Showing papers by "Thomas R. Sexton published in 2020"
TL;DR: A unified quality performance model for hospitals using publicly available data from the New York State Department of Health's Statewide Planning and Research Cooperative System database concluded that 79.2% of hospitals could improve their quality of care.
Abstract: EXECUTIVE SUMMARY The objective of this study was to build a unified quality performance model for hospitals using publicly available data. We obtained data from the New York State Department of Health's Statewide Planning and Research Cooperative System database for our model, which had three outcome measures that we wished to make smaller (deaths, readmissions, average length of stay). Because this was a performance model rather than an economic efficiency model, we excluded costs, which are affected significantly by local economic conditions. We included four site characteristics. With our data envelopment analysis model structure, we used logistic regression to analyze the output. We extracted data for 2,233,214 discharges in 2014 from 183 hospitals in the state. We found that 20.8% of the facilities were on the quality performance frontier-20.6% of the not-for-profit facilities and 21.4% of the other facilities. Hospitals with more discharges performed better with respect to mortality, readmission, and average length of stay. We found no difference in performance between not-for-profit hospitals and others. We concluded that 79.2% of hospitals could improve their quality of care. As an upper bound, if all hospitals increased each quality factor performance to 100%, there would have been 11,722 (24.8%) fewer deaths, 17,840 (15.8%) fewer readmissions, and the statewide average length of stay would have been 0.71 days (13.5%) less.
3 citations
Posted Content•
12 Jun 2020
TL;DR: A new marginal analysis framework is developed to approximate this complex multiple-decision model into a single-Decision model with an externality term, which internalizes the long-term impact of ordering decisions.
Abstract: The exact analysis of optimal ordering policies for periodic-review perishable inventory systems is challenging because of their high-dimensional state space arising from multiple interrelated ordering decisions over many periods and age distributions of on-hand products. We develop a new marginal analysis framework to approximate this complex multiple-decision model into a single-decision model with an externality term, which internalizes the long-term impact of ordering decisions. Our externality-based approximation utilizes a constant base-stock policy; it is fast and easy to apply. Numerical experiments show that our approach provides state-dependent ordering amounts almost identical to the optimal dynamic programming-based policy.
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TL;DR: In this article, the authors proposed a framework that uses an externality term to capture the long-term impact of ordering decisions on the average cost over an infinite horizon, and obtained a tractable approximate optimality condition.
Abstract: Finding the optimal policy for multi-period perishable inventory systems requires solving computationally-expensive stochastic dynamic programs (DP). To avoid the difficulty of solving DP models, we propose a framework that uses an externality term to capture the long-term impact of ordering decisions on the average cost over an infinite horizon. By approximating the externality term, we yield a tractable approximate optimality condition, which is solved through standard marginal analysis. The resulted policy is near-optimal in long-run average cost and ordering decisions.