Matching patient and physician preferences in designing a primary care facility network

This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.     

An Implicit Tour Scheduling Model with Applications in Healthcare

Motivated by difficult staff scheduling problems arising in healthcare institutions, we have developed an implicit tour scheduling model which includes full and part-time tour types as well as intra-tour start time flexibility. Potential benefits of intra-tour start time flexibility are demonstrated through a computational experiment. The model has been embedded in a decision support system at a large tertiary care hospital and has been used in numerous studies to help estimate staffing needs and to analyze the impact of scheduling policies and practices.     

Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach

The health care sector is one of the fastest growing sectors in the United States. Researchers are interested in conducting studies in the area of health economics in order to propose solutions to curb the rapid increase in health care spending and to improve the efficiency of the health care system in the United States. Specifically, hospital efficiency is one important research area in health economics. In this paper, data envelopment analysis (DEA) is used to assess hospital efficiency. An additive super-efficiency model is presented and applied to a sample of general acute care hospitals in Pennsylvania. In addition to the conventional choice of input and output variables, we include the survival rate as a quality measure of health outcome in the set of output variables. Thus our model takes both the quantity and the quality of the output into account. With the results obtained from our proposed DEA model, inefficiencies can be identified for hospitals to address without sacrificing the quality of care.     

The Will of the People

The paper discusses the cybernetic mechanisms whereby our institutions fail to translate the will of the people into effective policies, and those by which the will of the people is an attenuated version of human potentiality in the first place. A systemic model is developed to account for the observed phenomena in terms of a cybernetic theory of the management process, and this is then exemplified from current dilemmas facing humankind. The model is subsequently extended to encompass the theory of viable systems, the principle of self-reference, and a model of self-hood which promotes new concepts that close the model into its starting point of human potential. The total approach bears on the capability of individuals, groups, institutions, societies and nations to realize themselves, and to thwart the dangers in which our civilization is plunged.     

Choice of modelling technique for evaluating health care interventions

Economic evaluation, such as cost effectiveness analysis, provides a method for comparing healthcare interventions. These evaluations often use modelling techniques such as decision trees, Markov processes and discrete event simulations (DES). With the aid of examples from coronary heart disease, the use of these techniques in different health care situations is discussed. Guidelines for the choice of modelling technique are developed according to the characteristics of the health care intervention.The choice of modelling technique is shown to depend on the acceptance of the modelling technique, model ‘error’, model appropriateness, dimensionality and ease and speed of model development. Generally decision trees are suitable for acute interventions but they cannot model recursion and Markov models are suitable for simple chronic interventions. It is further recommended that population based models be used in order to provide health care outcomes for the likely cost, health benefits and cost effectiveness of the intervention. The population approach will complicate the construction of the model. DES will allow the modeller to construct more complex, dynamic and accurate systems but these may involve a corresponding increase in development time and expense. The modeller will need to make a judgement on the necessary complexity of the model in terms of interaction of individuals and model size and whether queuing for resources, resource constraints or the interactions between individuals are significant issues in the health care system.     

Register-based description of treatment processes in hospital care

This paper outlines the possibilities of establishing an information system based on the coherent processes a patient passes when getting in touch with hospital care. The information system represents an attempt to improve the basis for comparisons between hospital departments, for medical audits, for prognoses and for economic appraisals in health care. Current information systems on hospital care — in Denmark — only contain information on certain events, and these events cannot be seen consecutively.The work is based on register data from the county of Copenhagen for the year 1983.The work has shown that it is possible, with relatively few resources, to link elements in the treatment process to coherent processes for a single patient or to groups of patients. Furthermore the work has shown that even a simple registration of outpatient work constitutes a substantial improvement in hospital statistics.The paper was presented as a short paper at the 11th conference of EURO Working Group: OR Applied to Health Services, which took place in Enschede, The Netherlands 29th July – 2nd August 1985. The paper is based on a report [1] made up jointly by representatives from the County of Copenhagen and the Danish Hospital Institute.     

An algorithm to locate perinatal facilities in the municipality of Rio de Janeiro

In order to reduce infant mortality in the municipality of Rio de Janeiro it is desired to encourage all mothers-to-be to attend the appropriate health care facilities. A 3-level hierarchical prenatal–neonatal health care system is described together with a basic model for optimising accessibility to facilities. A genetic algorithm to solve the basic model is developed and some numerical experience reported. Extensions to the basic model and planned future research are briefly indicated.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.     

The Balance of Care Project: Modelling the Allocation of Health and Personal Social Services

The Balance of Care model was designed to predict from aggregate health and personal social service plans the care which will be received by different types of potential client. Planners can use the model to assess the implications of various competing strategies and hence choose the one which will yield a pattern of care closest to that desired. In order to predict the pattern of care inherent in a service strategy it is necessary to establish the priorities governing the allocation of services to clients. This paper concentrates on the structure of the model and the techniques developed to deduce priorities from the past behaviour of the health and personal social service system. Applications of the model are described more fully elsewhere.     

Modelling the flow of congestive heart failure patients through a hospital system

The number of hospital admissions in England due to heart failure is projected to increase by over 50% during the next 25 years. This will incur greater pressures on hospital managers to allocate resources in an effective manner. A reliable indicator for measuring the quantity of resources consumed by hospital patients is their length of stay (LOS) in care. This paper proposes modelling the length of time heart failure patients spend in hospital using a special type of Markov model, where the flow of patients through hospital can be thought of as consisting of three stages of care—short-, medium- and longer-term care. If it is assumed that new admissions into the ward are replacements for discharges, such a model may be used to investigate the case-mix of patients in hospital and the expected patient turnover during some specified period of time. An example is illustrated by considering hospital admissions to a Belfast hospital in Northern Ireland, between 2000 and 2004.     

Dimensioning hospital wards using the Erlang loss model

How many beds must be allocated to a specific clinical ward to meet production targets? When budgets get tight, what are the effects of downsizing a nursing unit? These questions are often discussed by medical professionals, hospital consultants, and managers. In these discussions the occupancy rate is of great importance and often used as an input parameter. Most hospitals use the same target occupancy rate for all wards, often 85%. Sometimes an exception is made for critical care and intensive care units. In this paper we demonstrate that this equity assumption is unrealistic and that it might result in an excessive number of refused admissions, particularly for smaller units. Queuing theory is used to quantify this impact. We developed a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units. We validated this model with hospital data over the years 2004–2006. Finally, we demonstrate the efficiency of merging departments.     

Using a Markov reward model to estimate spend-down costs for a geriatric department

A Markov model is used to describe movements of geriatric patients within a hospital system where the states of the Markov chain are acute/rehabilitative, long-stay care, discharge or death. By assigning costs to the states of this model, we can estimate the spend-down costs of running down services given that there are no more admissions and different costs are assigned to acute/rehabilitative and long-stay care. The model is used to estimate the spend-down costs using data previously validated for three Departments of Geriatric Medicine in the South West Thames Region of England. Our approach allows hospital planners to identify cost-effective strategies which take into account the fact that some geriatric patients remain in long-stay care for very long periods of time.     

A matheuristic algorithm for stochastic home health care planning

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances.     

Composing medical crews with equity and efficiency

The quality of the health care is directly connected to the equity and to the efficiency of the service delivered. Usually, the health care is delivered by crews composed of individuals working together sharing knowledge, experiences and skills. We consider the problem of composing medical crews in such a way that the health care service provided follows the principles of equity and efficiency. We present a general mathematical programming model for this problem and a solution algorithm based on Tabu Search methodology. Computational analysis proves the effectiveness of the proposed algorithm.     

Health network mergers and hospital re-planning

This paper presents an integer programming formulation for the hospital re-planning problem which arises after hospital network mergers. The model finds the best re-allocation of resources among hospitals, the assignment of patients to hospitals and the service portfolio to minimize the system costs subject to quality and capacity constraints. An application in the Turkish hospital networks case is illustrated to show the implications of consolidation of health insurance funds on resource allocations and flow of patients in the system.     

The development and use of tools for monitoring the occurrence of surgical wound infections

Infections acquired during patients' hospital stays are a major health care concern in the UK. They can be fatal, lead to excess morbidity and lengthen hospital stay. There is therefore considerable interest in using analytical tools for monitoring the occurrence of infections so that any problems with the quality of patient care can be quickly identified and rectified. The development and implementation of such tools are complicated as some infections can be difficult to diagnose and it can take several weeks before an infection manifests itself. Another important issue is that some patients are more likely to contract an infection than others, regardless of the standard of care they receive. This paper describes work that has been undertaken in collaboration with University College London Hospitals (UCLH) to develop appropriate outcome monitoring tools for surgical wound infections that are easy for hospital staff to use and interpret. The underlying risk model has been developed and validated locally at UCLH, and for more widespread implementation it would require revalidation for new centres.     

Emergency and on-demand health care: modelling a large complex system

This paper describes how system dynamics was used as a central part of a whole-system review of emergency and on-demand health care in Nottingham, England. Based on interviews with 30 key individuals across health and social care, a ‘conceptual map’ of the system was developed, showing potential patient pathways through the system. This was used to construct a stock-flow model, populated with current activity data, in order to simulate patient flows and to identify system bottle-necks. Without intervention, assuming current trends continue, Nottingham hospitals are unlikely to reach elective admission targets or achieve the government target of 82% bed occupancy. Admissions from general practice had the greatest influence on occupancy rates. Preventing a small number of emergency admissions in elderly patients showed a substantial effect, reducing bed occupancy by 1% per annum over 5 years. Modelling indicated a range of undesirable outcomes associated with continued growth in demand for emergency care, but also considerable potential to intervene to alleviate these problems, in particular by increasing the care options available in the community.     

Allocating nursing staff to hospital wards— A case study

In this case study a system for estimating daily nursing workloads on hospital wards is described and its potential is investigated. The system is to be adopted by the local health authority, and some of the preliminary results have already been used. A key feature of the work has been the active involvement of nursing staff. This style of work is proposed as one approach to improving efficiency within the Public Sector whilst maintaining acceptable standards of care.     

A Markovian queueing model for ambulance offload delays

Ambulance offload delays are a growing concern for health care providers in many countries. Offload delays occur when ambulance paramedics arriving at a hospital Emergency Department (ED) cannot transfer patient care to staff in the ED immediately. This is typically caused by overcrowding in the ED. Using queueing theory, we model the interface between a regional Emergency Medical Services (EMS) provider and multiple EDs that serve both ambulance and walk-in patients. We introduce Markov chain models for the system and solve for the steady state probability distributions of queue lengths and waiting times using matrix-analytic methods. We develop several algorithms for computing performance measures for the system, particularly the offload delays for ambulance patients. Using these algorithms, we analyze several three-hospital systems and assess the impact of system resources on offload delays. In addition, simulation is used to validate model assumptions.     

DEA方法在卫生经济学中的应用

自八十年代中期以来 ,对医院和医疗卫生系统进行效率评估引起了人们的广泛关注 ,越来越多的学者开始从事这一领域的研究 .数据包络分析方法 ( DEA)因其可对多输入 ,多输出的系统进行综合效率评价 ,这一特性正符合医院和医疗卫生系统中具有多投入和多产出的特点 ,从而在医院和医疗卫生系统的效率评估中受到重视并有着重要应用 .本文综述了现有文献中有关 DEA方法在医院和医疗卫生系统效率评估的应用 ,对已得到应用的 DEA模型作了简单的介绍 ,同时对 DEA模型和方法的进一步应用给出了建议 .