Clinician-Spoken Plain Language in Health Care Encounters: A Qualitative Analysis to Assess Measurable Elements.


Journal

Academic medicine : journal of the Association of American Medical Colleges
ISSN: 1938-808X
Titre abrégé: Acad Med
Pays: United States
ID NLM: 8904605

Informations de publication

Date de publication:
27 Feb 2024
Historique:
medline: 27 2 2024
pubmed: 27 2 2024
entrez: 27 2 2024
Statut: aheadofprint

Résumé

Good communication and use of plain language in health care encounters improves outcomes, including emotional health, symptom resolution, and functional status. Yet there is limited research on how to measure and report spoken plain language, which is the use of familiar, clear language. The authors aimed to describe key, measurable elements of spoken plain language that can be assessed and reported back to clinicians for self-reflection. The authors conducted secondary analysis of transcripts from recorded encounters between breast cancer surgeons and patients with early-stage breast cancer. Two coders used a hybrid qualitative analysis with a framework based on US Federal Plain Language Guidelines. To develop major themes, they examined (1) alignment with the Guidelines and (2) code frequencies within and across transcripts. They also noted minor themes. From 74 transcripts featuring 13 surgeons, the authors identified two major themes representing measurable elements of spoken plain language: (1) clinicians had a propensity to use both explained and unexplained medical terms, and (2) clinicians delivered information using either short turns (one unit of someone speaking) with one topic or long turns with multiple topics. There were three minor themes that were not indicative of whether or not clinicians used spoken plain language. First, clinicians regularly used absolute risk communication techniques. Second, question-asking techniques varied and included open-ended, close-ended, and comprehension checks. Third, some clinicians used imagery to describe complex topics. Clinicians' propensity to use medical terms with and without explanation and parse encounters into shorter or longer turns are measurable elements of spoken plain language. These findings will support further research on the development of a tool that can be used in medical education and other settings. This tool could provide direct and specific feedback to improve the plain language practices of clinicians in training and beyond.

Identifiants

pubmed: 38412476
doi: 10.1097/ACM.0000000000005666
pii: 00001888-990000000-00770
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 the Association of American Medical Colleges.

Auteurs

Renata W Yen (RW)

R.W. Yen is research scientist at The Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire. ORCID: http://orcid.org/0000-0002-6856-7631.

Robert Hagedorn (R)

R. Hagedorn is a medical student, University of Utah School of Medicine, Salt Lake City, Utah.

Marie-Anne Durand (MA)

M.A. Durand is chercheure inserm at University Toulouse, France, an adjunct associate professor at The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, and an adjunct scientist at Unisanté, Lausanne, Switzerland. ORCID: http://orcid.org/0000-0002-1127-9348.

JoAnna K Leyenaar (JK)

J.K. Leyenaar is professor of pediatrics, Geisel School of Medicine at Dartmouth College Lebanon, New Hampshire. ORCID: http://orcid.org/0000-0002-0555-0154.

A James O'Malley (AJ)

A.J. O'Malley is professor, The Dartmouth Institute for Health Policy and Clinical Practice and Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire. ORCID: http://orcid.org/0000-0001-8389-6217.

Catherine H Saunders (CH)

C.H. Saunders is assistant professor of medicine, Dartmouth Health and The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire. ORCID: http://orcid.org/0000-0003-0819-6610.

Talia Isaacs (T)

T. Isaacs is associate professor of applied linguistics and TESOL, IOE-UCL Faculty of Education and Society, University College London, 20 Bedford Way, London WC1H 0AL, United Kingdom. ORCID: http://orcid.org/0000-0003-4302-3379.

Glyn Elwyn (G)

G. Elwyn is professor, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire. ORCID: http://orcid.org/0000-0002-0917-6286.

Classifications MeSH