Application of discrete-event simulation in health care clinics: A survey

In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discrete-event simulation as an effective tool for allocating scarce resources to improve patient flow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.     

Bridging the gap between clinicians and health managers

This contribution is about bridging the gap between professionals and managers in health care: clinicians on the one hand and managers on the other hand. The paper looks at the problems along the road ahead: those that confront the governmental authorities and those that confront the health care sector. It identifies the driving forces that confront the health care sector and shows in what direction they are pushing management. Three steps are indicated to bridge the gap between clinicians and managers: (1) New discussion forums; (2) patient information systems; (3) communication tools. Operational Research can and should contribute. However, some shifts in emphasis are necessary. Three propositions are put forward: (1). Operational Research should infiltrate the discussion forums on health care; (2) Operational Research should orient itself to the “operationalisation” of the thousands of basic concepts and indicators used in health care rather than to the formulation of models which take all these concepts and indicators for granted; (3) Operational Research can contribute by helping to master the voluminous data in sensible ways in order to arrive at information on health, health care and health care management… under any type of constraint, resource or otherwise. This information needs, however, to be communicated.     

基于多维风险视角的政府卫生保健资源配置决策行为研究

研究在疾病风险和医学治疗风险同时共存的情形下,政府卫生保健资源的优先配置行为决策问题.当存在治疗风险(患病风险)时,分析病人患病的不确定性(治疗的不确定性)对政府卫生保健资源的配置效应,同时给出配置更多的卫生资源到更高风险病人群体的社会规划者的风险偏好条件.当这两类风险是局部的或正象限依赖的风险时,研究两种来源的风险对政府卫生保健资源的配置的联合影响.将之前学者提出的卫生保健资源的配置模型扩展到两类风险共存的情形,同时对于不确定下的卫生保健资源配置决策问题提供新的见解.     

基于中医健康云的健康养老服务平台设计与应用

中医健康云能将中医的特色化、标准化与大数据、人工智能应用完美融合。本文基于中医健康云技术和老龄群体对养老服务的迫切需求,对中医健康云服务平台和架构设计进行建设性研究,同时分析中医健康云平台的关键技术所在,进而提出中医健康云在健康养老服务中的应用设计。以期利用互联网与大数据,融合传统中医智慧,对老龄群体的健康进行动态跟踪与实时咨询,为老年群体提供及时、有效和针对性的健康养老服务。     

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.     

Promoting Participation in the Health Science Professions: Summer Programs for Talented,Underrepresented Students in Science

Across the nation, there remains a critical shortage of health professionals and of students choosing to pursue health-related careers. This problem is particularly pervasive in the state of Oklahoma. In response to this need, faculty, administrators, and health professionals on two campuses of the University of Oklahoma have, collaborated over the past 9 years to develop and deliver summer academy programs in the health sciences professions for talented students and primarily for students who are underrepresented in science. These summer academy programs feature classroom and hands-on clinical practicum experiences to introduce students to the rigors of academic preparation required for the health profession careers and the opportunity to be involved in direct patient care and laboratory research experiences. The academies have the goal of ensuring that bright, talented young students are aware of career options in health care delivery, health care education, and health care research arenas. A secondary objective is to spark student interest in pursuing higher education in the health sciences and in pursuing health-related careers. The academy goals complement the state and national ongoing efforts to attract top scholars and meet health work force needs.     

One hundred years of Operational Research in Health—UK 1948–2048

This paper presents a personal view, drawing on some 30 years of working in this area, of past, present and future contributions of operational research (OR) in health in the UK. It considers developments in health and care and in OR contributions to these at local and national level since the creation of the National Health Service 60 years ago; likely future developments in health and care; and associated priorities for preparing now for OR to make a major impact on health and care in the next 40 years. The aim is to stimulate reflective thinking and promote anticipatory action among health OR practitioners of the future.     

Performance measurement criteria in health care organizations: Review and future research directions

As pressures from government, insurance companies, communities, and individual consumers to lower the cost and improve health care quality increase, various health care performance measures have emerged as critical elements for cost containment. Previous research has not addressed the issue of standard performance measures for the health care industry. This paper has attempted to review the current literature and suggest future directions for both practitioners and researchers.     

The analytic hierarchy process in medical and health care decision making: A literature review

This paper presents a literature review of the application of the analytic hierarchy process (AHP) to important problems in medical and health care decision making. The literature is classified by year of publication, health care category, journal, method of analyzing alternatives, participants, and application type. Very few articles were published prior to 1988 and the level of activity has increased to about three articles per year since 1997. The 50 articles reviewed were classified in seven categories: diagnosis, patient participation, therapy/treatment, organ transplantation, project and technology evaluation and selection, human resource planning, and health care evaluation and policy. The largest number of articles was found in the project and technology evaluation and selection category (14) with substantial activity in patient participation (9), therapy/treatment (8), and health care evaluation and policy (8). The AHP appears to be a promising support tool for shared decision making between patient and doctor, evaluation and selection of therapies and treatments, and the evaluation of health care technologies and policies. We expect that AHP research will continue to be an important component of health care and medical research.     

Trends in labor efficiency among American hospital markets

The health care sector is one of the most labor intensive sectors of the economy. As a major player in health service delivery, hospitals must closely examine their largest cost — labor expenses. This study evaluates trends in the efficiency of health care labor among urban hospital markets. More specifically, it assesses the hypothesis that technological changes and market and regulatory pressures have increased aggregate labor efficiency. Using data envelopment analysis (DEA), this study evaluates labor efficiency in 1989 and 1993 in 298 metropolitan statistical areas (MSAs) with two or more hospitals. Results of this study suggest that between 1989 and 1993, hospital markets generally demonstrated higher labor inefficiencies. The U.S. health care system could save approximately $16.6 billion in 1993 by eliminating hospitals' excessive use of health care provider labor.     

A heuristic for direct consumer participation in primary health care planning

Recent attempts at consumer participation in the health care planning process have proved weak in their ability to responsively account for consumer health welfare. This can be attributed, in large part, to the mechanisms employed for identifying and utilizing the consumer's health care views and preferences. A heuristic planning procedure designed to overcome these problems by directly incorporating consumer preferences is developed. It identifies that (primary) health care delivery system which maximizes total incremental health benefit to a community subject to a prespecified budget constraint. The model assumes a methodology (previously developed by the author) for measuring, in aggregable units, the benefit, B_ip, from some health care facility p as perceived by some consumer i. Application of the procedure and subsequent sensitivity analyses demonstrate its ability to generate valid solutions that are robust to disturbances in the planning system.     

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.     

A primer: Health care databases,diagnostic coding,severity adjustment systems and improved parameter estimation

Data on outcomes of medical care are becoming much more available in health care organizations and systems of care. This will create new opportunities for operations researchers to make contributions to health care policy and management. To provide some background to those new to the health care area, in this article we do the following: (1) provide a brief exposure to major administrative databases that are available and useful for analyzing outcomes data; (2) discuss the strengths and limitations of the diagnostic information contained in these databases; (3) describe several systems that use this information, or in some cases information from the medical record, to determine patient severity, thus providing a basis for severity-adjustment before considering outcomes; and (4) finally, provide an overview of some recent advances in obtaining improved parameter estimates from large databases.     

Benchmarking physician practice patterns with DEA: A multi-stage approach for cost containment

Physician practice patterns in a Health Maintenance Organization (HMO) are analyzed using single and multi-stage applications of Data Envelopment Analysis (DEA). Best practice (BP) patterns are identified, which can serve as benchmark targets for inefficient physicians. Results suggest three health policy — resource utilization control strategies:

1. If managed care organizations could motivate primary care physicians to adopt the practice styles of the best practice primary care physicians, substantial clinical resource savings could be achieved, ranging from 12% to over 30% in the HMO that is the focus of this study;
2. some specialists who practice as primary care physicians (PCPs) provide more efficient care than some general practitioner PCPs, modifying the current perception that reducing specialists is the most effective way to achieve low cost practice patterns; and
3. groups of physicians in the HMO exhibit different resource use patterns, which may present opportunities to manage high cost groups as another path to contain costs.

The results suggest specific new paths which may prove effective at reducing health care costs within managed care organizations, the health care providers most likely to dominate the U.S. health system in the future. A multi-stage DEA technique is used to locate specific types of inefficient physicians. Methods to test the clinical viability of using DEA to realize the potential cost savings and extensions of this research are discussed.     

Simulation based analysis of patient arrival to health care systems and evaluation of an operations improvement scheme

Various demands of different patients over both medical resource and time domains in health care systems raise requests of strategies for balanced system capacity from an operations perspective. In this paper, a quantitative modeling technique with both patient arrival and associated treatment process integrated are used to characterize health care system performance and evaluate system efficiency. The patient arrival process is described as a dynamic random Poisson process and patient treatments are characterized as consumption processes of various health care resources over time with a view of the “product line” used. The waiting time of patients and usage of health care resources are proposed as system performance measures based on their means, variances, and confidence intervals. A simulation considering patients with several various diseases is given to find a mechanism of conflicting factors in decisions of balanced system capacity, and an operation scheme of “evenly balanced load for bottlenecks” is obtained based on analysis of simulation outputs. Simul8 provides the software environment for the simulation.     

Risk-adjusted control charts for health care assessment

The recent Joint Commission on Accreditation of Healthcare Organization (JCAHO) requirement that hospital accreditation be based upon a Total Quality Management (TQM) approach has focused the attention of health care administrations on the use of techniques such as control charts. However, control charts are not typically adjusted for severity of illness. This adjustment is needed because, unlike industrial organizations, hospitals are not able to control all of their inputs and must accept variances in their patients. In this paper, we present a methodology for adjusting a health care organization's control charts to reflect their patient population's severity of illness during different time intervals. We then demonstrate that risk-adjusting expected patient outcomes can change our assessments of the relative quality of care offered by a health care organization in different time periods.     

Primary care network development: the regulator’s perspective

Our research is motivated by the proliferation of primary care models in Ontario, Canada. Currently, primary care is mainly provided by facilities belonging to six models of care. These models are remunerated by various schemes—a mixture of fee-for-service, capitation and salary. In addition, they provide different levels of care and several are better adjusted than others to treat complex health needs. The proposed mixed integer programming model allows the regulator to test the outcomes of locating different types of primary care facilities on the overall cost, accessibility and appropriateness of provided care. The network design is fitted to the heterogeneity of the population residing in a defined geographical area, directly using an index (deprivation index) that was found to correlate with increased health needs and barriers to care. The model capabilities are illustrated on the geographical area of Kingston, Ontario.     

Shifting the balance of health care into the 21st century

Some of the present or likely future key shifts in the balance of health care in Europe are set out; shifts in when and where and how care is delivered, what is delivered and who is cared for. An illustrative assessment is given of ways in which ORMS can help in coping with the uncertainty, complexity and change that underlies many of the observed shifts in the balance of health care.     

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.