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Epic As Platform For Clinical Decision Support. Implications For Qi And Research.

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Epic Research Advisory Council Meeting April 1, 2009 Epic as a Platform Launching Decision Support Tools: Implications for Research and Population QI Yiscah Bracha, MS Minneapolis Medical Research Foundation Robert…
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  1. Epic Research Advisory Council Meeting April 1, 2009 Epic as a Platform Launching Decision Support Tools: Implications for Research and Population QI Yiscah Bracha, MS Minneapolis Medical Research Foundation Robert Grundmeier, MD The Children’s Hospital of Philadelphia
  2. Improving Asthma Care in an Integrated Safety Net through a Commercially Available Electronic Medical Record
    • Prime contractor:  Denver Health and Hospital Association .
    • Subcontractor:  Minneapolis Medical Research Foundation. Project site: Hennepin County Medical Center, Mpls MN
    • AHRQ Contract No. HHSA290200600020, Task Order No. 5
      • Staff and contractors – Minneapolis Medical Research Foundation
      • Gail Brottman, MD (Chief, Pediatric Pulmonology, HCMC)
      • Kevin Larsen, MD (Chief Medical Informatics Officer, HCMC)
      • Yiscah Bracha, MS (Research Director, Center for Urban Health)
      • Cherylee Sherry, MPH (Project Manager, Pediatric Research & Advocacy HCMC )
      • Touch Thouk (Administrative Manager, Center for Urban Health)
      • Angeline Carlson, PhD (Principle, Data Intelligence Inc.)
      • Staff – Denver Health and Hospital Association
      • Sherry Eisert, PhD (Director, Health Services Research)
      • Michael (Josh) Durfee (Research Projects Coordinator, Health Services Research)
      • Contributors of Ideas, Information & Effort:
      • Michael Barbouche (University of Wisconsin Medical Foundation); Robert Grundmeier, MD (Children’s Hospital of Philadelphia); Michael Kahn, MD, PhD (Denver Children’s Hospital)
      • Donald Uden, PharmD (University of Minnesota), Faith Dohman, RN (Hennepin Faculty Associates), Susan Ross, RN (Minnesota Department of Health)
  3. The Problem:
    • Docs practice within an information avalanche
    • Impossible for any human to keep up
  4. What is a busy physician to do?
  5. Be confident.
  6. Be concerned.
  7. Be oblivious.
  8. Medical informatics to the rescue!
    • Provide decision support during care, while doc viewing pt electronic record
    • Developmental generation beyond:
      • URL to PDF of guidelines on screen (clinicians don’t go there)
      • Automatic reminders & alerts (clinicians suffer from “alert fatigue”)
  9. Task-based decision support
    • Types of support
      • Which diagnostic tests to use
      • How to initiate therapy
      • How to adjust therapy over time
    • Electronic data:
      • EHR data automatically populate decision algorithms
      • Results from algorithms automatically populate patient data record
  10. ASTHMA REGISTRY Asthma info for Populations Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma Decision Support & Epic: Current model Electronic decision support for: Which diagnostic imaging tech? IMAGING REGISTRY Imaging info for Populations interface engine interface engine
  11. ASTHMA REGISTRY Asthma info for Populations Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma Current model and data warehousing: Electronic decision support for: Which diagnostic imaging tech? IMAGING REGISTRY Imaging info for Populations ASTHMA REGISTRY Asthma info for Populations interface engine interface engine
  12. Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma Current model and data warehousing: Electronic decision support for: Which diagnostic imaging tech? interface engine interface engine IMAGING REGISTRY Imaging info for Populations ASTHMA REGISTRY Asthma info for Populations
  13. Building the Warehouse: IMAGING REGISTRY Imaging info for Populations ASTHMA REGISTRY Asthma info for Populations
  14. The Warehouse Dream: ASTHMA REGISTRY Asthma info for Populations HIV REGISTRY HIV info for Populations STENT REGISTRY Stent info for Populations DIABETES REGISTRY Diabetes info for Populations IMAGING REGISTRY Imaging info for Populations COLONOSCOPY REGISTRY Colonoscopy info Populations
  15. ASTHMA REGISTRY Asthma info for Populations HIV REGISTRY HIV info for Populations STENT REGISTRY Stent info for Populations DIABETES REGISTRY Diabetes info for Populations IMAGING REGISTRY Imaging info for Populations COLONOSCOPY REGISTRY Colonoscopy info Populations The analytic dream:
  16. Our analytic reality: ASTHMA REGISTRY Asthma info for Populations HIV REGISTRY HIV info for Populations DIABETES REGISTRY Diabetes info for Populations IMAGING REGISTRY Imaging info for Populations WHY?
  17. Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma ASTHMA REGISTRY Asthma info for Populations The problem with the current model: Electronic decision support for: Which diagnostic imaging tech? IMAGING REGISTRY Imaging info for Populations interface engine interface engine
  18. Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma ASTHMA REGISTRY Asthma info for Populations Electronic decision support for: Which diagnostic imaging tech? IMAGING REGISTRY Imaging info for Populations Hard to build…. Hard to maintain…. interface engine interface engine
  19. Clarity Information for Populations Epic Information on Individual Patients Electronic decision support for: Asthma ASTHMA REGISTRY Asthma info for Populations So much effort focused on interface*: Electronic decision support for: Which diagnostic imaging tech? IMAGING REGISTRY Imaging info for Populations * Building complex decision support within Epic is just as much effort as interface interface engine interface engine
  20. No resources for data mgmt: ASTHMA REGISTRY Asthma info for Populations HIV REGISTRY HIV info for Populations DIABETES REGISTRY Diabetes info for Populations IMAGING REGISTRY Imaging info for Populations
  21. A low-cost alternative to current model:
    • Support provided by external applet
    • Applet invoked through Intranet hyperlink
    • Hyperlink opens applet in new window
    • Applet provides decision support
    • Applet saves data to its own database
    • User returns to Epic
  22. Example: HIT Asthma Project
    • Java Applet, called e-AAP, provides administrative & clinical decision support*
    • Invoked from Epic:
      • Create new Asthma Action Plan (procedure order)
      • View existing Asthma Action Plan
        • from Order Results
        • from asthma detail in problem list
    • Invocation sends @ 20 encrypted live Epic data elements to applet through URL
    • New asthma data saved to SQL server database
    * Support based on NAEPP-3 Guidelines, August 2007
  23. e-AAP Clinical Decision Support:
    • For new asthma patients
      • Assess severity
      • Initiate treatment given age & severity
    • For currently treated asthma patients
      • Assess control
      • Modify treatment given age, control, current treatment, pt adherence
        • Step-based treatment
        • Supports user in calculating current step
  24. e-AAP Administrative Support:
    • Document severity and/or control
    • Generate printable asthma action plan
      • Patient-specific
      • Patient-friendly (tested for literacy)
      • Records all JHACO-required data
    • Document production of AAP
    • Generate:
      • Template of asthma progress note that user can paste into patient’s chart
      • List of selected meds & instructions for use
  25. Launched as procedure order in Epic: Highlighted data transferred in URL. Clicking here launches applet..
  26. Live data passed to Applet in URL: Web link defined by print group Launches web browser with “garbled” pt data in URL
  27. Linking code required: Cache routine: Delivers hyperlink Cache global: Provides configuration
  28. 1 st screen user sees: User automatically logged in; audit trail initiated; patient data transferred directly from Epic.
  29. Applet generates data. Where do they go?
    • Individual patient data:
      • Saved to underlying database
      • Applet generates asthma progress note that doc can paste into record
    • Population data:
      • All Applet data stored to underlying registry
      • Clarity data extracted & merged with Applet data in registry
  30. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients ASTHMA REGISTRY Asthma info for Populations Patient & user context thru URL Individual pt data: Saved to SQL server database (registry)
  31. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients ASTHMA REGISTRY Asthma info for Populations Patient & user context thru URL Population data: Relevant data extracted from Clarity, merged with Applet data in registry
  32. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients ASTHMA REGISTRY Asthma info for Populations Patient & user context Decision Support & Epic: Alternative model
  33. How are applet data retrieved?
    • Individual patient data:
      • Previously created e-AAPs easily found & reviewed from Applet.
      • If user pasted asthma progress note, available in EHR.
    • Population data:
      • Extracted from underlying registry
      • Registry populated with Applet data and relevant data from Clarity
  34. To view individual pt data: To see previous AAPs, user clicks here
  35. Pop-up appears: User clicks on desired PDF
  36. Hyperlink opens applet:
  37. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients ASTHMA REGISTRY Asthma info for Populations Patient & user context thru URL To view population data:
  38. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients Imaging Applet Imaging info for Individual Patients ASTHMA REGISTRY Asthma info for Populations Imaging Registry Imaging info for Populations Patient & user context Patient & user context Multiple such registries possible:
  39. Clarity Information for Populations Epic Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients Imaging Applet Imaging info for Individual Patients ASTHMA REGISTRY Asthma info for Populations Imaging Registry HIV info for Populations Patient & user context Patient & user context ASTHMA REGISTRY Asthma info for Populations Alternate model and data warehousing:
  40. EHR Data Repository Information for Populations Local EHR Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients HIV APPLET HIV info for Individual Patients Patient & user context Patient & user context IMAGING REGISTRY Imaging info for Populations ASTHMA REGISTRY Asthma info for Populations Alternate model and data warehousing: Imaging Applet HIV info for Individual Patients
  41. Imaging Applet HIV info for Individual Patients EHR Data Repository Information for Populations Local EHR Information for Individual Patients ASTHMA APPLET Asthma info for Individual patients Patient & user context Patient & user context No major interface to build or maintain! ASTHMA REGISTRY Asthma info for Populations Imaging Registry Imaging info for Populations interface engine interface engine
  42. ASTHMA REGISTRY Asthma info for Populations HIV REGISTRY HIV info for Populations STENT REGISTRY Stent info for Populations DIABETES REGISTRY Diabetes info for Populations IMAGING REGISTRY Imaging info for Populations COLONOSCOPY REGISTRY Colonoscopy info Populations Maybe we can!
  43. Questions? Project supported by the Agency for Health Research and Quality . Contract No. HHSA290200600020, Task Order No. 5 The findings and conclusions are the responsibility of the authors, not the AHRQ. Clinical informatics at HCMC: Dr. Kevin Larsen ( [email_address] ) Invoking applet from Epic: Dr. Robert Grundmeier ( [email_address] ) Asthma details about applet: Dr. Gail Brottman ( [email_address] ) Project direction & all other Qs: Yiscah Bracha, MS ( [email_address] )
  44. Extra Slides
    • URL to live application demo
    • Why not build in Epic
      • Logical complexity
      • Volume of clinical material
  45. Why not build it in Epic?
    • Logical complexity
      • Existence of relevant data
      • Validity of relevant data
      • User choices
      • Patient age, adherence
    • Volume of up-to-date clinical info required
      • @37k med combos in guidelines
      • Guidelines contain @ 2% of all possibilities
  46. e-AAP: High-level process flow: Disease mgmt component
  47. Disease management component:
  48. Treatment decisions in guidelines:
  49.  
  50. Treatment data in Applet:
  51.  

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