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A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.

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Title: A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.
Authors: Mokashi, Anup C.1 mokashia@mskcc.org, Gardner, Ginger J.1 gardnerg@mskcc.org, Klotz, Adam D.1 klotza@mskcc.org, Burns, Jacquelyn J.1 burnsj@mskcc.org, Velzen, Jeena L.1 velzenj@mskcc.org
Source: Journal of Medical Systems. 6/28/2025, Vol. 49 Issue 1, p1-30. 30p.
Subjects: Cancer treatment, Hospital utilization, Computer simulation, Decision support systems, Patients, Management information systems, Health facility administration, Research funding, Medical specialties & specialists, Hospital admission & discharge, Systems development, Hospital care, Strategic planning, Discharge planning, Isolation (Hospital care), System analysis, Departments, Biotelemetry, Length of stay in hospitals, Health facilities, Data analysis software, Specialty hospitals
Geographic Terms: New York (State)
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.
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  Data: <searchLink fieldCode="AR" term="%22Mokashi%2C+Anup+C%2E%22">Mokashi, Anup C.</searchLink><relatesTo>1</relatesTo><i> mokashia@mskcc.org</i><br /><searchLink fieldCode="AR" term="%22Gardner%2C+Ginger+J%2E%22">Gardner, Ginger J.</searchLink><relatesTo>1</relatesTo><i> gardnerg@mskcc.org</i><br /><searchLink fieldCode="AR" term="%22Klotz%2C+Adam+D%2E%22">Klotz, Adam D.</searchLink><relatesTo>1</relatesTo><i> klotza@mskcc.org</i><br /><searchLink fieldCode="AR" term="%22Burns%2C+Jacquelyn+J%2E%22">Burns, Jacquelyn J.</searchLink><relatesTo>1</relatesTo><i> burnsj@mskcc.org</i><br /><searchLink fieldCode="AR" term="%22Velzen%2C+Jeena+L%2E%22">Velzen, Jeena L.</searchLink><relatesTo>1</relatesTo><i> velzenj@mskcc.org</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Medical+Systems%22">Journal of Medical Systems</searchLink>. 6/28/2025, Vol. 49 Issue 1, p1-30. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Cancer+treatment%22">Cancer treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+utilization%22">Hospital utilization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink><br /><searchLink fieldCode="DE" term="%22Management+information+systems%22">Management information systems</searchLink><br /><searchLink fieldCode="DE" term="%22Health+facility+administration%22">Health facility administration</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+specialties+%26+specialists%22">Medical specialties & specialists</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+admission+%26+discharge%22">Hospital admission & discharge</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+development%22">Systems development</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+care%22">Hospital care</searchLink><br /><searchLink fieldCode="DE" term="%22Strategic+planning%22">Strategic planning</searchLink><br /><searchLink fieldCode="DE" term="%22Discharge+planning%22">Discharge planning</searchLink><br /><searchLink fieldCode="DE" term="%22Isolation+%28Hospital+care%29%22">Isolation (Hospital care)</searchLink><br /><searchLink fieldCode="DE" term="%22System+analysis%22">System analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Departments%22">Departments</searchLink><br /><searchLink fieldCode="DE" term="%22Biotelemetry%22">Biotelemetry</searchLink><br /><searchLink fieldCode="DE" term="%22Length+of+stay+in+hospitals%22">Length of stay in hospitals</searchLink><br /><searchLink fieldCode="DE" term="%22Health+facilities%22">Health facilities</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Specialty+hospitals%22">Specialty hospitals</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22New+York+%28State%29%22">New York (State)</searchLink>
– Name: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10916-025-02206-y
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 30
        StartPage: 1
    Subjects:
      – SubjectFull: Cancer treatment
        Type: general
      – SubjectFull: Hospital utilization
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Decision support systems
        Type: general
      – SubjectFull: Patients
        Type: general
      – SubjectFull: Management information systems
        Type: general
      – SubjectFull: Health facility administration
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Medical specialties & specialists
        Type: general
      – SubjectFull: Hospital admission & discharge
        Type: general
      – SubjectFull: Systems development
        Type: general
      – SubjectFull: Hospital care
        Type: general
      – SubjectFull: Strategic planning
        Type: general
      – SubjectFull: Discharge planning
        Type: general
      – SubjectFull: Isolation (Hospital care)
        Type: general
      – SubjectFull: System analysis
        Type: general
      – SubjectFull: Departments
        Type: general
      – SubjectFull: Biotelemetry
        Type: general
      – SubjectFull: Length of stay in hospitals
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      – SubjectFull: Health facilities
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      – SubjectFull: Data analysis software
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      – SubjectFull: Specialty hospitals
        Type: general
      – SubjectFull: New York (State)
        Type: general
    Titles:
      – TitleFull: A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.
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              Text: 6/28/2025
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              Y: 2025
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