Using Electronic Health Records to Measure Quality Improvement Efforts: Findings from a Large Practice Facilitation Initiative

Liss, David T.; Peprah, Yaw; Brown, Tiffany; Ciolino, Jody D.; Jackson, Kathryn; Kho, Abel N.; Murakami, Linda; Walunas, Theresa L.; Persell, Stephen D.

doi:10.1016/j.jcjq.2019.09.006

Cited by 7 publications

(11 citation statements)

References 9 publications

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“…38 , 42 While EHRs remain a primary source of clinical quality calculations today, research supports that EHRs and associated workflow challenges often result in inadequate data for accurate quality measure calculations. 9 , 12 This study supports that alternative infrastructures for more accurate quality calculation may be possible.…”

Section: Discussionsupporting

confidence: 76%

“…While not directly measured in this research, lack of relevant measures in an EHR has been a documented concern. 8 , 9 The methods employed in this research support ongoing initiatives to integrate interoperability standards into the quality measurement process. 15 , 36 , 37…”

Section: Discussionmentioning

confidence: 99%

“…The time and costs associated with quality reporting are significant and many clinicians express frustration with the process. 6 , 9 This study’s methods perform quality measurement based on standards-based data exchange and incur no incremental time or effort on the behalf of sampled facilities. Data already exchanged using clinical data standards allow for calculation of many quality measures.…”

Section: Discussionmentioning

confidence: 99%

“…All selected measures are eligible for MIPS eCQM reporting and many have been profiled in previous research. 9 , 10 , 32–34 …”

Section: Methodsmentioning

confidence: 99%

“…7 While EHRs and other technology are required to have the capability to calculate care quality, many are neither easy to use nor liked by clinicians due to concerns about their validity. 2 , 8 , 9 …”

Section: Introductionmentioning

confidence: 99%

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Clinical data sharing improves quality measurement and patient safety

D'Amore

McCrary

Denson

et al. 2021

Journal of the American Medical Informatics Association

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Objective Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and Methods Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data. Results A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations. Discussion These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality. Conclusions Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%