Abstract
Introduction
Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and causing additional health conditions complicates prescribing decisions for these patients. Artificial Intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.
Methods and analysis
A qualitative study will be conducted using semi-structured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British healthcare professional bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide was designed to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes toward risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants’ opinions on how their experiences compare. Data will be analysed thematically using the Framework method.
Ethics and dissemination
This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer- reviewed publications, conferences and lay summaries.
Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and causing additional health conditions complicates prescribing decisions for these patients. Artificial Intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.
Methods and analysis
A qualitative study will be conducted using semi-structured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British healthcare professional bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide was designed to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes toward risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants’ opinions on how their experiences compare. Data will be analysed thematically using the Framework method.
Ethics and dissemination
This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer- reviewed publications, conferences and lay summaries.
| Original language | English |
|---|---|
| Article number | e077156 |
| Number of pages | 31 |
| Journal | BMJ open |
| Volume | 14 |
| DOIs | |
| Publication status | Published - 1 Feb 2024 |
Bibliographical note
Funding This work is independent research funded by the National Institute for Health and Care Research (NIHR) (OPTIMising therapies, disease trajectories, and AI assisted clinical management for patients Living with complex multimorbidity (OPTIMAL study), NIHR202632). The views expressed in this publication are those of the author(s) and not necessarily those of NIHR or The Department of Health and Social Care.Acknowledgments
The authors would like to thank the members of the PPAG (currently comprised of: Robert Jasper, Christine Michael, Jenny Negus, Gillian Richards, Lynne Wright and Janice Connelly) for their invaluable contribution to the conceptualisation and development of this study thus far.
Keywords
- multimorbidity
- patient perspectives
- clinical support
- artificial intelligence
- qualitative method
- shared decision-making