Clinical documentation of patient-reported medical cannabis use in primary care: Toward scalable extraction using natural language processing methods.

Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients’ electronic health records (EHRs). This study used natural language processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal.

Researchers analyzed EHRs of patients aged 18 or older who had been screened for past-year cannabis use (November 1, 2017 – October 31, 2018), to identify clinician-documented medical cannabis use. Medical use was defined as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. The study team developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes.

Analysis found documentation of medical cannabis use for 16,684 (5.6%) out of 299,597 outpatient encounters with routine screening for cannabis use among 203,589 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use, and 41 ambiguous terms. Implicit documentation of medical use (e.g., “edible THC nightly for lumbar pain“) was more common than explicit (e.g., “continues medical cannabis use”).

Conclusions: Clinicians use diverse and often ambiguous language to document patients’ reasons for cannabis use. Automating extraction of documentation about patients’ cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.

Related protocols: CTN-0077-Ot