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Dott. Davide Duma

Contacts

Tutor

Prof. Roberto Aringhieri

Curriculum vitae

Curriculum Vitae

Phd thesis

"Online optimization methods applied to the management of health services"

1) The surgical pathway

     - Operating room scheduling: Real Time Management

     - Non-elective patients: dedicated vs. shared operating rooms

2) The emergency pathway

     - Online allocation of ambulances

     - Online management of Emergency Department: Process Mining techniques 


Abstract: Dealing with decision-making in healthcare management, several online problems arise. Such problems are characterised by lack of information, which is updated over time. In contrast to stochastic optimization, which also deals with uncertainty, online approaches revise solutions with the enrichment of information. In this thesis, online optimization approaches are applied in different healthcare contexts. In the first part, a generic surgical clinical pathway is presented as example of well-structured process. For the first time, a real time approach for the operating room management is introduced, that is the decision problem arising during the fulfilment of the surgery process scheduling. Surgery delays and arrivals of non-elective patients are taken into account as uncertainty factors. In the second part, the emergency care pathway is studied. A first analysis at the system level is performed to show the effectiveness of adopting online approaches in this context. Then, the emergency care management is addressed into two phases: the ambulance allocation and the emergency department management. Online policies are analysed for the former in order to ensure the maximum time for rescue. The latter requires the resource optimization in a non-structured process, then the patient flow is modelled through process discovery techniques for replicating and predicting the patient paths, which allow us to act with online algorithms. The impact of the proposed online methods is evaluated performing quantitative analysis. Furthermore, competitive analysis compares the relative performance of an online and an ideal offline algorithm for the same problem instance.

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