Medical practitioner perspectives on AI in Emergency Triage

Bev Townsend, Katherine Plant, Victoria J. Hodge, Ol'Tunde Ashaolu, Radu Calinescu

Research output: Contribution to journalArticlepeer-review

Abstract

Background: A proposed Diagnostic AI System for Robot-Assisted Triage (‘DAISY’) is under development to support Emergency Department (‘ED’) triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England (‘NHS’) but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload.
Objective: The objective of this study was to explore NHS health practitioners’ perspectives and attitudes towards the future use of AI-supported technologies in ED triage.
Methods: Between July and August 2022 a qualitative-exploratory research study was conducted to collect and capture the perceptions and attitudes of nine NHS healthcare practitioners to better understand the challenges and benefits of a DAISY deployment. The study was based on a thematic analysis of semi-structured interviews. The study involved qualitative data analysis of the interviewees’ responses. Audio-recordings were transcribed verbatim, and notes included into data documents. The transcripts were coded line-by-line, and data were organised into themes and sub-themes. Both inductive and deductive approaches to thematic analysis were used to analyse such data.
Results: Based on a qualitative analysis of coded interviews with the practitioners, responses were categorised into broad main thematic-types, namely: trust; current practice; social, legal, ethical, and cultural concerns; and empathetic practice. Sub-themes were identified for each main theme. Further quantitative analyses explored the vocabulary and sentiments of the participants when talking generally about NHS ED practices compared to discussing DAISY. Limitations include a small sample size and the requirement that research participants imagine a prototype AI-supported system still under development. The expectation is that such a system would work alongside the practitioner. Findings can be generalisable to other healthcare AI-supported systems and to other domains.
Conclusions: This study highlights the benefits and challenges for an AI-supported triage healthcare solution. The study shows that most NHS ED practitioners interviewed were positive about such adoption. Benefits cited were a reduction in patient wait times in the ED, assistance in the streamlining of the triage process, support in calling for appropriate diagnostics and for further patient examination, and identification of those very unwell and requiring more immediate and urgent attention. Words used to describe the system were that DAISY is a ‘good idea’, ‘help’, helpful, ‘easier’, ‘value’, and ‘accurate’. Our study demonstrates that trust in the system is a significant driver of use and a potential barrier to adoption. Participants emphasised social, legal, ethical, and cultural considerations and barriers to DAISY adoption and the importance of empathy and non-verbal cues in patient interactions. Findings demonstrate how DAISY might support and augment human medical performance in ED care, and provide an understanding of attitudinal barriers and considerations for the development and implementation of future triage AI-supported systems.
Original languageEnglish
Article number1297073
Number of pages13
JournalFrontiers in Digital Health
Volume5
DOIs
Publication statusPublished - 6 Dec 2023

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Keywords

  • Diagnostic AI System for Robot-Assisted A&E Triage (DAISY), Emergency Department triage, perceptions, attitudes, medical practitioners.

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