Davide Duma

The Optimization of a Surgical Clinical Pathway

A Clinical Pathway (CP) can be conceived as an algorithm based on a flow chart that details all decisions and treatments related to a patient with a given pathology. CPs can be considered an operational tool in the clinical treatment of diseases, from a patient-focused point of view. Although it has been shown their benefits in clinical practices, little attention has been dedicated to study how CP can optimize the use of resources. We focus our attention on the analysis of a surgical CP from a patient-centred point of view in order to optimize the most critical resources of a surgical CP, and to evaluate the impact of the optimization with respect to a set of patient- and facility-centred indices.

The Real Time Management of Operating Rooms

At the operational decision level, the problem arising in the Operating Room (OR) planning is also called “surgery process scheduling”, which usually consists in selecting elective patients from a waiting list and assigning them to a specific operating room on a specific day, and determining the sequence of surgical procedures and the allocation of resources for each OR session. The Real Time Management (RTM) of operating rooms is the decision problem arising during the fulfillment of the surgery process scheduling, that is the problem of supervising the execution of such a schedule and, in case of delays, to take the more rational decision regarding the surgery cancellation or the overtime assignment. The RTM is characterized by the uncertainty of its main parameters, that is, for instance, the duration of a surgery and the arrivals of non-elective patients. In this chapter we propose online optimization approaches for the RTM capable to deal with (1) the elective and non-elective patient flows within a single surgical pathway (Non-Elective Worst Fit algorithm), and with (2) the resource sharing among different surgical pathways of elective patients (Flexible Overtime Allocation and Flexible Scheduling policies). We assess the effectiveness of the proposed solutions on simulated surgical clinical pathways under several scenarios. From a methodological point of view, our analysis suggested that online optimization can be a suitable methodology to deal with the inherent stochastic aspects arising in the majority of the health care problems.

A hybrid model for the analysis of a surgical pathway

In this paper we focus our attention on the analysis of a surgical pathway from a patient-centred point of view. The main concern of this work is the introduction of some optimization modules in the management of the most critical resources in a surgical pathway, that is the stay beds and the operating rooms, and to evaluate their impact with respect to a set of patient- and facility- centred indices. We propose a hybrid simulation and optimization model: simulation is used in order to generate a real situation with respect to the inherent stochasticity of the problem while optimization is used to take the best decisions in different points of the surgical pathway.

Simple but effective heuristics for the 2-constraint bin packing problem

The 2-constraint bin packing problem consists in packing a given number of items, each one characterised by two different but not related dimensions, into the minimum number of bins in such a way to do not exceed the capacity of the bins in either dimension. The development of the heuristics for this problem is challenged by the need of a proper definition of the criterion for evaluating the feasibility of the two capacity constraints on the two different dimensions. In this paper, we propose a computational evaluation of several criteria, and two simple but effective algorithms—a greedy and neighbourhood search algorithms—for solving the 2-constraint bin packing problem. An extensive computational analysis supports our main claim.

Ex post evaluation of an operating theatre

Abstract In this paper we present a general framework for the ex post evaluation of an operating theatre driven by different performance criteria. We tested such a framework on a real case study proposing also a comparison with the actual situation.

Patient–Centred Objectives as an Alternative to Maximum Utilisation: Comparing Surgical Case Solutions

Operating Room (OR) planning and scheduling is a research topic widely discussed in the literature, in which several performance criteria have been proposed to evaluate the OR planning decisions. Although the OR utilisation is the leading objective, from research experiences, long waiting lists lead to a satisfactory filling of ORs even fixing other objectives. In this paper we analyse the impact on OR utilisation of two patient–centred objectives: the waiting time minimisation and the workload balance. In the former the most commonly used patient–centred criterion is taken into account, while the latter leads to a smooth stay bed occupancies determining a smooth workload in the ward and, by consequence, an improved quality of care provided to patients. To the best of our knowledge, a comparison of the planning determined by these criteria is not yet available in literature.

Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data

The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time, ...) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the network of emergency departments operating in Piedmont, Italy: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. Further, we discuss how big data can enable a novel methodological approach to the health system analysis.

Mining the Patient Flow Through an Emergency Department to Deal with Overcrowding

The Emergency Department (ED) management presents a really high complexity due to the admissions of patients with a wide variety of diseases and different urgency, which require the execution of different activities involving human and medical resources. This have an impact on ED overcrowding that may affect the quality and access of health care. In this paper we apply Process Mining techniques to a real case study: from the ED database, discovery techniques identify the possible paths of a patient on the basis of the information available at the triage. Our purpose is to obtain precise process models for replicating and predicting the patient paths.