Key result
Data-driven inpatient flow policies significantly reduce simulated emergency department boarding times.
Why the study?
Effective and efficient healthcare service delivery methods are needed to improve decision-making in critical care and inpatient flow management, addressing challenges like ED overcrowding and resource use in CICU.
Data-driven operations research models, including evaluating open versus closed CICU care models and optimizing ED patient flow, show potential to improve hospital service delivery and resource utilization.
In recent years, there has been an increasing interest in developing novel methods for effective and efficient healthcare service delivery and data analytics has widely been recognized as being essential for decision-making at various healthcare service settings. This dissertation consists of three research projects each aiming to improve decisions at three different healthcare settings with one being related to critical care delivery and the other two being related to inpatient patient flow management. Each project combines statistical analysis of hospital data with techniques and methodologies from operations research. The first project concerns care delivery in the cardiac intensive care unit (CICU). We analyze a prospective study to describe admissions and care practices within the CICU of the UNC Hospital, and also evaluate the influence of an open versus closed model of care on patient outcomes and resource consumption. The second and third projects study management of patient flow from emergency department (ED) to hospital internal wards (IWs). Both projects develop effective inpatient flow management policies with the objective of reducing ED boarding time, which is defined as the time between the decision for admission for an ED patient and the time the patient is physically admitted to an IW. Delayed admission to IWs has been identified as a key factor for ED overcrowding and is a big challenge for many hospitals. We approach the problem from two different angles: early discharging patients to free up beds in IWs, and early requesting beds for ED patients based on early prediction of need for IW beds. In both projects, we develop relevant statistical models for analyzing the detailed hospital patient flow data and build mathematical decision models to develop new methods. We also build simulation models and use these models to investigate the benefits of the proposed methods. Simulation studies suggest significant potential improvements in various performance measures of interest.
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Dongqing Yu (2019) studied Critical care and emergency department patient flow. Data-driven inpatient flow management and care models was evaluated on Emergency department boarding time, patient outcomes, and resource consumption. Data-driven inpatient flow management policies, including early discharge and early bed requests, demonstrated significant potential improvements in emergency department boarding times in simulation models.
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