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  2. Collective reflections on health data .

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  • Collective reflections on health data

The “Artificial Intelligence & Health” Program of the Strategic Contract for the Health Industries and Technologies Sector (CST-ITS) includes Working Groups (WG) allowing dialogue between healthcare industry stakeholders and institutions. They address cross-cutting and fundamental topics to support, under the best conditions, the implementation of the most favorable ecosystem for the development of health data utilization in France.

These WGs were established as part of the signing of the CST-ITS contract on February 4, 2019:

GT1 : Valorization of shared data and economic model of the Health Data Hub

Context & objectives

The Health Data System (SNDS) managed by the Health Data Hub is governed by the law of July 24, 2019, concerning the organization and transformation of the healthcare system. Its article 41 expands the scope of data included in the SNDS and provides for the establishment of new governance with the creation of the Health Data Platform (Health Data Hub). 

At the heart of the “AI and Health” Program, there was a desire for coordination between healthcare and digital industries and government authorities on the valorization of shared data and the economic model of the Health Data Hub. In this context, a Working Group (WG1) was established to issue recommendations for an economic model for the provision of health data from the expanded SNDS.

Governance

The Working Group is co-chaired by Philippe Maugendre (Sanofi) and Frédérique Debroucker (Medtronic). It has engaged around twenty stakeholders from the healthcare ecosystem, including healthcare and digital industries, startups, academic research organizations, healthcare professionals and institutions, as well as government authorities and regulatory bodies heavily involved in this dialogue.

Deliverables

For its analysis, the Working Group particularly relied on the expertise and experience of its members, examples of public or private databases, both French and international, and on a few articles addressing the issue of database business models.

It thus implemented the following roadmap:

  • January 2020: Finalization of recommendations by the working group
  • February 11, 2020: Sharing of recommendations with the Health Data Hub and DREES
  • September 2020: Coordination with DREES for the implementation of recommendations, in connection with regulatory publications (decrees, orders)

Downloadables deliverables

Recommendations for an Economic Model for Providing Health Data from the National Health Data System - Final Version dated February 7, 2020

GT2 : Utilization of real-world data generation in decision support

Context & objectives

The real-world data serves as a lever to facilitate the transformation of the healthcare system. The main challenge is to create conditions that enable all stakeholders to generate results that have evidential value and can support decision-making. To achieve this, it is essential to:

  • Align particularly on objectives, methods, and uses
  • Define common priorities and intentions regarding the use of real-world data

Governance

This working group is co-led by representatives from ariis with Laurie Lévy-Bachelot (MSD) and Luca Mollo (Pfizer), and a representative from SNITEM with Anne Josseran (Resmed). It mobilizes around thirty stakeholders from the healthcare ecosystem, including healthcare and digital industry players, startups, academic research organizations, patient associations, healthcare professionals and establishments, as well as government authorities and regulatory bodies deeply involved in this dialogue

Deliverables​

This working group aims to establish:

  • A guidance tool on types of information sources: The goal of this tool is to guide stakeholders in the healthcare industry towards relevant types and sources of data to facilitate knowledge sharing among them and accelerate the learning curve of newcomers. This tool is part of a broader effort led by the Ministry of Research and AVIESAN on the quality and inventory of databases in France.

  • A guide to discovering algorithms in healthcare: The objective is to map and describe the various families of algorithms in healthcare to provide stakeholders with a better understanding and build a common understanding for the industry. This educational guide will contribute to improving the quality of methods applied to healthcare data.

The development of these deliverables has led to the emergence of points of consensus on areas for improvement where all stakeholders in the industry must commit. Indeed, three key themes unite all stakeholders: data quality, method quality, and data availability. These points of consensus have been translated into a list of recommendations presented to the members of the working group on September 2, 2020.

You can access the full set of deliverables from WG2 here and download the recommendations of WG2 here.

GT3 : Assessment and challenges of data sharing among industries

Context & objectives

The exponential growth in data collection, storage, and computing capabilities makes the use of healthcare data essential and promising for patients and the healthcare system as a whole.

Sharing data generated by healthcare companies is a crucial issue for which it is necessary to:

  • Define the terms that make up industrial data sharing.
  • Build a common vision on the purposes and prerequisites for this data sharing.

Governance

This working group is co-led by representatives from ariis, Sylvie Troy from Pfizer, and Rémy Choquet from Roche. It has brought together 19 experts from various healthcare companies, including AMGEN, ariis, ASTRAZENECA, EDWARDS, INCa, IPSEN, MEDTRONIC, RESMED, ROCHE Diagnostic, SANOFI, SERVIER, SNITEM, and WLGORE.

Deliverables​

Assessment and Challenges of Data Sharing Among Industries*

Survey: State of Data Sharing Generated by Industrial Players*

*This work is the result of a joint effort conducted within the framework of CSF-ITS. In no way does this represent a commitment by ariis members to share their data.