A Markov decision process to dynamically match hospital inpatient staffing to demand

Broyles, James R.; Montgomery, Douglas C.

doi:10.1080/19488300.2011.609523

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Further, this model could assist in creating lower-fidelity analytic models such as a (static) stochastic optimization model with endogenous uncertainty (Ryu and Jiang 2019), which captures the interdependence between the risk of infection and staffing/scheduling decision making. Integrated with an appropriate model for characterizing the underlying stochastic processes, this stochastic optimization model can also be further extended to a Markov decision process model (Broyles et al 2011), which prescribes optimal staffing and scheduling in a dynamic setting.…”

Section: Discussionmentioning

confidence: 99%

Team Based, Risk Adjusted Staffing During a Pandemic: An Agent Based Approach

Prabhu

Taaffe

Çaǧlayan

et al. 2020

2020 Winter Simulation Conference (WSC)

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Since the World Health Organization declared the novel coronavirus disease a pandemic, more than 2 million cases of infections and 140,000 deaths have been reported across the world. Specialty physicians are now working as frontline workers due to hospital overcrowding and a lack of providers, and this places them as a high-risk target of the epidemic. Within these specialties, anesthesiologists are one of the most vulnerable groups as they come in close contact with the patient's airway. An agent-based simulation model was developed to test various staffing policies within the anesthesiology department of the largest healthcare provider in Upstate South Carolina. We demonstrate the benefits of a restricted, no mixing shift policy, which segregates the anesthesiologists as groups and assigns them to a shift within a single hospital. Results consistently show a reduction in the number of deaths, anesthesiologists not available to work, and the number of infected anesthesiologists. INTRODUCTIONApproximately 2.9 million cases of infections and 200,000 deaths have been recorded around the world under the current outbreak of severe respiratory syndrome coronavirus 2 (SARS-CoV-2) (John Hopkins University and Medicine 2020). Of these, more than one-third of the infections and one-fourth of the deaths are recorded in the US, placing the country in a unique, unprecedented public health emergency situation (Centers for Disease Control and Prevention 2020a). The rapid proliferation of the virus and surge in cases has overwhelmed the health systems, first responders, and providers. Additionally, lack of resources, including hospital beds, testing kits, ventilators, and providers, has created a significant delay in providing patient care and has resulted in significant hospital overcrowding. This additional patient demand on healthcare facilities has various negative impacts, one being the increased risk of infection transmission within the healthcare facility (Ng et al. 2020). Given that a majority of the infected people act as asymptomatic carriers of the virus, the impact on the healthcare providers and on other patients is exacerbated (World Health Organization 2020a; Day 2020). A February 2020 case study observed that one patient potentially exposed 41 healthcare workers to the SARS-CoV-2 virus, forcing the hospital to quarantine these providers (Ng et al. 2020).Although various effective actions, including increasing the production of ventilators, testing kits, and setting up temporary hospital facilities, have been adopted to thwart the damage, one of the critical issues faced is the physician shortage (MedPage Today 2020). Currently, physicians from different specialties, retired physicians, and early-graduated medical students are all contributing to the workforce (Time 2020).

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Section: Discussionmentioning

confidence: 99%

Team Based, Risk Adjusted Staffing During a Pandemic: An Agent Based Approach

Prabhu

Taaffe

Çaǧlayan

et al. 2020

2020 Winter Simulation Conference (WSC)

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“…To assess Research Aim 2, the year to year hospital TPS data were categorized and examined to determine if and how hospitals, as a whole, migrated between categories of TPS performance. Much as in a Markovian process, the hospitals were identified as being in one of four performance categories (ie, four states) and then evaluated for their movement from one state in one year to another state, either higher or lower, in a subsequent year 15,16 . For example, a hospital identified as being in the Bottom 5% outlier group in one year could migrate to another percentile group in the next year.…”

Section: Methodsmentioning

confidence: 99%

“…Much as in a Markovian process, the hospitals were identified as being in one of four performance categories (ie, four states) and then evaluated for their movement from one state in one year to another state, either higher or lower, in a subsequent year. 15,16 For example, a hospital identified as being in the Bottom 5% outlier group in one year could migrate to another percentile group in the next year. Instead of following individual hospitals, a cohort of hospitals was followed.…”

Section: Me Thodsmentioning

confidence: 99%

Evaluating the robustness of the CMS Hospital Value‐Based Purchasing measurement system

Revere

Langland‐Orban

Large

et al. 2021

Health Services Research

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Background:The Hospital Value-Based Purchasing Program (HVBP) is a pay for performance system that impacts traditional Medicare fee-for-service payments to hospitals through rewards and penalties.Objectives: To explore variation in overall and individual-hospital total performance score (TPS) and embedded domains for hospitals during 2014-2018. Data Source: Hospital data were retrieved from the publicly available HOSArchive dataset. Study Design: Distribution of annual TPS and HVBP domain scores for 2014-2018 was evaluated using descriptive statistics. Transitional probabilities were analyzed to evaluate annual movement in the TPS ranking for outlier hospitals in the Top and Bottom 5%. Principal Findings: TPS scores are positively skewed while the distribution of domain scores vary with patient experience, (clinical) outcome, and efficiency domains having a large number of (positive) outliers. Mean TPS score decreased from 40.54 in 2014 to 38.04 by 2018. Improvement was shown in mean domain scores for clinicalprocess of care and clinical outcome using 95% confidence intervals, with hospitals gaining 10 points over the study period in clinical outcome. Changes in the mean scores for other domains did not show consistent increases or decreases. Chi-square analyses of hospital ranking categories showed some evidence that, as a group, hospitals initially ranked in the Bottom 5% are making consistent annual movements to higher categories. In contrast, over half of the hospitals ranking in the initial Top 5% remained in the top category across all study years. Conclusions: It may be time for CMS to redesign the HVBP incentive program to assure the measures accurately demonstrate sustained improvement, the domain weights appropriately reflect the level of importance, and the TPS comparative ranking methodology does not discourage lower-performing hospitals from actively improving the care they deliver and achieving top ranks.

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“…model, which estimates service process and transient patient inventory, was developed for an inpatient unit to match staffing with demand. When applied to a hospital, the model indicated that improved discharge practices can improve patient throughput and decrease the size of the premium staffing pool (Broyles, Cochran, and Montgomery, 2011). Abe et al, 2016c, states that, in publications from 2010 to January 2015, the most commonly used OR [operations research] methods were discrete event simulation and deterministic modeling (optimization), while the most common hospital operational areas where OR methods were applied were staff, room, and patient scheduling, as well as general patient flow assessment.…”

Section: Operation Research Methodsmentioning

confidence: 99%

Air Force Manpower Determinants: Options for More-Responsive Processes

Robbert¹,

Harrington²,

Mariano³

et al. 2020

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A Markov decision process to dynamically match hospital inpatient staffing to demand

Cited by 7 publications

References 32 publications

Team Based, Risk Adjusted Staffing During a Pandemic: An Agent Based Approach

Team Based, Risk Adjusted Staffing During a Pandemic: An Agent Based Approach

Evaluating the robustness of the CMS Hospital Value‐Based Purchasing measurement system

Air Force Manpower Determinants: Options for More-Responsive Processes

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