AI and mHealth in the global context: seeking efficiency and good healthcare in limited-resource settings
Artificial Intelligence and mobile technologies are often presented as efficient solutions to increasing costs in healthcare both in high- and low-income countries. By automatising triaging and decision-making systems and using simple technologies such as mobile phones, health provision is expected to become more efficient, able to learn from data and ultimately less costly. One such example is “GP at hand”, a primary care service provided by Babylon Health in London where primary care patients have opted to move from their offline general practice to an online one where their symptoms are automatically triaged and doctor appointments conducted through video calls. A similar technology is also rolled out in Rwanda, under the name “Babyl”, offering mobile consultations, quick access to laboratory tests and prescriptions, and soon an AI triaging system.
Concerns have been raised regarding the impact of these technologies on existing healthcare structures, and yet ethical and practical claims around efficiency, accessibility and good healthcare provisions seem to make a compelling case for their implementation, particularly in resource poor settings.
This project will analyse the relationship between efficiency and good healthcare mediated through AI and mHealth in the global context. It will focus, in particular, on the ethical/practical issues presenting at the systemic level and also at the doctor-patient relationship.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
This project will involve empirical bioethics methods which combine philosophical and ethical analysis with empirical research. It will provide a range of training opportunities in empirical bioethics research methods, including literature review, conceptual ethical analysis, qualitative research, data analysis.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
An ethnographic study will be done in Rwanda to explore how mHealth/AI interventions are implemented and experienced. Qualitative interviews will be required with mHealth/AI users as well as key stakeholders in the process of digitising medical records and developing AI health tools in a LMIC. Alternative research sites and case studies could be considered, but should be discussed early with supervisors.
The ideal candidate will have a Masters degree in a relevant area (e.g. Philosophy, bioethics, or a discipline in the social sciences), will have some experience in qualitative methods, wishing to develop expertise in the field of empirical bioethics with an interest in global health, new technologies, and health care.