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Improving hospital layout planning through clinical pathway mining

Arnolds, Ines Verena; Gartner, Daniel

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.Aunique feature of our approach is that we can generalize the data and include thoseCPs 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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000081552
Veröffentlicht am 12.07.2018
DOI: 10.1007/s10479-017-2485-4
Zitationen: 16
Zitationen: 20
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Operations Research (IOR)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 04.2018
Sprache Englisch
Identifikator ISSN: 0254-5330, 1572-9338
KITopen-ID: 1000081552
Erschienen in Annals of operations research
Verlag Springer
Band 263
Heft 1-2
Seiten 453–477
Nachgewiesen in Dimensions
Web of Science
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