Key result
An integrated framework combining a kernel approach and Principal Component Analysis was developed to optimize discharge timing and post-discharge monitoring to reduce hospital readmissions.
Why the study?
Traditional prediction models support only post-discharge interventions by predicting adverse event risk solely at discharge, rather than supporting both pre-discharge and post-discharge actions.
The study proposes a novel computational framework to optimize hospital discharge timing and post-discharge monitoring to reduce readmissions.
Answers are grounded in this paper and cite their sources.
Integrated risk-trajectory models may optimize discharge timing plus monitoring; hypothesis-generating pending prospective validation.
When patients leave the hospital for lower levels of care, they experience a risk of adverse events on a daily basis. The advent of value-based purchasing among other major initiatives has led to an increasing emphasis on reducing the occurrences of these post-discharge adverse events. This has spurred the development of new prediction technologies to identify which patients are at risk for an adverse event as well as actions to mitigate those risks. Those actions include pre-discharge and post-discharge interventions to reduce risk. However, traditional prediction models have been developed to support only post-discharge actions; predicting risk of adverse events at the time of discharge only. In this paper we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring. Our method combines a kernel approach for capturing the non-linear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting estimation tractable. We then demonstrate how this prediction model could be used to support both types of interventions by developing a simple and easily implementable discharge timing optimization.
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Alaeddini et al. (2019) studied Hospital readmissions and post-discharge adverse events. Integrated framework of risk prediction and discharge optimization vs. Traditional prediction models was evaluated. An integrated framework combining a kernel approach and Principal Component Analysis was developed to optimize discharge timing and post-discharge monitoring to reduce hospital readmissions.
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