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Role of Nursing Informatics in the Automation of Pneumonia Quality Measure Data Elements

Kalsy, Megha, PhD; Lin, Jau-Huei, MD, PhD; Bray, Bruce E., MD; Sward, Katherine A., PhD, RN

CIN: Computers, Informatics, Nursing: October 2018 - Volume 36 - Issue 10 - p 475–483
doi: 10.1097/CIN.0000000000000451
FEATURES

Core measures are standard metrics to reflect the processes of care provided by hospitals. Hospitals in the United States are expected to extract data from electronic health records, automated computation of core measures, and electronic submission of the quality measures data. Traditional manual calculation processes are time intensive and susceptible to error. Automated calculation has the potential to provide timely, accurate information, which could guide quality-of-care decisions, but this vision has yet to be achieved. In this study, nursing informaticists and data analysts implemented a method to automatically extract data elements from electronic health records to calculate a core measure. We analyzed the sensitivity, specificity, and accuracy of core measure data elements extracted via SQL query and compared the results to manually extracted data elements. This method achieved excellent performance for the structured data elements but was less efficient for semistructured and unstructured elements. We analyzed challenges in automating the calculation of quality measures and proposed a rule-based (hybrid) approach for semistructured and unstructured data elements.

Author Affiliations: IDEAS Center SLC VA Healthcare System, Salt Lake City, UT (Drs Kalsy and Bray); Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City (Drs Kalsy, Lin, Bray, and Sward); GE Healthcare, Medical Informatics, Portland, OR (Dr Lin); and College of Nursing, University of Utah, Salt Lake City (Drs Bray and Sward).

This study was approved by the University of Utah Institutional Review Board (IRB no. 00047333).

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

The views expressed are those of the authors and do not necessarily reflect those of the Department of Veterans Affairs, the US Government, GE Healthcare, or the academic affiliate organizations.

Corresponding author: Megha Kalsy, PhD, 521 S Lake St, Amherst, OH 44001 (megha.kalsy@utah.edu).

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