Improving hospital layout planning through clinical pathway mining

• Published in: Annals of operations research Vol. 263; no. 1-2; pp. 453 - 477
• Main Authors: Arnolds, Ines Verena, Gartner, Daniel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2018; Springer; Springer Nature B.V
• Subjects: Algorithms; Automata theory; Business and Management; Case studies; Combinatorics; Data mining; Data Mining and Analytics; Decision making; Design and construction; Engineering design; Evidence-based medicine; Health care; Hospitals; Machine learning; Mathematical models; Methods; Operations management; Operations research; Operations Research/Decision Theory; Patient care planning; Patients; Probability; Statistical analysis; Technology application; Theory of Computation; Clinical pathways; Hospital layout planning; Healthcare operations management; Sequential pattern mining; Machine learning
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-017-2485-4

Clinical pathways (CPs) are standardized, typically evidence-based health care processes. They define the set and sequence of procedures such as diagnostics, surgical and therapy activities applied to patients. This study examines the value of data-driven CP mining for strategic healthcare management. When assigning specialties to locations within hospitals—for new hospital buildings or reconstruction works—the future CPs should be known to effectively minimize distances traveled by patients. The challenge is to dovetail the prediction of uncertain CPs with hospital layout planning. We approach this problem in three stages: In the first stage, we extend a machine learning algorithm based on probabilistic finite state automata (PFSA) to learn significant CPs from data captured in hospital information systems. In that stage, each significant CP is associated with a transition probability. A unique feature of our approach is that we can generalize the data and include those CPs which have not been observed in the data but which are likely to be followed by future patients according to the pathway probabilities obtained from the PFSA. At the same time, rare and non-significant CPs are filtered out. In the second stage, we present a mathematical model that allows us to perform hospital layout planning decisions based on the CPs, their probabilities and expert knowledge. In the third stage, we evaluate our approach based on different performance measures. Our case study results based on real-world hospital data reveal that using our CP mining approach, distances traveled by patients can be reduced substantially as compared to using a baseline method. In a second case study, when using our approach for reconstructing a hospital and incorporating expert knowledge into the planning, existing layouts can be improved.