Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
NCT ID: NCT05174481
Last Updated: 2022-01-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
160000 participants
OBSERVATIONAL
2022-01-01
2022-12-31
Brief Summary
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Detailed Description
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Over the years, several predictive algorithms have been proposed ranging from generalized linear models to state space models and, more recently, deep learning algorithms. However, the performance of these algorithms has only been reported retrospectively and the clinically significant accuracy of these algorithms remains unclear.
In this study the investigators aim to investigate the accuracy of the previously reported ED forecasting algorithms in a prospective setting analogous to the way these tools would be used if used implemented as a decision-support system in a real-life clinical setting.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Early warning system for emergency department overcrowding
In this study, no interventions are performed.
Eligibility Criteria
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Inclusion Criteria
16 Years
ALL
No
Sponsors
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Tampere University Hospital
OTHER
Responsible Party
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Central Contacts
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References
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Gul M, Celik E. An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments. Health Syst (Basingstoke). 2018 Nov 19;9(4):263-284. doi: 10.1080/20476965.2018.1547348.
Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006 Mar 6;184(5):213-6. doi: 10.5694/j.1326-5377.2006.tb00204.x.
Other Identifiers
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ed-pro
Identifier Type: -
Identifier Source: org_study_id
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