Medical Information Commons to Support Learning Healthcare Systems: Examples From Canada

Frameworks, guidelines, and tools to develop a learning health system for Indigenous health: An environmental scan for Canada

Introduction

First Nations, Inuit, and Métis (FNIM) peoples experience systemic health disparities within Ontario's healthcare system. Learning health systems (LHS) is a rapidly growing interdisciplinary area with the potential to address these inequitable health outcomes through a comprehensive health system that draws on science, informatics, incentives, and culture for ongoing innovation and improvement. However, global literature is in its infancy with grounding theories and principles still emerging. In addition, there is inadequate information on LHS within Ontario's health care context.

Methods

We conducted an environmental scan between January and April 2021 and again in June 2022 to identify existing frameworks, guidelines, and tools for designing, developing, implementing, and evaluating an LHS.

Results

We found 37 relevant sources. This paper maps the literature and identifies gaps in knowledge based on five key pillars: (a) data and evidence-driven, (b) patient-centeredness, (c) system-supported, (d) cultural competencies enabled, and (e) the learning health system.

Conclusion

We provide recommendations for implementation accordingly. The literature on LHS provides a starting point to address the health disparities of FNIM peoples within the healthcare system but Indigenous community partnerships in LHS development and operation will be key to success.

Implementation strategies and outcome measures for advancing learning health systems: a mixed methods systematic review

BACKGROUND

Learning health systems strive to continuously integrate data and evidence into practice to improve patient outcomes and ensure value-based healthcare. While the LHS concept is gaining traction, the operationalization of LHSs is underexplored.

OBJECTIVE

To identify and synthesize the existing evidence on the implementation and evaluation of advancing learning health systems across international health care settings.

METHODS

A mixed methods systematic review was conducted. Six databases (CINAHL, Embase, Medline, PAIS, Scopus and Nursing at Allied Health Database) were searched up to July 2022 for terms related to learning health systems, implementation, and evaluation measures. Any study design, health care setting and population were considered for inclusion. No limitations were placed on language or date of publication. Two reviewers independently screened the titles, abstracts, and full texts of identified articles. Data were extracted and synthesized using a convergent integrated approach. Studies were critically appraised using relevant JBI critical appraisal checklists.

RESULTS

Thirty-five studies were included in the review. Most studies were conducted in the United States (n = 21) and published between 2019 and 2022 (n = 24). Digital data capture was the most common LHS characteristic reported across studies, while patient engagement, aligned governance and a culture of rapid learning and improvement were reported least often. We identified 33 unique strategies for implementing LHSs including: change record systems, conduct local consensus discussions and audit & provide feedback. A triangulation of quantitative and qualitative data revealed three integrated findings related to the implementation of LHSs: (1) The digital infrastructure of LHSs optimizes health service delivery; (2) LHSs have a positive impact on patient care and health outcomes; and (3) LHSs can influence health care providers and the health system.

CONCLUSION

This paper provides a comprehensive overview of the implementation of LHSs in various healthcare settings. While this review identified key implementation strategies, potential outcome measures, and components of functioning LHSs, further research is needed to better understand the impact of LHSs on patient, provider and population outcomes, and health system costs. Health systems researchers should continue to apply the LHS concept in practice, with a stronger focus on evaluation.

[Health data in France: Abundant but complex]

Alors que l’application de traçage des contacts (contact tracing)StopCovid(transformée à la mi-octobre 2020 enTousAntiCovid), débattue au Parlement en raison des inquiétudes qu’elle suscitait concernant la confidentialité des données personnelles et les libertés individuelles du fait qu’elle permet d’alerter un utilisateur s’il s’est trouvé à proximité d’une personne atteinte de la COVID-19, a été adoptée par près de 12 millions de personnes, un dispositif concernant les données individuelles de santé, aux conséquences potentiellement beaucoup plus importantes pour les citoyens et leurs données personnelles, a commencé à se mettre en place suite à la Loi du 24 juillet 2019 (Loi n° 2019-774) relative à l’organisation et à la transformation du système de santé : laplateforme des données de santé, communément appeléeHealth Data Hub, constituée sous la forme d’un groupement d’intérêt public (GIP). Il ne s’agit plus de simplement signaler qu’on a croisé une personne anonyme infectée par le SARS-Cov-2, mais de réunir, dans une infrastructure informatique unique, un immense ensemble de données personnelles particulièrement sensibles concernant la totalité de la population française. Ce projet suscite désormais un certain intérêt médiatique et un début d’inquiétude. Mais cette inquiétude ne concerne presque uniquement que le fait que ces données sont déposées et gérées dans uncloudappartenant à une société américaine, un nuage informatique qui tombe sous le coup de la loi américaine de 2018 dite « CLOUD act », qui ouvre la possibilité d’un transfert des données personnelles vers les États-Unis, comme s’en est inquiété récemment le Conseil d’État. Cet aspect est certes très important, mais il masque également de très nombreux enjeux liés au partage des données de santé, et qui sont largement méconnus de la population. Nous nous proposons de rappeler, tout d’abord, ce que sont les données de santé, ce qu’elles apportent et la nécessité d’en faciliter le partage, mais aussi les difficultés rencontrées pour leur accès et leur utilisation. Nous expliquerons ensuite, dans un deuxième article, en quoi cetteplateforme des données de santé, telle qu’elle est conçue et pilotée par les pouvoirs publics pour répondre à ces difficultés et pour promouvoir l’intelligence artificielle en santé, est un projet qui soulève de fortes inquiétudes pour les citoyens et la société dans son ensemble. Même si les problèmes posés se présentent sous une forme différente selon les pays, notre propos concernera spécifiquement la situation en France.

Rare disease data stewardship in Canada

The Canadian Genomics Partnership for Rare Diseases, spearheaded by Genome Canada, will integrate genome-wide sequencing to rare disease clinical care in Canada. Centralized and tiered models of data stewardship are proposed to ensure that the data generated can be shared for secondary clinical, research, and quality assurance purposes in compliance with ethics and law. The principal ethico-legal obligations of clinicians, researchers, and institutions are synthesized. Governance infrastructures such as registered access platforms, data access compliance offices, and Beacon systems are proposed as potential organizational and technical foundations of responsible rare disease data sharing. The appropriate delegation of responsibilities, the transparent communication of rights and duties, and the integration of data privacy safeguards into infrastructure design are proposed as the cornerstones of rare disease data stewardship.