This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes. We use hospital operational factors as instrumental variables to handle the endogeneity of the admission decisions and identify important specification issues that are required for this approach to be valid. Using the quantified cost estimates, we then provide a simulation framework for evaluating various admission strategies' performance. By simulating a hospital with 21 ICU beds, we find that we could save about $1.9 million per year by using an optimal policy based on observables designed to reduce readmissions and hospital length of stay. We also discuss the role of unobserved patient factors, which physicians may discretionarily account for when making admission decisions, and show that including these unobservables could result in a more than threefold increase in benefits compared to just optimizing the policy over the observable patient factors.
Kim, Song-Hee, Carri Chan, Marcelo Olivares, and Gabriel Escobar. "ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication for Patient Outcomes." Management Science 61, no. 1 (January 2015): 19-38.
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