Health Care Management Science

We analyze a sample of Washington State hospitals with a stochastic frontier panel data model, specifying the cost function as a generalized Leontief function which, according to a Hausman test, performs better in this case than the translog form. A one-stage FGLS estimation procedure which directly models the inefficiency effects improves the efficiency of our estimates. We find that hospitals with higher casemix indices or more beds are less efficient while for-profit hospitals and those with higher proportion of Medicare patient days are more efficient. Relative to the most efficient hospital, the average hospital is only about 67% efficient.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients’ arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches—beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

To make hospitals more patient-centered it is necessary to intervene on patient flow logistics. The study analyzes three innovative redesign projects implemented at three Italian hospitals. The three hospitals have reorganized patient flow logistics around patient care needs using, as proxies, the expected length of stay and the level of nursing assistance. In order to do this, they have extensively revised their logistical configuration changing: (1) the organization of wards, (2) the hospital’s physical lay-out, (3) the capacity planning system, and (4) the organizational roles supporting the patient flow management. The study describes the changes implemented as well as the results achieved and draws some general lessons that provide useful hints for those other hospitals involved in such type of redesign projects. The paper ends by discussing some policy implications. In fact, the results achieved in the three cases investigated provide interesting material for further discussion on clinical, operational, and economic issues.

The Authors present a new methodological approach in stochastic regime to determine the actual costs of an healthcare process. The paper specifically shows the application of the methodology for the determination of the cost of an Assisted reproductive technology (ART) treatment in Italy. The reason of this research comes from the fact that deterministic regime is inadequate to implement an accurate estimate of the cost of this particular treatment. In fact the durations of the different activities involved are unfixed and described by means of frequency distributions. Hence the need to determine in addition to the mean value of the cost, the interval within which it is intended to vary with a known confidence level. Consequently the cost obtained for each type of cycle investigated (in vitro fertilization and embryo transfer with or without intracytoplasmic sperm injection), shows tolerance intervals around the mean value sufficiently restricted as to make the data obtained statistically robust and therefore usable also as reference for any benchmark with other Countries. It should be noted that under a methodological point of view the approach was rigorous. In fact it was used both the technique of Activity Based Costing for determining the cost of individual activities of the process both the Monte Carlo simulation, with control of experimental error, for the construction of the tolerance intervals on the final result.

Accessibility and equity across populations are important measures in public health. This paper is specifically concerned with potential spatial accessibility, or the opportunity to receive care as moderated by geographic factors, and with horizontal equity, or fairness across populations regardless of need. Both accessibility and equity were goals of the 2009 vaccination campaign for the novel H1N1a influenza virus, including during the period when demand for vaccine exceeded supply. Distribution system design can influence equity and accessibility at the local level. We develop a general methodology that integrates optimization, game theory, and spatial statistics to measure potential spatial accessibility across a network, where we quantify spatial accessibility by travel distance and scarcity. We estimate and make inference on local (census-tract level) associations between accessibility and geographic, socioeconomic, and health care infrastructure factors to identify potential inequities in vaccine accessibility during the 2009 H1N1 vaccination campaign in the U.S. We find that there were inequities in access to vaccine at the local level and that these were associated with factors including population density and health care infrastructure. Our methodology for measuring and explaining accessibility leads to policy recommendations for federal, state, and local public health officials. The spatial-specific results inform the development of equitable distribution plans for future public health efforts.

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients’ medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R2 = 0.801 the outcome of interest; the time from diagnosis to a potential adverse event (TAE). Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, the goal of ML4CAD is to identify for every patient the therapy with the best expected TAE using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool.

The growing complexity of the United States Recommended Childhood Immunization Schedule has resulted in as many as five required injections during a single well-baby office visit. To reduce this number, vaccine manufacturers have developed combination vaccines that immunize against several diseases in a single injection. At the same time, a growing number of parents are challenging the safety and effectiveness of vaccinating children. They are also particularly concerned about the use of combination vaccines, since they believe that injecting a child with multiple antigens simultaneously may overwhelm a child’s immune system. Moreover, combination vaccines make it more likely that extraimmunization (i.e., administering more than the required amount of vaccine antigens) occurs, resulting in greater concerns by parents and vaccine wastage costs borne by an already strained healthcare system. This paper formulates an integer programming model that solves for the maximum number of vaccines that can be administered without any extraimmunization. An exact dynamic programming algorithm and a randomized heuristic for the integer programming model is formulated and the heuristic is shown to be a randomized ξ-approximation algorithm. Computational results are reported on three sets of test problems, based on existing and future childhood immunization schedules, to demonstrate their computational effectiveness and limitations. Given that future childhood immunization schedules may need to be solved for each child, on a case-by-case basis, the results reported here may provide a practical and valuable tool for the public health community.

In this work we propose the use of a graphical diagnostic tool (the funnel plot) to detect outliers among hospitals that treat patients affected by Acute Myocardial Infarction (AMI). We consider an application to data on AMI hospitalizations recorded in the administrative databases of our regional district. The outcome of interest is the in-hospital mortality, a variable indicating if the patient has been discharged dead or alive. We then compare the results obtained by graphical diagnostic tools with those arising from fitting parametric mixed effects models to the same data.