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Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05174481
Recruitment Status : Unknown
Verified January 2022 by Tampere University Hospital.
Recruitment status was:  Not yet recruiting
First Posted : December 30, 2021
Last Update Posted : January 10, 2022
Sponsor:
Information provided by (Responsible Party):
Tampere University Hospital

Study Description
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Brief Summary:
The aim of this study is to prospectively validate statistical forecasting tools that have been widely used retrospectively in forecasting ED overcrowding

Condition or disease Intervention/treatment
Emergencies Other: Early warning system for emergency department overcrowding

Detailed Description:

Emergency department (ED) overcrowding is a chronic international issue that has been repeatedly associated with detrimental treatment outcomes such increased 10-day-mortality. Forecasting future overcrowding would enable pre-emptive staffing decisions that could alleviate or prevent overcrowding along with its detrimental effects.

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.

Study Design
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Study Type : Observational [Patient Registry]
Estimated Enrollment : 160000 participants
Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration: 1 Day
Official Title: Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
Estimated Study Start Date : January 1, 2022
Estimated Primary Completion Date : February 28, 2022
Estimated Study Completion Date : December 31, 2022
Groups and Cohorts
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Intervention Details:
  • Other: Early warning system for emergency department overcrowding
    In this study, no interventions are performed.

Outcome Measures
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Primary Outcome Measures :
  1. Next day overcrowding [ Time Frame: 24 hours ]
    A day is defined as overcrowded if daily peak occupancy exceeds 80 patients, and severely overcrowded if daily peak occupancy exceeds 100 patients.


Secondary Outcome Measures :
  1. Number of hourly arrivals in the ED 24 hours ahead [ Time Frame: 24 hour ]
  2. Hourly occupancy in the ED 24 hours ahead [ Time Frame: 24 hour ]
  3. Number of daily arrivals in the ED 7 days ahead [ Time Frame: 24 hour ]
  4. Daily peak occupancy in the ED 7 days ahead [ Time Frame: 24 hours ]

Eligibility Criteria
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Ages Eligible for Study:   16 Years and older   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Tampere University Hospital is an academic hospital located in Tampere, Finland. It serves a population of 535,000 in the Pirkanmaa Hospital District and, as a tertiary hospital, an additional population of 365,700 and provides level 1 trauma centre capabilities.
Criteria

Inclusion Criteria:

  • All patients presenting in the Emergency Department

Exclusion Criteria:

  • No exclusion criteria
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05174481


Contacts
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Contact: Jalmari Tuominen, MD +358505961192 jalmari.tuominen@tuni.fi
Contact: Antti Roine, PhD antti.roine@tuni.fi

Sponsors and Collaborators
Tampere University Hospital
More Information
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Publications:

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Responsible Party: Tampere University Hospital
ClinicalTrials.gov Identifier: NCT05174481    
Other Study ID Numbers: ed-pro
First Posted: December 30, 2021    Key Record Dates
Last Update Posted: January 10, 2022
Last Verified: January 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Tampere University Hospital:
emergency department
overcrowding
forecasting
artificial intelligence
deep learning
Additional relevant MeSH terms:
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Emergencies
Disease Attributes
Pathologic Processes