Description
There are several validated measurement methods to assess the extent to which health professionals use SDM-related behaviors and engage patients in decision-making from an observer perspective. For example, OPTION and MAPPIN-SDM are widely used validated observational methods for this purpose.
Over the years, researchers internationally have created databases of observer ratings and associated audio/video recordings.
Artificial intelligence (AI) has the potential to standardize and streamline observer-based analysis and feedback from consultation recordings and could be incorporated into training programs, potentially enhancing efficiency, scale and spread.
Our consortium aims to develop research projects to support the implementation of SDM using AI.
Members and their students will have access to knowledge, networks and resources that can help them obtain research funding, conduct research projects, and collaborate with other stakeholders interested in exploring potential AI applications in SDM.
We are starting with a project on using AI to standardize the assessment of SDM processes during clinical consultations. Special Interest group members may have a range of interest areas within AI / SDM and are encouraged to explore these also.
- Collaborative, with and utilizing strengths of the ISDM Society.
- Equity, diversity, inclusion: from management to administration, partnership development, funding, ensuring diversity among staff, researchers, patient and public contributors and students, to attention to equity and diversity in its interdisciplinary research itself.
- Diversity across career stage, researcher demographics and countries, disciplines supported.
- Inclusive for participation and outputs.
Activities
Completed
1. We developed an international inventory of existing observer-based SDM assessment data.
2. We held a first virtual workshop on 2-3 October 2023, and the second on 22 January 2024.
3. We completed an environmental scan to identify key experts, existing AI applications in SDM and explore the views of stakeholders on the areas where AI could support SDM training or implementation (report in preparation).
Ongoing
1. We received a small grant to conduct a study to assess the feasibility of developing an AI system to evaluate the adoption of SDM processes in clinical consultations and plan a larger study.
2. We plan to develop a proposal for a larger research project based on the results of the feasibility studies.
Becoming a member
You are warmly invited to register your interest and to become a member of the ISDM Special Interest Group on use of AI in SDM.
We encourage members with interests from both AI and SDM domains.
Please contact us: ai-sdm@ulaval.ca
We are currently seeking to form a research team to develop grant applications on the use of AI for observer-based assessment of SDM processes during healthcare encounters as a basis for feedback and clinician training.
Please note that we wish to diversify our group as much as possible and are looking for experts coming from low- and middle-income countries as well as high-income countries.
To participate, applicants should be:
- Researchers or
- Supervised by a member or
- A patient/consumer/citizen partner
- Research development group: researchers who wish to participate as co-investigators in the funding proposal (subject to receiving funding for salary, if allowed by the funding stream, and doing the work).
- Collaborators: SDM experts who own data from observational instruments and agree to collaborate with the consortium, or experts who wish to participate to networking activities and be listed as collaborators with grant application (without receiving salary).
- Partner organization: We also invite organizations or networks to partner with us and to provide indication of support to the grant application. Specifically, members are invited to participate virtually in two workshops over the next year, with a view to the grant application.
We hope to be able to invite colleagues to participate in the funding proposal that will come out of this consortium, subject to the scale and eligibility of a chosen grant opportunity (please see Values).
To explore the potential of AI in SDM, including professional training and measurement, we have received a small funding award from the governments of Wales (UK) and Quebec (Canada).
The award recipients are:
Anik Giguère, Co-lead
Université Laval, Quebec City, Canada
Adrian Edwards, Co-lead
Cardiff University, Cardiff, UK
Denitza Williams
Cardiff University, Cardiff, UK
Samira Abbasgholizadeh-Rahimi
McGill University, Montreal, Canada
Natalie Joseph-Williams
Cardiff University, Cardiff, UK
France Légaré
Université Laval, Quebec City, Canada
Patient partner, Wales, UK
Patient partner, Ontario, Canada
Patient partner, Quebec, Canada
Other Researchers
Amsterdam University Medical Centers, Amsterdam, The Netherlands
Graphdata Ltd., Brazil
Bond University, Gold Coast, Australia
The Dartmouth Institute for Health Policy and Clinical Practice, USA, Lebanon, New Hampshire, USA
University of Exeter Medical School, Exeter, UK
Université de Sherbrooke, Sherbrooke, Canada
Université Laval, Quebec City, Canada
Plateforme de Recherche, Données, Intelligence et Santé (PREDIS), Quebec City, Canada
The Dartmouth Institute for Health Policy and Clinical Practice, USA, Lebanon, New Hampshire, USA
The Netherlands Cancer Institute, Amsterdam, The Netherlands
Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction,
Maastricht University Medical Centre+, Maastricht, The Netherlands
Susan B. Meister Child Health Evaluation and Research Center, Ann Arbor, Michigan, USA
Institute for Evidence-Based Health, Bond University, Gold Coast, Australia
Maastricht University, Maastricht, The Netherlands
Leiden University Medical Center, Leiden, The Netherlands / Mayo Clinic, Rochester, Minnesota, USA
Plateforme de Recherche, Données, Intelligence et Santé (PREDIS), Quebec City, Canada
Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota, USA
Lillebaelt Hospital-University Hospital of Southern Denmark, Vejle, Denmark
Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
UW-Madison School of Nursing, Madison, Wisconsin, USA
Generative AI Team, Tempus AI, Redwood City, California, USA.
The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire, USA
Dell medical school, The University of Texas at Austin, Texas, USA
University of Southampton, Southampton, UK
Maastricht University, Maastricht, The Netherlands
Lillebaelt Hospital-University Hospital of Southern Denmark, Vejle, Denmark
Susan B. Meister Child Health Evaluation and Research Center, Ann Arbor, Michigan, USA
Massachusetts General Hospital – Harvard Medical School, Boston, Massachusetts, USA
Amsterdam University Medical Centers, Amsterdam, The Netherlands
Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction,
Maastricht University Medical Centre+, Maastricht, The Netherlands
Monash University, Melbourne, Australia
Amsterdam University Medical Centers, Amsterdam, The Netherlands
Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
UMass Chan Medical School – Baystate, Massachusetts, USA
Medische Besliskunde, Afdeling Biomedical Data Sciences, Leids Universitair Medisch Centrum (LUMC), Netherlands
Dirección del Servicio Canario de la Salud (SESCS), Red de Investigación en Cronicidad, Atención, Primaria y Promoción de la Salud (RICAPPS),
Camino Candelaria s/n. C.S. El Chorrillo, El Rosario. S/C Tenerife, Spain
University of Melbourne, Melbourne, Australia
Chinese EQUATOR Centre, Hong Kong Baptist University, Hong Kong Special Administrative Region, China
