Doctoral Bursaries - Centre for Person-centred Practice Research (CPcPR)
There are currently no bursary topics in the Centre for Person-centred Practice Research (CPcPR) at QMU, Edinburgh.
The Future Role of Artificial Intelligence in Allied Health Professionals' Practice
Background
Artificial intelligence (AI) is rapidly transforming healthcare and has the potential to revolutionize AHP practice, improving the efficiency, accuracy, and effectiveness of care (Davenport and Kalakota 2019) and preventative interventions in complex scenarios (Ronquillo et al. 2022). To date, most research is focused on medical applications with little consideration given to understanding this fast-moving technology and its possible roles in the delivery of areas such as rehabilitation and health promotion. AI has the potential to reshape AHP practice further personalising therapy and care by empowering individuals to manage their own health, promoting self-management, and enhancing quality of life, particularly for people living with long term conditions and disabilities. Understanding how to support the current and future AHP workforce to engage with AI can enable the acceleration of practicable applications while ensuring that the professional and ethical imperatives that underpin contemporary person-centred practice can be represented in their development (Chen et al. 2022).
Research question
How can we ensure that AHPs are able to use AI effectively in their practice to enhance the health and wellbeing of people living with long term conditions and disability?
Research Objectives
This PhD research project will focus on the following objectives:
- To identify AI applications with the potential to promote self-management, independence, and quality of life for people living with long term conditions and disabilities.
- To explore the potential impact of AI on the therapeutic relationship between AHPs and the people they work with.
- To understand the ethical, social, legal and organisational implications of AI in AHP practice, including the effects on individual values, preferences, and autonomy..
- To identify the workforce development challenges associated with AI in AHP practice.
Proposed Research Methods
The research will use a mixed-methods approach, combining qualitative and quantitative data collection methods. The qualitative data collection methods will include interviews with AHPs, patients/service users, strategic leaders, AI developers and other relevant stakeholders to understand their perspectives on the current and future use of AI in AHP practice. Potential quantitative data collection methods include surveys and observational studies to collect data on the use of AI by AHPs and its impact on practice and outcomes.
Significance of the Research
This research project will make a significant contribution to our understanding of the future role of AI in AHP practice. The research findings will be used to develop recommendations for the implementation of AI in AHP practice in a safe, effective, and ethical manner. The research will also help to address the workforce challenges associated with AI in AHP practice and ensure that we understand how to equip current and future AHPs with the skills and knowledge they need to use AI effectively in their practice.
Benefits for QMU
Funding a PhD bursary in AI research for allied health will demonstrate the university's commitment to engaging with AI as an aspect of its research and innovation while maintain as focus on the commitment to work that improves the quality of healthcare. It may also support the development of expertise and organisational learning in an emerging but rapidly growing area.
Raising our organisation’s awareness of the potential application of AI in allied health will help the university to identify potential new research collaborations and opportunities for commercialization around new AI-powered tools and interventions that improve the quality and efficiency of care for the public.
References
Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res. 2023 Mar 24;25:e43251. doi: 10.2196/43251.
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
Gray K, Slavotinek J, Dimaguila GL, Choo D. Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. JMIR Med Educ. 2022 Apr 4;8(2):e35223. doi: 10.2196/35223.
Ronquillo CE, Mitchell J, Alhuwail D, Peltonen LM, Topaz M, Block LJ. The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review Yearb Med Inform 2022; 31(01): 094-099. DOI: 10.1055/s-0042-1742504
For inquiries, please contact Dr Duncan Pentland (DPentland@qmu.ac.uk)
