaston Mode contents

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

51. Reminder 1: A sinus X-ray is not adviced for children younger than 10 years of age

52. Executing guidelines