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11 results on '"MESH: Triage"'

1. Design of an ontology-based triage system for painful patients

Author

Saadi, Alexandre, Rogier, Alice, Burgun, Anita, Tsopra, Rosy, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and SAADI, Alexandre

Subjects
MESH: Humans, Clinical Decision Support System, [SDV.MHEP.PHY] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Ontology, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Chronic pain, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, MESH: Referral and Consultation, MESH: Systematized Nomenclature of Medicine, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], MESH: Chronic Pain, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, MESH: Triage
Abstract

International audience; Objective: Waiting time for a consultation for chronic pain is a widespread health problem. This paper present the design of an ontology use to assess patients referred to a consultation for chronic pain. Methods: We designed OntoDol, an ontology of pain domain for patient triage based on priority degrees. Terms were extracted from clinical practice guidelines and mapped to SNOMED-CT concepts through the Python module Owlready2. Selected SNOMED-CT concepts, relationships, and the TIME ontology, were implemented in the ontology using Protégé. Decision rules were implemented with SWRL. We evaluated OntoDol on 5 virtual cases. Results: OntoDol contains 762 classes, 92 object properties and 18 SWRL rules to assign patients to 4 categories of priority. OntoDol was able to assert every case and classify them in the right category of priority. Conclusion: Further works will extend OntoDol to other diseases and assess OntoDol with real world data from the hospital.

Published
2022

2. Design of an Ontology-Based Triage System for Patients with Chronic Pain

Author

Alexandre Saadi, Alice Rogier, Anita Burgun, Rosy Tsopra, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)

Subjects
MESH: Referral and Consultation, MESH: Humans, Clinical Decision Support System, MESH: Systematized Nomenclature of Medicine, Ontology, [SDV]Life Sciences [q-bio], Chronic pain, MESH: Chronic Pain, [INFO]Computer Science [cs], MESH: Triage
Abstract

International audience; Objective: Waiting time for a consultation for chronic pain is a widespread health problem. This paper presents the design of an ontology use to assess patients referred to a consultation for chronic pain. Methods: We designed OntoDol, an ontology of pain domain for patient triage based on priority degrees. Terms were extracted from clinical practice guidelines and mapped to SNOMED-CT concepts through the Python module Owlready2. Selected SNOMED-CT concepts, relationships, and the TIME ontology, were implemented in the ontology using Protégé. Decision rules were implemented with SWRL. We evaluated OntoDol on 5 virtual cases. Results: OntoDol contains 762 classes, 92 object properties and 18 SWRL rules to assign patients to 4 categories of priority. OntoDol was able to assert every case and classify them in the right category of priority. Conclusion: Further works will extend OntoDol to other diseases and assess OntoDol with real world data from the hospital.

Published
2022

3. An Interactive Interface for Displaying Recommendations on Emergency Phone Triage in Pediatrics

Author

Claire Durchon, Stephi Vanderlan, Alice Jegard, Hasini Saram, Marina Falchi, Florence Campeotto, Laurent Dupic, Anita Burgun, Benoît Vivien, Rosy Tsopra, GHU AP-HP Centre Université de Paris, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Physiopathologie et pharmacotoxicologie placentaire humaine : Microbiote pré & post natal (3PHM - UMR-S 1139), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), This work is part of an innovative program named 'Artificial Intelligence for Clinical Decision Support', dedicated to undergraduate medical students, at Université de Paris., École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and TSOPRA, Rosy

Subjects
Pediatric, Students, Medical, MESH: Humans, Clinical Decision Support System, [SDV]Life Sciences [q-bio], Usability, [INFO] Computer Science [cs], Pediatrics, [SDV] Life Sciences [q-bio], MESH: Emergencies, MESH: Students, Medical, MESH: Child, Emergency, MESH: Pediatrics, Humans, Digital Health, [INFO]Computer Science [cs], Emergencies, Triage, Child, MESH: Triage
Abstract

International audience; Emergency phone triage aims at identifying quickly patients with critical emergencies. Patient triage is not an easy task, especially in situations involving children, mostly due to the lack of training and the lack of clinical guidelines for children. To overcome these issues, we aim at designing and assessing an interactive interface for displaying recommendations on emergency phone triage in pediatrics. Four medical students formalized local guidelines written by the SAMU of Paris, into a decision tree and designed an interface according to usability principles. The navigation within the interface was designed to allow the identification of critical emergencies at the beginning of the decision process, and thus ensuring a quick response in case of critical emergencies. The interface was assessed by 10 medical doctors: they appreciated the ergonomics (e.g., intuitive colors), and found easy to navigate through the interface. Nine of them would like to use this interface during phone call triage. In the future, this interface will be improved and implemented in emergency call centers.

Published
2022

4. Towards a Clinical Decision Support System for Helping Medical Students in Emergency Call Centers

Author

Edouard Michot, Jules Woo, Louis Mouline, Catherine Sinnappan, Adrien Boukobza, Florence Campeotto, Laurent Dupic, Anita Burgun, Benoît Vivien, Rosy Tsopra, GHU AP-HP Centre Université de Paris, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Funding This work is part of an innovative program named 'Artificial Intelligence for Clinical Decision Support', dedicated to undergraduate medical students, at Université de Paris., École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and TSOPRA, Rosy

Subjects
MESH: Pandemics, MESH: Humans, Students, Medical, Clinical Decision Support System, [SDV]Life Sciences [q-bio], [INFO] Computer Science [cs], Phone call triage, Decision Support Systems, Clinical, [SDV] Life Sciences [q-bio], MESH: Call Centers, MESH: Students, Medical, MESH: Child, MESH: Decision Support Systems, Clinical, Digital Health, Humans, [INFO]Computer Science [cs], Pediatric Emergency, Triage, Child, Call Centers, Pandemics, MESH: Triage
Abstract

International audience; In critical situations such as pandemic, medical students are often called to help in emergency call centers. However, they may encounter difficulties in phone triage because of a lack of medical skills. Here, we aim at developing a Clinical Decision Support System for helping medical students in phone call triage of pediatric patients. The system is based on the PAT (Pediatric Assessment Triangle) and local guidelines. It is composed of two interfaces. The first allows a quick assessment of severity signs, and the second provides recommendations and additional elements such as “elements to keep in mind” or “medical advice to give to patient”. The system was evaluated by 20 medical students, with two fictive clinical cases. 75% of them found the content useful and clear, and the navigation easy. 65% would feel more reassured to have this system in emergency call centers. Further works are planned to improve the system before implementation in real-life.

Published
2022

5. COVID-19 pandemic

Author

W. J. Fokkens, Jürgen Schwarze, Cezmi A. Akdis, J Mullol, W. Czarlewski, Claudia Traidl-Hoffmann, Claus Bachert, D. Larenas-Linnemann, Tomas Chivato, M. Gotua, Mateo Bonini, Ludger Klimek, Vincenzo Patella, A. A. Cruz, Stephanie Dramburg, Kari C. Nadeau, H W Fritsch, K. Ohta, Thomas Eiwegger, Robert M. Naclerio, Antti Lauerma, A. Yorgancioglu, Aslı Gelincik, Piotr Kuna, Oliver Pfaar, Carmen Riggioni, Violeta Kvedariene, Markus Ollert, Sinthia Bosnic-Anticevich, V. Cardona, S. Del Giacco, Sanna Toppila-Salmi, Helen A. Brough, Heimo Breiteneder, Valérie Hox, B. Samolinski, Zuzana Diamant, G.W. Canonica, Lihong Zhang, María José Torres, Y. Okamoto, Liam O'Mahony, Radosław Gawlik, Jolanta Walusiak-Skorupa, Sharon Chinthrajah, Winfried Rief, T. Haatela, M. Morais-Almeida, Ioana Agache, Manfred Schedlowski, I Skypala, R. Brehler, D. Y. Wang, João Fonseca, I. J. Ansotegui, Robyn E O'Hehir, Oscar Palomares, Charlotte G. Mortz, J. C. Ivancevich, C. Suppli Ulrik, M. T. Ventura, P M Matricardi, S Untersmayr, Gabrielle L. Onorato, Amir Hamzah Abdul Latiff, Frederico S. Regateiro, Vanitha Sampath, Arũnas Valiulis, Marek Jutel, Luisa Brussino, Pedro Carreiro-Martins, Jean Bousquet, Nikolaos G. Papadopoulos, A. Bedbrook, Torsten Zuberbier, Karin Hoffmann-Sommergruber, Edward F. Knol, Ear, Nose and Throat, AII - Inflammatory diseases, UCL - SSS/IREC/PNEU - Pôle de Pneumologie, ORL et Dermatologie, UCL - (SLuc) Service d'oto-rhino-laryngologie, Philipps Universität Marburg = Philipps University of Marburg, Allergologie, Stimm und Sprachstörungen [Wiesbaden, Germany], Zentrum für Rhinologie und Allergologie [Wiesbaden, Germany], University of Wrocław [Poland] (UWr), ALL-MED, Swiss Institute of Allergy and Asthma Research (SIAF), Universität Zürich [Zürich] = University of Zurich (UZH), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Medizinische Universität Wien = Medical University of Vienna, Stanford University, Skane University Hospital [Malmo], Lund University [Lund], Charles University [Prague] (CU), University Medical Center Groningen [Groningen] (UMCG), Univ Toronto, Hosp Sick Children, Peter Gilgan Ctr Res & Learning, Mol Med, Toronto, ON M5G 0A4, Canada, The Hospital for sick children [Toronto] (SickKids), University of Toronto, Amsterdam UMC - Amsterdam University Medical Center, Alfred Health, Victoria University [Melbourne], University College Cork (UCC), Sean N. Parker Center for Allergy and Asthma Research [Stanford], Stanford Medicine, Stanford University-Stanford University, University Clinics of Essen, University of Essen, Allergy Unit [Malaga, Spain] (National Network ARADyAL), Hospital Regional Universitario de Málaga = Regional University Hospital of Malaga [Spain], Helmholtz Zentrum München = German Research Center for Environmental Health, University Hospital Augsburg, National University of Singapore (NUS), Beijing Tongren Hospital, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, National Heart and Lung Institute [London] (NHLI), Imperial College London-Royal Brompton and Harefield NHS Foundation Trust, University Hospital Münster - Universitaetsklinikum Muenster [Germany] (UKM), Evelina London Children's Hospital, King‘s College London, CEU-San Pablo University and HM-Hospitals School of Medicine, University of Cagliari, Medical University of Silesia (SUM), Istanbul University, Cliniques Universitaires Saint-Luc [Bruxelles], University Medical Center [Utrecht], Helsinki University Central Hospital, University of Helsinki, Odense University Hospital (OUH), Luxembourg Institute of Health (LIH), Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Hospital Sant Joan de Déu [Barcelona], Institut de Recerca Pediàtrica Hospital Sant Joan de Déu [Barcelona, Spain], University of Edinburgh, NHS Foundation Trust [London], The Royal Marsden, Nofer Institute of Occupational Medicine (NIOM), Hospital Quirónsalud Bizkaia [Bilbao], Ghent University Hospital, Sun Yat-Sen University [Guangzhou] (SYSU), Karolinska Institutet [Stockholm], Woolcock Institute of Medical Research [Sydney], The University of Sydney, University of Turin, Mauriziano Umberto I Hospital, Humanitas University [Milan] (Hunimed), Vall d'Hebron University Hospital [Barcelona], Hospital de Dona Estefania, NOVA Medical School - Faculdade de Ciências Médicas (NMS), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Federal University of Bahia School of Medicine, Global Alliance Against Chronic Respiratory Diseases (GARD-WHO), Medical Consulting Czarlewski, Faculdade de Medicina da Universidade do Porto (FMUP), Universidade do Porto = University of Porto, MEDIDA, Lda, David Tvildiani Medical University (DTMU), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Servicio de Alergia e ImmunologiaBuenos Aires (Clinica Santa Isabel), Barlicki University Hospital, Vilnius University [Vilnius], Hospital Medica Sur [Mexico City, Mexico], Pantai Hospital [Kuala Lumpur], Hospital CUF Descobertas, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona (UB), CIBER de Epidemiología y Salud Pública (CIBERESP), Johns Hopkins University School of Medicine [Baltimore], Fukujuji Hospital, Tokyo National Hospital, Chiba Rosai Hospital, Chiba University Hospital, Royal Manchester Children's Hospital, University of Manchester [Manchester], General Children's Hospital of Athens P & A Kyriakou, 'Santa Maria della Speranza' Hospital, Centro Hospitalar e Universitário [Coimbra], Coimbra Institute for Clinical and Biomedical Research [Coimbra, Portugal] (iCBR - Faculty of Medicine), University of Coimbra [Portugal] (UC), Medical University of Warsaw - Poland, Hvidovre Hospital, University of Copenhagen = Københavns Universitet (UCPH), Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Manisa Celal Bayar University, Transilvania University of Brasov, Salvy-Córdoba, Nathalie, Department of Dermatology, Allergology and Venereology, and HUS Inflammation Center

Subjects
0301 basic medicine, viruses, Eaaci Position Paper, Medizin, Cochrane Library, GUIDELINES, FOOD ALLERGY, allergen immunotherapy, allergy clinic, anaphylaxis, asthma, clinical trials, COVID-19, Position Paper, psychological impact, SARS-CoV-2, Allergists, Health Personnel, Humans, Hypersensitivity, Information Technology, Patient Care Team, Triage, SARS‐CoV‐2, DESENSITIZATION, 0302 clinical medicine, MESH: Patient Care Team, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, HDE ALER, Pandemic, Health care, Immunology and Allergy, ATOPIC-DERMATITIS, MESH: COVID-19, [SDV.IMM.ALL]Life Sciences [q-bio]/Immunology/Allergology, [SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, allergen immunotherapy (AIT), virus diseases, DRUG HYPERSENSITIVITY REACTIONS, 3. Good health, INFECTIONS, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, MESH: Triage, [SDV.IMM.ALL] Life Sciences [q-bio]/Immunology/Allergology, Allergen immunotherapy, medicine.medical_specialty, MESH: Information Technology, MESH: Hypersensitivity, Immunology, education, MEDLINE, DIAGNOSIS, psychological COVID, 03 medical and health sciences, MESH: Allergists, COVID‐19, medicine, MESH: SARS-CoV-2, ddc:610, RHINOSINUSITIS, MESH: Humans, business.industry, Clinical trial, Coronavirus, EXACERBATIONS, 030104 developmental biology, 030228 respiratory system, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Family medicine, 3121 General medicine, internal medicine and other clinical medicine, Position paper, MESH: Health Personnel, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

BackgroundThe Coronavirus disease 2019 (COVID‐19) has evolved as a pandemic infectious disease transmitted by the severe acute respiratory syndrome coronavirus (SARS‐CoV‐)2. Allergists and other health care providers (HCPs) in the field of allergies and associated airway diseases are in the front line, taking care of patients potentially infected with SARS‐CoV‐2. Hence, strategies and practices to minimize risks of infection for both HCPs and treated patients have to be developed and followed by allergy clinics.MethodThe scientific information on COVID‐19 was analyzed by a literature search in Medline, Pubmed, national and international guidelines from the European Academy of Allergy and Clinical Immunology (EAACI), the Cochrane Library and the Internet.ResultsBased on diagnostic and treatment standards developed by EAACI, on international information regarding COVID‐19, on guidelines of the World Health Organization (WHO) and other international organizations as well as on previous experience, a panel of experts including clinicians, psychologists, IT experts and basic scientists along with EAACI and the “Allergic Rhinitis and its Impact on Asthma (ARIA)” inititiative have developed recommendations for the optimal management of allergy clinics during the current COVID‐19 pandemic. These recommendations are grouped into nine sections on different relevant aspects for the care of patients with allergies.ConclusionsThis international Position Paper provides recommendations on operational plans and procedures to maintain high standards in the daily clinical care of allergic patients whilst ensuring necessary safety in the current COVID‐19 pandemic.

Published
2021

6. A COVID-19 Decision Support System for Phone Call Triage, Designed by and for Medical Students

Author

Rosy Tsopra, Anita Burgun, Adrien Boukobza, Jean-Patrick Vrel, Samy Oulmane, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), TSOPRA, Rosy, École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)

Subjects
MESH: Pandemics, Decision support system, medicine.medical_specialty, Students, Medical, Computer science, [SDV]Life Sciences [q-bio], Decision tree, Medical equipment, [INFO] Computer Science [cs], Clinical decision support system, Task (project management), MESH: Students, Medical, medicine, MESH: COVID-19, Humans, MESH: SARS-CoV-2, [INFO]Computer Science [cs], Pandemics, Medical Simulation, Medical education, MESH: Humans, Clinical Decision Support System, SARS-CoV-2, Medical simulation, COVID-19, Digital health, Triage, [SDV] Life Sciences [q-bio], Medical Education, Emergency, Digital Health, MESH: Triage
Abstract

International audience; During spring 2020, SARS-CoV-2 pandemic induced shortage of medical equipment, hospital capacity and staff. To tackle this issue, medical students have been strongly involved in early patient triage through medical phone call regulation. Here, we present an intelligent web-based decision support system for COVID-19 phone call regulation, developed by and for, medical students to help them during this difficult but crucial task. The system is divided into 5 tabs. The first tab displays administrative information, clinical data related to life-threatening emergency, and personalized recommendations for patient management. The second tab displays a PDF report summarizing the clinical situation; the third tab displays the guidelines used for establishing the recommendations, and the fourth tab displays the overall algorithm in the form of a decision tree. The fifth tab provided a short user guide. The system was assessed by 21 medical staff. More than 90% of them appreciated the navigation and the interface, and found the content relevant. 90,5% of them would like to use it during the medical regulation. In the future, we plan to use this system during simulation-based medical learning for the initial medical training of medical students.

Published
2021

7. Development of a bedside score to predict dengue severity

Author

A. Merlet, Carole Forfait, Myrielle Dupont-Rouzeyrol, Elise Klement-Frutos, Emilie Barsac, Cécile Cazorla, Sylvie Laumond, Daina Aubert, Jean-Paul Grangeon, Anabelle Valiame, Ann-Claire Gourinat, Catherine Inizan, Elodie Descloux, Arnaud Tarantola, Ingrid Marois, Centre hospitalier territorial Gaston-Bourret [Dumbea] (CHT), Centre hospitalier territorial Gaston-Bourret [Nouméa], Direction des affaires sanitaires et sociales de Nouvelle-Calédonie, Institut Pasteur de Nouvelle-Calédonie, Réseau International des Instituts Pasteur (RIIP), CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), and This work was supported by the government of New Caledonia.

Subjects
Male, Multivariate analysis, Hospital triage, MESH: Dengue, MESH: Hospitalization, Infectious and parasitic diseases, RC109-216, [SDV.IMM.II]Life Sciences [q-bio]/Immunology/Innate immunity, Dengue fever, Dengue, Medical microbiology, 0302 clinical medicine, Risk Factors, MESH: Risk Factors, 030212 general & internal medicine, MESH: Models, Theoretical, Severity score, 0303 health sciences, Univariate analysis, Area under the curve, Prognosis, Predictive value, MESH: Predictive Value of Tests, 3. Good health, Hospitalization, Infectious Diseases, [SDV.IMM.IA]Life Sciences [q-bio]/Immunology/Adaptive immunology, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Cohort, Female, MESH: Triage, Research Article, Operational tool, medicine.medical_specialty, 030231 tropical medicine, MESH: Prognosis, 03 medical and health sciences, New Caledonia, Predictive Value of Tests, medicine, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Retrospective Studies, 030304 developmental biology, MESH: Humans, business.industry, Univariate, Outbreak, MESH: Retrospective Studies, Models, Theoretical, MESH: New Caledonia, medicine.disease, MESH: Male, Pacific, Tropical medicine, Emergency medicine, Triage, business, MESH: Female, Arboviruses
Abstract

Background In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit. Methods We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient’s score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method. Results Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were: age, comorbidities, presence of at least one alert sign, platelets count 9/L, prothrombin time 10 N, and previous dengue infection. Severity was not influenced by the infecting dengue serotype nor by previous Zika infection. Two models to predict dengue severity were built according to sex. Best models for females and males had respectively a median Area Under the Curve = 0.80 and 0.88, a sensitivity = 84.5 and 84.5%, a specificity = 78.6 and 95.5%, a positive predictive value = 63.3 and 92.9%, a negative predictive value = 92.8 and 91.3%. Models were secondarily validated on 130 patients hospitalized for dengue in 2018. Conclusion We built robust and efficient models to calculate a bedside score able to predict dengue severity in our setting. We propose the spreadsheet for dengue severity score calculations to health practitioners facing dengue outbreaks of enhanced severity in order to improve patients’ medical management and hospitalization flow.

Published
2020

8. CEdRIC: Strategy for Patient Education During COVID-19 Triage

Author

Benoit Pétré, Jean-Christophe Servotte, Justine Piazza, Alexandre Ghuysen, Aurore Margat, Remi Gagnayre, Dieudonné Leclercq, Université de Liège, Be.Hive, Interdisciplinarity Primary Care Chair, Centre Hospitalier Universitaire de Liège (CHU-Liège), Laboratoire éducations et promotion de la santé (LEPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Nord, Université de Liège - Faculté de Psychologie et des Sciences de l'Education (UNIVERSITÉ DE LIÈGE - FACULTÉ DE PSYCHOLOGIE ET DES SCIENCES DE L'EDUCATION), and DE ANDRADE, Vincent

Subjects
MESH: Pandemics, MESH: Coronavirus Infections, Pneumonia, Viral, MESH: Delivery of Health Care, lcsh:Medicine, MESH: Patient Education as Topic, Betacoronavirus, Belgium, Patient Education as Topic, MESH: Belgium, Humans, MESH: COVID-19, Endemic Infections, MESH: SARS-CoV-2, Pandemics, MESH: Humans, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, SARS-CoV-2, lcsh:R, lcsh:Medical emergencies. Critical care. Intensive care. First aid, COVID-19, lcsh:RC86-88.9, MESH: Pneumonia, Viral, Commentary, MESH: Betacoronavirus, Triage, Coronavirus Infections, Delivery of Health Care, MESH: Triage, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; The current coronavirus disease 2019 (COVID-19) pandemic is forcing healthcare systems around the word to organise care differently than before. Prompt detection and effective triage and isolation of potentially infected and infectious patients are essential to preventing unnecessary community exposure. Since there are as yet no medications to treat or vaccines to prevent COVID-19, prevention focuses on self-management strategies, creating patient education challenges for physicians doing triage and testing. This article describes a five-step process for effectively educating, at discharge, patients who are suspected of being infectious and instructed to self-isolate at home. We are proposing the CEdRIC strategy as a practical, straightforward protocol that meets patient education and health psychology science requirements. The main goal of the CEdRIC process is to give patients self-management strategies aimed at preventing complications and disease transmission. The COVID-19 pandemic is challenging clinicians to rapidly teach their patients self-management strategies while managing the inherent pressures of this emergency situation. The CEdRIC strategy is designed to deliver key information to patients and standardize the discharge process. CEdRIC is currently being tested at triage centres in Belgium. Formal assessment of its implementation is still needed.

Published
2020

9. What if the worst consequences of COVID-19 concerned non-COVID patients?

Author

Pierre Géraud Claret, Radjiv Goulabchand, Benoit Lattuca, Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), and Unité de réanimation médicale [CHU de Carémeau, Nîmes]

Subjects
0301 basic medicine, MESH: Intensive Care Units / supply & distribution, MESH: Emergency Service, Hospital* / organization & administration, 0302 clinical medicine, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Pandemic, Medicine, 030212 general & internal medicine, MESH: Pneumonia, Viral / epidemiology, MESH: Pneumonia, Viral / therapy, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, MESH: Health Resources / supply & distribution, biology, lcsh:Public aspects of medicine, General Medicine, Public health system, 3. Good health, Intensive Care Units, Infectious Diseases, Health Resources, MESH: Betacoronavirus, France, Coronavirus Infections, Emergency Service, Hospital, MESH: Triage, 2019-20 coronavirus outbreak, medicine.medical_specialty, MESH: Pandemics, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 030106 microbiology, Pneumonia, Viral, Article, lcsh:Infectious and parasitic diseases, 03 medical and health sciences, Betacoronavirus, Humans, lcsh:RC109-216, MESH: SARS-CoV-2, Intensive care medicine, Pandemics, MESH: Humans, business.industry, SARS-CoV-2, Public Health, Environmental and Occupational Health, COVID-19, lcsh:RA1-1270, MESH: Coronavirus Infections / therapy, biology.organism_classification, Triage, MESH: Delivery of Health Care / organization & administration, MESH: France, MESH: Coronavirus Infections / epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Delivery of Health Care
Abstract

International audience; We highlight in this short article the side-effects of COVID-19 pandemic on the management of non-COVID patients, with potential detrimental and irreversible complications. We thus propose adjusted strategies to deal with both COVID and non-COVID patients.

Published
2020
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10. Preparing for emerging respiratory pathogens such as SARS-CoV, MERS-CoV, and SARS-CoV-2

Author

Ja, Al-Tawfiq, Ma, Garout, Philippe Gautret, Johns Hopkins University School of Medicine [Baltimore], Umm Al-Qura University, Vecteurs - Infections tropicales et méditerranéennes (VITROME), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut de Recherche Biomédicale des Armées (IRBA), Institut Hospitalier Universitaire Méditerranée Infection (IHU Marseille), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] (IRBA)

Subjects
MESH: Protective Devices, MESH: Coronavirus Infections, Severe Acute Respiratory Syndrome, Communicable Diseases, Emerging, Patient Isolation, MESH: Infection Control, COVID-19 Testing, Hospital Administration, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, MESH: COVID-19, MESH: Hospital Administration, MESH: Communicable Diseases, Emerging, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, Severe acute respiratory syndrome-related coronavirus, MESH: Guideline Adherence, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Middle East Respiratory Syndrome Coronavirus, MESH: Betacoronavirus, MESH: Interdisciplinary Communication, Guideline Adherence, Symptom Assessment, Coronavirus Infections, MESH: Triage, MESH: Pandemics, Pneumonia, Viral, Saudi Arabia, MESH: SARS Virus, MESH: Civil Defense, MESH: Personnel, Hospital, Betacoronavirus, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, MESH: Severe Acute Respiratory Syndrome, Humans, MESH: SARS-CoV-2, [SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology, Pandemics, MESH: Clinical Laboratory Techniques, Infection Control, MESH: Humans, Clinical Laboratory Techniques, SARS-CoV-2, Protective Devices, MESH: Symptom Assessment, MESH: Middle East Respiratory Syndrome Coronavirus, MESH: COVID-19 Testing, COVID-19, Civil Defense, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, MESH: Patient Isolation, MESH: Saudi Arabia, Personnel, Hospital, MESH: Pneumonia, Viral, Interdisciplinary Communication, Triage
Abstract

International audience; Preparing for emerging respiratory pathogens is a fundamental requirement for enhancements of the safeguard in healthcare settings. We are facing an increasing pressure to be prepared more than before. Healthcare organizations should be ready to deal with such emerging infectious disease. Here, we share some points that are essential to be considered while we prepare our institutions to prevent the transmission of emerging respiratory pathogens such as MERS-CoV and the recently emerging pandemic of SARS-CoV-2, the causative agent of COVID-19.

Published
2020

11. Validation of a Self-administered Inflammatory Arthritis Detection Tool for Rheumatology Triage

Author

Peter Tugwell, Mary J. Bell, George A. Wells, Vivian P. Bykerk, Francis Guillemin, Ruben Tavares, McMaster University [Hamilton, Ontario], University of Ottawa [Ottawa] (uOttawa), Hospital for Special Surgery, Centre d'Investigation Clinique - Epidemiologie Clinique/essais Cliniques Nancy, Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale (INSERM), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université Paris Descartes - Paris 5 (UPD5)-Université de Lorraine (UL), Sunnybrook Health Sciences Centre, and University of Ottawa [Ottawa]

Subjects
Male, Inflammatory arthritis, Logistic regression, Arthritis, Rheumatoid, 0302 clinical medicine, MESH: Aged, 80 and over, MESH: Osteoarthritis, Scoring algorithm, Lupus Erythematosus, Systemic, Immunology and Allergy, 030212 general & internal medicine, Aged, 80 and over, MESH: Aged, MESH: Arthritis, Rheumatoid, MESH: Middle Aged, Intensive treatment, Middle Aged, MESH: Predictive Value of Tests, MESH: Diagnostic Self Evaluation, Waiting list, Female, MESH: Pain, Algorithms, Case identification, MESH: Spondylarthropathies, MESH: Triage, Adult, medicine.medical_specialty, Immunology, Pain, MESH: Algorithms, Sensitivity and Specificity, Diagnostic Self Evaluation, 03 medical and health sciences, Rheumatology, Predictive Value of Tests, Internal medicine, MESH: Rheumatology, Osteoarthritis, medicine, Humans, MESH: Lupus Erythematosus, Systemic, Aged, 030203 arthritis & rheumatology, MESH: Humans, business.industry, MESH: Adult, medicine.disease, Triage, MESH: Sensitivity and Specificity, MESH: Male, Physical therapy, Spondylarthropathies, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH: Female
Abstract

Objective.The benefits of early intensive treatment of inflammatory arthritis (IA) are dependent on timely and accurate case identification. In our study, a scoring algorithm for a self-administered IA detection tool was developed and validated for the rheumatology triage clinical setting.Methods.A total of 143 consecutive consenting adults, newly referred to 2 outpatient rheumatology practices, completed the tool. A scoring algorithm was derived from the best-fit logistic regression model using age, sex, and responses to the 12 tool items as candidate predictors of the rheumatologists' blinded classification of IA. Bootstrapping was used to internally validate and refine the model.Results.The 30 IA cases were younger than the 113 non-cases (p < 0.0001) and included clinical diagnoses of early IA (n = 10), rheumatoid arthritis (n = 9), and spondyloarthropathies (n = 11). Non-cases included osteoarthritis (n = 46), pain syndromes (n = 19), systemic lupus erythematosus (n = 5), and miscellaneous, noninflammatory musculoskeletal complaints (n = 43). The best-fit model included younger age, male sex, “trouble making a fist,” “morning stiffness,” “ever told you have RA,” and “psoriasis diagnosis.” The overall predictive performance (standard error, SE) of the derivation model was 0.91 (0.03). Internal validation of the derivation model across 200 bootstrap samples resulted in a mean predictive performance (SE) of 0.904 (0.002). The refined tool had a mean predictive performance (SE) of 0.915 (0.002), a sensitivity of 0.855 (0.005), and specificity of 0.873 (0.003).Conclusion.A simple, self-administered tool was developed and internally validated for the sensitive and specific detection of IA in a rheumatology waiting list sample. The tool may be used to triage IA from rheumatology referrals.

Published
2013

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