Identifying Medication Discontinuations Using Text Searching and Clinical Data

Glenn K Goodrich, Elizabeth A Bayliss, James Lagrotteria, Jennifer C Barrow, Bill Harding, Kathy S Gleason, et al.

Summary: Researchers developed and tested a method to identify when a clinician deliberately decided to stop a patient's medication — as distinct from a patient simply not refilling a prescription or experiencing a gap in insurance coverage. Working with records from adults over 65 across five medication types, the team combined keyword searches of clinical notes with information from medication order records. When the two sources were used together, the method correctly identified intended discontinuations between 74% and 82% of the time, and correctly identified non-discontinuations between 75% and 95% of the time, depending on the medication type. Using either source alone performed worse.

Why it matters: Understanding whether a medication was deliberately stopped by a clinician — a practice called deprescribing — is important for studying whether reducing medications in older adults is beneficial or harmful. The problem is that standard insurance billing records, which researchers often rely on, cannot distinguish between a deliberate clinical decision and a patient simply not picking up a refill. If researchers cannot make that distinction reliably, studies of deprescribing can produce misleading results. This tool offers a practical step toward solving that problem using information already available in electronic health records.

Expert perspective: The approach is methodologically sound and the problem it addresses is real and underappreciated in medication research. However, there are important limitations to keep in mind. The tool was developed and tested at a single health system, so it may not work as well in settings with different documentation habits or different electronic record systems. The groups of records used to test each medication type were relatively small — as few as 80 for one category — which means the accuracy figures reported are estimates with meaningful uncertainty around them. A miss rate of roughly one in four to one in five intended discontinuations could meaningfully distort the conclusions of a study depending on what is being measured. Researchers who want to use this approach should consider testing it in their own setting before relying on it, and should think carefully about how any remaining errors might affect their particular analysis.

Relevance: Real-World Data; Real-World Evidence; Clinical Informatics; Data Quality; Observational Research

Source: https://pubmed.ncbi.nlm.nih.gov/42593400/

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