The healthcare and long-term care (LTC) sector is experiencing severe workforce challenges. Nurses and caregivers are frequently confronted with excessive workloads, mandatory overtime, and the constraints of complex union agreements. These pressures contribute to burnout, high turnover rates, and rising labour costs, while simultaneously undermining care quality and patient satisfaction. Conventional workforce management (WFM) tools are largely focused on record-keeping and compliance auditing, which lack predictive and optimization capabilities, making them inadequate for the highly dynamic and complex nature of healthcare scheduling. In this thesis, we aim to develop a next-generation, AI-driven, compliance-aware, and human-centered intelligent scheduling platform, that can satisfy regulatory and union requirements while also respecting staff preferences and improving organizational outcomes.
Thesis Type |
|
Status |
Running |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Yongli Mou |
Contact |
mou@dbis.rwth-aachen.de |
Research Questions
This thesis project will explore the following core scientific and engineering questions:
- AI-Optimized scheduling: How can predictive models and optimization algorithms be leveraged to generate optimal schedules under multiple constraints, including demand forecasting, skill matching, union rules, and staff preferences?
- Compliance modelling: How can complex and evolving collective agreements (e.g., ONA and CUPE in Canada) be formalized and integrated into a scheduling engine?
- Human factors and user experience: How can efficiency and compliance be optimized while also balancing staff fairness, preferences, and quality of life?
Thesis Objectives
- System development: Design and implement a prototype system incorporating forecasting modules, AI agent modules (supporting RAG and related capabilities), an optimization engine, a compliance rule engine, and web/mobile user interfaces.
- Scientific exploration: Conduct experimental research in automated compliance rule modelling and optimization algorithms (e.g., MIP, reinforcement learning), producing results with publication potential.
- Applied validation: Evaluate the system through simulated case studies (e.g., a 300-bed hospital) or pilot hospitals, measuring reductions in overtime costs, improvements in staff satisfaction, and reductions in administrative coordination time.
- Expected outcome: Deliver not only a functioning prototype but also publishable research contributions.
- Academic background:
- Master students in Computer Science, Data Science, or related fields.
- Technical skills:
- Proficiency in Python (FastAPI, Gurobi, PyTorch) and TypeScript (Next.js, React Native);
- Experience with AI/optimization algorithms is highly desirable.
- Knowledge of databases, cloud computing, and CI/CD deployment pipelines is a strong asset.
- Research interests:
- Strong interest in applying AI to healthcare;
- Motivated to pursue interdisciplinary scientific research and real-world system development.
- Soft skills:
- Ability to work independently and collaboratively;
- Openness to interdisciplinary collaboration with healthcare institutions and industry partners.