Nan Kong (Committee Member), Pratik Parikh (Advisor), Xinhui Zhang (Committee Member)
Master of Science in Engineering (MSEgr)
Hospitals provide a complex array of services to patient populations with highly variable conditions, needs, and preferences. These services are delivered with limited resources, and therefore, the synchronization of patient flow between various units in the hospital is essential for minimizing patient delay and congestion. Unfortunately, patient flow optimization is a topic in its research infancy, and, in current practice, hospitals struggle with the numerous ramifications of delays in care provision and increased costs. Emergency Department (ED) crowding and boarding have become a topic of increased interest as the ED becomes an increasingly popular entry point to acute care hospitals. A key factor to these issues comes from the lack of available beds within the inpatient units to which newly admitted patients can be transferred. The inpatient day-of-discharge process plays a vital role in synchronizing supply with demand as the inpatient beds are released and made available to arriving bed requests. The objective of this study was to reduce inpatient discharge lateness and overnight stays for patients being released from inpatient units and to alleviate patient boarding in upstream units. Alternative strategies for discharge order writing time and discharge process length were evaluated to identify strategies that had the greatest impact on advancing discharge completion time and reducing upstream boarding. We collected both observational job shadowing and retrospective patient data iv from a trauma unit at a local acute care hospital. Job shadowing helped us gain an in-depth understanding of the day-of-discharge process. The retrospective data contained elements on bed requests, order writing times, and discharge completion times, which helped us understand the underlying statistical distributions that drive process variability. This information was used to build a discrete event simulation (DES) model of the day-of-discharge process for the unit. Outcomes analyzed in the model were inpatient discharge completion time and upstream patient boarding time. After the model was validated with real hospital data, we evaluated three general categories of alternative strategies for a total of nine specific alternative strategies. Statistical comparison of outcomes showed that all nine had a significant effect on advancing discharge completion time and reducing upstream boarding time (p < 0.05). Results showed that strategies combining both discharge order writing time and discharge process length (referred to as n-by-T) had the greatest impact on measured system outcomes. In the n-by-T strategy, "n" is a set number of inpatients (e.g., 1 or 2) to be discharged by time "T" (e.g., 10 a.m. or 12 p.m.). Variations of this strategy indicated 25-40% reductions in upstream boarding time. This research can be explored further in several directions. Analysis of the impact of seasonality and trends in occupancy rate, patient arrivals, and discharge times could be studied based upon day of the week, week of the month, and month of the year. An in-depth consideration of the components composing the day-of-discharge process such as physical therapy, occupational therapy, laboratory work, and transportation would add greater detail to the model. Incorporating discharge prioritization and provider workload optimization will allow for a comprehensive approach to inpatient discharge planning.
Department or Program
Department of Biomedical, Industrial & Human Factors Engineering
Year Degree Awarded
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