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Cited by 119Patient-Provider Communication in HealthcareArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.

Using artificial intelligence to analyse and teach communication in healthcare

Open access
Butow, Phyllis, Hoque, Ehsan·2020·The Breast
DOI 10.1016/j.breast.2020.01.008PMC PMC7375542
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Abstract

Communication is a core component of effective healthcare that impacts many patient and doctor outcomes, yet is complex and challenging to both analyse and teach. Human-based coding and audit systems are time-intensive and costly; thus, there is considerable interest in the application of artificial intelligence to this topic, through machine learning using both supervised and unsupervised learning algorithms. In this article we introduce health communication, its importance for patient and health professional outcomes, and the need for rigorous empirical data to support this field. We then discuss historical interaction coding systems and recent developments in applying artificial intelligence (AI) to automate such coding in the health setting. Finally, we discuss available evidence for the reliability and validity of AI coding, application of AI in training and audit of communication, as well as limitations and future directions in this field. In summary, recent advances in machine learning have allowed accurate textual transcription, and analysis of prosody, pauses, energy, intonation, emotion and communication style. Studies have established moderate to good reliability of machine learning algorithms, comparable with human coding (or better), and have identified some expected and unexpected associations between communication variables and patient satisfaction. Finally, application of artificial intelligence to communication skills training has been attempted, to provide audit and feedback, and through the use of avatars. This looks promising to provide confidential and easily accessible training, but may be best used as an adjunct to human-based training.

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Fig. 1 Example of computerised visualisation of a consultation used by Angus et al., 2012.
Fig. 1 Example of computerised visualisation of a consultation used by Angus et al., 2012.CC BY-NC-ND · PubMed Central
Fig. 2 Comparative histograms of the number of words spoken by doctor for best rated doctors and other doctors. From Sen et al., 2017.
Fig. 2 Comparative histograms of the number of words spoken by doctor for best rated doctors and other doctors. From Sen et al., 2017.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Butow, P., & Hoque, E.
Affiliations
The University of Sydney (AU)
Corresponding author
Butow, Phyllis
ORCID
Butow, Phyllis
Venue
The Breast
Publisher
Elsevier BV
Published
2020-04
Volume
50
Pages
49-55
ISSN
0960-9776 (ISSN-L)
DOI
10.1016/j.breast.2020.01.008
PMC
PMC7375542
OpenAlex
W2999911199
Cited by
118
References
49
Citations by year
2026: 21 · 2025: 24 · 2024: 23 · 2023: 18 · 2022: 12 · 2021: 14 · 2020: 6
FWCI
13.05
Type
journal article
Language
en
Open access
Licence
Locations
  • The Breast (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
health communication, physician-patient communication, artificial intelligence, machine learning, communication skills training, audit and feedback, avatars, patient satisfaction, textual transcription
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Reproducibility of Results, Health Communication, Health Information Systems

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