Year: 2017
Type: article
Abstract: An Intensive Care Unit (ICU) is a specialized section of a hospital that provides comprehensive and continuous care for patients in critical conditions. Around 20% of hospital operating costs are due to ICUs, and this percentage has been increasing. Modeling patient flow through an ICU is challenging due to significant heterogeneity of patient cases and high variability of evolving patient conditions. Using a highly detailed data set of ICU patients from a single health system, we build a stochastic and dynamic model of patient physiology that can significantly improve ICU operations and predictions. Scoring systems that assess the severity at admission or progression of severity during the stay have been used to predict the outcome (mortality or readmission). Existing scores are not sufficient to predict readmissions or mortalities after transferring to a lower level care unit. We present ICU outcome prediction models that perform better than existing models and could be used to benchmark ICU discharge policies and guide post-ICU resource needs. We consider the transfer operations of patients to a downstream unit. In current practice, downstream beds are requested once a patient is clinically ready to be transferred. We investigate anticipative bed requests that can be made before a patient is ready for transfer. Patient health is described via a novel transfer readiness score created using our readmission prediction model that we incorporate into a Markov decision process model. Our numerical results indicate that an anticipative transfer request policy can significantly improve the system performance. We investigate the sensitivity of policy change upon cost parameter estimation errors by using robust models, and demonstrate that proactive strategies are more beneficial than reactive current policy in most scenarios. We present an explicit stochastic length of stay model considering patient physiology modeled by the transfer readiness score as well as transfer delays. We characterize the stochastic process under certain assumptions. We show that the model demonstrates a moderate performance in fitting the underlying distribution of the length of stay, and improvements on the score will improve the predictive power of the model.
Author: M. Yasin Ulukuş
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2776344614