Research evidence supporting the use of digital technologies and innovation has existed for a while, yet uptake/integration of advanced digital technologies such as machine learning algorithms, has been limited by tight control of the healthcare space. Health stakeholders partly exercise this control, often with good intentions for their end users (i.e., health consumers, patients, participants). Concerns about data privacy, breaches of confidentiality, lack of patient’s consent for use of data, ambiguity over data ownership, and mistrust over the use of healthcare data for non-medical purposes limit the widespread adoption/implementation of virtual and digital care. Funding to take proven solutions to scale is also urgently needed.

This collection will explore problems and solutions to catalyze conversations, spark research, and improve methodological frameworks and ask deeper questions that can enable a rapid integration of advanced technologies such as:

• Use of digital health technologies for population health management
• Telemedicine & wearables
• Machine learning applications for natural language processing, predictive models & personalized medicine
• Chronic and infectious disease management
• Ethical & integration challenges
• Data Governance and ownership structures.

It will explore the problems faced in implementing digital solutions, setting up frameworks of integrated care, catching up on regulatory and approval processes, and issues in methodologies and technical implementation guidance documents that will help inform normative policy guidance, in Canada and worldwide.

New submissions welcome

This collection is currently open and will be continually updated with relevant articles published in FACETS. The submission deadline is November 15, 2024. To have your article considered for this collection, at step 3 of the submission process in ScholarOne, specify that your manuscript is intended for the Digital health and machine learning integrated health care systems collection.