Improving surgical patient flow through simulation of scheduling heuristics

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013. === Cataloged from PDF version of thesis. === Includes bib...

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Main Author: Range, Ashleigh Royalty
Other Authors: Retsef Levi and David Simchi-Levi.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2013
Subjects:
Online Access:http://hdl.handle.net/1721.1/81017
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-810172019-05-02T15:55:55Z Improving surgical patient flow through simulation of scheduling heuristics Range, Ashleigh Royalty Retsef Levi and David Simchi-Levi. Leaders for Global Operations Program. Sloan School of Management. Massachusetts Institute of Technology. Engineering Systems Division. Leaders for Global Operations Program. Sloan School of Management. Engineering Systems Division. Leaders for Global Operations Program. Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 79). Massachusetts General Hospital (MGH) is currently the nation's top ranked hospital and is the largest in New England. With over 900 hospital beds and approximately 38,000 operations performed each year, MGH's operating rooms (ORs) run at 90% utilization and their hospital beds at 99% operational occupancy. MGH is faced with capacity constraints throughout the perioperative (pre-, intra-, and postoperative) process and desires to improve throughput and decrease patient waiting time without adding expensive additional resources. This project focuses on matching the intraday scheduling of elective surgeries with the discharge rate and pattern of patients from the hospital floor by investigating ways surgeons could potentially schedule their cases within a given OR block. To do this, various scheduling rules are modeled to measure the impact of shifting patient flow in each step of the perioperative process. Currently the hospital floor proves to be the biggest bottleneck in the system. Delays in discharging patients result in Same Day Admits (patients that will be admitted to the hospital post-surgery) waiting for hospital beds in the Post Anesthesia Care Unit (PACU). These patients wait more than sixty minutes on average after being medically cleared to depart the PACU. A simulation model is built to evaluate the downstream effects of each scheduling rule and discharge process change. The model takes into account physical and staff resource limitations at each of the upstream and downstream steps in the perioperative process. By scheduling Same Day Admits last in each OR block, patient wait time in the PACU can be reduced up to 49%. By implementing the recommended changes the system will realize lower wait times for patients, less stress on the admitting and nursing staff, and a better overall use of the limited physical resources at MGH. by Ashleigh Royalty Range. S.M. M.B.A. 2013-09-24T19:37:17Z 2013-09-24T19:37:17Z 2013 2013 Thesis http://hdl.handle.net/1721.1/81017 857790332 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 79 p. application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
spellingShingle Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
Range, Ashleigh Royalty
Improving surgical patient flow through simulation of scheduling heuristics
description Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 79). === Massachusetts General Hospital (MGH) is currently the nation's top ranked hospital and is the largest in New England. With over 900 hospital beds and approximately 38,000 operations performed each year, MGH's operating rooms (ORs) run at 90% utilization and their hospital beds at 99% operational occupancy. MGH is faced with capacity constraints throughout the perioperative (pre-, intra-, and postoperative) process and desires to improve throughput and decrease patient waiting time without adding expensive additional resources. This project focuses on matching the intraday scheduling of elective surgeries with the discharge rate and pattern of patients from the hospital floor by investigating ways surgeons could potentially schedule their cases within a given OR block. To do this, various scheduling rules are modeled to measure the impact of shifting patient flow in each step of the perioperative process. Currently the hospital floor proves to be the biggest bottleneck in the system. Delays in discharging patients result in Same Day Admits (patients that will be admitted to the hospital post-surgery) waiting for hospital beds in the Post Anesthesia Care Unit (PACU). These patients wait more than sixty minutes on average after being medically cleared to depart the PACU. A simulation model is built to evaluate the downstream effects of each scheduling rule and discharge process change. The model takes into account physical and staff resource limitations at each of the upstream and downstream steps in the perioperative process. By scheduling Same Day Admits last in each OR block, patient wait time in the PACU can be reduced up to 49%. By implementing the recommended changes the system will realize lower wait times for patients, less stress on the admitting and nursing staff, and a better overall use of the limited physical resources at MGH. === by Ashleigh Royalty Range. === S.M. === M.B.A.
author2 Retsef Levi and David Simchi-Levi.
author_facet Retsef Levi and David Simchi-Levi.
Range, Ashleigh Royalty
author Range, Ashleigh Royalty
author_sort Range, Ashleigh Royalty
title Improving surgical patient flow through simulation of scheduling heuristics
title_short Improving surgical patient flow through simulation of scheduling heuristics
title_full Improving surgical patient flow through simulation of scheduling heuristics
title_fullStr Improving surgical patient flow through simulation of scheduling heuristics
title_full_unstemmed Improving surgical patient flow through simulation of scheduling heuristics
title_sort improving surgical patient flow through simulation of scheduling heuristics
publisher Massachusetts Institute of Technology
publishDate 2013
url http://hdl.handle.net/1721.1/81017
work_keys_str_mv AT rangeashleighroyalty improvingsurgicalpatientflowthroughsimulationofschedulingheuristics
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