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Managing admission and discharge processes in intensive care units.

ABSTRACT: The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

SUBMITTER: Bai J

PROVIDER: S-EPMC8189840 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Managing admission and discharge processes in intensive care units.

Bai Jie J Fügener Andreas A Gönsch Jochen J Brunner Jens O JO Blobner Manfred M

Health care management science 20210610 4

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of ...[more]

PMID: 34110549

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Project description:Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Project description:INTRODUCTION: The discharge of patients from the intensive care unit (ICU) to a hospital ward is a common transition of care that is associated with error and adverse events. Risk stratification tools may help identify high-risk patients for targeted interventions, but it is unclear if proper tools have been developed. METHODS: We searched Ovid EMBASE, Ovid MEDLINE, CINAHL, PUBMED and Cochrane Central Register of Controlled Trials from the earliest available date through March 2013, plus reference lists and citations of all studies included in the systematic review. Cohort studies were selected that described the derivation, validation or clinical impact of tools for predicting medical emergency team activation, ICU readmission or mortality following patient discharge from the ICU. Data were extracted on the study design, setting, population, sample size, tool (components, measurement properties) and outcomes. RESULTS: The literature search identified 9,926 citations, of which eight studies describing eight tools met the inclusion criteria. Reported outcomes included ICU readmission (n=4 studies), hospital mortality (n=3 studies) and both ICU readmission and hospital mortality (n=1 studies). Seven of the tools were comprised of distinct measurable component variables, while one tool used subjective scoring of patient risk by intensive care physicians. The areas under receiver operator curves were reported for all studies and ranged from 0.66 to 0.92. A single study provided a direct comparative analysis between two tools. We did not find any studies evaluating the impact of risk prediction on processes and outcomes of care. CONCLUSIONS: Eight risk stratification tools for predicting severe adverse events following patient discharge from ICU have been developed, but have undergone limited comparative evaluation. Although risk stratification tools may help clinician decision-making, further evaluation of the existing tools' effects on care is required prior to clinical implementation.

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Managing admission and discharge processes in intensive care units.

Publications

Managing admission and discharge processes in intensive care units.

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