Most healthcare organizations (HCOs) engage Group Purchasing Organizations (GPOs) as an outsourcing strategy to secure their supplies and materials. When an HCO outsources the procurement function to a GPO, this GPO will directly interact with the HCO's supplier on the HCO's behalf. This study investigates how an HCO's dependence on a GPO affects supply chain relationships and power in the healthcare medical equipment supply chain. Hypotheses are tested through factor analysis and structural equation modeling, using primary survey data from HCO procurement managers. An HCO's dependence on a GPO is found to be positively associated with a GPO's reliance on mediated power, but, surprisingly, negatively associated with a GPO's mediated power. Furthermore, analysis indicates that an HCO's dependence on a GPO is positively associated with an HCO's dependence on a GPO-contracted Original Equipment Manufacturer (OEM). HCO reliance on GPOs may lead to a buyer's dependence trap, where HCOs are increasingly dependent on GPOs and OEMs. Implications for HCO procurement managers and recommended steps for mitigation are offered. Power-dependence relationships in the medical equipment supply chain are not consistent with relationships in other, more traditional, supply chains. While dependence in a supply chain relationship typically leads to an increase in reliance on mediated power, GPO-dependent HCOs instead perceive a decrease in GPO mediated power. Furthermore, HCOs that rely on procurement service from GPOs are increasingly dependent on the OEMs.

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Accountable care organizations (ACO) in the United States show promise in controlling health care costs while preserving patients' choice of providers. Understanding the effects of patient choice is critical in novel payment and delivery models like ACO that depend on continuity of care and accountability. The financial, utilization, and behavioral implications associated with a patient's decision to forego local health care providers for more distant ones to access higher quality care remain unknown. To study this question, we used an agent-based simulation model of a health care market composed of providers able to form ACO serving patients and embedded it in a conditional logit decision model to examine patients capable of choosing their care providers. This simulation focuses on Medicare beneficiaries and their congestive heart failure (CHF) outcomes. We place the patient agents in an ACO delivery system model in which provider agents decide if they remain in an ACO and perform a quality improving CHF disease management intervention. Illustrative results show that allowing patients to choose their providers reduces the yearly payment per CHF patient by $320, reduces mortality rates by 0.12 percentage points and hospitalization rates by 0.44 percentage points, and marginally increases provider participation in ACO. This study demonstrates a model capable of quantifying the effects of patient choice in a theoretical ACO system and provides a potential tool for policymakers to understand implications of patient choice and assess potential policy controls.

Payment innovations that better align incentives in health care are a promising approach to reduce health care costs and improve quality of care. Designing effective payment systems, however, is challenging due to the complexity of the health care system with its many stakeholders and their often conflicting objectives. There is a lack of mathematical models that can comprehensively capture and efficiently analyze the complex, multi-level interactions and thereby predict the effect of new payment systems on stakeholder decisions and system-wide outcomes. To address the need for multi-level health care models, we apply multiscale decision theory (MSDT) and build upon its recent advances. In this paper, we specifically study the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and determine how this incentive program affects computed tomography (CT) use, and how it could be redesigned to minimize unnecessary CT scans. The model captures the multi-level interactions, decisions and outcomes for the key stakeholders, i.e., the payer, ACO, hospital, primary care physicians, radiologists and patients. Their interdependent decisions are analyzed game theoretically, and equilibrium solutions - which represent stakeholders' normative decision responses - are derived. Our results provide decision-making insights for the payer on how to improve MSSP, for ACOs on how to distribute MSSP incentives among their members, and for hospitals on whether to invest in new CT imaging systems.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

Health care providers are under pressure to improve both efficiency and quality. The two objectives are not always mutually consistent, because achieving higher levels of quality may require additional resources. The aim of this study is to demonstrate how the nonparametric conditional approach can be used to integrate quality into the analysis of efficiency and to investigate the mechanisms through which quality enters the production process. Additionally, we explain how the conditional approach relates to other nonparametric methods that allow integrating quality into efficiency analysis and provide guidance on the selection of an appropriate methodology. We use data from 178 departments of interventional cardiology and consider three different measures of quality: patient satisfaction, standardized mortality ratio, and patient radiation exposure. Our results refute the existence of a clear trade-off between efficiency and quality. In fact, the impact of quality on the production process differs according to the utilized quality measure. Patient satisfaction does not affect the attainable frontier but does have an inverted U-shaped effect on the distribution of inefficiencies; mortality ratio negatively impacts the attainable frontier when the observed mortality more than doubles the predicted mortality; and patient radiation exposure is not associated with the production process.

We address the problem of ambulance dispatching, in which we must decide which ambulance to send to an incident in real time. In practice, it is commonly believed that the 'closest idle ambulance' rule is near-optimal and it is used throughout most literature. In this paper, we present alternatives to the classical closest idle ambulance rule. Most ambulance providers as well as researchers focus on minimizing the fraction of arrivals later than a certain threshold time, and we show that significant improvements can be obtained by our alternative policies. The first alternative is based on a Markov decision problem (MDP), that models more than just the number of idle vehicles, while remaining computationally tractable for reasonably-sized ambulance fleets. Second, we propose a heuristic for ambulance dispatching that can handle regions with large numbers of ambulances. Our main focus is on minimizing the fraction of arrivals later than a certain threshold time, but we show that with a small adaptation our MDP can also be used to minimize the average response time. We evaluate our policies by simulating a large emergency medical services region in the Netherlands. For this region, we show that our heuristic reduces the fraction of late arrivals by 18 % compared to the 'closest idle' benchmark policy. A drawback is that this heuristic increases the average response time (for this problem instance with 37 %). Therefore, we do not claim that our heuristic is practically preferable over the closest-idle method. However, our result sheds new light on the popular belief that the closest idle dispatch policy is near-optimal when minimizing the fraction of late arrivals.

This paper evaluates the technical efficiency of 71 Greek public hospitals and examines potential efficiency gains from 13 candidate mergers among them. Efficiency assessments are performed using bootstrapped Data Envelopment Analysis (DEA) whilst merger analysis is conducted by applying the Bogetoft and Wang methodology which allows the overall potential merger gains to be decomposed into three main components of inefficiency, namely technical (or learning), scope (or harmony) and scale (or size) effects. Thus, the analysis provides important insights not only on the magnitude of the potential total efficiency gains but also on their sources. The overall analysis is conducted in the context of a complete methodological framework where methods for outlier detection, returns to scale identification, and bias corrections for DEA estimations are also applied. Mergers are analyzed under the assumptions of constant, variable and non-decreasing returns to scale in an input oriented DEA model with three inputs and three outputs. The main finding of the study indicates that almost all mergers show substantial potential room for efficiency improvement, which is mainly attributed to the pre-merger technical inefficiencies of the individual hospitals and therefore it might be possible to be achieved without the need of implementing full-scale mergers. The same -though, at a lower extent- applies to the harmony effect whilst the size effect shows marginal or even negative gains.

When faced with a medical problem, patients contact their primary care physician (PCP) first. Here mainly two types of patient requests occur: non-scheduled patients who are walk-ins without an appointment and scheduled patients with an appointment. Number and position of the scheduled appointments influence waiting times for patients, capacity for treatment and the utilization of PCPs. As the number of patient requests differs significantly between weekdays, the challenge is to match capacity with patient requests and provide as few appointment slots as necessary. In this way, capacity for walk-ins is maximized while overall capacity restrictions are met. Decisions as to the optimal appointment capacity per day on a tactical decision level has gained little attention in the literature. A mixed integer linear model is developed, where the minimum number of appointments scheduled for a weekly profile is determined. We are thus able to give the answer as to how many appointments to offer on each day in a week in order to create a schedule that takes patient preferences as well as PCP preferences into account. Appointment schedules are often influenced by uncertain demands due to the number of urgent patients, interarrivals and service times. Based on an exemplary case study, the advantages of the optimal appointment schedule on different performance criteria are shown by detailed stochastic simulations.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.