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Quality Performance Measurement Using the Text of Electronic Medical Records

Department of Pharmaceutical Care and Health Systems, University of Minnesota, Rochester, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota

Susan Bjornsen, RN, MA

Department of Endocrinology, Mayo Clinic, Rochester, Minnesota

Penny Hanson

Department of Endocrinology, Mayo Clinic, Rochester, Minnesota

Steven Smith, MD

Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, pakh0002{at}umn.edu, Department of Endocrinology, Mayo Clinic, Rochester, Minnesota

Background. Annual foot examinations (FE) constitute a critical component of care for diabetes. Documented evidence of FE is central to quality-of-care reporting; however, manual abstraction of electronic medical records (EMR) is slow, expensive, and subject to error. The objective of this study was to test the hypothesis that text mining of the EMR results in ascertaining FE evidence with accuracy comparable to manual abstraction. Methods. The text of inpatient and outpatient clinical reports was searched with natural-language (NL) queries for evidence of neurological, vascular, and structural components of FE. A manual medical records audit was used for validation. The reference standard consisted of 3 independent sets used for development (n=200 ), validation (n=118), and reliability (n=80). Results. The reliability of manual auditing was 91% (95% confidence interval [CI]= 85—97) and was determined by comparing the results of an additional audit to the original audit using the records in the reliability set. The accuracy of the NL query requiring 1 of 3 FE components was 89% (95% CI=83—95). The accuracy of the query requiring any 2 of 3 components was 88% (95% CI=82—94). The accuracy of the query requiring all 3 components was 75% (95% CI= 68— 83). Conclusions. The free text of the EMR is a viable source of information necessary for quality of health care reporting on the evidence of FE for patients with diabetes. The low-cost methodology is scalable to monitoring large numbers of patients and can be used to streamline quality-of-care reporting.

Key Words: performance measures • quality indicators • data mining.

This version was published on July 1, 2008

Medical Decision Making, Vol. 28, No. 4, 462-470 (2008)
DOI: 10.1177/0272989X08315253


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