DEMONSTRATING CLOUD-BASED CLINICAL DECISION SUPPORT AT SCALE: THE CLINICAL DECISION SUPPORT CONSORTIUM

Transcription

1 DEMONSTRATING CLOUD-BASED CLINICAL DECISION SUPPORT AT SCALE: THE CLINICAL DECISION SUPPORT CONSORTIUM Brian E. Dixon, MPA, PhD, FHIMSS Marilyn D. Paterno, MBI

2 Outline Part 1 Introductions Overview of the CDSC and its Aims Theoretical Framework Models to accelerate knowledge to practice Unified theory of CDS Knowledge Representation The Four layers Authoring tools The KM portal

3 Outline Part 2 CDS in the Cloud The web services approach CDSC Implementation Sites Demo of the Regenstrief system CDS Dashboard Assessing CDS effectiveness Future Directions Acknowledgements

4 Spectrum of CDS Systems Clinical Reminder Clinical Alert Corollary Order Population Health

5 CDS Grand Challenges Summarize patient-level information Prioritize recommendations to users Combine recommendations for patients with co-morbidities Improve the human-computer interface Use free text information in clinical decision support Manage large clinical knowledge databases Create a internet-accessible, clinical decision support repository Prioritize CDS content development and implementation Disseminate best practices Create an architecture for sharing executable CDS modules Mine large clinical databases to create new CDS Sittig et al., JBI, 2008

6 CDS Consortium: Goal and Significance Goal: To assess, define, demonstrate, and evaluate best practices for knowledge management and CDS in health care IT at scale across multiple ambulatory care settings and EHR technology platforms Significance: The CDS Consortium will carry out a variety of activities to improve knowledge about decision support, with the ultimate goal of supporting and enabling widespread sharing and adoption of CDS. 2. Knowledge Specification 1. Knowledge Management Life Cycle 3. Knowledge Portal and Repository 4. CDS Public Services and Content 5. Evaluation Process for each CDS Assessment and Research Area 6. Dissemination Process for each Assessment and Research Area

7 5-Year Timeline 2008 October Content, KM, KT Development Analysis of Best Practices 2010 MU Stage 2 Final ARRA/HITECH Passed July Pilots, Demonstrations Expand CDSC Membership Expand CDSC Content Evaluation

8 Three Models to Accelerate Knowledge -> Practice Current paper-based approach Guideline EMR Knowledge artifact import into EMR Computer Interpretable Guideline Cloud-based clinical decision support services Web Services CCD/VMR Patient Data Object Decision Support

9 The Unified Theory for CDS Clinical Knowledge GEM CDSC L2 Structured Knowledge Implementable/ Executable Knowledge CDSC L3 CDSC CCD+ CDSC L4 EHR EHR EHR Service CDSC Action Recommendation

10 Knowledge Translation and Specification: Four-Layer Model derived from Level 1 Unstructured Format :. jpeg,. html,. doc,. xl Level 2 Semi - structured Format : xml Level 3 Structured Format : xml Level 4 Machine Execution Format : any + metadata + metadata + metadata + metadata derived from derived from Initial evaluation results: Structured recommendation (L3) was considered more implementable than the semi-structured recommendation (L2). Boxwala, A.A., et al. A multi-layered framework for disseminating knowledge for computer-based decision support. JAMIA 2011.doi: /amiajnl

11 The Four Layers Illustrated Published Guideline knowledge evoke: Arden Syntax Rule Semi-structured data: Structured Recommendation Recommendation Semi-Structured Recommendation PCP Structured := read { }; Recommendation Executable Rules BPRecordedInLastYear := read last{table= RES, code= } Meta data Title: Screening Meta data for High Blood Pressure Reaffirmation Title: Recommendation Screening Adult for := High ; Statement Blood Pressure Narrative Developer: Guideline Developer: U.S. Preventive CDS Consortium Services Task Force Screening for High (USPSTF) Blood Pressure Derived from: logic: USPSTF BP Screening Semistructured Rec. Reaffirmation Recommendation Strength of recommendation: Statement if (adult is Grade false) then A U.S. Preventive Services Task Applicable Force (USPSTF) Scenario conclude false; Clinical Scenario: Data Mapping: if (BPRecordInLastYear BPRecordedInLastYear: is null) Observation then = VitalSign-> Order Sets in Patient age select(code.equals(bploinccode) 18 years conclude true; and vsdatatime.within(12, CPOE months)) system The U.S. Preventive Blood Services pressure Task not obtained Force in the last year (USPSTF) recommends screening Logical Condition: for action: high BPRecordedInLastYear->notEmpty() blood pressure Clinical adults Action: aged 18 and Write older. Patient has not had a blood pressure screening in the last year (This is a grade Obtain "A" recommendation) and Recommended record blood PCP ; pressure Action: VitalSign(code: BPLoincCode)

12 L3 Knowledge Module Single knowledge representation approach for different CDS modalities Order sets, reminders, alerts, documentation templates Modality features tend to mix-and-match Single representation for different modalities Unified framework for tools development Enables consistency checking

13 Knowledge Module Structure Knowledge Module Metadata Action Behavior Presentation Patient Data

14 Knowledge Authoring Tool

15 Knowledge Authoring Tool

16 Knowledge Authoring Tool

17 Knowledge Management Portal

18 Search Results for diabetes KM Portal

19 CDS IN THE CLOUD

20 Web Services Approach: Hypothesis & Goal CDSC CDS Services team hypothesis A service-oriented architecture (SOA) approach to decision support is feasible and will provide benefits in interoperability, reliability, and reusability of knowledge content used in clinical decision support across multiple sites. Interoperability goal A web service that is External to the application and/or system Agnostic to the technology of the calling site Capable of being called from inside or outside its firewall Supports / makes use of emerging standards

21 Web Services Approach: Service Flexibility Service Input Formats

22 Web Services Approach: Flexible yet Standard Output Action Recommendation Model Utilizes HL7 Datatypes R1 standard Makes the decomposing task easier Mappable to local EHRs that use coded data Actionable for clinical use, as in: Create Orders Medications, Procedures Record Observations Problems Display Messages Text-based Alerts Provide Knowledge Assets Patient Education Material Recommend Encounters Referrals

23 Sample Message Observation Encounter Knowledge Asset

24 Web Services Approach: Implementations WVP Health Authority Salem,Oregon PHS Host Boston, MA Wishard Hospital Indianapolis,IN RWJ Medical Group New Brunswick,NJ

25 Current Status Site - Guideline Clinics Providers Partners - CDSC 2 48 Partners - Immunization 4 40 Regenstrief - CDSC 2 72 NextGen - CDSC 4 10 GE CDSC (Expects to start late August) 1 10 * * Estimate

26 Performance over 90 days * Site - Guideline # days Type + Average Calls/day Avg time (secs) Partners - CDSC 90 S 1, Partners - Immunization 80 S Regenstrief - CDSC 64 A NextGen - CDSC 87 A Synchronous / Asynchronous * May 13, 2013 August 10, 2013

27 Reminders appear on Summary Screen Partners LMR CDSC Guidelines

28 And on Reminder Screen Partners LMR CDSC Guidelines

29 Partners LMR CDSC Guidelines And at time of signing

30 Partners LMR CDC Immunizations

31 Contraindication warning Partners LMR CDC Immunizations

32 Regenstrief Wishard Hospital

33 CDSC Guidelines at Wishard - Gopher DEMONSTRATION

34 WVP Health Authority

35 GE Rutgers Robert Wood Johnson Medical Group

36 CDS DASHBOARD

37 Develop performance reporting tools and CDS dashboards to Purpose review adherence to CDS Consortium guidelines assess effectiveness of CDS on patient care outcomes How does the dashboard help assess CDS effectiveness?

38 Putting Reminders in Context Epidemiology of CDS: the CDS/Reminder Lifecycle T 0 T 1 Measurement Period Patient becomes member of eligible population Reminder logic becomes true Reminder displayed Reminder accepted Right action documented Clinical outcome Prevalence Logic Display Acknowledged Performance Outcome Patients with Type2 DM Overdue for A1 C Test Reminder displayed to user User clicks on reminder and chooses coded response A1 C test result documented A1 C < =7. 0

39 How well are the reminders working? To measure the Effectiveness of CDS, dashboard uses Display (Counts) Acknowledged (%) Performance ( Right action taken) Contribution to Clinical Performance Number Needed to Remind (NNTR) the number of reminders needed to be displayed to a provider for that provider to take the recommended action

40 Dashboard CDS Designer View Total patients 47,782 Performing total 28,476 Patients where reminders displayed 2,757 Total count of displays 14,944 NNTR 3.87

41 Reminders in Context CDS Reminders Displayed

42 Number Needed to Remind (NNTR) NNTR is: 2757 patients with reminder displayed divided by 713 patients who had reminder displayed and then had aspirin added to the Med List = 3.87 (If look at total #reminder displays rather than #patients, then NNTR is 20.96)

43 CDS Reminder Effectiveness Less effective Fewer patients Less effective More patients More effective Fewer patients More effective More patients

44 Uses for Dashboard Results Systematically monitor and evaluate effectiveness of clinical decision support Prioritize which decision support guidelines or reminders to implement or enhance

45 FUTURE PLANS

46 Future Directions for the CDSC Current demonstrations Complete site trials currently running Publish multi-site trial results, analysis Continuing to expand Technology development (e.g., support for DSS, SMART) Clinical content offerings (e.g., pharmacogenomic, other rules in development) Relocating Administrative Support Vanderbilt University Medical Center

47 FOR MORE INFORMATION CDSC Blackford Middleton Four-Layer Process and Knowledge Authoring Tool Aziz Boxwala KM Portal Tonya Hongsermeier ECRS Web Service Howard Goldberg Action-Recommendation Model Beatriz Rocha CDS Dashboard Jonathan Einbinder

48 THANK YOU Presenters: Brian E. Dixon, MPA, PhD, FHIMSS Marilyn D. Paterno, MBI Contact: Blackford Middleton, MD, MPH, MSc