Identification of documented medication non-adherence in physician notes.
Medication non-adherence is common and the physicians awareness of it may be an important factor in clinical decision making. Few sources of data on physician awareness of medication non-adherence are available. We have designed an algorithm to identify documentation of medication non-adherence in the text of physician notes. The algorithm recognizes eight semantic classes of documentation of medication non-adherence. We evaluated the algorithm against manual ratings of 200 randomly selected notes of hypertensive patients. The algorithm detected 89% of the notes with documented medication non-adherence with specificity of 84.7% and positive predictive value of 80.2%. In a larger dataset of 1,000 documents, notes that documented medication non-adherence were more likely to report significantly elevated systolic (15.3% vs. 9.0%; p = 0.002) and diastolic (4.1% vs. 1.9%; p = 0.03) blood pressure. This novel clinically validated tool expands the range of information on medication non-adherence available to researchers.
Turchin
Alexander
A
Clinical Informatics Research and Development, Partners HealthCare, Boston, MA USA.
Wheeler
Holly I
HI
Labreche
Matthew
M
Chu
Julia T
JT
Pendergrass
Merri L
ML
Einbinder
Jonathan S
JS
Einbinder
Jonathan Seth
JS
D000465:Algorithms; D000959:Antihypertensive Agents / Q000008:administration & dosage; D001185:Artificial Intelligence; D006973:Hypertension / Q000188:drug therapy / Q000453:epidemiology; D016247:Information Storage and Retrieval / Q000379:methods*; D008404:Massachusetts; D008487:Medical History Taking / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D010349:Patient Compliance / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings
D000959:Antihypertensive Agents