This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration which might ultimately require readmission; models of these risks are currently unavailable to providers. These readmissions in turn impose an additional load on capacity-limited ICU resources.
The present work studies the impact of different ICU discharge strategies on total readmission load. Our study focuses on a certain index policy for discharge that is predicated on a model of readmission risk. We use empirical data from over 6000 actual ICU patient flows to calibrate our model and judge the efficacy of our approach relative to several benchmark strategies. The empirical study suggests that a predictive model of the readmission risks associated with discharge decisions in tandem with simple index policies of the type proposed can provide very meaningful throughput gains in actual ICUs. In addition to our empirical work, we conduct a rigorous performance analysis for our discharge policy. We show that our policy is optimal in certain regimes, and is otherwise guaranteed to incur readmission loads no larger than a factor of (ˆρ + 1) of an optimal discharge strategy, where ˆρ is a certain natural measure of system utilization.
Chan, Carri, Vivek Farias, Nick Bambos, and Gabriel Escobar. "Maximizing throughput of hospital intensive care units with patient readmissions." MIT, 2010.
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