ArticleJournal of medical systems2015
StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- A scenario-driven simulation approach to sustainable hospital resource management: aging society, pandemic preparedness and referral enhancement.BMC health services research · 2025Article
- A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.Journal of medical systems · 2025Article
- Review
- The Capacity-to-Serve Model as a Data-Driven Process for Provider Capacity Management in Outpatient Community Mental Health.Community mental health journal · 2024Article
- Article
- An Overview of Hospital Capacity Planning and Optimisation.Healthcare (Basel, Switzerland) · 2022Review
- Machine learning based forecast for the prediction of inpatient bed demand.BMC medical informatics and decision making · 2022Article
- Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination.Healthcare (Basel, Switzerland) · 2022Article
- Resource management framework using simulation modeling and multi-objective optimization: a case study of a front-end department of a public hospital in Thailand.BMC medical informatics and decision making · 2022Article
- Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review.International journal of environmental research and public health · 2021Review
- Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.Health care management science · 2021Article
- Adjusting Daily Inpatient Bed Allocation to Smooth Emergency Department Occupancy Variation.Healthcare (Basel, Switzerland) · 2020Article
- Rapid cancer diagnosis for patients with vague symptoms: a cost-effectiveness study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2020Article
- A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China.Computational and mathematical methods in medicine · 2019Article
- A Discrete Event Simulation Model of Patient Flow in a General Hospital Incorporating Infection Control Policy for Methicillin-Resistant Staphylococcus Aureus (MRSA) and Vancomycin-Resistant Enterococcus (VRE).Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- A systematic literature review of simulation models for non-technical skill training in healthcare logistics.Advances in simulation (London, England) · 2018Article
- Simulation-Based Design of ED Operations with Care Streams to Optimize Care Delivery and Reduce Length of Stay in the Emergency Department.Journal of medical systems · 2017Article
- Optimizing nurse capacity in a teaching hospital neonatal intensive care unit.Health care management science · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.