Angela Testi

A model to prioritize access to elective surgery on the basis of clinical urgency and waiting time

BackgroundPrioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times. In addition, administrative and standardized data on waiting lists are generally lacking in Italy, where no detailed national reports are available. This is true although since 2002 the National Government has defined implicit Urgency-Related Groups (URGs) associated with Maximum Time Before Treatment (MTBT), similar to the Australian classification. The aim of this paper is to propose a model to manage waiting lists and prioritize admissions to elective surgery.MethodsIn 2001, the Italian Ministry of Health funded the Surgical Waiting List Info System (SWALIS) project, with the aim of experimenting solutions for managing elective surgery waiting lists. The project was split into two phases. In the first project phase, ten surgical units in the largest hospital of the Liguria Region were involved in the design of a pre-admission process model. The model was embedded in a Web based software, adopting Italian URGs with minor modifications. The SWALIS pre-admission process was based on the following steps: 1) urgency assessment into URGs; 2) correspondent assignment of a pre-set MTBT; 3) real time prioritization of every referral on the list, according to urgency and waiting time. In the second project phase a prospective descriptive study was performed, when a single general surgery unit was selected as the deployment and test bed, managing all registrations from March 2004 to March 2007 (1809 ordinary and 597 day cases). From August 2005, once the SWALIS model had been modified, waiting lists were monitored and analyzed, measuring the impact of the model by a set of performance indexes (average waiting time, length of the waiting list) and Appropriate Performance Index (API).ResultsThe SWALIS pre-admission model was used for all registrations in the test period, fully covering the case mix of the patients referred to surgery. The software produced real time data and advanced parameters, providing patients and users useful tools to manage waiting lists and to schedule hospital admissions with ease and efficiency. The model protected patients from horizontal and vertical inequities, while positive changes in API were observed in the latest period, meaning that more patients were treated within their MTBT.ConclusionThe SWALIS model achieves the purpose of providing useful data to monitor waiting lists appropriately. It allows homogeneous and standardized prioritization, enhancing transparency, efficiency and equity. Due to its applicability, it might represent a pragmatic approach towards surgical waiting lists, useful in both clinical practice and strategic resource management.

A pre-assignment heuristic algorithm for the Master Surgical Schedule Problem (MSSP)

In this paper a 0–1 linear programming model and a solution heuristic algorithm are developed in order to solve the so-called Master Surgical Schedule Problem (MSSP). Given a hospital department made up of different surgical units (i.e. wards) sharing a given number of Operating Rooms (ORs), the problem herein addressed is determining the assignment among wards and ORs during a given planning horizon, together with the subset of patients to be operated on during each day. Different resource constraints related to operating block time length, maximum OR overtime allowable by collective labour agreement and legislation, patient length of stay (LOS), available OR equipment, number of surgeons, number of stay and ICU beds, are considered. Firstly, a 0–1 linear programming model intended to minimise a cost function based upon a priority score, that takes into proper account both the waiting time and the urgency status of each patient, is developed. Successively, an heuristic algorithm that enables us to embody some pre-assignment rules to solve this NP-hard combinatorial optimisation problem, is presented. In particular, we force the assignment of each patient to a subset of days depending on his/her expected length of stay in order to allow closing some stay areas during the weekend and hence reducing overall hospitalisation cost of the department. The results of an extensive computational experimentation aimed at showing the algorithm efficiency in terms of computational time and solution effectiveness are given and analysed.

A two level metaheuristic for the operating room scheduling and assignment problem

Given a surgery department comprising several specialties that share a fixed number of operating rooms and post-surgery beds, we study the joint operating room (OR) planning and advanced scheduling problem. More specifically, we consider the problem of determining, over a one week planning horizon, the allocation of OR time blocks to specialties together with the subsets of patients to be scheduled within each time block. The aim of this paper is to extend and generalize existing approaches for the joint OR planning and scheduling problem. First, by allowing schedules that include patients requiring weekend stay beds which was not the case previously. Second, by tackling simultaneously both the OR planning and patient scheduling decision levels, instead of taking them into account in successive phases. To achieve this, we exploit the inherent hierarchy between the two decision levels, i.e.,the fact that the assignment decisions of OR time blocks to surgical specialties directly affect those regarding the scheduling of patients, but not the reverse. The objective function used in this study is an extension of an existing one. It seeks to optimize both patient utility (by reducing waiting time costs) and hospital utility (by reducing production costs measured in terms of the number of weekend stay beds required by the surgery planning). 0-1 linear programming formulations exploiting the stated hierarchy are proposed and used to derive a formal proof that the problem is NP-hard. A two level metaheuristic is then developed for solving the problem and its effectiveness is demonstrated through extensive numerical experiments carried out on a large set of instances based on real data.

Material versus social deprivation and health: a case study of an urban area.

Socioeconomic factors are one of the main determinants of health inequalities. However, which component of socioeconomic status affects health most and how that relationship should be measured remains an open question. The aim of this study was to compare material and social deprivation indexes in order to determine which better explains health inequalities within an urban area. Following a review of the literature on small area deprivation indexes, a case study of the Italian city Genoa is presented. The city of Genoa is split into 71 small areas [urbanistic units (UU)], each of which has about 9,500 inhabitants. For each small area, socioeconomic indicators were extracted from the 2001 Census, whereas health indicators were computed from the death registry for 2001–2003. Factorial analyses was used to choose the deprivation variables, which were utilised to create two distinct deprivation indexes referring to material and social deprivation, respectively. Both deprivation indexes are positively correlated with health status proxied by standardised mortality ratios (SMRs) under 65. The material index, however, correlates more highly with SMRs than the social index, and thus the material index is the more suitable measure to explain variations in premature mortality within an urban area. Moreover, the two indexes must be kept distinct.

A simulation-based modeling framework to deal with clinical pathways

In this paper we focus our attention on the analysis and management of Clinical Pathways (CPs) in health care systems. From an operational point of view, the CP is “the path” followed by a patient with a given pathology through the health-care system. We start by a global vision and propose a modeling framework based on a discrete event simulation model to identify the critical activities and scarce resources that represent the process bottlenecks both from a patient-centered and facility-centered point of view. Moreover, we face the challenging problem of integrating simulation and optimization in order to put together the capability of the simulation in the scenario analysis (“what-if” analysis) and in describing the dynamics of the system considered and the decisional strength of the optimization, i.e., the “what-best” analysis. The framework is applied to a case study for the thyroid surgical treatment.

Improving surgery department performance via simulation and optimization

The production process inside a hospital surgery department is made up of three distinct main sub-processes: waiting list management, operating theatre planning and scheduling, stay area sizing and organization. Overall department performance depends on how these sub-processes are managed as well as they are integrated. In the literature, they are mainly treated separately recurring to two approaches: optimization or simulation. The complexity of the system often makes the optimization models intractable, whereas simulation seems to be preferred because of its ability to evaluate what if scenarios. The novelty of this paper is to propose an integrated approach to deal with the described issues. The approach is integrated under two points of view: firstly, because it concerns all the three sub-processes together, i.e. from the moment the patient enters the system to the moment it is discharged, and, secondly, because it utilizes both the methods, i.e. simulation and optimization. The proposed holistic integrated approach can be used as a decision support tool to compare alternative operative scenarios by means of a complete set of performance indexes, regarding all the different sub-processes. The framework has been applied to the analysis of a surgical department of a university public hospital sited in Genova (Italy).

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach

This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients’ clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives—meeting patient needs and optimal utilization of beds and operating rooms.Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

Vaccinating Italian infants with a new multicomponent vaccine (Bexsero®) against meningococcal B disease: A cost-effectiveness analysis

ABSTRACT The European Medicines Agency has approved a multicomponent serogroup B meningococcal vaccine (Bexsero®) for use in individuals of 2 months of age and older. A cost-effectiveness analysis (CEA) from the societal and Italian National Health Service perspectives was performed in order to evaluate the impact of vaccinating Italian infants less than 1 y of age with Bexsero®, as opposed to non-vaccination. The analysis was carried out by means of Excel Version 2011 and the TreeAge Pro® software Version 2012. Two basal scenarios that differed in terms of disease incidence (official and estimated data to correct for underreporting) were considered. In the basal scenarios, we considered a primary vaccination cycle with 4 doses (at 2, 4, 6 and 12 months of age) and 1 booster dose at the age of 11 y, the societal perspective and no cost for death. Sensitivity analyses were carried out in which crucial variables were changed over probable ranges. In Italy, on the basis of official data on disease incidence, vaccination with Bexsero® could prevent 82.97 cases and 5.61 deaths in each birth cohort, while these figures proved to be three times higher on considering the estimated incidence. The results of the CEA showed that the Incremental Cost Effectiveness Ratio (ICER) per QALY was €109,762 in the basal scenario if official data on disease incidence are considered and €26,599 if estimated data are considered. The tornado diagram indicated that the most influential factor on ICER was the incidence of disease. The probability of sequelae, the cost of the vaccine and vaccine effectiveness also had an impact. Our results suggest that vaccinating infants in Italy with Bexsero® has the ability to significantly reduce meningococcal disease and, if the probable underestimation of disease incidence is considered, routine vaccination is advisable.

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.

A simulation-based decision support tool to analyze clinical pathways in hospital

In this chapter we analyze the patient flow inside a hospital surgical department, adopting a patient-centered perspective focused on clinical pathways (CPs). The specific aim is to develop a decision support tool to make the clinical point of view, implicit in the CP approach, compatible with the economic and managerial requirements of the hospital. The simulation-based modeling framework herein proposed analyzes the three main operative areas, or sub-processes, of the flow of surgical patients within the hospital, i.e. waiting list management, operating theatre planning and bed ward organization. The framework has been applied to a university public hospital in Genova (Italy). Firstly, the simulation model has been used to compare alternative scenarios, changing waiting list and bed management strategic decisions, through a complete set of indexes able to capture the performance of the three sub-processes, with parity of resources. Secondly, the model has been adapted to evaluate the impact on the overall department performance of iteratively introducing the solutions given by a 0–1 optimization model developed to solve the tactical operating room planning problem. The reported results show that the integrated decision tool can be helpful in supporting decisions that are particularly hard in healthcare delivery because they usually imply a tradeoff between different performance indexes.