Literature DB >> 29188913

[A new artificial intelligence tool for assessing symptoms in patients seeking emergency department care: the Mediktor application].

Elvira Moreno Barriga1, Irene Pueyo Ferrer2, Miquel Sánchez Sánchez2, Montserrat Martín Baranera3, Josep Masip Utset1.   

Abstract

OBJECTIVES: To analyze agreement between diagnoses issued by the Mediktor application and those of an attending physician, and to evaluate the usefulness of this application in patients who seek emergency care.
MATERIAL AND METHODS: Prospective observational study in a tertiary care university hospital emergency department. Patients with medical problems and surgical conditions (surgery and injuries) who did not require immediate emergency care responded to the Mediktor questions on a portable computer tablet. The software analyzed the answers and provided a list of 10 possible preliminary diagnoses in order of likelihood. The patient and the attending physician were blinded to the list to so that the usual care process would not be altered. The level of agreement between the physician's diagnosis and the Mediktor diagnosis was analyzed.
RESULTS: A total of 1015 patients were included; 622 cases were considered valid for study. Cases were excluded if the patients did not meet the inclusion criteria, they did not have a discharge diagnosis, they had a final diagnosis expressed as a symptom or their final diagnosis was not included in the Mediktor database. The physician's diagnosis (the gold standard) coincided with one of the 10 MEDIKTOR diagnoses in 91.3% of the cases, with one of the first 3 diagnoses in 75.4%, and with the first diagnosis in 42.9%. Sensitivity was over 92% and specificity over 91% in the majority of common diagnostic groups; the κ statistic ranged from 0.24 to 0.98.
CONCLUSION: The Mediktor application is a reliable diagnostic aid for the most prevalent problems treated in a hospital emergency department. The general public finds it easy to use.

Entities:  

Keywords:  Agreement; Cirugía; Concordancia; Diagnóstico médico; Emergency department; Injuries; Physician diagnosis; Servicio de Urgencias; Symptoms; Síntoma; Traumatología

Mesh:

Year:  2017        PMID: 29188913

Source DB:  PubMed          Journal:  Emergencias        ISSN: 1137-6821            Impact factor:   3.881


  5 in total

1.  Investigating the Potential for Clinical Decision Support in Sub-Saharan Africa With AFYA (Artificial Intelligence-Based Assessment of Health Symptoms in Tanzania): Protocol for a Prospective, Observational Pilot Study.

Authors:  Marcel Schmude; Nahya Salim; Hila Azadzoy; Mustafa Bane; Elizabeth Millen; Lisa O'Donnell; Philipp Bode; Ewelina Türk; Ria Vaidya; Stephen Gilbert
Journal:  JMIR Res Protoc       Date:  2022-06-07

2.  Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease.

Authors:  Lingzhi Hong; Xufang Cheng; Deming Zheng
Journal:  Contrast Media Mol Imaging       Date:  2021-11-24       Impact factor: 3.161

3.  Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients.

Authors:  Severin Hennemann; Sebastian Kuhn; Michael Witthöft; Stefanie M Jungmann
Journal:  JMIR Ment Health       Date:  2022-01-31

4.  Study protocol for a pilot prospective, observational study investigating the condition suggestion and urgency advice accuracy of a symptom assessment app in sub-Saharan Africa: the AFYA-'Health' Study.

Authors:  Elizabeth Millen; Nahya Salim; Hila Azadzoy; Mustafa Miraji Bane; Lisa O'Donnell; Marcel Schmude; Philipp Bode; Ewelina Tuerk; Ria Vaidya; Stephen Henry Gilbert
Journal:  BMJ Open       Date:  2022-04-11       Impact factor: 2.692

5.  Safety of Triage Self-assessment Using a Symptom Assessment App for Walk-in Patients in the Emergency Care Setting: Observational Prospective Cross-sectional Study.

Authors:  Fabienne Cotte; Tobias Mueller; Stephen Gilbert; Bibiana Blümke; Jan Multmeier; Martin Christian Hirsch; Paul Wicks; Joseph Wolanski; Darja Tutschkow; Carmen Schade Brittinger; Lars Timmermann; Andreas Jerrentrup
Journal:  JMIR Mhealth Uhealth       Date:  2022-03-28       Impact factor: 4.773

  5 in total

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