BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Limics - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Limics
X-ORIGINAL-URL:https://www.limics.fr
X-WR-CALDESC:Évènements pour Limics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20251209T090000
DTEND;TZID=Europe/Paris:20251209T140000
DTSTAMP:20260614T031815
CREATED:20250515T055938Z
LAST-MODIFIED:20251120T073237Z
UID:1710-1765270800-1765288800@www.limics.fr
SUMMARY:Soutenance de thèse Cynthia Abi Khalil
DESCRIPTION:  \n\n\n\nTitle: VIGIL CARE: Development\, validation\, and evaluation of a Nursing Process Decision Support System\n\n\nLieu: Limics\, Campus des Cordeliers\, salle de conférence\n\n\n\n\n\nSummary:\n\n\nNurses are central to patient safety and care quality\, ensuring continuous monitoring\, coordination\, and timely interventions. In intensive care units (ICUs)\, workforce shortages\, heavy workloads\, and high-acuity cases heighten stress and cognitive demands raising the risk of errors and adverse outcomes. This underscores the need for decision support. The Nursing Process (NP) provides a structured framework for clinical reasoning through assessment\, diagnosis\, planning\, implementation\, and evaluation. Accurate Nursing Diagnoses (NDs)\, documented with standardized terminologies such as NANDA-I\, are essential yet hindered by cognitive overload\, complex data environments\, and time pressure. Limited NP integration within electronic health records (EHRs) further restricts decision-making\, reinforcing the value of Clinical Decision Support Systems (CDSSs). NP-focused CDSSs (NP-CDSSs) can improve diagnostic accuracy\, care planning\, and intervention prioritization. Despite the NP-CDSS standard (2016) defining ergonomic\, functional\, and integration requirements\, adoption remains limited. Most systems only partially support NP steps and face barriers including poor workflow fit\, documentation burden\, interoperability gaps\, limited training\, and low user trust. These issues highlight the need for standard-compliant NP-CDSSs enabling accurate ND formulation\, individualized care plans\, and adaptability to diverse digital contexts. We developed VIGIL Care\, an NP-CDSS for ICUs with low digital maturity. It integrates rule-based and machine-learning (ML) decision support\, standardized nursing terminologies\, and NP-CDSS standard principles to generate timely NDs and strengthen clinical reasoning. Using an electronic assessment\, it captures demographic\, clinical\, and functional data. Combined with administrative\, laboratory\, pharmacy\, and billing inputs\, these feed a rule engine that suggests NDs using NANDA-I terminology and displays triggering factors. Nurses can confirm\, reject\, or add diagnoses\, ranked by criticality and adjustable by context. Development followed three steps: (1) Delphi method: 13 ICU experts reduced 277 NANDA-I diagnoses to 32\, prioritizing 18; (2) Indicator refinement: a second panel validated indicators\, retaining 449 and adding 52 with weighted rules; and (3) Prototype development: a web-based tool built to process assessments and generate\, explain\, and rank diagnoses. Pilot testing on pseudonymized ICU cases showed higher sensitivity and specificity versus manual methods. A randomized controlled trial (RCT) with 32 ICU nurses compared diagnostic accuracy with and without VIGIL Care using 12 validated cases. Correctness increased from 73% to 81%\, improving ND accuracy\, sensitivity\, and specificity—especially for complex cases. Some errors (e.g.\, fluid balance) persisted. Initial decision times were longer but declined with practice\, indicating a learning curve. User acceptance was high (87%)\, though occasional automation bias occurred when suggestions were incorrect. Usability (SUS 78%) and satisfaction ratings were strong\, especially for data integration\, explainability\, and workflow fit\, with minor interface concerns. Planned enhancements include full EHR interoperability\, AI-driven diagnostics integrating ML and natural language processing\, improved explainability\, alerts for conflicting data\, workload metrics\, and continuous feedback loops. VIGIL Care also includes ML-based risk prediction (e.g.\, hospital-acquired pressure injuries)\, offering dynamic\, interpretable\, data-driven support that complements rule-based reasoning and reduces cognitive load. By combining intelligent decision support with clinical oversight\, VIGIL Care shows strong potential to optimize reasoning\, foster critical thinking\, and enhance patient-centered care—particularly in ICUs and low-digital-resource environments.
URL:https://www.limics.fr/event/soutenance-de-these-cynthia-abi-khalil/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20251209T140000
DTEND;TZID=Europe/Paris:20251209T180000
DTSTAMP:20260614T031815
CREATED:20251129T154302Z
LAST-MODIFIED:20251129T154453Z
UID:1915-1765288800-1765303200@www.limics.fr
SUMMARY:Soutenance de thèse Bénédicte Melot
DESCRIPTION:Titre: Évaluation et amélioration des pratiques en antibiothérapie en soins primaires : preuve de concept à partir des données de téléconsultation \nTitle: Assessing and improving antibiotic prescriptions in primary care : a proof of concept using teleconsultation data \nLieu: Limics\, 15 rue de l’école de médecine\, 75006 PARIS
URL:https://www.limics.fr/event/soutenance-de-these-benedicte-melot/
END:VEVENT
END:VCALENDAR