The Association Between Hospital Capacity Strain and Inpatient Outcomes in Highly Developed Countries: A Systematic Review

Eriksson, Carl; Stoner, Ryan C.; Eden, Karen; Newgard, Craig D.; Guise, Jeanne Marie

doi:10.1007/s11606-016-3936-3

Cited by 134 publications

(127 citation statements)

References 69 publications

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“…In terms of curative beds per 100,000 population as reported by Eurostat, we observed for most countries (all except Bulgaria, Ireland, Poland and Romania), a downward trend in the past 10 years. Moreover, hospital capacity strain was recently found to be associated with increased mortality and decreased health outcomes [11]. Note that if we would assume that the extent of downsizing in Italy and other EU countries was the same, our relative comparisons would not change.…”

Section: Usefulness and Limitations Of These Comparisonsmentioning

confidence: 92%

Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020

2020

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European healthcare systems face extreme pressure from coronavirus disease (COVID-19). We relate country-specific accumulated COVID-19 deaths (intensity approach) and active COVID-19 cases (magnitude approach) to measures of healthcare system capacity: hospital beds, healthcare workers and healthcare expenditure. Modelled by the intensity approach with a composite measure for healthcare capacity, the countries experiencing the highest pressure on 25 March 2020 - relative to Italy on 11 March - were Italy, Spain, the Netherlands and France (www.covid-hcpressure.org).

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Section: Usefulness and Limitations Of These Comparisonsmentioning

confidence: 92%

Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020

2020

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“…Hospital capacity strain negatively impacts quality of care 1. Overcrowding creates many challenges to efficient patient navigation of the hospital system from initial assessment in the emergency department (ED), to admission onto inpatient wards, and ultimately to patient discharge.…”

Section: Introductionmentioning

confidence: 99%

Mortality of hospitalised internal medicine patients bedspaced to non-internal medicine inpatient units: retrospective cohort study

et al. 2017

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Objective To compare inhospital mortality of general internal medicine (GIM) patients bedspaced to off-service wards with GIM inpatients admitted to assigned GIM wards. Method A retrospective cohort study of consecutive GIM admissions between 1 January 2015 and 1 January 2016 was conducted at a large tertiary care hospital in Canada. Inhospital mortality was compared between patients admitted to off-service wards (bedspaced) and assigned GIM wards using a Cox proportional hazards model and a competing risk model. Sensitivity analyses included propensity score and pair matching based on GIM service team, workload, demographics, time of admission, reasons for admission and comorbidities. Results Among 3243 consecutive GIM admissions, more than a third (1125, 35%) were bedspaced to off-service wards with the rest (2118, 65%) admitted to assigned GIM wards. In hospital, 176 (5%) patients died: 88/1125 (8%) bedspaced patients and 88/2118 (4%) assigned GIM ward patients. Compared with assigned GIM wards patients, bedspaced patients had an HR of 3.42 (95% CI 2.23 to 5.26; P<0.0001) for inhospital mortality at admission, which then decreased by HR of 0.97 (95% CI 0.94 to 0.99; P=0.0133) per day in hospital. Competing risk models and sensitivity analyses using propensity scores and pair matching yielded similar results. Conclusions Bedspaced patients had significantly higher inhospital mortality than patients admitted to assigned GIM wards. The risk was highest at admission and subsequently declined. The results of this single centre study may not be generalisable to other hospitals and may be influenced by residual confounding. Despite these limitations, the relationship between bedspacing and patient outcomes requires investigation at other institutions to determine if this common practice represents a modifiable patient safety indicator.

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“…Under normal conditions, hospitals with a large influx of patients tend to struggle to manage them in a timely manner (Morley et al 2018). Studies have shown that as the time to see admitted patients increases, so does the risk for adverse events (Eriksson et al 2017). Any time delay in this crisis can lead to a missed opportunity or limited time to save a life.…”

Section: Main Textmentioning

confidence: 99%

Machine learning to assist clinical decision-making during the COVID-19 pandemic

et al. 2020

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Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for MLsupported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

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The Association Between Hospital Capacity Strain and Inpatient Outcomes in Highly Developed Countries: A Systematic Review

Abstract: In highly developed countries, hospital capacity strain is associated with increased mortality and worsened health outcomes. Evidence-based solutions to improve outcomes during times of capacity strain are needed.

Cited by 134 publications

References 69 publications

Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020

Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020

Mortality of hospitalised internal medicine patients bedspaced to non-internal medicine inpatient units: retrospective cohort study

Machine learning to assist clinical decision-making during the COVID-19 pandemic

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