Hospital-Level Changes in Adult ICU Bed Supply in the United States

Wallace, David J.; Seymour, Christopher W.; Kahn, Jeremy M.

doi:10.1097/ccm.0000000000002051

Cited by 52 publications

(45 citation statements)

References 42 publications

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“…Arguably, sustained strain contributes to inefficient and inequitable utilization of finite ICU resources. While opening additional ICU beds may seem the simplest response, this is not necessarily sustainable, and likely only a short-term reprieve [ 29 ]. Rather, in order to explain the breadth of effect strain may have on patients, professionals and operations, a constellation of evidence-informed quality indicators are likely to be necessary.…”

Section: Discussionmentioning

confidence: 99%

Indicators of intensive care unit capacity strain: a systematic review

et al. 2018

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BackgroundStrained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.MethodsWe performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.ResultsOf 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.ConclusionsWe identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.Systematic review registrationThis systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931).Electronic supplementary materialThe online version of this article (10.1186/s13054-018-1975-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Indicators of intensive care unit capacity strain: a systematic review

et al. 2018

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“…3 Another study of more than 4,400 hospitals found a significant increase in the number of adult intensive care beds between 2009 and 2011, but this growth was seen mostly in large urban teaching facilities, rather than in rural areas or smaller, less-resourced hospitals, which often serve residents with little or no other access to care. 4 In addition, there has been an increase in the number of rural hospitals closing each year. 5 During a public health emergency, federal, state, or local stockpiled ventilators should be deployed in a way that optimizes the effectiveness, efficiency, and equity of this scarce resource.…”

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confidence: 99%

Strategies to Inform Allocation of Stockpiled Ventilators to Healthcare Facilities During a Pandemic

Koonin

Pillai

Kahn³

et al. 2020

Health Security

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During a severe pandemic, especially one causing respiratory illness, many people may require mechanical ventilation.Depending on the extent of the outbreak, there may be insufficient capacity to provide ventilator support to all of those in need. As part of a larger conceptual framework for determining need for and allocation of ventilators during a public health emergency, this article focuses on the strategies to assist state and local planners to allocate stockpiled ventilators to healthcare facilities during a pandemic, accounting for critical factors in facilities' ability to make use of additional ventilators. These strategies include actions both in the pre-pandemic and intra-pandemic stages. As a part of pandemic preparedness, public health officials should identify and query healthcare facilities in their jurisdiction that currently care for critically ill patients on mechanical ventilation to determine existing inventory of these devices and facilities' ability to absorb additional ventilators. Facilities must have sufficient staff, space, equipment, and supplies to utilize allocated ventilators adequately. At the time of an event, jurisdictions will need to verify and update information on facilities' capacity prior to making allocation decisions. Allocation of scarce life-saving resources during a pandemic should consider ethical principles to inform state and local plans for allocation of ventilators. In addition to ethical principles, decisions should be informed by assessment of need, determination of facilities' ability to use additional ventilators, and facilities' capacity to ensure access to ventilators for vulnerable populations (eg, rural, inner city, and uninsured and underinsured individuals) or high-risk populations that may be more susceptible to illness.

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“…6,7,9 Upon examining this new model, we observed that the highest tier ICU bed adjustment (≥43 ICU beds) was lower than the number of ICU beds at many large US hospitals, and there was no adjustment for oncology units and/or hematopoietic cell transplant units (ONC-HCT units) within general hospitals, despite wide recognition that these patient populations are at increased risk for CDI (Table 1). 6,10 We hypothesized that inadequate adjustment for these patients might explain why large, academic hospitals tend to be poor performers in CDI SIR rankings and decided to investigate the effect of removing ICU and ONC-HCT unit data on the CDI SIRs of large, general, acute-care facilities. [11][12][13]…”

mentioning

confidence: 99%

Evaluation of the National Healthcare Safety Network standardized infection ratio risk adjustment for healthcare-facility-onset Clostridioides difficile infection in intensive care, oncology, and hematopoietic cell transplant units in general acute-care hospitals

Polage

Quan

Madey

et al. 2020

Infect. Control Hosp. Epidemiol.

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Objective:To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.Design:Retrospective cohort study.Setting:Eight tertiary-care referral general hospitals in California.Methods:We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.Results:For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15–1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, −25%; IQR, −20% to −29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%–105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, −15%; IQR, −14% to −21%) and decreased the SIR at all hospitals (median, −8%; IQR, −4% to −11%).Conclusions:For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.

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Hospital-Level Changes in Adult ICU Bed Supply in the United States

Cited by 52 publications

References 42 publications

Indicators of intensive care unit capacity strain: a systematic review

Indicators of intensive care unit capacity strain: a systematic review

Strategies to Inform Allocation of Stockpiled Ventilators to Healthcare Facilities During a Pandemic

Evaluation of the National Healthcare Safety Network standardized infection ratio risk adjustment for healthcare-facility-onset Clostridioides difficile infection in intensive care, oncology, and hematopoietic cell transplant units in general acute-care hospitals

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