EmergenciesNCT05174481
Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
An observational study of Emergencies, run by Tampere University Hospital.
Trial summary
The aim of this study is to prospectively validate statistical forecasting tools that have been widely used retrospectively in forecasting ED overcrowding Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Emergencies
- What is being tested?
- Early warning system for emergency department overcrowding
- How many people
- 160,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2022to 31 Dec 2022
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- All patients presenting in the Emergency Department
Who cannot join
- No exclusion criteria
Trial locations and contacts
No sites are listed yet.
Antti Roine, PhD contact
About this study
The aim of this study is to prospectively validate statistical forecasting tools that have been widely used retrospectively in forecasting ED overcrowding
Read the 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.
- Conditions
- Emergencies
- Being tested
- Early warning system for emergency department overcrowding
- Keywords
- emergency department, overcrowding, forecasting, artificial intelligence, deep learning
- Medical subject headings
- Emergencies, Crowding
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Early warning system for emergency department overcrowdingOther
In this study, no interventions are performed.
What is being measured
The primary outcome is the main question the study is designed to answer.
Next day overcrowding
A day is defined as overcrowded if daily peak occupancy exceeds 80 patients, and severely overcrowded if daily peak occupancy exceeds 100 patients.
Measured: 24 hours
Secondary outcomes (4)
Number of hourly arrivals in the ED 24 hours ahead
Measured: 24 hour
Hourly occupancy in the ED 24 hours ahead
Measured: 24 hour
Number of daily arrivals in the ED 7 days ahead
Measured: 24 hour
Daily peak occupancy in the ED 7 days ahead
Measured: 24 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2021
- Study start1 Jan 2022 (estimated)
- Primary completion28 Feb 2022 (estimated)
- Study completion31 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for EmergenciesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 160,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Tampere University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05174481
- Sponsor's protocol ID
- ed-pro
- Status last verified
- Jan 2022
- First submitted
- 13 Dec 2021
- Last updated
- 10 Jan 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33354320Richardson 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.
PubMed 16515430
Study at a glance
From the registry record, last updated by the sponsor on 10 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
- ClinicalTrials.gov ID
- NCT05174481
- Acronym / sponsor ID
- ed-pro
- Conditions
- Emergencies
- Keywords
- emergency department, overcrowding, forecasting, artificial intelligence, deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 160,000 participants (estimated)
- Lead sponsor
- Tampere University HospitalUniversity, hospital or other
- Study start
- 1 Jan 2022 (estimated)
- Primary completion
- 28 Feb 2022 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 30 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The aim of this study is to prospectively validate statistical forecasting tools that have been widely used retrospectively in forecasting ED 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
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- All patients presenting in the Emergency Department
Key exclusion criteria (1 listed)
- No exclusion criteria
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Early warning system for emergency department overcrowdingIn this study, no interventions are performed. | Other | – |
Primary outcome
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.
4 secondary outcomes
Number of hourly arrivals in the ED 24 hours ahead
Time frame: 24 hour
Hourly occupancy in the ED 24 hours ahead
Time frame: 24 hour
Number of daily arrivals in the ED 7 days ahead
Time frame: 24 hour
Daily peak occupancy in the ED 7 days ahead
Time frame: 24 hours
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Jan 2022.
- Background: PubMed 33354320Gul 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.
- Background: PubMed 16515430Richardson 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Antti Roine, PhD contact
Questions about this trial
What is this clinical trial studying?
An observational study of Emergencies, run by Tampere University Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 10 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jalmari Tuominen, MD (+358505961192, jalmari.tuominen@tuni.fi), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Next day overcrowding (time frame: 24 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05174481, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Jan 2022.