Job Description Despite the potential benefits of integrating AI into clinical care paths, healthcare organizations face substantial challenges in successful implementation. Concerns such as potential errors, patient harm, bias, lack of transparency, and organizational readiness gaps hinder the adoption of AI technologies. Addressing these challenges requires a deliberate focus on recognizing and addressing organizational, contextual, and behavioral determinants essential for successful integration.
This project aims to address the lack of integration of AI into clinical care paths. The research will involve critical review of existing implementation frameworks with the goal of identifying what likely works best, either through tailoring existing frameworks, or through amalgamating several existing frameworks. An implementation plan will be developed based on the framework selected in the critical review. The plan will be created with key stakeholders (patients, doctors, AI developers, management, and support staff) using participatory design and co-creation methods. Furthermore, implementation and monitoring of the plan through a mixed-methods approach is also part of the project with a focus on four outcomes: acceptability, appropriateness, feasibility, and costs.
Your responsibility is to fulfill all the requirements to obtain a PhD degree at Maastricht University, including but not limited to:
- Write and co-write academic publications which will form the basis of your PhD thesis, together with the members of your supervision team and others,
- Collect data according to a well-considered research design,
- Further build your qualitative and mixed-methods data analysis skills,
- Participate in required and jointly decided elements of your PhD education
You will be working in and with two interdisciplinary groups: Clinical Data Science (CDS) at Maastricht University and Strategy and Entrepreneurship at Tilburg University. You will be expected to actively participate in the colloquia and meeting of both groups and will benefit from peers with diverse expertises. At any given moment, your reliance on one of these groups and their expertise may take priority, but for the whole duration of the work we expect this to be balanced.