Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction And Encoding System (MedLEE)

Jung Hsien Chiang, Jou Wei Lin, Chen Wei Yang

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

The objective of this study was to develop and validate an automated acquisition system to assess quality of care (QC) measures for cardiovascular diseases. This system combining searching and retrieval algorithms was designed to extract QC measures from electronic discharge notes and to estimate the attainment rates to the current standards of care. It was developed on the patients with ST-segment elevation myocardial infarction and tested on the patients with unstable angina/non-STsegment elevation myocardial infarction, both diseases sharing almost the same QC measures. The system was able to reach a reasonable agreement (k value) with medical experts from 0.65 (early reperfusion rate) to 0.97 (b-blockers and lipid-lowering agents before discharge) for different QC measures in the test set, and then applied to evaluate QC in the patients who underwent coronary artery bypass grafting surgery. The result has validated a new tool to reliably extract QC measures for cardiovascular diseases.

Original languageEnglish
Pages (from-to)245-252
Number of pages8
JournalJournal of the American Medical Informatics Association
Volume17
Issue number3
DOIs
Publication statusPublished - 2010 May

All Science Journal Classification (ASJC) codes

  • Health Informatics

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