Using artificial intelligence to assess clinicians’ communication skills

Ryan, Padhraig; Luz, Saturnino; Albert, Pierre; Vogel, Carl; Normand, Charles; Elwyn, Glyn

doi:10.1136/bmj.l161

Cited by 53 publications

(38 citation statements)

References 27 publications

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“…Health information technology (IT) systems support the smooth flow of information between services, and to and from citizens and their families. Artificial intelligence might facilitate this and help improve interactions with patients [41]. Quality measures: Mr. G was invited to download an app after his first myocardial infarction where he can follow the development of symptoms and well-being and contact health care services for help and support with formulating his personal health plan.…”

Section: The Illness Story Of Mr G: An Example Of the Enablers Of Pcmentioning

confidence: 99%

Supporting Innovative Person-Centred Care in Financially Constrained Environments: The WE CARE Exploratory Health Laboratory Evaluation Strategy

Lloyd

Ekman

Rogers

et al. 2020

IJERPH

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The COST CARES project aims to support healthcare cost containment and improve healthcare quality across Europe by developing the research and development necessary for person-centred care (PCC) and health promotion. This paper presents an overview evaluation strategy for testing 'Exploratory Health Laboratories' to deliver these aims. Our strategy is theory driven and evidence based, and developed through a multi-disciplinary and European-wide team. Specifically, we define the key approach and essential criteria necessary to evaluate initial testing, and on-going large-scale implementation with a core set of accompanying methods (metrics, models, and measurements). This paper also outlines the enabling mechanisms that support the development of the "Health Labs" towards innovative models of ethically grounded and evidenced-based PCC.

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Section: The Illness Story Of Mr G: An Example Of the Enablers Of Pcmentioning

confidence: 99%

Supporting Innovative Person-Centred Care in Financially Constrained Environments: The WE CARE Exploratory Health Laboratory Evaluation Strategy

Lloyd

Ekman

Rogers

et al. 2020

IJERPH

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show abstract

“…Glyn Elwyn and colleagues have recently provided a useful overview of potential applications of AI in medical communication training [45]. These authors suggest that AI analysis of communication elements such as words and phrases, turn-taking, tone and style could be used to provide detailed and confidential feedback and comparison with peers to trainees, in a safe, confidential setting, at regular intervals.…”

Section: Artificial Intelligence Applications In Training and Auditmentioning

confidence: 99%

Using artificial intelligence to analyse and teach communication in healthcare

Butow

Hoque

2020

The Breast

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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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“…have found applications in the analysis of clinician-patient communication [1], education [2], and entertainment [3,4]. In this paper, the social signals of presenters in monological discourse is explored.…”

Section: Introductionmentioning

confidence: 99%

Attitude Recognition Using Multi-resolution Cochleagram Features

Haider

Luz

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Attitudes play an important role in human communication. Models and algorithms for automatic recognition of attitudes therefore may have applications in areas where successful communication and interaction are crucial, such as healthcare, education and digital entertainment. This paper focuses on the task of categorizing speaker attitudes using speech features. Data extracted from video recordings are employed in training and testing of predictive models consisting of different sets of speech features. A novel attitude recognition approach using Multi-Resolution Cochleagram (MRCG) features is proposed. The results show that MRCG feature set outperforms the feature sets most commonly used in computational paralinguistic tasks, including emobase, eGeMAPS and ComParE, in terms of attitude recognition accuracy for decision tree, 1-nearest neighbour and random forest classifiers. Analysis of the results suggests that MRCG features contribute information not captured by these existing feature sets. Indeed, while the ComParE feature set provides slightly better results than MRCG features for support vector machine classifiers, the fusion of the existing feature sets with the new MRCG features improves on those results. Overall, with the addition of MRCG, the attitude recognition method proposed in this study achieves accuracy scores approximately 11 points higher than reported in previous studies.

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Using artificial intelligence to assess clinicians’ communication skills

Abstract: Most doctors have never had their communication skills formally assessed and do not know how they compare with their peers. Glyn Elwyn and colleagues explain how AI might facilitate this and help improve interactions with patients

Cited by 53 publications

References 27 publications

Supporting Innovative Person-Centred Care in Financially Constrained Environments: The WE CARE Exploratory Health Laboratory Evaluation Strategy

Supporting Innovative Person-Centred Care in Financially Constrained Environments: The WE CARE Exploratory Health Laboratory Evaluation Strategy

Using artificial intelligence to analyse and teach communication in healthcare

Attitude Recognition Using Multi-resolution Cochleagram Features

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