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Improving Health with
     Healthcare Intelligence

    iHT2 Health IT Summit Seattle
      Thursday, 23 August 2012


         Dick Gibson MD PhD
   Chief Healthcare Intelligence Officer
Providence Health & Services – Renton WA
                                           1
Agenda

• External and Internal Environment.
• Three Data Platforms:
   • EMR Reporting.
   • Microsoft Amalga.
   • Enterprise Data Warehouse.
• Continuously Learning Organization.
• Big Data.
• Conclusions.



                                        2
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
Our overall motivation




                         4
What does this mean for our healthcare?




                                          5
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
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
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
Health Care Intelligence
with three overlapping platforms


             Epic
                                     Microsoft               Enterprise
            Clarity
                                      Amalga                   Data
           Reporting
                                                             Warehouse
           Database


Routine 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
Epic
Hyperspace
Transaction
                Community Lead
  System

                                        Single Epic
                                     Clarity Reporting
         (Enterprise                     Database
         Master File EMFI
       Infrastructure)




              Three
            Identical
           instances
  AK                    WA/MT    OR/CA               10
Increased use of benchmarking
means more data need to be collected




                                       11
And a lot of the data must come
from doctors at the point of care




                              Thanks to Jeff Westcott MD
                                      at Swedish

                                                      12
For Epic Clarity Reporting: SAP Business Objects

Crystal Reports
• Operational reports built by IT, read by manager.
• Precise, pixel perfect formatting.
• High volume publishing.
• Predictable questions.                Gradual trend from
                                        reports to analytics
Web Intelligence
• Query and analysis, sort, filter, drill down.
• Business user or analyst interacts with the data.
• Basic formatting only.
• Unpredictable questions.
                                                               13
Crystal Reports

 Drop Down Lists
    To Select
Report Parameters




               14
Crystal Reports
   Printout
                  15
Web Intelligence

Pull 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
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 Use
Raw
data
                                                                                       S
feeds
                                                                                       E
                                                                                       C
        Message    Message      Message
                                               Parsers
        Receiver    Filer        Queue                                                 U
                                                                                       R
                                                                                       I
                                                                                       T
                              Lifetime Raw                                             Y
                             Message Archive




                                                                                            17
Amalga collects data from multiple disparate
transaction 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
Modified Early Warning System (MEWS)




                                       19
Users can sort, filter, exclude columns




                                          20
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
Enterprise Data Warehouse Stack

 Sources                 Data Management Layers
             Staging   Integration     Data       Data Marts
  Regist                             Services       Finance
   EHR
                                                    Surgery
 Costing
 Mat Mgt                                            Office
Gen Ledger                                          Quality
Time & Att
                                                   Bundles
   Lab
 MD Cred                                            Revenue
 Pt Satis
 Claims
                  Patient   MD     Meds
             Master Data & Conformed Dimensions
                                                       22
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 and
Time & 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
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
Data Integration Layer
                         • Patient, provider, & med names are
Staging                    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
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
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
Benefits of the Semantic Layer




 90% time extracting data           10% time extracting data
10% time interpreting data         90% time interpreting data

                                                         28
Microsoft Presentation Tools




                               29
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
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
Nov 2011


An example of prescriptive analytics




                                       32
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
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
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
Number of Primary Care Physicians at
        a given Treatment Quality score
250
                                     2010
200

150                2005
100

50

 0
      64%




      90%
      60%
      62%

      66%
      68%
      70%
      72%
      74%
      76%
      78%
      80%
      82%
      84%
      86%
      88%

      92%
      94%
      96%
      98%
                                             36
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
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
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
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
Questions?

    iHT2 Health IT Summit Seattle
      Thursday, 23 August 2012


         Dick Gibson MD PhD
   Chief Healthcare Intelligence Officer
Providence Health & Services – Renton WA


                                           41

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

  • 1. Improving Health with Healthcare Intelligence iHT2 Health IT Summit Seattle Thursday, 23 August 2012 Dick Gibson MD PhD Chief Healthcare Intelligence Officer Providence Health & Services – Renton WA 1
  • 2. Agenda • External and Internal Environment. • Three Data Platforms: • EMR Reporting. • Microsoft Amalga. • Enterprise Data Warehouse. • Continuously Learning Organization. • Big Data. • Conclusions. 2
  • 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
  • 5. What does this mean for our healthcare? 5
  • 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. 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. 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. Health Care Intelligence with three overlapping platforms Epic Microsoft Enterprise Clarity Amalga Data Reporting Warehouse Database Routine 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. Epic Hyperspace Transaction Community Lead System Single Epic Clarity Reporting (Enterprise Database Master File EMFI Infrastructure) Three Identical instances AK WA/MT OR/CA 10
  • 11. Increased use of benchmarking means more data need to be collected 11
  • 12. And a lot of the data must come from doctors at the point of care Thanks to Jeff Westcott MD at Swedish 12
  • 13. For Epic Clarity Reporting: SAP Business Objects Crystal Reports • Operational reports built by IT, read by manager. • Precise, pixel perfect formatting. • High volume publishing. • Predictable questions. Gradual trend from reports to analytics Web Intelligence • Query and analysis, sort, filter, drill down. • Business user or analyst interacts with the data. • Basic formatting only. • Unpredictable questions. 13
  • 14. Crystal Reports Drop Down Lists To Select Report Parameters 14
  • 15. Crystal Reports Printout 15
  • 16. Web Intelligence Pull 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. 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 Use Raw data S feeds E C Message Message Message Parsers Receiver Filer Queue U R I T Lifetime Raw Y Message Archive 17
  • 18. Amalga collects data from multiple disparate transaction 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. Modified Early Warning System (MEWS) 19
  • 20. Users can sort, filter, exclude columns 20
  • 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. Enterprise Data Warehouse Stack Sources Data Management Layers Staging Integration Data Data Marts Regist Services Finance EHR Surgery Costing Mat Mgt Office Gen Ledger Quality Time & Att Bundles Lab MD Cred Revenue Pt Satis Claims Patient MD Meds Master Data & Conformed Dimensions 22
  • 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 and Time & 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. 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. Data Integration Layer • Patient, provider, & med names are Staging 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. 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. 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. Benefits of the Semantic Layer 90% time extracting data 10% time extracting data 10% time interpreting data 90% time interpreting data 28
  • 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. 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. Nov 2011 An example of prescriptive analytics 32
  • 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. 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. 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. Number of Primary Care Physicians at a given Treatment Quality score 250 2010 200 150 2005 100 50 0 64% 90% 60% 62% 66% 68% 70% 72% 74% 76% 78% 80% 82% 84% 86% 88% 92% 94% 96% 98% 36
  • 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. 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. 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. 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. Questions? iHT2 Health IT Summit Seattle Thursday, 23 August 2012 Dick Gibson MD PhD Chief Healthcare Intelligence Officer Providence Health & Services – Renton WA 41