Challenges to electronic clinical quality measurement using third-party platforms in primary care practices: the healthy hearts in the heartland experience

Author:

Ahmad Faraz S123ORCID,Rasmussen Luke V3,Persell Stephen D45,Richardson Joshua E6,Liss David T45,Kenly Pauline2,Chung Isabel2,French Dustin D789,Walunas Theresa L24,Schriever Andy10,Kho Abel N234

Affiliation:

1. Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

2. The Center for Health Information Partnerships (CHiP), Institute of Public Health & Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

3. Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

4. Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

5. The Center for Primary Care Innovation, Institute of Public Health & Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

6. RTI International, Chicago, Illinois, USA

7. Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

8. Center for Healthcare Studies, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA

9. Veterans Affairs Health Services Research and Development Service, Chicago, Illinois, USA

10. Apertiva, Inc, Chicago, Illinois, USA

Abstract

Abstract Third-party platforms have emerged to support small primary care practices for calculating and reporting electronic clinical quality measures (eCQM) for federal programs like The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and Merit-based Incentive Payment System (MIPS). Yet little is known about the capabilities and limitations of electronic health record systems (EHRs) to enable data access for these programs. We connected 116 small- to medium-sized practices with seven different EHRs to popHealth, an open-source eCQM platform. We identified the prevalence of following problems with eCQM data for data extraction in seven different EHRs: (1) Lack of coded data in five of seven; (2) Incorrectly categorized data in four of seven; (3) Isosemantic data (data within the incorrect context) in four of seven; (4) Coding that could not be directly evaluated in six of seven; (5) Errors in date assignment and labeled as historical values in five of seven; and (6) Inadequate data to assign the correct code in two of seven. We recommend specific enhancements to EHR systems that can promote effective eCQM implementation and reporting to MACRA and MIPS.

Funder

Agency for Healthcare Research and Quality

National Heart, Lung, and Blood Institute

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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