Perspectives on illness-related stigma and electronically sharing psychiatric health information by people with multiple sclerosis.


Journal

Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073

Informations de publication

Date de publication:
01 03 2021
Historique:
received: 03 06 2020
accepted: 24 12 2020
entrez: 19 2 2021
pubmed: 20 2 2021
medline: 27 4 2021
Statut: ppublish

Résumé

Electronic medical records (EMRs) facilitate more integrated and comprehensive care. Despite this, EMRs are used less frequently in psychiatry compared to other medical disciplines, in part due to concerns regarding stigma surrounding mental health. This paper explores the willingness to share medical information among patients with multiple sclerosis (MS), who experience higher rates of psychiatric comorbidities compared to the general population, and the role that stigma plays in patient preferences. MS patients were surveyed about their co-occurring psychiatric and non-psychiatric diagnoses, willingness to share their health information electronically among their treating doctors, and levels of self and societal stigma associated with their diagnoses. Participants were slightly more willing to share their non-psychiatric medical information vs. psychiatric information. Despite the presence of stigma decreasing patient willingness to share medical records, those with psychiatric co-occurring disorders, compared to those without, endorsed significantly greater willingness to electronically share their health records. The majority of diagnoses for which participants experienced the greatest difference in self vs. societal stigmas were psychiatric ones, including substance use, eating and mood disorders. Societal stigma strongly correlated with decreased non-psychiatric medication sharing, while self stigma was strongly correlated with decreased psychiatric medications sharing. Standardized scales were not used to assess patient stigma and there is a potential lack of generalizability of results beyond patients with MS. These insights into patient preferences toward sharing their medical information should inform decisions to implement EMRs, particularly for patient populations experiencing higher than average levels of psychiatric comorbidities.

Sections du résumé

BACKGROUND
Electronic medical records (EMRs) facilitate more integrated and comprehensive care. Despite this, EMRs are used less frequently in psychiatry compared to other medical disciplines, in part due to concerns regarding stigma surrounding mental health. This paper explores the willingness to share medical information among patients with multiple sclerosis (MS), who experience higher rates of psychiatric comorbidities compared to the general population, and the role that stigma plays in patient preferences.
METHODS
MS patients were surveyed about their co-occurring psychiatric and non-psychiatric diagnoses, willingness to share their health information electronically among their treating doctors, and levels of self and societal stigma associated with their diagnoses.
RESULTS
Participants were slightly more willing to share their non-psychiatric medical information vs. psychiatric information. Despite the presence of stigma decreasing patient willingness to share medical records, those with psychiatric co-occurring disorders, compared to those without, endorsed significantly greater willingness to electronically share their health records. The majority of diagnoses for which participants experienced the greatest difference in self vs. societal stigmas were psychiatric ones, including substance use, eating and mood disorders. Societal stigma strongly correlated with decreased non-psychiatric medication sharing, while self stigma was strongly correlated with decreased psychiatric medications sharing.
LIMITATIONS
Standardized scales were not used to assess patient stigma and there is a potential lack of generalizability of results beyond patients with MS.
CONCLUSIONS
These insights into patient preferences toward sharing their medical information should inform decisions to implement EMRs, particularly for patient populations experiencing higher than average levels of psychiatric comorbidities.

Identifiants

pubmed: 33601726
pii: S0165-0327(20)33257-2
doi: 10.1016/j.jad.2020.12.167
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

840-845

Informations de copyright

Copyright © 2020. Published by Elsevier B.V.

Auteurs

Eileen Yu (E)

Johns Hopkins Department of Psychiatry & Behavioral Sciences, 1800 Orleans Street, Baltimore, MD 21287, United States.

Alexis Adams-Clark (A)

University of Oregon Department of Psychology, 1227 University St., Eugene, OR 97403, United States.

Alison Riehm (A)

Duke Psychiatry & Behavioral Sciences, 2301 Erwin Rd, Durham, NC 27710, United States.

Caroline Franke (C)

Cleveland Clinic Lerner College of Medicine, 9501 Euclid Ave, Cleveland, OH 44195, United States.

Ryoko Susukida (R)

Johns Hopkins Bloomberg School of Public Health, 615 North Broadway Street, Baltimore, MD 21205, United States.

Melissa Pinto (M)

University of California Irvine School of Nursing, 802 West Peltason Drive, Irvine, CA 92697, United States.

Steven Arenberg (S)

Johns Hopkins Medicine, 1800 Orleans Street, Baltimore, MD 21287, United States.

Dominique Tosi (D)

Medstar Health, 3333 N Calvert St, Baltimore, MD 21218, United States.

Abbey Hughes (A)

Johns Hopkins Department of Psychiatry & Behavioral Sciences, 1800 Orleans Street, Baltimore, MD 21287, United States.

Amanda Montague (A)

Multiple Sclerosis Association of America, 375 Kings Highway North, Cherry Hill, NJ 08034, United States.

Anu Kumar (A)

Johns Hopkins Department of Psychiatry & Behavioral Sciences, 1800 Orleans Street, Baltimore, MD 21287, United States.

Kay Jamison (K)

Johns Hopkins Department of Psychiatry & Behavioral Sciences, 1800 Orleans Street, Baltimore, MD 21287, United States.

Adam Kaplin (A)

Johns Hopkins Department of Psychiatry & Behavioral Sciences, 1800 Orleans Street, Baltimore, MD 21287, United States. Electronic address: akaplin@jhmi.edu.

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Classifications MeSH