Publications

Submitted by ctuttle on
Go back to Resources

Clinical Documentation of Patient-Reported Medical Cannabis Use in Primary Care: Toward Scalable Extraction Using Natural Language Processing Methods

Mar-22

Journal Article

Authors:

Carrell, D.S.
Cronkite, D.J.
Shea, M.
Oliver, M.
Luce, C.
Matson, T.E.
Bobb, J.F.
Hsu, C.
Binswanger, I.A.
Browne, K.C.
Saxon, A.J.
McCormack, J.
Jelstrom, E.
Ghitza, U.E.
Campbell, C.I.
Bradley, K.A.
Lapham, G.T.

Secondary:
Subst Abus

Volume:
43

Pagination:
917-924

Issue:
1

PMID:
35254218

URL:
https://pubmed.ncbi.nlm.nih.gov/35254218/

DOI:
10.1080/08897077.2021.1986767

Keywords:
medical marijuana; natural language processing; observational study; screening

Abstract:
<p>Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients&#039; electronic health records (EHRs). We 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. We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use 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. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 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., &quot;edible THC nightly &quot;) was more common than explicit (e.g., &quot;continues cannabis use&quot;). Clinicians use diverse and often ambiguous language to document patients&#039; reasons for cannabis use. Automating extraction of documentation about patients&#039; cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.</p>

Go back to Resources