Abstract

Choosing which bed to assign an admitted patient to in a hospital is a complex problem. There are numerous factors to consider including the patient’s gender and isolation requirements, current bed availability, and unit configurations. This problem must be solved each time a new patient seeks admission resulting in rearrangement of already admitted patients. Each movement of an already admitted patient increases the workload for hospital staff and also increases the risk of nosocomial infections for the patient. In order to alleviate these problems we propose optimizing the patient admission process through a multi-objective model which first maximizes the overall criticality of patients admitted, then minimizes movements of previously admitted patients while creating space for incoming patients. Using this model we perform three sets of experiments. The first experiments seek to determine the ideal number of private and semi-private rooms in a multi-occupancy unit with a fixed number of total rooms. This results in a tool to enable the unit to manage the tradeoffs between moving previously admitted patients and bed utilization. The second experiments seek to determine the ideal timeframe over which to batch patient admissions. These results suggest more frequent admissions have minimal impact on inpatient rearrangement. The third experiments seek to determine the potential benefit of using a centralized admitting entity and finds managing bed assignment from a central perspective far out performs individual units managing their bed assignments.

Library of Congress Subject Headings

Hospital care--Data processing; Hospital size--Data processing; Hospital patients--Classification; Nosocomial infections--Prevention

Publication Date

8-8-2017

Document Type

Thesis

Student Type

Graduate

Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)

Advisor

Ruben Proano

Advisor/Committee Member

Marcos Esterman

Comments

Physical copy available from RIT's Wallace Library at RA971 .H64 2017

Campus

RIT – Main Campus

Plan Codes

ISEE-MS

Download

Share

COinS