Identifying Medication Discontinuations Using Text Searching and Clinical Data

Identifying clinically intended medication discontinuations at scale may help generate evidence to inform deprescribing. We developed an algorithmic approach to identifying such discontinuations, combining text strings applied to clinical documentation with medication order data for 5 different drug groups: oral hypoglycemics, statins, antihypertensives, bladder antimuscarinics, and antithrombotics.

The study population (n=1588) comprised individuals aged older than 65 with ≥90-day gaps in dispensing classified through manual review as having a clinically intended medication discontinuation or not. This gold-standard cohort was randomly divided into development and validation subsamples for each drug group. We developed text strings from clinical documentation reflecting clinical intent to discontinue a medication (or not). Text strings were usually simple [eg, "stop (medication)"], but required tailoring to drug groups [eg, temporarily "hold (antithrombotic)"]. Text strings queried clinical documentation and were supplemented with order discontinuation data, if available. We calculated sensitivity and specificity for identifying intended discontinuations for text alone, discrete data alone, and the full algorithm across validation samples.

Sensitivity and specificity for the full algorithm were 80% and 85% for oral hypoglycemics (n=467), 75% and 95% for statins (n=282), 82%, 75% for antihypertensives (n=599), 77% and 80% for bladder antimuscarinics (n=80), and 74% and 78% for antithrombotics (n=160). The full algorithm had higher sensitivity than either text or order data alone.

Text-based approaches supplemented by medication order data can identify clinically intended medication discontinuations at scale with moderate specificity and sensitivity. This may reduce misclassification relative to using claims data to generate evidence about deprescribing.