1. Using artificial intelligence to analyse and teach communication in healthcare
- Author
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Phyllis Butow and Ehsan Hoque
- Subjects
Artificial intelligence ,Audit ,lcsh:RC254-282 ,Machine Learning ,Health Information Systems ,03 medical and health sciences ,0302 clinical medicine ,Patient satisfaction ,Health care ,business.product_line ,Humans ,Medicine ,Confidentiality ,030212 general & internal medicine ,Health communication ,business.industry ,Communication ,Healthcare ,Reproducibility of Results ,General Medicine ,Virtual special issue: Artificial Intelligence in Breast Cancer Care ,Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Communication skills training ,Health Communication ,030220 oncology & carcinogenesis ,Unsupervised learning ,Surgery ,business ,Coding (social sciences) - 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., Highlights • Artificial intelligence (AI) applied to health professional-patient communication enables efficient audit and feedback. • Very recent advances have increased the ability of AI to encode the complexity in human interaction. • AI can now encode words as well as a person does, as well as emotion and non-verbal aspects of communication. • AI coding has been shown to be moderately to substantially reliable. • Translation into the real world has yet to be demonstrated.
- Published
- 2020