Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE).
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-ST-segment elevation myocardial infarction, both diseases sharing almost the same QC measures. The system was able to reach a reasonable agreement (kappa value) with medical experts from 0.65 (early reperfusion rate) to 0.97 (beta-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.
Chiang
Jung-Hsien
JH
Institute of Medical Informatics and Department of Computer Science, National Cheng Kung University, Tainan, Taiwan. jchiang@mail.ncku.edu.tw
Lin
Jou-Wei
JW
Yang
Chen-Wei
CW
D000789:Angina, Unstable; D002318:Cardiovascular Diseases; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D009203:Myocardial Infarction; D009323:Natural Language Processing; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D010351:Patient Discharge; D012986:Software Validation; D013624:Taiwan