EmergenciesMADLADNCT04757194
Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch
A study testing openTriage - Alitis algorithm in people with Emergencies, run by Uppsala University Hospital at 2 sites in Sweden.
Trial summary
BACKGROUND: At Emergency Medical Dispatch (EMD) centers, Resource Constrained Situations (RCS) where there are more callers requiring an ambulance than there are available ambulances are common. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Emergencies
- What is being tested?
- openTriage - Alitis algorithm
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,499 participants
- Where
- 2 sitesSweden
- Study dates
- 1 Feb 2021to 30 Nov 2024
- Phase
- Interventional
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Identification of a resource constrained situation by ambulance director (i.e., 2 or more patients awaiting an ambulance response)
- Assigned priority 2A or 2B (Low-priority ambulance response) by dispatch nurse call-taker
- Valid Swedish personal identification number collected at dispatch
- Age >= 18 years
Who cannot join
- Relevant calls received more than 30 minutes apart
- Logistical factors (eg. the patients' geographical locations) affect the ambulance assignment decision
- On scene risk factors (eg. a patient is outdoors and risks hypothermia) or risk mitigators (eg. healthcare staff already on-scene with a patient) affect the ambulance assignment decision
Trial locations and contacts
2 sites in 1 country.
Sweden2 sites
Uppsala University Hospital
Uppsala
Västmanland hospital Västerås
Västerås, Västmanland County
About this study
Official title: Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch: A Randomized Controlled Trial
BACKGROUND:
At Emergency Medical Dispatch (EMD) centers, Resource Constrained Situations (RCS) where there are more callers requiring an ambulance than there are available ambulances are common. At the EMD centers in Uppsala and Västmanland, patients experiencing these situations are typically assigned a low-priority response, are often elderly, and have non-specific symptoms. Machine learning techniques offer a promising but largely untested approach to assessing risks among these patients.
Read more
OBJECTIVES:
To establish whether the provision of machine learning-based risk scores improves the ability of dispatchers to identify patients at high risk for deterioration in RCS.
DESIGN:
Multi-centre, parallel-grouped, randomized, analyst-blinded trial.
POPULATION:
Adult patients contacting the national emergency line (112), assessed by a dispatch nurse in Uppsala or Västmanland as requiring a low-priority ambulance response, and experiencing an RCS.
OUTCOMES:
Primary:
- Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS) score
Secondary:
- Difference in composite risk score consisting of ambulance interventions, emergent transport, hospital admission, intensive care, and mortality between patients receiving immediate vs. delayed ambulance response during RCS.
- Difference in NEWS between patients receiving immediate vs. delayed ambulance response during RCS.
INTERVENTION:
A machine learning model will estimate the risk associated with each patient involved in the RCS, and propose a patient to receive the available ambulance. In the intervention arm only, the assessment will be displayed in a user interface integrated into the dispatching system.
TRIAL SIZE:
1500 RCS each consisting of multiple patients randomized 1:1 to control and intervention arms
- Conditions
- Emergencies
- Being tested
- openTriage - Alitis algorithm
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
openTriage - Alitis algorithmDiagnostic test
A machine learning algorithm (Gradient boosting) applied to structured data collected in the Alitis Clinical Decision Support system, patient demographics, and free-text notes.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Calculation of risk assessment score by machine learning algorithm and display of risk assessment information to dispatch nurses. Staff encouraged but not required to comply with suggested ranking. Receives: Diagnostic Test: openTriage - Alitis algorithm |
| Control | No intervention | Ambulance dispatch per standard of care |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS).
NEWS is a widely used and well-validated scoring algorithm based on objective patient vital signs, which are not causally dependent on the outcomes used to train the machine learning models investigated in this study. NEWS values will be based on the first set of vital signs obtained by ambulance nurses upon making contact with the patient. NEWS is measured on a 0-21 scale, with higher values corresponding to patients at higher risk for deterioration.
Measured: Upon ambulance response (Within 8 hours of dispatch)
Secondary outcomes (2)
Difference in composite outcome measure score between patients with immediate vs. delayed response.
This measure investigates a composite score consisting of the outcomes used to train the machine learning models. The composite score is generated by identifying the following patient outcomes and assigning the corresponding weights:
Abnormal intitial Arway/Breathing/Circulation findings by ambulance crew (4) Emergent (lights and sirens) transport to the hospital (2) Provision of prehospital interventions (1) Admission to in-patient care or mortality within 30 days (1)
This results in a score from 0-8, with higher scores representing more
Measured: Up to 30 days
Difference in National Early Warning Score (NEWS) between patients with immediate vs. delayed response.
Per primary outcome
Measured: Upon ambulance response (Within 8 hours of dispatch)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2021
- Study start1 Feb 2021
- Primary completion30 Nov 2024
- Study completion30 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Groups of patients experiencing a resource constrained situation randomized 1:1 at time of inclusion to control/intervention arms
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 2,499 participants (actual)
Sponsor and investigators
- Lead sponsor
- Uppsala University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Region Västmanland
- Responsible party
- Principal investigator, Hans Blomberg, Uppsala University Hospital
- Investigators
- Hans Blomberg, MD, PhD, Principal investigator, Uppsala University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04757194
- Sponsor's protocol ID
- SVLC001
- Acronym
- MADLAD
- Status last verified
- Jan 2025
- First submitted
- 3 Feb 2021
- Last updated
- 8 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual level data available upon reasonable request to authors after publication
Publications
Spangler D, Hermansson T, Smekal D, Blomberg H. A validation of machine learning-based risk scores in the prehospital setting. PLoS One. 2019 Dec 13;14(12):e0226518. doi: 10.1371/journal.pone.0226518. eCollection 2019.
PubMed 31834920Spangler D, Edmark L, Winblad U, Collden-Benneck J, Borg H, Blomberg H. Using trigger tools to identify triage errors by ambulance dispatch nurses in Sweden: an observational study. BMJ Open. 2020 Mar 19;10(3):e035004. doi: 10.1136/bmjopen-2019-035004.
PubMed 32198303Spangler DN, Morelli S, Smekal D, Edmark L, Blomberg H. Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial. PLoS Med. 2026 Mar 31;23(3):e1004770. doi: 10.1371/journal.pmed.1004770. eCollection 2026 Mar.
PubMed 41915716
Study at a glance
From the registry record, last updated by the sponsor on 8 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT04757194
- Acronym / sponsor ID
- MADLAD / SVLC001
- Conditions
- Emergencies
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 2,499 participants (actual)
- Lead sponsor
- Uppsala University HospitalUniversity, hospital or other
- Collaborators
- Region Västmanland
- Study start
- 1 Feb 2021 (actual)
- Primary completion
- 30 Nov 2024 (actual)
- Study completion
- 30 Nov 2024 (actual)
- First posted
- 17 Feb 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.
BACKGROUND:
At Emergency Medical Dispatch (EMD) centers, Resource Constrained Situations (RCS) where there are more callers requiring an ambulance than there are available ambulances are common. At the EMD centers in Uppsala and Västmanland, patients experiencing these situations are typically assigned a low-priority response, are often elderly, and have non-specific symptoms. Machine learning techniques offer a promising but largely untested approach to assessing risks among these patients.
OBJECTIVES:
To establish whether the provision of machine learning-based risk scores improves the ability of dispatchers to identify patients at high risk for deterioration in RCS.
DESIGN:
Multi-centre, parallel-grouped, randomized, analyst-blinded trial.
POPULATION:
Adult patients contacting the national emergency line (112), assessed by a dispatch nurse in Uppsala or Västmanland as requiring a low-priority ambulance response, and experiencing an RCS.
OUTCOMES:
Primary:
- Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS) score
Secondary:
- Difference in composite risk score consisting of ambulance interventions, emergent transport, hospital admission, intensive care, and mortality between patients receiving immediate vs. delayed ambulance response during RCS.
- Difference in NEWS between patients receiving immediate vs. delayed ambulance response during RCS.
INTERVENTION:
A machine learning model will estimate the risk associated with each patient involved in the RCS, and propose a patient to receive the available ambulance. In the intervention arm only, the assessment will be displayed in a user interface integrated into the dispatching system.
TRIAL SIZE:
1500 RCS each consisting of multiple patients randomized 1:1 to control and intervention arms
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
- Model description
- Groups of patients experiencing a resource constrained situation randomized 1:1 at time of inclusion to control/intervention arms
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Diagnostic Test: openTriage - Alitis algorithmCalculation of risk assessment score by machine learning algorithm and display of risk assessment information to dispatch nurses. Staff encouraged but not required to comply with suggested ranking. |
| Control | No intervention | –Ambulance dispatch per standard of care |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Identification of a resource constrained situation by ambulance director (i.e., 2 or more patients awaiting an ambulance response)
- Assigned priority 2A or 2B (Low-priority ambulance response) by dispatch nurse call-taker
- Valid Swedish personal identification number collected at dispatch
- Age >= 18 years
Key exclusion criteria (3 listed)
- Relevant calls received more than 30 minutes apart
- Logistical factors (eg. the patients' geographical locations) affect the ambulance assignment decision
- On scene risk factors (eg. a patient is outdoors and risks hypothermia) or risk mitigators (eg. healthcare staff already on-scene with a patient) affect the ambulance assignment decision
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 |
|---|---|---|
| openTriage - Alitis algorithmA machine learning algorithm (Gradient boosting) applied to structured data collected in the Alitis Clinical Decision Support system, patient demographics, and free-text notes. | Diagnostic test | Intervention |
Primary outcome
Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS).
Time frame: Upon ambulance response (Within 8 hours of dispatch)
NEWS is a widely used and well-validated scoring algorithm based on objective patient vital signs, which are not causally dependent on the outcomes used to train the machine learning models investigated in this study. NEWS values will be based on the first set of vital signs obtained by ambulance nurses upon making contact with the patient. NEWS is measured on a 0-21 scale, with higher values corresponding to patients at higher risk for deterioration.
2 secondary outcomes
Difference in composite outcome measure score between patients with immediate vs. delayed response.
Time frame: Up to 30 days
Difference in National Early Warning Score (NEWS) between patients with immediate vs. delayed response.
Time frame: Upon ambulance response (Within 8 hours of dispatch)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jan 2025.
- Background: PubMed 31834920Spangler D, Hermansson T, Smekal D, Blomberg H. A validation of machine learning-based risk scores in the prehospital setting. PLoS One. 2019 Dec 13;14(12):e0226518. doi: 10.1371/journal.pone.0226518. eCollection 2019.
- Background: PubMed 32198303Spangler D, Edmark L, Winblad U, Collden-Benneck J, Borg H, Blomberg H. Using trigger tools to identify triage errors by ambulance dispatch nurses in Sweden: an observational study. BMJ Open. 2020 Mar 19;10(3):e035004. doi: 10.1136/bmjopen-2019-035004.
- Derived from this study: PubMed 41915716Spangler DN, Morelli S, Smekal D, Edmark L, Blomberg H. Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial. PLoS Med. 2026 Mar 31;23(3):e1004770. doi: 10.1371/journal.pmed.1004770. eCollection 2026 Mar.
- Source code for risk assessment tool used in interventionLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. Individual level data available upon reasonable request to authors after publication
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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.
Hans Blomberg, MD, PhD principal investigator
Uppsala University Hospital
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing openTriage - Alitis algorithm in people with Emergencies, run by Uppsala University Hospital at 2 sites in Sweden.
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 4 inclusion and 3 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: finished, as of the sponsor's last update on 8 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS). (time frame: Upon ambulance response (Within 8 hours of dispatch)). 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 NCT04757194, which also lists 3 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 8 Jan 2025.