Including Quality, Access, and Efficiency in Healthcare Cost Models

Fulton, Lawrence V.; McDaniel, Reuben R.; Coppola, Nicholas

doi:10.3200/htps.86.4.3-17

Cited by 12 publications

(10 citation statements)

References 7 publications

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“…Four years of data were used to make predictions for the next 2 years. Similarly, we found another article forecasting resources for US Army health care (25). That study used ordinary least squares estimation, ridge regression, and robust regression and concluded that, although all the models produced nearly the same estimates, ordinary least squares was desirable because it had the simplest interpretations.…”

Section: Discussionmentioning

confidence: 52%

Forecasting Participants in the All Women Count! Mammography Program

2018

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IntroductionThe All Women Count! (AWC!) program is a no-cost breast and cervical cancer screening program for qualifying women in South Dakota. Our study aimed to identify counties with similar socioeconomic characteristics and to estimate the number of women who will use the program for the next 5 years.MethodsWe used AWC! data and sociodemographic predictor variables (eg, poverty level [percentage of the population with an annual income at or below 200% of the Federal Poverty Level], median income) and a mixture of Gaussian regression time series models to perform clustering and forecasting simultaneously. Model selection was performed by using Bayesian information criterion (BIC). Forecasting of the predictor variables was done by using an autoregressive integrated moving average model.ResultsBy using BIC, we identified 5 clusters showing the groups of South Dakota counties with similar characteristics in terms of predictor variables and the number of participants. The mixture model identified groups of counties with increasing or decreasing trends in participation and forecast averages per cluster.ConclusionThe mixture of regression time series model used in this study allowed for the identification of similar counties and provided a forecasting model for future years. Although several predictors contributed to program participation, we believe our forecasting analysis by county may provide useful information to improve the implementation of the AWC! program by informing program managers on the expected number of participants in the next 5 years. This, in turn, will help in data-driven resource allocation.

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Section: Discussionmentioning

confidence: 52%

Forecasting Participants in the All Women Count! Mammography Program

2018

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“…Note that these specified data parameters are typical of government hospitals [48] and are often used as variables in MHS performance measurement and evaluation studies [3,4,19,21,22]. As shown in Table 1, the MHS input resources to be manipulated include EXP , ENROLL, and F T E, whereas the output resources are listed as RV U and QU AL.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Ozcan et al [48] evaluated trends in hospital efficiency using longitudinal data. Fulton et al [22] proposed regression-based military hospital cost models that included DEA efficiency scores in addition to the variables of quality, access, and satisfaction. Nayar and Ozcan [44] provided a DEA comparison of Virginia hospitals in terms of efficiency and quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Bastian

Ekin²,

Kang

et al. 2016

Health Care Manag Sci

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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.

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“…Table 1 describes the set of health system input and output variables selected for analyzing MHS performance. These output and input parameters are typical of government facilities (e.g., Ozcan and Bannick 1994;O'Neill et al 2008) and are routinely used as predictors of MHS performance (Fulton et al 2007(Fulton et al , 2008.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…They used DEA and stochastic frontier analysis to identify the cost drivers for performance-based resource allocation. Then, Fulton et al (2008) proposed regression-based military hospital cost models that included DEA efficiency scores in addition to the variables of quality, access, and efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%