Background: Few studies have rigorously evaluated the associations between organizational characteristics and intervention activities of health care organizations participating in quality improvement collaboratives (QICs).
Objective: To examine the relationship between clinic characteristics and intervention activities by primary care clinics that provide HIV care and that participated in a QIC.
Design: Cross-sectional study of Ryan White CARE Act (now called Ryan White HIV/AIDS Treatment Modernization Act) funded clinics that participated in a QIC over 16 months in 2000 and 2001. The QIC was originally planned to be a more typical 12 months long, but was extended to increase the likelihood of success. Data were collected using surveys of clinicians and administrators in participating clinics and monthly reports of clinic improvement activities.
Measures: Number of interventions attempted, percent of interventions repeated, percent of interventions evaluated, and organizational characteristics.
Results: Clinics varied significantly in their intervention choices. Organizations with a more open culture and a greater emphasis on quality improvement attempted more interventions (P < 0.01, P < 0.05) and interventions that were more comprehensive (P < 0.01, P < 0.10). Presence of multidisciplinary teams and measurement of progress toward quantifiable goals also were associated with comprehensiveness of interventions (P < 0.01, P < 0.05).
Conclusion: Clinic characteristics predicted intervention activities during a QIC. Further research is needed on how these organizational characteristics affect quality of care through their influence on intervention activities.

This paper is inspired by the recurring mismatch between demand and supply in the U.S. influenza vaccine market. Economic theory predicts that an oligopolistic market with unregulated but costly entry will experience excess entry and oversupply, not the undersupply observed in the market for influenza vaccine in recent years. In this paper, we examine the interaction between yield uncertainty, a key characteristic of many production processes, including that for influenza vaccine, and firms’ strategic behavior. We find that yield uncertainty can contribute to a high degree of concentration in an industry and a reduction in the industry output and the expected consumer surplus in equilibrium. We use parameter values loosely based on the U.S. influenza vaccine market to numerically illustrate the impact of yield uncertainty.

Consider a retailer that rents products to customers for a pre-specified rental duration. By considering the dynamics of uncertain rental demand and return processes, we first present a base model that is intended to analyze the impact of rental duration on the stocking level, the rental price, and the retailer’s profit. Due to the complexity of the base model, we develop an approximation scheme to obtain tractable results. Also, we apply the base model to analyze a situation in which a retailer enters a revenue sharing agreement with a distributor. Moreover, we expand our base model to address the issue of competition in rental duration and rental price. The analysis of our competitive model in a duopolistic environment suggests that the market equilibrium depends on the market potential and the rental duration sensitivity. Furthermore, we establish conditions under which one firm will charge a lower rental price while the other firm will offer a longer rental duration in equilibrium.

Consider a video rental retailer who procures DVDs or video cassettes from a distributor and rents them to the customers. To meet the time-varying rental demand, the retailer needs to develop cost-effective procurement and disposal policies. In this paper, we first present a base model in which the underlying rental demand is decreasing over time, backorders are not allowed and the disposal price is exogenous. For this base model, we show that the optimal procurement quantity is equal to the sum of effective demands (rental demand net of returns) over an integral number of periods, and the optimal disposal policy can be determined by solving a simple dynamic program with polynomial complexity. We then analyze the case of endogenous disposal prices and derive optimal disposal policies by solving a quadratic optimization problem with tree constraints. We also extend the base model to allow for backorders and to cases where the retailer has multiple procurement opportunities and a contractual period where disposals are not allowed. We show that the qualitative nature of the procurement policy is preserved in these cases and the optimal procurement and selling policies can be determined using similar dynamic programming algorithms.

We develop a model for the flu vaccine supply chain that includes a profit maximizing manufacturer with an unreliable production process selling to rational consumers who consider only their private benefit from vaccination (and not the social benefit of protecting others via reduced infectiousness) thus creating a negative externality. We characterize the equilibrium demand and planned production quantity for this decentralized supply chain and compare them with the corresponding outcomes in a centralized system, where a benevolent social planner chooses the socially optimal quantity and demand. We find that the planned production quantity is always lower in the decentralized setting in accordance with previous supply chain models of yield uncertainty. However, contrary to the existing economic models of vaccination, we find that the expected demand in equilibrium can be higher than the socially optimal demand. The main driver for this result is the interaction between the incentives of the manufacturer and those of the individuals leading to a second (positive) externality due to limited availability, which we refer to as availability effect. In order to disentangle these effects, we construct two partially centralized scenarios, where the social planner intervenes either on the demand-side or the supply-side but not both. We then quantify the relative effectiveness of these two interventions under various demand and supply characteristics using extensive numerical analysis. Since previous work on flu vaccine supply chain has focused only on supply-side inefficiency due to yield uncertainty, we investigate the value of incorporating rational consumer behavior in the model of flu vaccine supply chain. Using parameter values loosely based on the U.S. market, we find that implementing the first best quantity and ignoring consumer behavior can result in lower social welfare compared to that in the decentralized system.

In recent years, growth in the demand for emergency medical services along with decline in the number of hospitals with emergency departments (EDs) has raised concerns about the ability of the EDs to provide adequate service. Many EDs frequently report periods of overcrowding during which they are forced to divert incoming ambulances to neighboring hospitals, a phenomenon known as "ambulance diversion". The objective of this paper is to study the impact of key structural characteristics of the hospitals such as the number of ED beds, the number of inpatient beds, and the utilization of inpatient beds on the extent to which hospitals go on ambulance diversion. We propose a simple queueing model to describe the patient flow between the ED and the inpatient department. We analyze this model using two different approximations - heavy traffic and fluid - to derive two separate sets of measures for inpatient occupancy and ED size. We use these sets of measures to form hypotheses and test them by estimating a sample selection model using data on a cross-section of hospitals from California. We find that the measures derived from the heavy traffic approximation provide better explanation of the data than those derived from the fluid approximation. For the former specification, we find that the fraction of time that the ED spends on diversion is decreasing in the spare capacity of the inpatient department and in the size of the ED, where both are appropriately normalized for the size of the inpatient department. In addition, controlling for these hospital-specific factors, we find that the fraction of time on diversion at a hospital increases with number of hospitals in its neighborhood. We also find that certain hospitals, owing to their location, ownership and trauma center status, are more likely to choose ambulance diversion to mitigate overcrowding than others.

We present a model of dynamic resource allocation in a setting where continuity of service is important and future resource availability is uncertain. The paper is inspired by the challenges faced by HIV clinics in resource-limited settings in the allocation of scarce HIV treatment among a large pool of eligible patients. Many clinics receive insufficient supply to treat all patients and the supply they do receive is highly uncertain. This supply uncertainty, combined with the clinical importance of an uninterrupted treatment throughout patients’ life, requires the clinics to make a trade-off between providing access to treatment for new patients and ensuring continuity of treatment for current patients. Setting aside other aspects of the treatment rationing problem, we model the decisions of a clinic facing this trade-off using stochastic dynamic programming. We derive sufficient conditions under which the optimal policy coincides with the clinically preferred policy of prioritizing previously enrolled patients. We use numerical examples to investigate the impact of supply uncertainty on the performance of enrollment policies used in practice. We also discuss how our model applies to other intertemporal resource allocation decisions such as that faced by non-profit organizations where continuity of service is crucial to meeting the organization’s social objective, or that faced by an entrepreneur who wants to attract new customers without reducing service quality to existing customers.