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Dominik Aronsky pour la journée e-health 2013

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Dominik Aronsky pour la journée e-health 2013

  1. 1. Computerized Decision Support:From Data to InformationDominik Aronsky, MD, PhDDept. of Biomedical Informatics &Emergency MedicineVanderbilt University Medical CenterNashville Tennesseeandii4sm, Basel, Switzerland
  2. 2. 2?Clinicians:MD,RN,admin….Lhunter & gathererComputerized Decision SupportClinicalinformationsystemJinformation manager
  3. 3. Sir William Osler3“Medicine isa science of uncertaintyandan art of probability”Fundamental impacton how we dealwith data in medicine
  4. 4. 4Computerized Decision Supportcollect reportstore3.18 elevatedIF…THEN…ELSE….ENDIFDecision Support Systems
  5. 5. 5Practicing Medicine in the ED Multitasking Communication challenges Interruptions Workflow disruptions Hand-offs Team workChallenges: Information managementWorkflow optimization12
  6. 6. 6Computerized Decision SupportED Information System Infrastructure:ED whiteboard: “patient tracking”Applications / Research:Pneumonia detection systemAsthma decision support systemForecasting ED overcrowding12
  7. 7. 7Tracking in Other Industries
  8. 8. 8Tracking Patients in Healthcare –It is a Simple World2001
  9. 9. 9Electronic WhiteboardElectronic Tracking Board(Version 0.1a)2001
  10. 10. 10
  11. 11. 11ADT SystemRegistrationinformationDispositioninformationHospitalBed BoardApplicationComputerizedPatient RecordComputerizedProvider OrderEntry SystemRadiologySystemEnterpriseDataWarehouseWhiteboardInformationRadiologyExamStatusBedRequestStatus ofBedRequest &DiversionStatusPatientinformationPatientlocationOrdersHospital Information SystemED TriageED OrderTrackerTriageInformationOrderStatusWhiteboardScreenshotViewerWhiteboardScreenshotsED Information SystemSubjectRecruitmentWaitingRoomED BedBoardRegistrationlogTreatmentAreaStaffRosterRecentDischargesED Patient Tracking Board
  12. 12. 12
  13. 13. 13ED DischargeApplication
  14. 14. 14Discharge
  15. 15. 15ED Whiteboard “Movie”Original intent:• Bridging downtime periodsUnintentional (positive) consequences• Review: appropriateness of ED diversion episodes• Malpractice claims• State investigations
  16. 16. 16Whiteboard:Return on InvestmentDirect benefit:Additional revenues:> $ 1.4 million / year……Indirect benefit:more accurate documentation> $ 1.5 increased MD billingJCAHO visit 2009……
  17. 17. 17Computerized Decision SupportED Information System Infrastructure:ED whiteboardApplications / Research:Pneumonia detection systemAsthma decision support systemForecasting ED overcrowding12
  18. 18. 18PneumoniaPneumonia ?
  19. 19. 19
  20. 20. 21Pneumonia Care Process
  21. 21. 22ResultsMinutesMonth
  22. 22. 23Computerized Decision SupportED Information System Infrastructure:ED whiteboardApplications / Research:Pneumonia detection systemAsthma decision support systemForecasting ED overcrowding12
  23. 23. 24Asthma Detection: ObjectivesScreening:• Identify eligible patients early• Screen all ED patients automatically• Screen all ED patients in real-timeWorkflow Integration:• No additional data entry• Inform clinicians before initial evaluationGeneralizability:• Use only electronically recorded data• Use only common data elementsGoal  Alert clinicians about asthma guideline eligible patients Overcome behavioral barrier of initiating guideline
  24. 24. 25Asthma Detection SystemComputerizedNurse Triage• Coded chief complaint• Coded asthma history• Vital signs• DemographicsBilling RecordDatabase• Prior visit codes– In- or outpatient– ICD-9 = 493.*ElectronicMedical Record• Problem list (text)– History of asthma• Medication List (text)– Asthma medications
  25. 25. 26Asthma Detection SystemMethod: Developed & implemented a Bayesian Network
  26. 26. 27Prospective Evaluation• Study period: 4 weeks (Jan 27 - Feb 24, 2006)• 2,006 encounters; 153 asthma patients (7.6%)Sensitivity (fixed) Specificity Positive PV Negative PV90% 89.9% 42.5% 99.1%AUC = 97.1%(CI: 95.5% - 98.1%)
  27. 27. 28Initiating Asthma Guideline
  28. 28. 29Computerized Decision SupportED Information System Infrastructure:ED whiteboardApplications / Research:Pneumonia detection systemPneumococcal vaccination systemForecasting ED overcrowding12
  29. 29. 30death was “a result of gross deviations from the standard ofcare that a reasonable person would have exercised in this situation.”
  30. 30. 31
  31. 31. 32Forecasting ED CrowdingProblem No tools available to measure objectivelyand manage proactivelyResearch opportunityUsing ED whiteboard data:Develop a real-time prediction instruments to alert about impending EDdiversion
  32. 32. 33Forecasting ED Crowdinghttp://mac01xd.mc.vanderbilt.edu:8080/crowd-war/neticaBayesian network:Data collection from ED, OR, hospital, access center, etc., over 2 yearsIdentified 11 variables predictive of ED diversion: prospective evaluation
  33. 33. Forecasting ED Crowding34http://mac01xd.mc.vanderbilt.edu:8080/crowd-war/netica
  34. 34. 35
  35. 35. The “Divide”IT in Medicine Medical Informatics36Computer scientistIT managernursesphysicianstechnicians(Bio-) MedicalInformatics
  36. 36. 37ED: Computerized Decision Supportfastintuitiverich in contentoptimized for workflow
  37. 37. 38Psycho-social, organizational, political aspects…“Change Management”Decision Support Systems
  38. 38. 39Creating a Culture of Informaticsbillinginformaticsphysicianshospitalregistration……nursingAmbulanceservices
  39. 39. Lessons learnt40- “Is it an important problem?” (Don Lindberg)- Who cares?- A very long way from design, implementation,to evaluation.- “Get (institutional) support”- “If it can happen - it will” (Murphy)- People – Process – Technology: understand thedata, workflow and processes- “So what?” (Reed Gardner)- “Change management” (Nancy Lorenzi)- “Medical Informatics is a behavioral science.”(Homer Warner)… if ONE of them does not apply: Have Fun J
  40. 40. 41AcknowledgmentNational Library of Medicine:• R21 LM009747• R21 LM009002• T15 007450 (BiomedicalInformatics Training Program)
  41. 41. 42Questions

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