Healthcare Operations Planning

Reliable planning of operational processes in the healthcare sector is of particular importance, and not just in the wake of a worsening demographic crisis and increasing demand for healthcare services. The high workload on staff, cost-intensive treatments and capacity bottlenecks require the targeted use of scarce resources to ensure adequate patient care. Efficiency potentials are identified, among other things, in a higher degree of digitalisation, made possible by the associated increasing availability of large amounts of data.

The dissertation focuses on the application of predictive and prescriptive analytics to current problems in the hospital sector, both at the patient level to support individual care and at the institutional level for the management of hospital resources. Uncertainties, manifested in unplanned patient admissions, complications or bottlenecks, pose particular difficulties. The integration of machine learning methods into decision-support OR approaches should enable the problems to be modelled and solved in a practical manner.