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. B Daniel Gartner

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In general, layout planning problems can be classified as in-house location problems where the aim is to minimize traveling or material handling costs based on distances by deciding on the relative positions of any kind of organizational units inside a building. This class of operations research problems originates from industrial applications, for example, planning the location of different machines of an assembly line needed to manufacture a product or the arrangement of racks and shelves within a warehouse.The special case of layout planning problems in health care has been first introduced by Elshafei in 1977(Elshafei 1977. He modeled a hospital layout problem as a quadratic assignment problem (QAP) and developed heuristics to solve it. In the framework for hospital planning and control the hospital layout planning problem is classified as a resource capacity planning problem on a strategic level . Although it is a long-term decision, the spatial organization within hospitals also directly influences the quality and efficiency of health care and secondary services of the daily routine (Choudhary et al. 2010; Hignett and Lu 2010) as well as patient satisfaction (Chaudhury et al. 2005). In practice, hospital buildings are commonly planned by architects based on experience, design aspects and legal regulations. Instead of that, it is important to develop and follow a holistic approach in order to combine the architectural and legal aspects with logistics, i.e., patient, personnel and material flows inside the future hospital building. In this context, the established operations research methodologies, especially optimization and simulation techniques, can be applied in order to support finding an optimal or robust hospital layout. On the one hand, optimal can mean to minimize traveling costs for I. Arnolds ( ) · S. Nickel Discrete Optimization and Logistics at the IOR,

There was no linear correlation between the number of transfer rooms and the number of ORs. The shorter the average duration of surgery, the earlier an additional transfer room is required. Thus, hospitals with shorter duration of surgery and fewer ORs may need the same or more transfer rooms than a hospital with longer duration of surgery and more ORs. However, with respect to an economic analysis, the costs and benefits of installing additional OR transfer rooms need to be calculated using the profit margins of the specific hospital.