Do clinicians follow heuristics in prescribing antidepressants?

Isaac Lage; Melanie F Pradier; Thomas H McCoy Jr; Roy H Perlis; Finale Doshi-Velez

doi:10.1016/j.jad.2022.04.128

Do clinicians follow heuristics in prescribing antidepressants?

J Affect Disord. 2022 Aug 15:311:110-114. doi: 10.1016/j.jad.2022.04.128. Epub 2022 Apr 23.

Authors

Isaac Lage¹, Melanie F Pradier¹, Thomas H McCoy Jr², Roy H Perlis³, Finale Doshi-Velez⁴

Affiliations

¹ Harvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, USA.
² Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
³ Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA. Electronic address: rperlis@mgh.harvard.edu.
⁴ Harvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, USA. Electronic address: finale@seas.harvard.edu.

PMID: 35472480
DOI: 10.1016/j.jad.2022.04.128

Abstract

Background: While clinicians commonly learn heuristics to guide antidepressant treatment selection, surveys suggest real-world prescribing practices vary widely. We aimed to determine the extent to which antidepressant prescriptions were consistent with commonly-advocated heuristics for treatment selection.

Methods: This retrospective longitudinal cohort study examined electronic health records from psychiatry and non-psychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Patients included 45,955 individuals with a major depressive disorder or depressive disorder not otherwise specified diagnosis who were prescribed at least one of 11 common antidepressant medications. Specific clinical features that may impact prescribing choices were extracted from coded data, and analyzed for association with index prescription in logistic regression models adjusted for sociodemographic variables and provider type.

Results: Multiple clinical features yielded 10% or greater change in odds of prescribing, including overweight and underweight status and sexual dysfunction. These heuristics were generally applied similarly across hospital systems and psychiatrist and non-psychiatrist providers.

Limitations: These analyses rely on coded clinical data, which is likely to substantially underestimate prevalence of particular clinical features. Additionally, numerous other features that may impact prescribing choices are not able to be modeled.

Conclusion: Our results confirm the hypothesis that clinicians apply heuristics on the basis of clinical features to guide antidepressant prescribing, although the magnitude of these effects is modest, suggesting other patient- or clinician-level factors have larger effects.

Funding: This work was funded by NSF GRFP (grant no. DGE1745303), Harvard SEAS, the Center for Research on Computation and Society at Harvard, the Harvard Data Science Initiative, and a grant from the National Institute of Mental Health (grant no. 1R01MH106577).

Keywords: Antidepressive agents; Depression; Depressive disorder; Electronic health records; Heuristics; Prescriptions.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't

MeSH terms

Antidepressive Agents / therapeutic use
Depressive Disorder, Major* / drug therapy
Drug Prescriptions*
Heuristics
Humans
Longitudinal Studies
Retrospective Studies

Substances

Antidepressive Agents

Grants and funding

R01 MH123804/MH/NIMH NIH HHS/United States