In this paper, we report on the development of an analytical model and a decision support tool for meeting the complex challenge of scheduling dialysis patients. The tool has two optimization objectives: First, waiting times for the start of the dialysis after the patients’ arrivals must be minimized. Second, the minimization of lateness after the scheduled finish time, which is relevant for transport services, are pursued. We model the problem as a mathematical program considering clinical pathways, a limited number of nurses managing the patients, and dialysis stations. Furthermore, information about patients' drop-off and pick-up time windows at/from the dialysis unit are considered. We develop a platform in Microsoft Excel and implement the analytical model using an Open Source optimization solver. A case study from a dialysis unit in the UK shows that a user can compute a schedule efficiently and the results provide useful information for patients, caregivers, clinicians and transport services.
Learning Objective: After participation of the session, the learner should be able to:
- Understand the current issues and developments in patient scheduling systems including patient-related objectives.
- Learn current issues and pitfalls when hemodialysis patients are scheduled inefficiently.
- Learn how analytical approaches can help to improve patient scheduling decisions in hemodialysis treatment to deliver high quality services.
- Learn how OpenSolver can be used as a technology to improve patient schedules.
- Formulate a patient scheduling problem in which clinical pathways constrain the solution space.
- Learn how to structure the decision process around assigning patients to scarce dialysis resources such as staff and machines.
Rosie Fleming, Cardiff University
Daniel Gartner, Cardiff University
Rema Padman (Presenter)
Carnegie Mellon University
Dafydd James, Abertawe Bro Morgganwg University Health Board