Bayes network analysis for data-driven decision support in hospital catering
At a glance
- Project leader : Dr. Georg Spinner
- Project team : Dr. Nicole Gerber, Dr. Lukas Hollenstein
- Project budget : CHF 250'000
- Project status : ongoing
- Funding partner : Internal
- Contact person : Georg Spinner
Description
Constantly rising costs in the healthcare sector require
economic action without compromising the quality of care. Hospital
catering is cost-intensive, but also very significant for patient
satisfaction. In addition to purely economic optimization, numerous
qualitative factors such as sustainability and employee
satisfaction are also of key importance. Of particular interest
here are their interdependencies, which are often not directly
apparent.
The increasing collection of data in the health sector (including
hospital catering) enables a systematic analysis of such questions.
In particular, the use of Bayesian networks enables the causal and
probabilistic modeling of numerous factors that are in a complex,
hierarchical interdependency. Bayesian networks are a particular
kind of graph, which allow the representation of variables and
their interdependencies in an easily accessible manner – for both
computation and interpretation. In the present project, extensive
data collected from various hospitals is examined. In addition,
simulated data is analyzed to improve the model quality.
The resulting models in the form of Bayesian networks will allow
conclusions to be drawn about how various factors influence one
another directly or indirectly. In this way, systematic foundations
for management decisions can be found and a contribution can be
made towards patient-centered and resource-optimized services in
health institutions.