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DESCRIPTION:Jean Nikiema\, MD\, PhD\n\nAssistant Professor | School of Publ
 ic Health\n	Department of Health Management\, Evaluation and Policy Univers
 ité de Montréal\n\nThe Seminars in Epidemiology organized by the Departmen
 t of Epidemiology\, Biostatistics and Occupational Health at the ��ɫ��Ƶ Sc
 hool of Population and Global Health is a self-approved Group Learning Act
 ivity (Section 1) as defined by the maintenance of certification program o
 f the Royal College of Physicians and Surgeons of Canada. Physicians requi
 ring accreditation\, please complete the Evaluation Form and send to admin
 coord.eboh [at] mcgill.ca \n\nWHEN: Monday\, January 26\, 2026\, from 3:30
 -4:30 p.m.\n	WHERE: Hybrid | Onsite at 2001 ��ɫ��Ƶ College\, Rm 1140 | Zoom
 \n	NOTE: Jean Nikiema will present in-person\n\nAbstract\n\nReal-world data
  (RWD)\, especially information extracted from electronic health records\,
  holds enormous potential for clinical decision support\, cost analysis\, 
 and public health planning. Yet this potential is frequently limited by fr
 agmented data pipelines\, inconsistent clinical semantics\, incomplete pro
 venance\, and uncertain fitness-for-use. The CIRCULATE consortium\, paired
  with the Provem platform\, were created to address these challenges throu
 gh an integrated ecosystem that turns raw RWD into decision-ready assets.
 \n\nThis talk will describe how Provem and the CIRCULATE consortium operat
 ionalizes a pragmatic “AI for Health” mindset: starting with data provenan
 ce and quality signals\, enabling interoperability through ontology-based 
 harmonization\, and supporting clinical trajectory reconstruction (hospita
 l stays\, transitions\, and longitudinal care pathways). We will present h
 ow pathway discovery (clustering\, labeling\, and phenotyping of stays/tra
 jectories) is combined with knowledge-based standardization\, and how a hu
 man-in-the-loop workflow supports validation\, traceability\, and clinical
  relevance of AI algorithms. Finally\, we will highlight how the ecosystem
  creates a continuous transparency loop so that improvements to data gover
 nance and data production directly translate into more reliable analytics 
 and AI capabilities.\n\nLearning Objectives\n\nAt the end of this talk\, a
 ttendees will be able to:\n\n\n	Present the limitations of RWD for AI: data
  quality\, bias\, and recognize what “realistic expectations” look like in
  clinical settings\;\n	Present the evaluation of a responsible AI system th
 at identifies and convert patterns in EHR data into clinically useful info
 rmation for decision support and cost analysis\;\n	Understand the operation
 alized responsible\, and scalable analytics by combining data-driven pathw
 ay discovery with knowledge-based standardization while keeping human-in-t
 he-loop for algorithm validation.\n\n\nSpeaker Bio\n\nJean-Noël Nikiema is
  an Assistant Professor at the Université de Montréal School of Public Hea
 lth (ESPUM)\, a regular researcher at the Centre de recherche en santé pub
 lique (UdeM–CIUSSS du Centre-Sud-de-l’Île-de-Montréal)\, a researcher with
  OBVIA (Sustainable Health Axis)\, and Co-Director of LabTNS\, focused on 
 digital transformation in health\, and co responsible of the infrastructur
 e axe at Québec Digital Health Network. His research centers on the real-w
 orld conditions required for successful health innovation\, data quality\,
  interoperability\, governance\, organizational impact\, and uptake in pra
 ctice settings\, to support learning health systems aligned with operation
 al constraints. His training integrates clinical\, public health\, and inf
 ormatics perspectives: MD (Université Nazi Boni)\, MSc in Public Health-Me
 dical Informatics (Université de Bordeaux)\, PhD in Public Health-Informat
 ics and Health (Université de Bordeaux)\, followed by postdoctoral trainin
 g at CHUM. Prior to joining ESPUM\, he was research visiting scholar at th
 e U.S. National Library of Medicine.\n
DTSTART:20260126T203000Z
DTEND:20260126T213000Z
SUMMARY:From Data to Decisions: Building a Data-Quality Ecosystem for Clini
 cal Decision Support and Care Pathway Analytics
URL:/spgh/channels/event/data-decisions-building-data-
 quality-ecosystem-clinical-decision-support-and-care-pathway-analytics-370
 260
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