1. Achieving automated health data linkages for learning healthcare systems: Lessons learned Allison Devlin, MS Program Director, Comparative Effectiveness Research & Translation Network University of Washington

2. CERTAIN Network Evidence Generation Clinical Practice Partners Dissemination & Implementation Healthcare Data Patient Voices Stakeholder Input Clinician Offices Long-term Care Facilities Hospitals

3. Project Overview Hospital n Hospital 2 Hospital 1 Hospital 2 Hospital n NLP SCOAP Existing workflow: Manual medical records review and data entry to registry Planned workflow: Electronic data delivery to a central data repository. Structured data and text data are processed through SCOAP data definitions to automate data collection for the registry. Anticipated Outcomes: Continuous performance surveillance and benchmarking Reduced staff burden Increased scalability Increased access to additional data fields

4. Hospital Recruitment 19 approached 15 C-level 5 C-level + IT availability Compared to those who did not participate, hospitals completing data linkage generally were: Greater in size More experienced with SCOAP More likely to be considered a teaching hospital More likely to be urban Similar in affiliation with a research institute or formal research program

5. SCOAP Champion support did not always translate to C- level support, and C-level support did not always translate to project approval or procession across the organization  Disease- or treatment-based QI initiatives may be too narrow to achieve hospital-wide buy-in IT resources will always first be directed to pursue hospital core business objectives  EHR transitions, Meaningful Use, ICD-10 CER and PCOR not considered a priority Recruitment Findings EHR-based projects need hospital-wide prioritization. Incentives for learning healthcare system participation are needed.

6. Data Oversight Hospitals Information Sharing Agreement Business Associate Data Use Agreement Business Associate De-identified Data Validation of Results Quality Improvement Research

7. The concept that hospitals retain physical control of raw data while providing for aggregation as limited datasets was not widely embraced Trying to accommodate hospitals that wanted to only export data about patients undergoing a SCOAP-included procedure was a tremendous technical challenge. The distinction between QI and research was time-intensive in convening stakeholders to gain consensus but otherwise was solvable Data Oversight Findings Many hospitals were not ready to engage in broader concepts of data aggregation for future or undefined purposes related to Quality Improvement or Research.

8. EHRs do not currently optimize entry or storage of discrete data elements at the point of care EHR systems generally do not have built-in capacity to standardize data and communicate it to other systems Idiosyncrasies in patient data introduced by hospital individualization of a widely used EHR brand Required ongoing participation of hospital IT subject matter experts and IT resources to create workaround formats Data Connection Findings EHR systems are generally not configured to facilitate a project like this without significant investment of time.

9. The net result was that the major barriers were not simply technical, but included a number of organizational barriers to achieving the goals of a learning healthcare system across multiple unaffiliated institutions

10. Learning in other ways

11. This project was supported by grant number R01 HS 20025-01 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The Surgical Care and Outcomes Assessment Program (SCOAP) is a Coordinated Quality Improvement Program of the Foundation for Health Care Quality. CERTAIN is a program of the University of Washington, the academic research and development partner of SCOAP. Personnel contributing to this study: Centers for Comparative and Health Systems Effectiveness (CHASE Alliance), University of Washington, Seattle, WA: Daniel Capurro, MD; Allison Devlin, MS; E. Beth Devine, PharmD, MBA, PhD; Marisha Hativa, MSHS; Prescott Klassen, MS; Kevin Middleton; Michael Tepper, PhD; Peter Tarczy-Hornoch, MD; Erik Van Eaton, MD; N. David Yanez III, PhD; Meliha Yetisgen-Yildiz, PhD, MSc; Megan Zadworny, MHA Acknowledgements

12. www.becertain.org www.becertain.org Allison Devlin adevlin@uw.edu adevlin@uw.edu