A5073455590- Health Sciences Research and Education
- Health Literacy and Information Accessibility
- Healthcare Systems and Technology
- Machine Learning in Healthcare
- Explainable Artificial Intelligence (XAI)
- Artificial Intelligence in Healthcare and Education
- Mobile Health and mHealth Applications
- Healthcare Quality and Management
- Health Systems, Economic Evaluations, Quality of Life
- Telemedicine and Telehealth Implementation
- Electronic Health Records Systems
- Healthcare cost, quality, practices
- Patient Satisfaction in Healthcare
- COVID-19 diagnosis using AI
- Patient-Provider Communication in Healthcare
- Pharmaceutical studies and practices
- Health Education and Validation
McGill University
2014-2024
Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
2024
Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
2024
Jewish General Hospital
2024
Cambridge Health Alliance
2015
Tufts University
2015
University of Virginia Health System
2015
The Family Centre
2014
Background Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation lagged. Incorporating explainable (XML) through the development of a decision support tool using design thinking approach is expected lead greater uptake such tools. Objective This work aimed explore how constant engagement clinician end users can address lack adoption ML tools in contexts due their transparency and challenges related presenting...
Background: Online consumer health information addresses problems, self-care, disease prevention, and care services is intended for the general public. Using this information, people can improve their knowledge, participation in decision-making, health. However, there are no comprehensive instruments to evaluate value of from a perspective.
Abstract Inspired by the acquisition–cognition–application model (T. Saracevic & K.B. Kantor, 1997 ), we developed a tool called Information Assessment Method to more clearly understand how physicians use clinical information. In primary healthcare, conducted naturalistic and longitudinal study of searches for Forty‐one family received handheld computer with linked one commercial electronic knowledge resource. Over an average 320 days, 83% 2,131 information were rated using Method....
<h3>PURPOSE</h3> We wanted to describe family physicians' use of information from an electronic knowledge resource for answering clinical questions, and their perception subsequent patient health outcomes; estimate the number needed benefit (NNBI), defined as patients whom was retrieved 1 benefit. <h3>METHODS</h3> undertook a mixed methods research study, combining quantitative longitudinal qualitative studies. Participants were 41 physicians primary care clinics across Canada. Physicians...
Background: Patient satisfaction is a complex, multidimensional concept that difficult to measure. However, there agreement understanding the expectations of patient community or "what important them" an essential consideration. We chose participatory approach address in context primary care teaching clinic.
A new impact assessment method facilitates knowledge transfer by promoting a two-way exchange between providers of health information and family doctors. Participants reported high degree conceptual use email alerts. Interviews (research-in-progress) will document the meaning all ten scale items in support content validity method.Une nouvelle méthode d’évaluation facilitant le transfert des connaissances préconise l’échange réciproque entre les fournisseurs d’information médicale et médecins...
<sec> <title>BACKGROUND</title> Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation lagged. Incorporating explainable (XML) through the development of a decision support tool using design thinking approach is expected lead greater uptake such tools. </sec> <title>OBJECTIVE</title> This work aimed explore how constant engagement clinician end users can address lack adoption ML tools in contexts due their transparency...