Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals
Objective To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.Methods The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5...
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BMJ Publishing Group
2024-12-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/31/1/e101124.full |
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| author | Clair Sullivan Jacqueline Daly Oliver J Canfell Wilkin Chan Jason D Pole Teyl Engstrom Tim Saul |
| author_facet | Clair Sullivan Jacqueline Daly Oliver J Canfell Wilkin Chan Jason D Pole Teyl Engstrom Tim Saul |
| author_sort | Clair Sullivan |
| collection | DOAJ |
| description | Objective To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.Methods The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows. Co-designed workflows were deductively mapped to a validated feasibility framework to inform future clinical piloting. Qualitative data underwent inductive thematic analysis.Results Between 2020 and 2022 (n=16 health services), 175 282 PREMs inpatient surveys received 23 982 open-ended responses (mean response rate, 13.7%). Existing PREMs workflows were problematic due to overwhelming data volume, analytical limitations, poor integration with health service workflows and inequitable resource distribution. Three potential semiautomated, AI-based (unsupervised machine learning) workflows were developed to address the identified problems: (1) no code (simple reports, no analytics), (2) low code (PowerBI dashboard, descriptive analytics) and (3) high code (Power BI dashboard, descriptive analytics, clinical unit-level interactive reporting).Discussion The manual analysis of free-text PREMs data is laborious and difficult at scale. Automating analysis with AI could sharpen the focus on consumer input and accelerate quality improvement cycles in hospitals. Future research should investigate how AI-based workflows impact healthcare quality and safety.Conclusion AI-based clinical informatics workflows to routinely analyse free-text PREMs data were co-designed with multidisciplinary end-users and are ready for clinical piloting. |
| format | Article |
| id | doaj-art-626fcbd6c02547a3a7ded06c8c84833c |
| institution | OA Journals |
| issn | 2632-1009 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-626fcbd6c02547a3a7ded06c8c84833c2025-08-20T01:58:11ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092024-12-0131110.1136/bmjhci-2024-101124Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitalsClair Sullivan0Jacqueline Daly1Oliver J Canfell2Wilkin Chan3Jason D Pole4Teyl Engstrom5Tim Saul6Queensland Digital Health Centre (QDHeC), Centre for Health Services Research, The University of Queensland Faculty of Medicine, Woolloongabba, Queensland, Australia10East Galway and Midlands Cancer Support, Galway, IrelandDepartment of Nutritional Sciences, Faculty of Life Sciences & Medicine, King`s College London, London, UKQueensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Saint Lucia, Queensland, AustraliaQueensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Saint Lucia, Queensland, AustraliaQueensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Saint Lucia, Queensland, AustraliaQueensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Saint Lucia, Queensland, AustraliaObjective To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.Methods The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows. Co-designed workflows were deductively mapped to a validated feasibility framework to inform future clinical piloting. Qualitative data underwent inductive thematic analysis.Results Between 2020 and 2022 (n=16 health services), 175 282 PREMs inpatient surveys received 23 982 open-ended responses (mean response rate, 13.7%). Existing PREMs workflows were problematic due to overwhelming data volume, analytical limitations, poor integration with health service workflows and inequitable resource distribution. Three potential semiautomated, AI-based (unsupervised machine learning) workflows were developed to address the identified problems: (1) no code (simple reports, no analytics), (2) low code (PowerBI dashboard, descriptive analytics) and (3) high code (Power BI dashboard, descriptive analytics, clinical unit-level interactive reporting).Discussion The manual analysis of free-text PREMs data is laborious and difficult at scale. Automating analysis with AI could sharpen the focus on consumer input and accelerate quality improvement cycles in hospitals. Future research should investigate how AI-based workflows impact healthcare quality and safety.Conclusion AI-based clinical informatics workflows to routinely analyse free-text PREMs data were co-designed with multidisciplinary end-users and are ready for clinical piloting.https://informatics.bmj.com/content/31/1/e101124.full |
| spellingShingle | Clair Sullivan Jacqueline Daly Oliver J Canfell Wilkin Chan Jason D Pole Teyl Engstrom Tim Saul Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals BMJ Health & Care Informatics |
| title | Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals |
| title_full | Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals |
| title_fullStr | Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals |
| title_full_unstemmed | Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals |
| title_short | Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals |
| title_sort | artificial intelligence after the bedside co design of ai based clinical informatics workflows to routinely analyse patient reported experience measures in hospitals |
| url | https://informatics.bmj.com/content/31/1/e101124.full |
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