Medical practitioner perspectives on AI in emergency triage
IntroductionA 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 global...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1297073/full |
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| author | Beverley A. Townsend Katherine L. Plant Victoria J. Hodge Ol’Tunde Ashaolu Radu Calinescu |
| author_facet | Beverley A. Townsend Katherine L. Plant Victoria J. Hodge Ol’Tunde Ashaolu Radu Calinescu |
| author_sort | Beverley A. Townsend |
| collection | DOAJ |
| description | IntroductionA 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. 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.MethodsBetween 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.ResultsBased 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.DiscussionThis 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. |
| first_indexed | 2024-03-09T02:37:06Z |
| format | Article |
| id | doaj.art-143bffc4a24d44278bf0cace3516580e |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-03-09T02:37:06Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-143bffc4a24d44278bf0cace3516580e2023-12-06T08:28:11ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2023-12-01510.3389/fdgth.2023.12970731297073Medical practitioner perspectives on AI in emergency triageBeverley A. Townsend0Katherine L. Plant1Victoria J. Hodge2Ol’Tunde Ashaolu3Radu Calinescu4York Law School, University of York, York, United KingdomFaculty of Engineering & Physical Sciences, University of Southampton, Southampton, Hampshire, United KingdomDepartment of Computer Science, University of York, York, United KingdomYork and Scarborough Teaching Hospitals, York, United KingdomDepartment of Computer Science, University of York, York, United KingdomIntroductionA 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. 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.MethodsBetween 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.ResultsBased 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.DiscussionThis 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.https://www.frontiersin.org/articles/10.3389/fdgth.2023.1297073/fullDiagnostic AI System for Robot-Assisted A & E Triage (DAISY)emergency department triageperceptionsattitudesmedical practitioners |
| spellingShingle | Beverley A. Townsend Katherine L. Plant Victoria J. Hodge Ol’Tunde Ashaolu Radu Calinescu Medical practitioner perspectives on AI in emergency triage Frontiers in Digital Health Diagnostic AI System for Robot-Assisted A & E Triage (DAISY) emergency department triage perceptions attitudes medical practitioners |
| title | Medical practitioner perspectives on AI in emergency triage |
| title_full | Medical practitioner perspectives on AI in emergency triage |
| title_fullStr | Medical practitioner perspectives on AI in emergency triage |
| title_full_unstemmed | Medical practitioner perspectives on AI in emergency triage |
| title_short | Medical practitioner perspectives on AI in emergency triage |
| title_sort | medical practitioner perspectives on ai in emergency triage |
| topic | Diagnostic AI System for Robot-Assisted A & E Triage (DAISY) emergency department triage perceptions attitudes medical practitioners |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1297073/full |
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