Michele Sonnessa

A Discrete Event Simulation model to support bed management

In recent years, due to the overcrowding of Emergency Department (ED) and the growing concern in reducing the number of inpatient ward beds, it has become crucial to improve the capacity planning and control activities, which manage the patient flows from EDs to hospital wards. Bed Management has a key role in this context. This study starts by a collaboration with the Local Health Government (LHG) of the Liguria region aimed at studying the impact of supporting bed management with some operational strategies without increasing the bed capacity. A large amount of data was collected over a one-year period at public hospital in Genova and a preliminary observational analysis was conducted to get the main information about the flow of emergency and elective patients from ED to inpatient wards. A Discrete Event Simulation (DES) model has been then developed in order to represent the real system. A scenarios analysis is proposed to assess the best strategy to improve the system performance without increasing bed capacity, by simply synchronizing bed supply and demand. The model can be used as a decision support tool to optimise the use of the available resources as well as to improve the quality of the patient pathway inside the hospital.

Multiobjective bed management considering emergency and elective patient flows

In recent years, hospitals have increasingly been faced with a growing proportion of their inpatient work coming from the fluctuating and unpredictable demand of emergency admissions. The opportunity to move emergency patients who have been selected for admission out of the emergency department (ED) is linked to the ability of the hospital network to actually admit them. The latter is, in turn, correlated to the availability of inpatient beds in the hospital wards, which are shared resources between elective and emergency patients. Due to the overcrowding of EDs and the growing concern regarding reducing the number of inpatient ward beds, it is thus becoming crucial to improve the bed capacity planning and the management of emergency and elective patient admissions. In this direction, greater coordination and communication among the different healthcare providers involved in the pathway flows is required, and the so-called “bed management” function plays a key role. This study starts with collaboration with the local health government (LHG) of the Liguria region aimed at studying the hospital bed management function. A large quantity of data records have been collected during one year of activity to obtain information related to the flow of emergency and elective patient pathways. A medium-sized hospital located in the city of Genova has been chosen as a case study, and a discrete event simulation model has been developed to reproduce the multiple patient flows involved in the system. Multiobjective optimization analysis has been performed to choose the best bed allocations considering both operational and tactical decisions characterized by various trade-offs among alternative conflicting objectives. The model can be used to help decision makers find a representative set of Pareto-optimal solutions and quantify trade-offs when satisfying different objectives.

Managing Emergent Patient Flow to Inpatient Wards: A Discrete Event Simulation Approach

In recent years, a growing proportion of patients flowing through inpatient hospital wards come from Emergency Departments (EDs). Because of ED overcrowding and the reduction of hospital beds, it is becoming crucial to improve the management of emergent patient flows to be admitted into inpatient wards. This study evaluates the impact and potential of introducing the so-called Bed Management function in a large city’s health district. Thanks to the collaboration with the Local Health Authority of the Liguria region, an observational analysis was conducted based on data collected over a 1-year period to develop a discrete event simulation model. The model has been utilised to evaluate several bed management strategies. Two scenarios at a tactical level, i.e. the opening of a discharge room and blocking elective arrivals, have also been simulated. The effects of such scenarios have been compared with respect to a set of performance metrics, such as waiting times, misallocated patients, trolleys in EDs, and inpatient bed occupancy rates.

System Dynamics Modelling of Emergent and Elective Patient Flows

This paper introduces a tool to assess the impact of organizational strategies that are intended to allocate inpatient beds amongst emergent and elective flows inside a hospital. The tool, based on a System Dynamics model, is able to reproduce the entire system and the relationship between the various flows. In the absence of corrective strategies, an exogenous increase in the rate of arrivals at the Emergency Department (e.g., in winter) can trigger a reinforcing loop increasing elective waits and further overcrowding emergency rooms. The model can be used to discover the best strategies aimed at managing bed capacity between emergent and elective flows. Some preliminary results are given in the context of a public hospital located in Genova (Italy).

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.

An agent-based simulation model to evaluate alternative co-payment scenarios for contributing to health systems financing

Ageing populations, rapid technological progress and recent public budget cuts currently threaten the sustainability of public health systems. To meet growing needs with declining resources, decision-makers must identify new ways to avoid reducing the quality of services offered to citizens. This paper focuses on the so-called “co-payment” tools aimed to obtain additional resources for the public health budget directly from citizens. Whereas certain forms of co-payments have always been introduced within health systems to prevent moral hazard behaviours, other co-payment mechanisms are explicitly intended to help finance public healthcare systems. Literature and empirical findings do not agree about the final impact of such co-payment tools, particularly whether they can attain system sustainability and guarantee equitably delivered services. In this paper, we develop an agent-based simulation model which can be used by decision-makers as a decision support tool to compare different co-payment rules and evaluate their impact on the public budget and the health expense of different groups of citizens.

A Hybrid Simulation Approach to Analyse Patient Boarding in Emergency Departments

This paper deals with the problem of patient boarding in Emergency Department (ED) due to the lack of availability of stay beds in inpatient hospital wards. The boarding of emergent patients is a major reason of ED overcrowding, which in turn creates long waiting times in ED, as well as patient and staff dissatisfaction. In this paper a hybrid simulation framework is proposed to describe how emergent and elective patients flows interact each other inside the hospital. The framework combines System Dynamics (SD) and Discrete Event Simulation (DES). Flows are generated at macro level by the SD model, while in DES model they are disentangled into single entities following a detailed process-oriented pathway. The hybrid model has been then applied and validated to data from a medium-size public hospital located in Genova (Italy). Furthermore, some metrics (such as waiting times to be admitted in hospital, number of trolleys in ED, inpatient bed occupancy rates and elective patients delayed) are proposed as key indicators to assess the system performance.

Evaluating the Introduction of Deductibles in the Health Insurance System

Helderman and Peelen (2004), described how public decision- makers were evaluating the possibility of introducing deductibles in the social health system, to provide a scheme of regulated competition among insurers and providers. Testi et al. (2012) showed how deductibles deeply modify the actual co-payment rules and therefore the prices paid by patients. Indeed, demand depends on elasticity and therefore price increases reduce demand. The impact of co-payment over the public budget and over different citizen groups should be then carefully evaluated.