iHT2 Health IT Summit in Seattle 2012 – Keynote Presentation "Improving Health with Healthcare Intelligence"

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Is "healthcare intelligence" an oxymoron? What can we expect to accomplish with the data we have in healthcare? How do we transform data in electronic health records into superior clinical and financial outcomes? What are the information building blocks for a continuously learning health system? How important is technology in healthcare intelligence? What is the role of Big Data in healthcare and how do we prepare for it?

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iHT2 Health IT Summit in Seattle 2012 – Keynote Presentation "Improving Health with Healthcare Intelligence"

  1. 1. Improving Health with Healthcare Intelligence iHT2 Health IT Summit Seattle Thursday, 23 August 2012 Dick Gibson MD PhD Chief Healthcare Intelligence OfficerProvidence Health & Services – Renton WA 1
  2. 2. Agenda• External and Internal Environment.• Three Data Platforms: • EMR Reporting. • Microsoft Amalga. • Enterprise Data Warehouse.• Continuously Learning Organization.• Big Data.• Conclusions. 2
  3. 3. What we believe about the future• More care done for lower Per Member Per Month.• Mental Health Care & Post Acute Care will grow significantly.• Less reliance on physicians & more on alternative providers.• More care delivered at home, at work, & on mobile devices.• More self-care with Internet information sources.• More scrutiny of our care by regulatory & consumer bodies.• More telehealth, teleradiology, telepharmacy, etc.• Genomic and proteomic data will revolutionize healthcare but not for a few years.• More reimbursement by Health Savings Accounts (more retail a la carte buying) and by global premium. 3
  4. 4. Our overall motivation 4
  5. 5. What does this mean for our healthcare? 5
  6. 6. If it is not indicated, we don’t do it. If it is indicated, we do it reliably. If we do it, we do it flawlessly.We study our results and we continuously improve. 6
  7. 7. Including Swedish Health Services• 32 hospitals• 7,000 beds• 64,000 employees• 2,300 employed physicians• 285 clinics• 400,000 member health plan• $10Billion Net Revenue 7
  8. 8. Two kinds of information systems• Transaction Systems: Epic Hyperspace for healthcare. • Captures all characterizations of the patient’s status. • Both Pre-intervention & Post-intervention. • Captures all our interventions: diagnostic & therapeutic. • Point-of-care Clinical Decision Support guides providers.• Reporting Systems: Retrospectively examine outcomes. • Epic Clarity Reporting Database. • Amalga. • Enterprise Data Warehouse. 8
  9. 9. Health Care Intelligencewith three overlapping platforms Epic Microsoft Enterprise Clarity Amalga Data Reporting Warehouse DatabaseRoutine scheduled Current care of active inpts. Review care over time. operations reports. Data updated immediately. All patients, all settings.Everything within Epic. Alert leads to immediate Data updated nightly.Can be integrated with intervention. Retrospective analysis transaction system. Data from multiple clinical guides decision–making.Updated nightly. systems. All clinical & financial data.Meaningful Use reports. Used by clinician, RN Used by manager or analyst.Used by dept manager. manager, or MD manager. 9
  10. 10. EpicHyperspaceTransaction Community Lead System Single Epic Clarity Reporting (Enterprise Database Master File EMFI Infrastructure) Three Identical instances AK WA/MT OR/CA 10
  11. 11. Increased use of benchmarkingmeans more data need to be collected 11
  12. 12. And a lot of the data must comefrom doctors at the point of care Thanks to Jeff Westcott MD at Swedish 12
  13. 13. For Epic Clarity Reporting: SAP Business ObjectsCrystal Reports• Operational reports built by IT, read by manager.• Precise, pixel perfect formatting.• High volume publishing.• Predictable questions. Gradual trend from reports to analyticsWeb Intelligence• Query and analysis, sort, filter, drill down.• Business user or analyst interacts with the data.• Basic formatting only.• Unpredictable questions. 13
  14. 14. Crystal Reports Drop Down Lists To SelectReport Parameters 14
  15. 15. Crystal Reports Printout 15
  16. 16. Web IntelligencePull the Data Fields here that you want to see on the screen.Pull the Data Fields here to determine what records to include in the output. 16
  17. 17. Microsoft Amalga is a new entry in data management GET STORE SHOW Data Acquisition & Data Store Amalga Distribution Engine (DADE) Client Data Tables & SQL Views Elements Optimized by UseRawdata Sfeeds E C Message Message Message Parsers Receiver Filer Queue U R I T Lifetime Raw Y Message Archive 17
  18. 18. Amalga collects data from multiple disparatetransaction systems into one alerting engine• 117 servers.• 87 Terabytes of provisioned storage.• 150 realtime interfaces.• Outbound alerts connected to paging system.• Data presented in simple Excel-like row & column format. 18
  19. 19. Modified Early Warning System (MEWS) 19
  20. 20. Users can sort, filter, exclude columns 20
  21. 21. Providence’s Use of Amalga Currently Active • Modified Early Warning System (MEWS). • Sepsis Scoring. • Catheter Associated Urinary Tract Infection. • Central Line Associated Blood Stream Infection. In Process • Readmission Manager • Infection Control. • Antimicrobial Stewardship. • Pressure Ulcer Reduction. • Falls Prevention. 21
  22. 22. Enterprise Data Warehouse Stack Sources Data Management Layers Staging Integration Data Data Marts Regist Services Finance EHR Surgery Costing Mat Mgt OfficeGen Ledger QualityTime & Att Bundles Lab MD Cred Revenue Pt Satis Claims Patient MD Meds Master Data & Conformed Dimensions 22
  23. 23. Source to Staging & Data Quality Sources Staging Regist • Bring over data, table for table, EHR without changing the data. Costing • Relieve transaction system from the Mat Mgt CPU slowdown of reporting.Gen Ledger • Examine Staging data quality andTime & Att give feedback to Operations. Lab MD Cred • Looking for null fields, Discharge Pt Satis dates before Admission dates, Claims surgical stays without surgeon, etc. DATA QUALITY 23
  24. 24. Master Data Management & Data Stewards• A few key tables of the most important assets. • Patients. • Providers. • Orderables: implants, medications, surgical supplies. • Departments.• Single source of truth for entire organization.• These become the “Conformed Dimensions” of the facts.• A Data Steward is appointed to manage the master table for a given data type for entire organization. Patient MD Meds 24
  25. 25. Data Integration Layer • Patient, provider, & med names areStaging replaced by keys from Master Tables. Data • Addresses are validated & cleansed by Integration outside Reference Tables. • A patient’s multiple identities from multiple EMRs are associated with a Max unique key in the Patient Master Table data break and that key is used in this layer. down • Normalized tables are created for each of the major entities: Patients, Encounters, Orderables, etc. Patient MD Meds 25
  26. 26. Data Services Layer & Data Marts• A large, denormalized Surgery Table Data Data is created by joining the Integration Services Marts Layer Patient, Encounter, Procedure, Cost tables. Layer Finance• Services is a permanent data store. Surgery• Data Marts are built expressly for Office Data rapid visualization, query, and regrouped Quality interactive analytics. for• Data Marts can be built and torn reporting Bundles down rapidly to meet specific needs. Revenue 26
  27. 27. Semantic & Presentation Layers Data Presentation• Semantic Layer uses column Marts Layer names that are familiar to Standard SEMANTIC LAYER Finance business users. Reporting• Standard Reporting & Surgery Dashboard Dashboards for routine Office monitoring by untrained users. Query & Quality• Special training for ad hoc query Analysis and interactive analytics. Bundles Data• Statistical packages used for data Mining Revenue mining. 27
  28. 28. Benefits of the Semantic Layer 90% time extracting data 10% time extracting data10% time interpreting data 90% time interpreting data 28
  29. 29. Microsoft Presentation Tools 29
  30. 30. What can Healthcare Intelligence do?• Analyze emergency department patient throughput.• Provide insight to revenue cycle performance in each work queue.• Assess PMG physician clinical and productivity performance.• Calculate cost of an individual encounter or average cost of a service line in preparation for bundled or global payment.• Predict nurse staffing need for a shift in two weeks.• Highlight sources and cost of physician variation in normal vaginal delivery and newborn care.• Link physician office waiting time with client satisfaction. 30
  31. 31. Three stages of healthcare intelligence Prescriptive-we can suggest best diagnostic & treatment approach for patients with multiple chronic conditions.Predictive-we know who is likely to be severely ill next year. Descriptive-we know what we did and what works. 31
  32. 32. Nov 2011An example of prescriptive analytics 32
  33. 33. What did their own patients tell them?• Overall 98 patients with lupus, 10 of them developed thrombosis (blood clots).• 15x: Relative risk of thrombosis with lupus and persistent proteinuria (protein in urine) vs lupus without proteinuria.• 12x: Relative risk of thrombosis with lupus and pancreatitis (inflammation of pancreas) versus lupus without pancreatitis. 33
  34. 34. Importance of this NEJM report• First report of using EMR patient data search to aid immediate care of a patient.• More EMRs lead to more data.• Idea can scale with large combined data sets.• Potentially better than anecdotal or expert opinion.• Challenge will be system speed and relevance of findings. 34
  35. 35. Basis ofand Amalga/EDW promote a Epic a continuously learning system continuous improvement cycle Patient status is Clinicians use Patient captured using best practice outcome is Documentation Order Sets captured using Templates Documentation Templates Documentation HC Intelligence and Order Sets analyzes data for are changed based most effective on new information treatment 35
  36. 36. Number of Primary Care Physicians at a given Treatment Quality score250 2010200150 200510050 0 64% 90% 60% 62% 66% 68% 70% 72% 74% 76% 78% 80% 82% 84% 86% 88% 92% 94% 96% 98% 36
  37. 37. What is Big Data?• Data that are hard to process by routine computing methods.• Gartner calls it “Extreme Computing.”• Any one of three characteristics can make data “Big.” Often it is more than one characteristic. • Volume. • Velocity. • Variety. 37
  38. 38. What are sources of Big Data in healthcare?• Physician freetext dictation.• EHR access logs.• Medical images.• Ubiquitous vital signs and fluid sampling from microchips embedded in garments worn at home and office.• Detailed patient histories of all their habits, symptoms, families, food, activities, moods, purchases, thoughts.• Electronic medical record entries nationwide.• Freetext textbook and journal articles.• Genomics, proteomics, human microbiomics. 38
  39. 39. Big Data will revolutionize healthcare.• Will require massive scale computing.• But it will take 5-10 years.• It’s not Either/Or – it’s Both/And.• Meanwhile we need to master regular data. • Indications for diagnostic & therapeutic intervention. • High reliability healthcare. • Patient throughput. • Client satisfaction.• Full value when Big Data combined with clinical, financial, and operational database across millions of patients. 39
  40. 40. Conclusions• Do the Right Thing, Do the Right Thing Right (David Eddy).• Different data platforms serve different needs.• We will need to use our EMRs to collect specific physician data.• It’s the people and effort behind the technology that count.• The EMR is the collector of data and it is also the Action Arm where knowledge is put back into practice.• We need to continue to master regular data while we get ready for the revolution of Big Data. 40
  41. 41. Questions? iHT2 Health IT Summit Seattle Thursday, 23 August 2012 Dick Gibson MD PhD Chief Healthcare Intelligence OfficerProvidence Health & Services – Renton WA 41

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