<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T06:32:42Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/111536" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/111536</identifier><datestamp>2022-01-28T15:11:36Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Retsef Levi and David Simchi-Levi.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Ugarph, Elizabeth</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Leaders for Global Operations Program.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Leaders for Global Operations Program at MIT</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Engineering Systems Division</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Institute for Data, Systems, and Society</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Sloan School of Management</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2017-09-15T15:38:30Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2017-09-15T15:38:30Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2017</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2017</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/111536</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1003324821</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, in conjunction with the Leaders for Global Operations Program at MIT, 2017.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 119-120).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">In 2015, there were approximately 17,000 General Medicine admissions in the Department of Medicine (DOM) at MGH. General Medicine patients regularly experience significant non-clinical delays caused by bed and care team unavailability, with approximately 25% of patients waiting ten hours or more for a bed. Delays in bed and care team assignments result in decreased patient satisfaction, congestion in the ED and ICUs, and increased overall hospital length-of-stay. This project studies General Medicine patient flow, develops and evaluates interventions to improve this flow, and provides recommendations to hospital leadership. To this end, we construct a discrete-event simulation based on historical data. Intervention effectiveness is measured primarily based on patient-wait-for-bed, the time from when patient is medically ready for an inpatient bed until the bed is assigned to and ready for the patient. We find that the simulation model accurately represents the wait times of General Medicine patients. We propose a new algorithm, which when implemented could reduce overall average patient-wait-for-bed by 9% from 7.36 to 6.67 hours. Implementation of additional capacity and reorganization of the physician care teams (known as the DOM redesign) is shown to result in a further 31% reduction in average wait time (from 6.67 to 4.59 hours). Other interventions tested such as early assignment of patients to care teams based on predicted discharges, and increased flexibility of care teams to cover different units are shown to have modest effects on overall patient-wait-for-bed.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Elizabeth Ugarph.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.B.A.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M. in Engineering Systems</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">120 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Sloan School of Management.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Institute for Data, Systems, and Society.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Leaders for Global Operations Program.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Patient flow optimization in the Department of Medicine at MGH</dim:field>
   <dim:field mdschema="dc" element="title" qualifier="alternative" lang="en_US">Patient flow optimization in the DOM at Massachusetts General Hospital</dim:field>
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   	&lt;Title>Patient flow optimization in the Department of Medicine at MGH&lt;/Title>
   	&lt;Subtitle>Patient flow optimization in the DOM at Massachusetts General Hospital&lt;/Subtitle>
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   	&lt;PublicationDate>2017&lt;/PublicationDate>
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        	&lt;DisplayName>Ugarph, Elizabeth&lt;/DisplayName>
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    &lt;Keyword>Sloan School of Management.&lt;/Keyword>
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   	&lt;Abstract>In 2015, there were approximately 17,000 General Medicine admissions in the Department of Medicine (DOM) at MGH. General Medicine patients regularly experience significant non-clinical delays caused by bed and care team unavailability, with approximately 25% of patients waiting ten hours or more for a bed. Delays in bed and care team assignments result in decreased patient satisfaction, congestion in the ED and ICUs, and increased overall hospital length-of-stay. This project studies General Medicine patient flow, develops and evaluates interventions to improve this flow, and provides recommendations to hospital leadership. To this end, we construct a discrete-event simulation based on historical data. Intervention effectiveness is measured primarily based on patient-wait-for-bed, the time from when patient is medically ready for an inpatient bed until the bed is assigned to and ready for the patient. We find that the simulation model accurately represents the wait times of General Medicine patients. We propose a new algorithm, which when implemented could reduce overall average patient-wait-for-bed by 9% from 7.36 to 6.67 hours. Implementation of additional capacity and reorganization of the physician care teams (known as the DOM redesign) is shown to result in a further 31% reduction in average wait time (from 6.67 to 4.59 hours). Other interventions tested such as early assignment of patients to care teams based on predicted discharges, and increased flexibility of care teams to cover different units are shown to have modest effects on overall patient-wait-for-bed.&lt;/Abstract>
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