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Supporting healthcare management decisions via robust clustering of event logs

Delias Pavlos, Michael Doumpos, Grigoroudis Evangelos, Manolitzas Panagiotis, Matsatsinis Nikolaos

Πλήρης Εγγραφή

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Περίληψη

Business processes constitute an essential asset of organizations while the related process models help to better comprehend the process and therefore to enable effective process analysis or redesign. However, there are several working environments where flows are particularly flexible (e.g., healthcare, customer service) and process models are either very hard to get created, or they fail to reflect reality. The aim of this paper is to support decision-making by providing comprehensible process models in the case of such flexible environments. Following a process mining approach, we propose a methodology to cluster customers’ flows and produce effective summarizations. We propose a novel method to create a similarity metric that is efficient in downgrading the effect of noise and outliers. We use a spectral technique that emphasizes the robustness of the estimated groups, therefore it provides process analysts with clearer process maps. The proposed method is applied to a real case of a healthcare institution delivering valuable insights and showing compelling performance in terms of process models’ complexity and density.

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