Computerized Extraction of Information on the Quality of Diabetes Care from Free Text in Electronic Patient Records of General Practitioners

TLDR

The study evaluated a computerized method for extracting numeric diabetes‑care measurements from free text in general practitioner electronic patient records. The method was tested by comparing its accuracy to manual chart abstraction across two common electronic record systems, with processing times averaging 7.8 minutes per 100 patients. The extraction achieved 94–100% sensitivity and 85–100% PPV for most measurements, with post‑processing raising PPV to 100%, and 80% of relevant data resided in free text, demonstrating high‑accuracy conversion to structured data for research and quality assessment.

Abstract

Objective: This study evaluated a computerized method for extracting numeric clinical measurements related to diabetes care from free text in electronic patient records (EPR) of general practitioners. Design and Measurements: Accuracy of this number-oriented approach was compared to manual chart abstraction. Audits measured performance in clinical practice for two commonly used electronic record systems. Results: Numeric measurements embedded within free text of the EPRs constituted 80% of relevant measurements. For 11 of 13 clinical measurements, the study extraction method was 94%–100% sensitive with a positive predictive value (PPV) of 85%–100%. Post-processing increased sensitivity several points and improved PPV to 100%. Application in clinical practice involved processing times averaging 7.8 minutes per 100 patients to extract all relevant data. Conclusion: The study method converted numeric clinical information to structured data with high accuracy, and enabled research and quality of care assessments for practices lacking structured data entry.