A mathematical model for simulating daily bed occupancy in an intensive care unit*

Barado, Julio; Guergué, Juan María; Esparza, Laida; Azcárate, Cristina; Giménez, Ferran; Ochoa, Susana

doi:10.1097/ccm.0b013e3182374828

Cited by 52 publications

(29 citation statements)

References 26 publications

(25 reference statements)

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“…Prior studies have developed models to simulate varying hospital unit sizes, nurse or physician staffing levels, and different triage, discharge, and bed assignment policies to examine the impact on bed use, wait times, lengths of stay, readmissions, and mortality (17)(18)(19)(20)(21)(22). Although many models capture patient flow through multidisciplinary ICUs (23), often with elective admissions (24), no studies included both an ICU and SDU with patients with different acuity levels within a single model. This study's aim is to present a framework for understanding and improving patient flow through the medical ICU and SDU, and to present illustrative analysis for a single hospital.…”

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confidence: 99%

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

Mathews

Long

2015

Annals ATS

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Rationale: High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays.Objectives: To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes.Methods: A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy. Measurements and Main Results:With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds.Conclusions: Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.

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confidence: 99%

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

Mathews

Long

2015

Annals ATS

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“…The taskforce further recognized that any physician staffing model may still get overwhelmed in times of high patient volume or acuity and may, at other times, seem to be overstaffed. There is clearly a need to develop tools to better predict both the regularity and the severity of surges and such modeling has already been proposed by some (63). Nevertheless, a good staffing model would recognize that these high-volume days occur with some measurable frequency and be able to handle the increased workload without significant compromise in patient care.…”

Section: Recommendationsmentioning

confidence: 97%

Intensivist/Patient Ratios in Closed ICUs

Ward

Afessa

Kleinpell

et al. 2013

Critical Care Medicine

119

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The taskforce concluded that while advocating a specific maximum number of patients cared for is unrealistic, an approach that uses the following principles is essential: 1) proper staffing impacts patient care; 2) large caseloads should not preclude rounding in a timely fashion; 3) staffing decisions should factor surge capacity and nondirect patient care activities; 4) institutions should regularly reassess their staffing; 5) high staff turnover or decreases in quality-of-care indicators in an ICU may be markers of overload; 6) telemedicine, advanced practice professionals, or nonintensivist medical staff may be useful to alleviate overburdening the intensivist, but should be evaluated using rigorous methods; 7) in teaching institutions, feedback from faculty and trainees should be sought to understand the implications of potential understaffing on medical education; and 8) in academic medical ICUs, there is evidence that intensivist/patient ratios less favorable than 1:14 negatively impact education, staff well-being, and patient care.

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“…An exception is the model developed by Griffin et al [21] which focuses on obstetric unit patient discharge times sampled from an empirical probability distribution of discharge times. Barado et al [22] developed a statistical model for simulating patient occupancy in an intensive care unit and found that inclusion of length of stay and discharging rules was critical to the success of the simulation. Cohen et al [23] account for regular and emergent transfers between units, but they assume that LOS at a unit is independent of the unit from which the patient arrived.…”

Section: Previous Approaches To Bed Capacity Decisionsmentioning

confidence: 99%

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

et al. 2015

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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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A mathematical model for simulating daily bed occupancy in an intensive care unit*

Abstract: It is possible to develop simulation models that can be used to predict future intensive care unit resource needs.

Cited by 52 publications

References 26 publications

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

Intensivist/Patient Ratios in Closed ICUs

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

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