A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.
This paper describes the development and application of an analytical solution to assist with inpatient flow and capacity management at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City. We present a discrete-event simulation model that captures several key aspects of the complex patie...
| Published in: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 31 |
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| Main Authors: | , , , , |
| Format: | algorithm research tables/charts Journal Article |
| Published: |
Springer Nature
6/28/2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186463864&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186463864 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 6/28/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186463864 186463864 186463864 10.1007/s10916-025-02206-y 186463864 ppf: 1 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment. aug: au: Mokashi, Anup C. Gardner, Ginger J. Klotz, Adam D. Burns, Jacquelyn J. Velzen, Jeena L. affil: https://ror.org/02yrq0923 Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA sug: subj: Cancer Care Facilities Administration Patient Admission Bed Occupancy Computer Simulation Decision Support Systems, Management Systems Development Health Facility Administration Human Inpatients New York Length of Stay Health Facility Departments Data Analysis Software Telemetry Patient Isolation Funding Source Transfer, Intrahospital Systems Analysis Specialties, Medical Strategic Planning Patient Discharge ab: This paper describes the development and application of an analytical solution to assist with inpatient flow and capacity management at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City. We present a discrete-event simulation model that captures several key aspects of the complex patient flow patterns at MSKCC in the inpatient setting. The model captures the variation in admission patterns based on various patient cohorts and admit locations. The model also accounts for the variability in specialized care needs for distinct patient cohorts using categorical distributions. Durations for various patient flow states from admission till discharge are modeled as probability distributions. Key patient-and resource attributes are also incorporated to accurately capture the constraints affecting resource allocation. A comprehensive set of output metrics is used to validate the model, and to compare alternative scenarios. We present results for a scenario that tests the impact of resource allocation changes aimed at consolidating patients on certain floors based on the hospital department tasked with their inpatient care. Outputs for the scenario are compared with baseline using the following output metrics: mean bed utilization by floor, mean admit boarding times by service, proportion of home floor admissions by service, and wait times for step-down care beds. Our results show an estimated reduction in average admit wait times by 30 minutes or more across 4 inpatient services (an annual reduction of ∼ 116 days), with a neutral impact on other output metrics. The analysis from the scenario was utilized by hospital leadership to implement actual bed allocation changes in the hospital. The model demonstrates a structured analytical approach to evaluate the impact of strategic or tactical changes prior to implementing them in practice, specifically in an inpatient setting. It also provides the flexibility to design and test a wide variety of scenarios, and has proved its utility as a decision support tool that can be leveraged periodically by leadership at MSKCC. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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