Showing papers in "Health Care Management Science in 2007"

Optimization of surgery sequencing and scheduling decisions under uncertainty.

Abstract: Operating rooms (ORs) are simultaneously the largest cost center and greatest source of revenues for most hospitals. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. Longer than average surgery durations result in late starts not only for the next surgery in the schedule, but potentially for the rest of the surgeries in the day as well. Late starts also result in direct costs associated with overtime staffing when the last surgery of the day finishes later than the scheduled shift end time. In this article we describe a stochastic optimization model and some practical heuristics for computing OR schedules that hedge against the uncertainty in surgery durations. We focus on the simultaneous effects of sequencing surgeries and scheduling start times. We show that a simple sequencing rule based on surgery duration variance can be used to generate substantial reductions in total surgeon and OR team waiting, OR idling, and overtime costs. We illustrate this with results of a case study that uses real data to compare actual schedules at a particular hospital to those recommended by our model.

Optimal outpatient appointment scheduling

Abstract: In this paper optimal outpatient appointment scheduling is studied. A local search procedure is derived that converges to the optimal schedule with a weighted average of expected waiting times of patients, idle time of the doctor and tardiness (lateness) as objective. No-shows are allowed to happen. For certain combinations of parameters the well-known Bailey-Welch rule is found to be the optimal appointment schedule.

A three-phase approach for operating theatre schedules

Abstract: In this paper we develop a three-phase, hierarchical approach for the weekly scheduling of operating rooms. This approach has been implemented in one of the surgical departments of a public hospital located in Genova (Genoa), Italy. Our aim is to suggest an integrated way of facing surgical activity planning in order to improve overall operating theatre efficiency in terms of overtime and throughput as well as waiting list reduction, while improving department organization. In the first phase we solve a bin packing-like problem in order to select the number of sessions to be weekly scheduled for each ward; the proposed and original selection criterion is based upon an updated priority score taking into proper account both the waiting list of each ward and the reduction of residual ward demand. Then we use a blocked booking method for determining optimal time tables, denoted Master Surgical Schedule (MSS), by defining the assignment between wards and surgery rooms. Lastly, once the MSS has been determined we use the simulation software environment Witness 2004 in order to analyze different sequencings of surgical activities that arise when priority is given on the basis of a) the longest waiting time (LWT), b) the longest processing time (LPT) and c) the shortest processing time (SPT). The resulting simulation models also allow us to outline possible organizational improvements in surgical activity. The results of an extensive computational experimentation pertaining to the studied surgical department are here given and analyzed.

A comprehensive simulation for wait time reduction and capacity planning applied in general surgery

Abstract: This paper describes the use of operational research techniques to analyze the wait list for the Division of General Surgery at the Capital District Health Authority in Halifax, Nova Scotia, Canada. A discrete event simulation model was developed to aid capacity planning decisions and to analyze the performance of the division. The analysis examined the consequences of redistributing beds between sites, and achieving standard patient lengths of stay, while contrasting them to current and additional resource options. From the results, multiple independent and combined options for stabilizing and decreasing waits for elective procedures were proposed.

Surgical block scheduling in a system of hospitals: an application to resource and wait list management in a British Columbia health authority

Abstract: Scheduling surgical specialties in a medical facility is a very complex process. The choice of schedules and resource availability impact directly on the number of patients treated by specialty, cancellations, wait times, and the overall performance of the system. In this paper we present a system-wide model developed to allow management to explore tradeoffs between OR availability, bed capacity, surgeons' booking privileges, and wait lists. We developed a mixed integer programming model to schedule surgical blocks for each specialty into ORs and applied it to the hospitals in a British Columbia Health Authority, considering OR time availability and post-surgical resource constraints. The results offer promising insights into resource optimization and wait list management, showing that without increasing post-surgical resources hospitals could handle more cases by scheduling specialties differently.

The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta

Abstract: We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.

Modeling the emergency cardiac in-patient flow: an application of queuing theory.

Abstract: This study investigates the bottlenecks in the emergency care chain of cardiac in-patient flow. The primary goal is to determine the optimal bed allocation over the care chain given a maximum number of refused admissions. Another objective is to provide deeper insight in the relation between natural variation in arrivals and length of stay and occupancy rates. The strong focus on raising occupancy rates of hospital management is unrealistic and counterproductive. Economies of scale cannot be neglected. An important result is that refused admissions at the First Cardiac Aid (FCA) are primarily caused by unavailability of beds downstream the care chain. Both variability in LOS and fluctuations in arrivals result in large workload variations. Techniques from operations research were successfully used to describe the complexity and dynamics of emergency in-patient flow.

Effects of clinical characteristics on successful open access scheduling.

Abstract: Many outpatient clinics are experimenting with open access scheduling. Under open access, patients see their physicians within a day or two of making their appointment request, and long term patient booking is very limited. The hope is that these short appointment lead times will improve patient access and reduce uncertainty in clinic operations by reducing patient no-shows. Practice shows that successful implementation can be strongly influenced by clinic characteristics, indicating that open access policies must be designed to account for local clinical conditions. The effects of four variables on clinic performance are examined: (1) the fraction of patients being served on open access, (2) the scheduling horizon for patients on longer term appointment scheduling, (3) provider care groups, and (4) overbooking. Discrete event simulation, designed experimentation, and data drawn from an intercity clinic in central Indiana are used to study the effects of these variables on clinic throughput and patient continuity of care. Results show that, if correctly configured, open access can lead to significant improvements in clinic throughput with little sacrifice in continuity of care.

Contact tracing to control infectious disease: when enough is enough.

Abstract: Contact tracing (also known as partner notification) is a primary means of controlling infectious diseases such as tuberculosis (TB), human immunodeficiency virus (HIV), and sexually transmitted diseases (STDs). However, little work has been done to determine the optimal level of investment in contact tracing. In this paper, we present a methodology for evaluating the appropriate level of investment in contact tracing. We develop and apply a simulation model of contact tracing and the spread of an infectious disease among a network of individuals in order to evaluate the cost and effectiveness of different levels of contact tracing. We show that contact tracing is likely to have diminishing returns to scale in investment: incremental investments in contact tracing yield diminishing reductions in disease prevalence. In conjunction with a cost-effectiveness threshold, we then determine the optimal amount that should be invested in contact tracing. We first assume that the only incremental disease control is contact tracing. We then extend the analysis to consider the optimal allocation of a budget between contact tracing and screening for exogenous infection, and between contact tracing and screening for endogenous infection. We discuss how a simulation model of this type, appropriately tailored, could be used as a policy tool for determining the appropriate level of investment in contact tracing for a specific disease in a specific population. We present an example application to contact tracing for chlamydia control.

Staggered work shifts: a way to downsize and restructure an emergency department workforce yet maintain current operational performance.

Abstract: Starting from the last decade of the twentieth century, most hospital Emergency Department (ED) budgets did not keep up with the demand for ED services made by growing populations and aging societies. Since labor consumes over 50% of the total monies invested in EDs and other healthcare systems, any downsizing, streamlining and reorganization plan needs to first address staffing issues such as determining the correct size of the workforce and its work shift scheduling. In this context, it is very important to remember that downsizing certainly does not mean a general cut-across-the-board. This study shows that a selective downsizing process in which each resource is treated separately (increasing the work capacity of some resources is also possible), based on its unique contribution to the overall ED operational performance, can approximately maintain current ED operational measures in terms patient length of stay (LOS) despite an overall reduction in staff hours. A linear optimization model (S-model) and a heuristic iterative simulation based algorithm (SWSSA) are used in this study for scheduling the resources’ work shifts, one resource at a time. The algorithm was tested using data that was gathered from five general hospital EDs. By leveling the workload of the different resources in the ED, SWSSA was able to achieve LOS values within −19 to 4% of the original values despite a reduction of 8–17.5% in physicians’ work hours and a reduction of 13–47% in the nurses’ work hours.

Modeling patient service centers with simulation and system dynamics.

Abstract: We report on the use of simulation modeling for redesigning phlebotomy and specimen collection centers (or patient service centers) at a medical diagnostic laboratory. Research was performed in an effort to improve patient service, in particular to reduce average waiting times as well as their variability. Discrete-event simulation modeling provided valuable input into new facility design decisions and showed the efficacy of pooling sources of variation, particularly patient demand and service times. Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of service centers eventually resulted in unanticipated performance problems. We show how a system dynamics model might have helped predict these implementation problems and suggest some ways to improve results.

Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center.

Abstract: Over the past several decades healthcare delivery systems have received increased pressure to become more efficient from both a managerial and patient perspective. Many researchers have turned to simulation to analyze the complex systems that exist within hospitals, but surprisingly few have published guidelines on how to analyze models with multiple performance measures. Moreover, the published literature has failed to address ways of analyzing performance along more than one dimension, such as performance by day of the week, patient type, facility, time period, or some combination of these attributes. Despite this void in the literature, understanding performance along these dimensions is critical to understanding the root of operational problems in almost any daily clinic operation. This paper addresses the problem of multiple responses in simulation experiments of outpatient clinics by developing a stratification framework and an evaluation construct by which managers can compare several operationally different outpatient systems across multiple performance measure dimensions. This approach is applied to a discrete-event simulation model of a real-life, large-scale oncology center to evaluate its operational performance as improvement initiatives affecting scheduling practices, process flow, and resource levels are changed. Our results show a reduction in patient wait time and resource overtime across multiple patient classes, facilities, and days of the week. This research has already proven to be successful as certain recommendations have been implemented and have improved the system-wide performance at the oncology center.

Short term hospital occupancy prediction

Abstract: Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data.

A linear programming model for allocating HIV prevention funds with state agencies: a pilot study.

Abstract: Given the initiatives to improve resource allocation decisions for HIV prevention activities, a linear programming model was designed specifically for use by state and local decision-makers. A pilot study using information from the state of Florida was conducted and studied under a series of scenarios depicting the impact of common resource allocation constraints. Improvements over the past allocation strategy in the number of potential infections averted were observed in all scenarios with a maximal improvement of 73%. When allocating limited resources, policymakers must balance efficiency and equity. In this pilot study, the optimal allocation (i.e., most-efficient strategy) would not distribute resources in an equitable manner. Instead, only 12% of at-risk people would receive prevention funds. We find that less efficient strategies, where 58% fewer infections are averted, result in significantly more equitable allocations. This tool serves as a guide for allocating funds for prevention activities.

Evaluating hospital design from an operations management perspective

Abstract: This paper describes an evaluation method for the assessment of hospital building design from the viewpoint of operations management to assure that the building design supports the efficient and effective operating of care processes now and in the future. The different steps of the method are illustrated by a case study. In the case study an experimental design is applied to assess the effect of used logistical concepts, patient mix and technologies. The study shows that the evaluation method provides a valuable tool for the assessment of both functionality and the ability to meet future developments in operational control of a building design.

Development of a patient safety culture measurement tool for ambulatory health care settings: analysis of content validity.

Abstract: The development of patient safety culture in health care organizations is a necessary precursor to patient safety improvement. However, existing tools to measure patient safety culture are intended for implementation in hospitals. A new, abbreviated patient safety culture survey was developed for use in ambulatory health care settings. This survey was tested for content validity utilizing a panel of six experts. It had a clarity interrater agreement (IR) of 0.75, a clarity content validity index (CVI) of 0.95, a representativeness IR of 0.75 and a representativeness CVI of 0.95. The content validity analysis served as a useful tool for assessing the relevance and comprehensiveness of this survey of patient safety culture in ambulatory care organizations.

Simulation tools for developing policies for complex systems: Modeling the health and safety of refugee communities

Abstract: The U.S. Committee for Refugees and Immigrants estimated that there were over 33 million refugees and internally displaced persons (IDPs) in the world at the beginning of 2005. IDP/Refugee communities behave in complex ways making it difficult to make policy decisions regarding the provision of humanitarian aid and health and safety. This paper reports the construction of an agent-based model that has been used to study humanitarian assistance policies executed by governments and NGOs that provide for the health and safety of refugee communities. Agent-based modeling (ABM) was chosen because the more widely used alternatives impose unrealistic restrictions and assumptions on the system being modeled and primarily apply to aggregate data. We created intelligent agents representing institutions, organizations, individuals, infrastructure, and governments and analyzed the resulting interactions and emergent behavior using a Central Composite Design of Experiments with five factors. The resulting model allows policy makers and analysts to create scenarios, to make rapid changes in parameters, and provides a test bed for concepts and strategies. Policies can be examined to see how refugee communities might respond to alternative courses of action and how these actions are likely to affect the health and well-being of the community.

A mixed integer programming model to locate traumatic brain injury treatment units in the Department of Veterans Affairs: a case study.

Abstract: For the Department of Veterans Affairs (VA), traumatic brain injury (TBI) is a significant problem facing active duty military personnel, veterans, their families, and caregivers. The VA has designated TBI treatment as one of its physical medicine and rehabilitation special emphasis programs, thereby providing a comprehensive array of treatment services to those military personnel and veterans with TBI. Timely treatment of TBI is critical in achieving maximal recovery, and being in geographical proximity to a medical center with specialized TBI treatment services is a major determinant of whether such treatment is utilized. We present a mixed integer programming model for locating TBI treatment units in the VA. This model was developed for the VA Rehabilitation Strategic Healthcare Group to assist in locating new TBI treatment units. The optimization model assigns TBI treatment units to existing VA medical centers while minimizing the sum of patient treatment costs, patient lodging and travel costs, and the penalty costs associated with foregone treatment revenue and excess capacity utilization. We demonstrate our model with VA TBI admission data from one of the VA’s integrated service networks, and discuss the expected service and cost implications for a range of TBI treatment unit location options.

Safety-cost trade-offs in medical device reuse: a Markov decision process model

Abstract: Healthcare expenditures in the US are approaching $2 trillion, and hospitals and other healthcare providers are under tremendous pressure to rein in costs. One cost-saving approach which is gaining popularity is the reuse of medical devices which were designed only for a single use. Device makers decry this practice as unsanitary and unsafe, but a growing number of third-party firms are willing to sterilize, refurbish, and/or remanufacture devices and resell them to hospitals at a fraction of the original price. Is this practice safe? Is reliance on single-use devices sustainable? A Markov decision process (MDP) model is formulated to study the trade-offs involved in these decisions. Several key parameters are examined: device costs, device failure probabilities, and failure penalty cost. For each of these parameters, expressions are developed which identify the indifference point between using new and reprocessed devices. The results can be used to inform the debate on the economic, ethical, legal, and environmental dimensions of this complex issue.

Predicting joint replacement waiting times

Abstract: Currently, the median waiting time for total hip and knee replacement in Ontario is greater than 6 months. Waiting longer than 6 months is not recommended and may result in lower post-operative benefits. We developed a simulation model to estimate the proportion of patients who would receive surgery within the recommended waiting time for surgery over a 10-year period considering a wide range of demand projections and varying the number of available surgeries. Using an estimate that demand will grow by approximately 8.7% each year for 10 years, we determined that increasing available supply by 10% each year was unable to maintain the status quo for 10 years. Reducing waiting times within 10 years required that the annual supply of surgeries increased by 12% or greater. Allocating surgeries across regions in proportion to each region's waiting time resulted in a more efficient distribution of surgeries and a greater reduction in waiting times in the long-term compared to allocation strategies based only on the region's population size.

Healthcare system design and parttime working doctors

Abstract: More doctors would like to work parttime. Since research on fitting healthcare system design with the structure of parttime jobs is lacking, we studied how parttime work for medical doctors could be enabled from a system design perspective. A theoretical analysis was performed, illustrated by two case studies. We conclude that introducing parttime work can provide the opportunity for improving system design and, therewith, performance. From the case studies it seems that work redesign can enable parttime work, and at the same time improve system performance. Better managing variability in the system contributed to this. The case studies results also showed that systems characterized by different levels of variability fit with different work contracts.

Dynamic compartmental model of trends in Australian drug use.

Abstract: A five-state compartment model of trends in illicit drug use in Australia is parameterized using data from multiple sources. The model reproduces historical prevalence and supports what-if analyses under the assumption that past trajectories of drug escalation and desistance persist. For fixed initiation, the system has a unique stable equilibrium. The chief qualitative finding is that even though some users escalate rapidly, regular injection drug use still adjusts to changes in incidence with considerable inertia and delay. This has important policy implications, e.g., concerning the timing of reductions in drug-related social cost generated by interventions that reduce the social cost per injection user versus those that cut drug initiation.

A consensus process for identifying a prioritised list of study questions.

Abstract: A consensus process has been developed to assist the UK National Confidential Enquiry into Patient Outcome and Death in identifying questions to be addressed in its studies. The process utilises the knowledge and experience of a panel of experts via a facilitated brainstorming exercise and employs a robust voting system to produce a list of candidate questions ordered in terms of the preferences expressed by individual panel members. The process which is described has been used successfully to assist the design of two national studies and it is anticipated that the process will be adopted for many studies in the future.

A Bayesian approach to assess heart disease mortality among persons with diabetes in the presence of missing data

Abstract: Some states’ death certificate form includes a diabetes yes/no check box that enables policy makers to investigate the change in heart disease mortality rates by diabetes status. Because the check boxes are sometimes unmarked, a method accounting for missing data is needed when estimating heart disease mortality rates by diabetes status. Using North Dakota’s data (1992–2003), we generate the posterior distribution of diabetes status to estimate diabetes status among those with heart disease and an unmarked check box using Monte Carlo methods. Combining this estimate with the number of death certificates with known diabetes status provides a numerator for heart disease mortality rates. Denominators for rates were estimated from the North Dakota Behavioral Risk Factor Surveillance System. Accounting for missing data, age-adjusted heart disease mortality rates (per 1,000) among women with diabetes were 8.6 during 1992–1998 and 6.7 during 1999–2003. Among men with diabetes, rates were 13.0 during 1992–1998 and 10.0 during 1999–2003. The Bayesian approach accounted for the uncertainty due to missing diabetes status as well as the uncertainty in estimating the populations with diabetes.

HMO versus non-HMO private managed care plans: an investigation on pre-switch consumption

Abstract: This paper investigates the switching behavior of enrollees in U.S. managed care plans through treatment effect analyses of the disaggregated expenditures of the plan switchers and stayers prior to switching. Propensity score matching methods are used to estimate the average treatment effects on the treated where switching is the treatment. Analyses on subsamples provide detailed insights into pre-switch consumption behavior. The results, which are based on a national representative data set from the Medical Expenditure Panel Survey, indicate that switchers (from HMO to non-HMO) spend more on hospitalization, utilize less cholesterol checks and flu shots before switching. The other type of switchers (from non-HMO to HMO) spends less on prescribed medicine and office-based physician visits, while female switchers use less breast exams, Pap smears and mammograms prior to switching. The findings suggest that the non-HMO private managed care plans provide better coverage on hospitalization, office-based physician visits and prescribed medicine than the HMO plans.

What determines a patient’s treatment? Evidence from out of hours primary care co-op data in the Republic of Ireland

Abstract: This study explores consistency in healthcare. It investigates whether vulnerable groups in the population receive the most appropriate care. This is achieved by considering the case study of individuals who present to out of hours (OOH) primary care services in the Republic of Ireland with gastroenteritis. Specifically an individual can potentially receive four services; nurse advice, doctor advice, a treatment centre consultation or a home visit. Results show that service choice is influenced by patient, call and seasonal characteristics to varying degrees. Patient symptoms are the primary driver of the type of service the patients receives. Results also indicate that the OOH primary care facilities individual characteristics do not affect service choice. This suggests a degree of consistent care across these organisations. It also provides evidence that service choice is exogenous to the organisation.

Probabilistic master lists: integration of patient records from different databases when unique patient identifier is missing

Abstract: We show how Bayesian probability models can be used to integrate two databases, one of which does not have a key for uniquely identifying clients (e.g., social security number or medical record number). The analyst selects a set of imperfect identifiers (last visit diagnosis, first name, etc.). The algorithm assesses the likelihood ratio associated with the identifier from the database of known cases. It estimates the probability that two records belong to the same client from the likelihood ratios. As it proceeds in examining various identifiers, it accounts for inter-dependencies among them by allowing overlapping and redundant identifiers to be used. We test that the procedure is effective by examining data from the Medical Expenditure Panel Survey (MEPS) Population Characteristics data set, a publicly available data set. We randomly selected 1,000 cases for training data set--these constituted the known cases. The algorithm was used to identify if 100 cases not in the training data set would be misclassified in terms of being a case in the training set or a new case. With 12 fields as identifiers, all 100 cases were correctly classified as new cases. We also selected 100 known cases from the training set and asked the algorithm to classify these cases. Again, all 100 cases were correctly classified. Less accurate results were obtained when the training data set was too small (e.g., less than 100 records) or the number of fields used as identifiers was too small (e.g., less than seven fields). In a test of performance of the algorithm, when the ratio of testing to training data set exceeds 4 to 1, the accuracy of the algorithm exceeded 90% of cases. As the ratio increases, the accuracy of algorithm improves further. These data suggest the accuracy of our automated and mathematical procedure to merge data from two different data sets without the presence of a unique identifier. The algorithm uses imperfect and overlapping clues to re-identify cases from information not typically considered to be a patient identifier.

Special issue on health care simulation

Abstract: Health-related issues continue to be global challenges. Both developed and developing nations are struggling with ways to provide affordable access to health services. The threat of global epidemics including HIV, SARS and the spread of other diseases worldwide threaten to undo decades of gains in life expectancy. Poverty and political unrest breeds new diseases that are rapidly spread around the globe. At the same time the organization and delivery of health services are increasingly complex and costly. Papers in this issue demonstrate the power of simulation in addressing these challenges. Computer simulation has become state of the art in many areas and its use is still growing. Two trends can be observed. First, the conceptual scope of both simulation software and models is becoming broader and more flexible. This allows investigators to combine several modelling approaches in the same application. For example, agentbased models allow researchers to combine technical as well as behavioural aspects of systems. Second, partly as a result of the first trend, we see the use of computer simulation increasingly being applied in areas where there used to be reluctance to use this approach, e.g., in policy modelling. The first three papers address health care policy issues. Pasdirtz examines the issue of control of health care expenditures in the US. State-space models of the US health care system and the US economy were developed, estimated with time series data for the late twentieth century and analyzed using modern control theory. The results suggest that control strategies designed to contain health care costs need to be targeted at health care capital investment, prices for physicians’ services and prescription drugs. Policies directed purely at cutting costs in general or increasing efficiency are likely to have little effect. The paper by Anderson and others reports the construction of an agent-based model that has been used to study humanitarian assistance policies executed by governments and non-government organizations (NGOs) for the health and safety of refugees. An experimental design was used to study the relationships among five factors: basic needs, food and water, sanitation, medical resources and security. The simulation demonstrates the critical role of security in providing for the health and well-being of refugees. The importance of security was highlighted in the recommendations that Amnesty International made regarding the African Union Mission in Sudan (AMIS). A major strength of the model is that it allows policy makers to incorporate specific characteristics of refugees and the governmental and nongovernmental organizations that are providing humanitarian aid to the camp. Policy simulations are valuable because of their ability to facilitate training and feedback concerning potential impact of decisions. In the real world policy decisions may have serious consequences. Policy simulation models are useful because they accelerate creation of scenarios, allow rapid changes in parameters, and provide a test bed for concepts and strategies. In the present application, policies can be examined to see how refugee communities might respond to alternative courses of action and how these actions are likely to affect the health and well-being of the community. The third paper in this issue demonstrates the value of simulation as a tool to analyze policies designed to contain Health Care Manage Sci (2007) 10:309–310 DOI 10.1007/s10729-007-9031-x

Controlling the US health care system with policy wedges

Abstract: Two state-space models, one for the US health care system and one for the US economy, were developed and estimated for the period 1950–1999. The output from the US economy model was then used as a reference input to control the growth of the health care system model. The counterfactual history produced by simulating the controlled model shows that a reduction in investment and volume-based services would have been needed to bring the growth of the health care system in line with the US economy. Specifically, a 13% reduction in capital expenditure, a 15% reduction in drug prices and a 32% reduction in prices for physician’s services would have been needed over the late twentieth century. The methodology also suggests how universal health care programs might be designed using planning and economic incentives without either over-engineering plan provisions or using centralized, command-and-control approaches.