Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

Author:

Schorer Anna E.1ORCID,Moldwin Richard2ORCID,Koskimaki Jacob3,Bernstam Elmer V.4ORCID,Venepalli Neeta K.5,Miller Robert S.3ORCID,Chen James L.6ORCID

Affiliation:

1. Oncology Analytics, Atlanta, GA

2. College of American Pathologists, Northfield, IL

3. CancerLinQ, American Society of Clinical Oncology, Alexandria, VA

4. The University of Texas School of Biomedical Informatics at Houston and Division of General Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, TX

5. Department of Medicine, The University of Illinois, Chicago, IL

6. Departments of Internal Medicine and Biomedical Informatics, The Ohio State University, Columbus, OH

Abstract

PURPOSE The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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