Artificial Intelligence (IA) Advanced Triage Tool for G&O Emergencies

NCT05382000 | Active Not Recruiting DMC

• 1 other identifier: IIBSP-TIA-2021-81
• Study Type: interventional
• Target: 450
• Locations: 1 country
• Sites: 1

Brief Summary

Triage represents the first opportunity to classify patients who come to an Emergency Department (ED) and to be able to identify, prioritize high-risk patients and efficiently allocate the limited resources that are available. Therefore, the purpose of triage in the ED is to prioritize patients, detecting those that are urgent (that cannot wait to be attended). Urgency is defined as that clinical situation with the capacity to generate deterioration or danger to the health or life of the patient, depending on the time elapsed between its appearance and the establishment of an effective treatment, which determines a healthcare episode with significant intervention needs in a short period of time. There are currently six triage systems or models systematically structured into 5 levels. Although simple in concept, the practice of triage is challenging due to time pressure, the limitations of available information, the various medical conditions of the patients, and a great reliance on intuition on the part of the professionals who perform it. which conditions a great variability in it. On the other hand, almost half of adult ED visits nationwide are classified as level 3 in a 5-level structured triage system, which makes level 3 a heterogeneous group with patients with diverse pathologies, in which triage is not capable of accurately differentiating them, and this inability poses safety risks for the most severely ill patients ("under-triage") and may influence the accuracy and efficiency in resource allocation when patients with low acuity are overrated. Therefore, it seems necessary to develop new triage procedures that allow us to improve their accuracy and reduce inter-individual variability. TIAGO is a prospective, single-center, observational, comparative study to determine the validity of the Mediktor ® Triage and its effectiveness with respect to the current triage system and the "gold standard" (physician's diagnosis).

Conditions

Emergencies

Outcome Measures

Primary Outcomes (1)

• Number of patients with equivalence between emergency triage classifications

  Correspondence of emergency grading between Advanced IA Triage Tool (Mediktor Hospital) and the current triage system.

  3 days

Secondary Outcomes (2)

• Number of patients with the same diagnosis on advanced triage tool and emergency discharge report (gold-standard)

  3 days

• Number of patients with good correlation between complimentary tests requested by the advanced triage tool with gold-standard

  3 days

Study Arms (1)

Intervention

OTHER

Inclusion in the study will be proposed to all patients who come to the Emergency Department of the Gynecology and Obstetrics Service The planned procedures for this group are as follows: 1. Obtaining informed consent. 2. Sequential triage - Patients will be evaluated sequentially using the MAT system according to standard practice. Next, another professional from the center, trained in the use of the Mediktor Hospital ® tool, will perform the advanced triage in the same space, both professionals being blind to the result of each of the tools. 3. Once the sequential triage is finished, the patient's care will be carried out according to usual clinical practice, following the triage assessment carried out with the MAT system. 4. Retrieval and introduction of data in DRF - Data of the study variables will be retrieved from the emergency report issued in the Gynecology and Obstetrics Emergency area and will be entered into an electronic DRF for subsequent analysis and processing.

Diagnostic Test: Advanced triage tool for Gynecology and Obstetrics emergencies based on artificial intelligence algorithms.

Interventions

Advanced triage tool for Gynecology and Obstetrics emergencies based on artificial intelligence algorithms. DIAGNOSTIC_TEST

After the conventional triage, a second independent doctor will make the suit with the Mediktor Hospital tool.. In less than 3 minutes and with an average of 14 questions, Mediktor performs an interrogation very similar to what an emergency doctor would do. The professional version allows the health professional to modify the course of the questions in the middle of the evaluation, if he considers it necessary to go deeper into some aspect of the anamnesis. The system allows you to see in real time the diseases that Mediktor considers possible during the evaluation. At the end of the triage process, Mediktor offers the level of urgency and a list of possible diagnoses based on the signs and symptoms answered. The professional can change the level of urgency if he considers it beneficial for the patient. Once the two triages (Conventional and Mediktor) have been carried out, the patient will be seen according to the care protocols of the center.

Intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Being over 18 years
• Understand and accept the study procedures
• Sign the informed consent.

You may not qualify if:

• Not being able to understand the nature of the study and/or the procedures to be followed
• Not signing the informed consent
• Be under 18 years of age
• Emergency level 1 through current triage system

Sponsors & Collaborators

• Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau: lead

Study Sites (1)

Hospital de la Santa Creu i Sant Pau

Barcelona, Spain

Location

Related Publications (10)

• Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: a narrative review. Acute Med Surg. 2022 Mar 1;9(1):e740. doi: 10.1002/ams2.740. eCollection 2022 Jan-Dec.

  PMID: 35251669 BACKGROUND

• Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, Succi MD, Yun BJ. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018 Aug;36(8):1515-1517. doi: 10.1016/j.ajem.2018.01.017. Epub 2018 Jan 4. No abstract available.

  PMID: 29321109 BACKGROUND

• Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019 Feb 22;23(1):64. doi: 10.1186/s13054-019-2351-7.

  PMID: 30795786 BACKGROUND

• Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, Dugas A, Linton B, Kirsch T, Kelen G. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med. 2018 May;71(5):565-574.e2. doi: 10.1016/j.annemergmed.2017.08.005. Epub 2017 Sep 6.

  PMID: 28888332 BACKGROUND

• Moreno Barriga E, Pueyo Ferrer I, Sanchez Sanchez M, Martin Baranera M, Masip Utset J. [A new artificial intelligence tool for assessing symptoms in patients seeking emergency department care: the Mediktor application]. Emergencias. 2017 Dic;29(6):391-396. Spanish.

  PMID: 29188913 BACKGROUND

• Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emerg Med J. 2017 Nov;34(11):703-710. doi: 10.1136/emermed-2016-206295. Epub 2017 Jul 27.

  PMID: 28751363 BACKGROUND

• Julian-Jimenez A, Palomo de los Reyes MJ, Lain Teres N. [Coment on the original article: modelo predictor de ingreso hospitalario a la llegada al servicio de Urgencias]. An Sist Sanit Navar. 2012 Sep-Dec;35(3):493-6; author reply 497-9. doi: 10.23938/ASSN.0113. No abstract available. Spanish.

  PMID: 23296233 BACKGROUND

• Elias P, Damle A, Casale M, Branson K, Churi C, Komatireddy R, Feramisco J. A Web-Based Tool for Patient Triage in Emergency Department Settings: Validation Using the Emergency Severity Index. JMIR Med Inform. 2015 Jun 10;3(2):e23. doi: 10.2196/medinform.3508.

  PMID: 26063343 BACKGROUND

• Storm-Versloot MN, Ubbink DT, Kappelhof J, Luitse JS. Comparison of an informally structured triage system, the emergency severity index, and the manchester triage system to distinguish patient priority in the emergency department. Acad Emerg Med. 2011 Aug;18(8):822-9. doi: 10.1111/j.1553-2712.2011.01122.x.

  PMID: 21843217 BACKGROUND

• Moll HA. Challenges in the validation of triage systems at emergency departments. J Clin Epidemiol. 2010 Apr;63(4):384-8. doi: 10.1016/j.jclinepi.2009.07.009. Epub 2009 Oct 28.

  PMID: 19875271 BACKGROUND

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Josep Estadella Tarriel

  Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Once the patient's consent has been obtained, the patient will be assessed sequentially in the same triage space. Initially, a nurse will classify the patient in the triage box using the MAT system according to the usual practice. Then, another professional from the center, trained in the use of the Mediktor Hospital ® tool, and who has not been present in the conventional sorting, will perform the advanced IA triage in the same space, both professionals being blind to the result of each of the tools. Attention in the Emergency Service Once the sequential triage is completed, the patient will return to the Gynecology and Obstetrics Emergency Room. The patient's care will be performed according to usual clinical practice, following the triage assessment performed with the MAT system.

Study Record Dates

• First Submitted: April 28, 2022
• First Posted: May 19, 2022
• Study Start: May 11, 2022
• Primary Completion: August 1, 2024
• Study Completion: December 1, 2024
• Last Updated: May 17, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: All data will be available once the study is published . It will be available for 5 years after publication date.
• Access Criteria: Requests will be reviewed by Primary Investigator.

Locations

ES (1)