An automated acquisition system using MedLEE achieved reasonable agreement with medical experts (k values 0.65 to 0.97) in extracting quality of care measures from electronic discharge notes.
Can an automated system (MedLEE) reliably extract quality of care measures from electronic discharge notes compared to medical experts in patients with cardiovascular diseases?
An automated natural language processing system (MedLEE) can reliably extract cardiovascular quality of care measures from electronic discharge notes, showing good agreement with expert manual review.
Effect estimate: k value 0.65 to 0.97
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.
Chiang et al. (Sat,) conducted a other in Cardiovascular diseases. Automated acquisition system (MedLEE) vs. Medical experts was evaluated on Agreement (k value) with medical experts for quality of care measures (k value 0.65 to 0.97). An automated acquisition system using MedLEE achieved reasonable agreement with medical experts (k values 0.65 to 0.97) in extracting quality of care measures from electronic discharge notes.