Angela Testi

The growth of health care needs and the hospital establishment capacity has led to increased costs together with a deterioration in the finances of national health public systems. Hospitals need to understand how they can improve efficiency and service quality to survive and keep on operating with a sustainable activity for the future. Hospitals have to face hard budget constraints and on the other side maintain a high quality of care and improved health outcomes. The hospital production is a topic that the microeconomists analyzed in the last fifty years, looking at the hospital efficiency able to provide the best outputs and outcomes using limited number of inputs. The modelling of this function was mostly performed using parametric methods (such as Translog and Cobb-Douglas functional forms or Stochastic Frontiers), and later with non-parametric methods (such as Data Envelopment Analysis). The objective of this preliminary study consists in providing an overview of the economic evaluation of hospital production, comparing the two methods and showing the main benefits and limitations of each approach.

Purpose: The ageing of the world’s population is causing an increase in the number of frail patients admitted to hospitals. In the absence of appropriate management and organisation, these patients risk an excessive length of stay and poor outcomes. To deal with this problem, the purpose of this paper is to propose a conceptual model to facilitate the pathway of frail elderly patients across acute care hospitals, focussed on avoiding improper wait times and treatment during the process. Design/methodology/approach: The conceptual model is developed to enrich the standard flowchart of a clinical pathway in the hospital. The modified flowchart encompasses new organisational units and activities carried out by new dedicated professional roles. The proposed variant aims to provide a correct assessment of frailty at the entrance, a better management of the patient’s stay during different clinical stages and an early discharge, sending the patient home or to other facilities, avoiding a delayed discharge. The model is completed by a set of indicators aimed at measuring performance improvements and creating a strong database of evidence on the managing of frail elderly’s pathways, providing proper information that can validate the model when applied in current practice. Findings: The paper proposes a design of the clinical path of frail patients in acute care hospitals, combining elements that, according to an evidence-based management approach, have proved to be effective in terms of outcomes, costs and organisational issues. The authors can, therefore, expect an improvement in the treatment of frail patients in hospital, avoiding their functional decline and worsening frailty conditions, as often happens in current practice following the standard path of other patients. Research limitations/implications: The framework proposed is a conceptual model to manage frail elderly patients in acute care wards. The research approach lacks application to real data and proof of effectiveness. Further work will be devoted to implementing a simulation model for a specific case study and verifying the impact of the conceptual model in real care settings. Practical implications: The paper includes suggestions for re-engineering the management of frail elderly patients in hospitals, when a reduction of lengths of stay and the improvement of clinical outcomes is required. Originality/value: This paper fulfils an identified need to study and provide solutions for the management of frail elderly patients in acute care hospitals, and generally to produce value in a patient-centred model.

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.

The Operating Room (OR) is a key resource of all major hospitals, but it also accounts for up 40 % of resource costs. Improving cost effectiveness, while maintaining a quality of care, is a universal objective. These goals imply an optimization of planning and a scheduling of the activities involved. This is highly challenging due to the inherent variable and unpredictable nature of surgery. A Business Process Modeling Notation (BPMN 2.0) was used for the representation of the “OR Process” (being defined as the sequence of all of the elementary steps between “patient ready for surgery” to “patient operated upon”) as a general pathway (“path”). The path was then both further standardized as much as possible and, at the same time, keeping all of the key-elements that would allow one to address or define the other steps of planning, and the inherent and wide variability in terms of patient specificity. The path was used to schedule OR activity, room-by-room, and day-by-day, feeding the process from a “waiting list database” and using a mathematical optimization model with the objective of ending up in an optimized planning. The OR process was defined with special attention paid to flows, timing and resource involvement. Standardization involved a dynamics operation and defined an expected operating time for each operation. The optimization model has been implemented and tested on real clinical data. The comparison of the results reported with the real data, shows that by using the optimization model, allows for the scheduling of about 30 % more patients than in actual practice, as well as to better exploit the OR efficiency, increasing the average operating room utilization rate up to 20 %. The optimization of OR activity planning is essential in order to manage the hospital’s waiting list. Optimal planning is facilitated by defining the operation as a standard pathway where all variables are taken into account. By allowing a precise scheduling, it feeds the process of planning and, further up-stream, the management of a waiting list in an interactive and bi-directional dynamic process.