ReviewChest2020
Lessons on Outbreak Preparedness From the Cleveland Clinic.
Review in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Hospital crisis management in the epidemic: A qualitative study.Health science reports · 2024Article
- Advancing crush syndrome management: the potent role of Sodium zirconium cyclosilicate in early hyperkalemia intervention and survival enhancement in a rat model.Frontiers in pharmacology · 2024Article
- Critical Care Among Disadvantaged Minority Groups Made Equitable: Trends Throughout the COVID-19 Pandemic.Journal of racial and ethnic health disparities · 2023Article
- Evaluating Efficacy of a COVID-19 Alternative Care Site Preparedness Assessment Tool for Catastrophic Healthcare Surge Capacity during Pandemic Response.Healthcare (Basel, Switzerland) · 2023Article
- Respond quickly and effectively! Components of the military health surveillance system in natural disasters: A qualitative study.Journal of education and health promotion · 2023Article
- Lessons from COVID-19 for future disasters: an opinion paper.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2022Review
- Assessment of COVID-19 prevention and protection measures in hospitals.Cleaner engineering and technology · 2022Article
- COVID-19 Pandemic Support Programs for Healthcare Workers and Implications for Occupational Mental Health: A Narrative Review.The Psychiatric quarterly · 2022Review
- Factors Affecting the Decontamination Process in Hospitals in Saudi Arabia.Risk management and healthcare policy · 2021Article
- Cancer Imaging and Patient Care during the COVID-19 Pandemic.Radiology. Imaging cancer · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disasters, including infectious disease outbreaks, are inevitable. Hospitals need to plan in advance to ensure that their systems can adapt to a rapidly changing environment if necessary. This review provides an overview of 10 general principles that hospitals and health-care systems should consider when developing disaster plans. The principles are consistent with an "all-hazards" approach to disaster mitigation. This approach is adapted to planning for a multiplicity of threats but emphasizes highly relevant scenarios, such as the coronavirus disease 2019 pandemic. We also describe specific ways these principles helped prepare our hospital for this pandemic. Key points include acting quickly, identifying and engaging key stakeholders early, providing accurate information, prioritizing employee safety and mental health, promoting a fully integrated clinical response, developing surge plans, preparing for ethical dilemmas, and having a cogent exit strategy for post-disaster recovery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.