1. Decision support linked to Laboratory Information systems Dr Gerard Boran Adelaide and Meath Hospital Dublin Incorporating the National Children’s Hospital

2. Overview of presentation Definition of DSS What do they do? Target areas and users Methodologies Some examples of applications

3. Decision Support Systems Support for Health Care Professionals What is a decision support system? –"A DSS/KBS is any computer program designed to help health professionals make clinical decisions" [Shortliffe, 1987] e.g... Information management Focussing attention (alarms) Consultation

4. Decision Support Systems Support for Health Care Professionals Desirable DSS Features: –can be configured by the local users –have measurable benefits for patients and staff –control “data intoxication” –promote cost-effectiveness and efficient use of resources –improve co-operation between central and remote labs –based on appropriate informatics and telematic standards –can be integrated with existing LIS, order communication systems, and relevant clinical information systems

5. DSS versus KBS Knowledge-based systems (KBS) are computer programs which seek to imitate human intelligence and expertise through the use of symbolic reasoning DSS emphasise SUPPORT for the decision- making process

6. Do labs need DSS? Advances in laboratory technology –Automation –Integrated laboratories –distributed laboratories (satellite labs, point-of- care facilities, etc) Increases in workload Limitations on staff and resources

7. What should they do? Have measurable benefits for patients and staff Measurable improvements in quality and efficiency Be configurable by local users Control data intoxication promote efficient use of resources

8. What do they do? Information management –e.g activity, financial reports Focusing attention –alarms on critical data Consultation –Looking up manuals, protocols

9. Target Users Medical Staff Nurses, e.g. ICU nurses General Practitioners e.g... –Test ordering protocols –Access to lab manuals –Alarms/alerts for critical data –Interpretative reports

10. Target Users Laboratory Scientists –QC procedures –instrument fault diagnosis –preventive maintenance Managers –Monitor changes in costs, activity,etc

11. Decision Support Systems Support for Health Care Professionals Module Development –Structured Software Engineering Approach

12. Decision Support Systems Support for Health Care Professionals Techniques available –statistical/mathematical/graphical –algorithms –biodynamic models –knowledge-based systems (KBS) –Neural networks –Hypertext markup language

13. Decision Support Systems Support for Health Care Professionals Features of KBS technology –Reasoning ability –Explanation facilities –Learning by experience –Sensory perception (vision, hearing) –Language understanding (speech, writing) –Motor functions (robots, speech synthesis)

14. Decision Support Systems Support for Health Care Professionals KBS Structure –Knowledge Base Rule List List of comments/interpretations Database –Inference Engine Human-computer interface Rule handling procedures

15. Decision Support Systems Support for Health Care Professionals Forward Chaining propagation Rule (1) IF ((Condition-1 is TRUE) (Condition-2 is TRUE) (...................)) THEN ((Condition-3 is TRUE) (Output Solution-1)) (Output Solution-1)).. Rule (209) IF ((Condition-3 is TRUE) (Condition-4 is TRUE)) THEN ((Output Solution-1) (Terminate))

16. Decision Support Systems Support for Health Care Professionals Support for ordering investigations Support for performing investigations Support for interpretation

17. Physician Test Requesting Result Interpretation Sample Collection Result Reporting Analysis Sample Preparation Decision Support Systems Total Testing Cycle

18. Decision Support Systems Support for Health Care Professionals Support for ordering investigations –Scheduling of Investigations –Dynamic Scheduling of Tests –Lab Information Need to work with order communication systems

19. Support for performing investigations –Advanced Instrument Interface –Remote Maintenance of Instruments –Instrument Fault Diagnosis/Troubleshooting –Quality Control –Validation of Results Decision Support Systems Support for Health Care Professionals

20. Support for interpretation –Alarms and Alerts –Graphical Presentation –Interpretative Reporting –Drug Alarms Feedback for use with order communication systems

21. Decision Support Systems Relevant Decision Support Modules –Patient Result Validation –Thyroid Function –Lipid –Alarm/Alert –Acid-Base –Drug Interference –Haematology Image Interpretation –MI markers –Organ Profile interpretation –Cytology applications –Microbiology applications

22. Integration Integrate with routinely used IS Data collection a by-product of routine activity Absence of key data (often clinical data) hampers progress

23. Integration With LIS, e.g –HELP system –OpenLabs –Connolly With HIS, e.g. –Order Communication systems With other Clinical Systems, e.g. –Departmental systems (data feeds...) –Shared Care system

27. OpenLabs architecture

28. General Practicioner GP DMS Patient St. James ConsultantSynapses Server Hos. DB Laboratory Renal Clinic Diabetic Day Centre Diabetic Clinic Eye Clinic Lipid Clinic Consultant Synapses ServerHos. DBLaboratoryRenal ClinicDiabetic Day CentreDiabetic ClinicEye ClinicLipid Clinic Tallaght SHARED CARE Integrating Lab Data with other clinical systems

29. The Test Cycle PRE INTRA POST

30. InvestigateInterpret NPT/Satellite Lab 1 34 2 4. Reporting 3. QC/Validation 2. Analysis 1. Sample Prep Transport to Lab PTS/Porters Collect Sample (Phlebotomy,etc) Request Form/OCS Order Main Lab Clinician

31. Pre-laboratory applications Ordering protocols Order communications LUMPS/BUMPS (Peters et al, 1991) Dutch GP Guidelines

32. Order communications

43. Intra-laboratory Applications QA Server Patient Result Validation (Valdiguie, OpenLabs) Lab Watch

48. Post-laboratory Applications Thyroid interpretation protein electrophoresis interpretation interpretive reporting (college guidelines) Alarm systems Data feeds to other DSS - e.g. diabetes register