ArticleJAMIA open2018
Using electronic health records to characterize prescription patterns: focus on antidepressants in nonpsychiatric outpatient settings.
Article in JAMIA open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A method to enable clinical and translational research teams with custom real-world data from electronic health record systems.Journal of clinical and translational science · 2026Article
- Caregiving at end-of-life: How do family structure and dementia status impact antidepressant and anxiolytic prescriptions among families?Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Optimising the use of electronic medical records for large scale research in psychiatry.Translational psychiatry · 2024Review
- Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Article
- An architecture for research computing in health to support clinical and translational investigators with electronic patient data.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Off-Label Use of Antineoplastic Drugs to Treat Malignancies: Evidence From China Based on a Nationwide Medical Insurance Data Analysis.Frontiers in pharmacology · 2021Article
- Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.Journal of speech, language, and hearing research : JSLHR · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data.
methodsA retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured "medication-diagnosis" pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments.
resultsOf 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as "on-label/evidence-based uses." Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of "off-label use" prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation.
conclusionsThe results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient's recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics.
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