Harnessing Artificial Intelligence to Enable Equitable Pharmacovigilance Research Capacity Building

Training and education play a pivotal role in enhancing research capacity, and subsequently strengthening health systems worldwide. Research capacity strengthening (RCT) is of particular importance in low-resource settings, where it can offer a sustainable, long-term approach to tackling deep-rooted health systems challenges. Nevertheless, access to high-quality training resources is often hindered by economic, technological, sociocultural and geographical barriers. These barriers disproportionately impact researchers in low-resource settings.

Although a plethora of high-quality, open-access training materials exists, current efforts to collate, review, classify and disseminate these resources typically rely on manual internet searches or user submission / crowd sourcing - labour intensive and unsystematic approaches. These inefficiencies not only result in a limited understanding of the health research training landscape, but prevent the accurate identification of global training gaps, and obscure our understanding of the inequities embedded in training resource development, including disparities in funding allocation and beneficiary demographics.

Advances in artificial intelligence (AI) offer an exciting opportunity to address these challenges by automating the collation and analysis of open-access research training materials. Leveraging this technology could i) provide a comprehensive database of free, open-access research training resources for any scientific discipline, ii) allow the rapid, systematic and comprehensive identification of global training gaps, which may be used, for example, in times of public health emergency, to guide resource allocation toward the most pressing gaps, and iii) identify structural inequities in training development and the global health research training landscape. Automating these processes would not only pave the way for more efficient resource utilization, but crucially enable participation in health research training in settings where it is needed most, ultimately contributing to the equitable strengthening of global health systems.

The specific area of health research that this project will focus on is medicines safety (pharmacovigilance), as this work builds upon previous research exploring global inequities in medicines safety research led by Dr Walker. Hence, the project will initially focus on learning and training materials designed to support medicines safety (pharmacovigilance) research. Nevertheless, it is envisaged that the project outputs will be adaptable to any health research, and more broadly, any research discipline, with significant potential for uptake and global impact.

Aim

The final scope will be tailored with the successful applicant. The following serves as a potential example:

To develop an AI-based system for the identification, collation, analysis and sharing of trusted, high-quality, open-access learning resources for pharmacovigilance research.

Candidate profile

• A strong undergraduate (minimum 2:1 honours degree) or Master’s degree (or equivalent) in Computer Science, Data Science, Pharmacy, Medicine or a related discipline.

• Candidates who do not hold a degree in Computer Science, Data Science or a related discipline will be expected to demonstrate a strong understanding of / technical ability in these areas.

• Demonstrable interest in health research capacity / health research systems strengthening.

• Ability to work independently and communicate results clearly in different formats.

• English language proficiency meeting institutional requirements. Full details can be found here: https://www.birmingham.ac.uk/study/postgraduate/subjects/medicine-courses/pharmacy-phd

Desirable

• Experience in / familiarity with the field of pharmacovigilance / medicines safety

How to apply

Informal enquiries to [email protected].

Please enclose a CV (including research experience and any publications) and cover letter (1–2 pages) describing your interest in this project, how it aligns to your previous experience, and what you would like to gain from studying for a PhD.

Funding notes:

This is a self-funded PhD opportunity (UK or international). Applicants must be able to cover the tuition fees (rate depends on fee status), living costs and any other relevant costs (to be confirmed once the experimental plan has been agreed).

You can view sources of funding here: https://www.birmingham.ac.uk/study/postgraduate/taught/fees-funding