Domain

Learning Health System

Type

Case Study or Comparative Case Study

Theme

effectiveness; quality

Start Date

7-6-2014 10:25 AM

End Date

7-6-2014 11:45 AM

Structured Abstract

Introduction

Delivering more appropriate, safer and highly effective health care requires a learning healthcare system. The Agency for Healthcare Research and Quality funded enhanced registry projects to: 1) create and analyze valid data for comparative effectiveness research (CER); and 2) enhance the ability to monitor and advance clinical quality improvement (QI).

Methods

The Comparative Effectiveness Research and Translation Network (CERTAIN) deployed the commercially available Amalga Unified Intelligence System™ as a central data repository for an existing QI registry. The project planned to electronically link participating hospitals to: 1) reduce the medical records review workflow and staffing burden for on-going participation in QI; 2) demonstrate capacity and scalability to incorporate new clinical disciplines into QI; and 3) provide access to more data on patients across healthcare encounter types and longitudinal records. An eight-step implementation process included site recruitment, technical review of electronic medical record (EMR) systems, site-specific interface planning, data ingestion into the repository, and data validation. Data ownership and security protocols were established, along with formal methods to separate data management for QI purposes from data analysis done for research purposes.

Findings

CERTAIN approached 19 hospitals in Washington State operating within 12 unaffiliated healthcare systems. Of 19 hospitals approached, 5 completed all implementation steps. Four hospitals did not participate due to lack of perceived institutional value by the Chief Medical Officer (CMO) or Chief Information Officer (CIO). Ten hospitals with CMO or CIO interest did not participate because their informatics engineers were oversubscribed with institutional Meaningful Use objectives. Additionally, one organization providing administrative services to 22 hospitals expressed engagement and commitment of information technology (IT) resources, but were unable to overcome significant data governance barriers during the funded project timeline. Data ingestion found interface errors that could not be solved by the research team alone, requiring each hospital to supply ongoing IT resources at an additional cost. While 50% of the QI registry data elements were targeted for automated delivery to the project’s enhanced registry, 14.5% were successfully transmitted.

Discussion

CERTAIN demonstrated success in recruiting unaffiliated hospitals to create an enhanced registry to achieve AHRQ goals. However, this project uncovered several distinct barriers to central data aggregation for QI and CER across unaffiliated hospitals. At some hospitals, the CMO or CIO committed to joining the project but there were not enough IT resources to follow through. Beyond this, concerns about data governance, the distinction between QI versus CER data use, and the physical security of the data leaving a hospital electronically were also cited as reasons against participation. Finally, there were unpredictable costs at each site because of idiosyncrasies among unaffiliated hospitals in how EMR data are stored and made available for transmission, even among hospitals using the same vendor’s EMR.

Conclusion

The most challenging barriers to this process were not ultimately the technology, but included significant site recruitment challenges, alignment with existing health IT projects, governance and security issues, and unexpected costs raised by the differences in EMR implementation.

Acknowledgements

This project was supported by grant number R01HS020025 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official vies 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: Peter Tarczy-Hornoch, MD; Erik Van Eaton, MD; Daniel Capurro, MD; Allison Devlin, MS; E. Beth Devine, PharmD, MBA, PhD; Michael Tepper, PhD; Meliha Yetisgen-Yildiz, PhD, MSc; Marisha Hativa, MSHS; Kevin Middleton; Megan Zadworny, MHA. Institutions contributing to this study: EvergreenHealth (Kirkland, WA); Harborview Medical Center (Seattle, WA); Northwest Hospital and Medical Center (Seattle, WA); University of Washington Medical Center (Seattle, WA); Virginia Mason Medical Center (Seattle, WA).

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Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Jun 7th, 10:25 AM Jun 7th, 11:45 AM

Achieving Automated Health Data Linkages for Learning Healthcare Systems: Lessons Learned

Introduction

Delivering more appropriate, safer and highly effective health care requires a learning healthcare system. The Agency for Healthcare Research and Quality funded enhanced registry projects to: 1) create and analyze valid data for comparative effectiveness research (CER); and 2) enhance the ability to monitor and advance clinical quality improvement (QI).

Methods

The Comparative Effectiveness Research and Translation Network (CERTAIN) deployed the commercially available Amalga Unified Intelligence System™ as a central data repository for an existing QI registry. The project planned to electronically link participating hospitals to: 1) reduce the medical records review workflow and staffing burden for on-going participation in QI; 2) demonstrate capacity and scalability to incorporate new clinical disciplines into QI; and 3) provide access to more data on patients across healthcare encounter types and longitudinal records. An eight-step implementation process included site recruitment, technical review of electronic medical record (EMR) systems, site-specific interface planning, data ingestion into the repository, and data validation. Data ownership and security protocols were established, along with formal methods to separate data management for QI purposes from data analysis done for research purposes.

Findings

CERTAIN approached 19 hospitals in Washington State operating within 12 unaffiliated healthcare systems. Of 19 hospitals approached, 5 completed all implementation steps. Four hospitals did not participate due to lack of perceived institutional value by the Chief Medical Officer (CMO) or Chief Information Officer (CIO). Ten hospitals with CMO or CIO interest did not participate because their informatics engineers were oversubscribed with institutional Meaningful Use objectives. Additionally, one organization providing administrative services to 22 hospitals expressed engagement and commitment of information technology (IT) resources, but were unable to overcome significant data governance barriers during the funded project timeline. Data ingestion found interface errors that could not be solved by the research team alone, requiring each hospital to supply ongoing IT resources at an additional cost. While 50% of the QI registry data elements were targeted for automated delivery to the project’s enhanced registry, 14.5% were successfully transmitted.

Discussion

CERTAIN demonstrated success in recruiting unaffiliated hospitals to create an enhanced registry to achieve AHRQ goals. However, this project uncovered several distinct barriers to central data aggregation for QI and CER across unaffiliated hospitals. At some hospitals, the CMO or CIO committed to joining the project but there were not enough IT resources to follow through. Beyond this, concerns about data governance, the distinction between QI versus CER data use, and the physical security of the data leaving a hospital electronically were also cited as reasons against participation. Finally, there were unpredictable costs at each site because of idiosyncrasies among unaffiliated hospitals in how EMR data are stored and made available for transmission, even among hospitals using the same vendor’s EMR.

Conclusion

The most challenging barriers to this process were not ultimately the technology, but included significant site recruitment challenges, alignment with existing health IT projects, governance and security issues, and unexpected costs raised by the differences in EMR implementation.