Open Call HORIZON-HLTH-2023-IND-06-07: Development and Harmonisation of Methodologies for Assessing Digital Health Technologies in Europe

European CommissionThis topic aims at addressing digital transition challenges through supporting activities that are enabling or contributing to one or several expected impacts of destination 6 "Maintaining an innovative, sustainable and globally competitive health industry". More specifically, this topic aims at supporting activities that are contributing to the following impact area: "High quality digital services for all". To that end, proposals under this topic should aim to deliver results that are directed towards and contributing to all of the following expected outcomes:
  • Policymakers in the EU have at their disposal a methodological framework and standardised approaches for assessing digital health technologies, that helps them make evidence-based decisions regarding the introduction of digital health technologies in their health and care systems with added value for patients and society.
  • Regulators have access to robust, scientifically underpinned evaluation methodologies.
  • EU citizens gain faster access to safe and well-performing person-centred digital technologies and are empowered through these tools.
  • Health technology developers are better informed and dispose of more guidance on the evidence needed to demonstrate the added value of digital health technologies and have better insights on market predictability.
  • (Digital) Health Industry/digital health technology developers and HTA bodies can contribute to the development of EU harmonised Health Technology Assessment (HTA) rules based on common principles.
  • Improved cross-border use and interoperability of digital health tools and services throughout the EU and Associated Countries.
  • Increased trust in digital health technologies and better integration of digital health tools and services in health and care systems.

Digital health technologies have been driving a revolution in health and care ranging from general use of computers to algorithms designed to assist radiologists and radiotherapists in detecting and treating diseases, from robotic surgery to artificial intelligence, machine learning, computer aided decision models, and from mobile apps helping patients to self-manage their disease to electronic health records.

Digital health technologies are expected to further contribute to better people-centred health and care systems and have the vast potential to improve our ability to accurately prevent, diagnose and treat diseases.

However, assessing the added value and health benefits for patients and society pose a number of challenges in particular of methodological and technical nature. Best practice for common approaches in methodology for digital health are lacking, especially in the digital health tools that include artificial intelligence algorithms. A framework for the assessment of the digital transformation of health services and its impact is vital to generate the evidence required for decision-making on stimulating, using and/or funding digital health strategies at various levels in the health and care systems.

The Expert Panel on effective ways of investing in Health (EXPH) recommended in its report 'Assessing the impact of digital transformation of health services', further investment in the development of assessment methodologies and in a European repository for evaluation methods and evidence of digital health services.

To date, such assessment frameworks are relatively scarce, especially those addressing the transformative aspects of healthcare delivery on the organisational and operational level.

The proposals are expected to develop and harmonise methodologies for assessing digital health technologies (including mhealth apps and telehealth, as well as Artificial Intelligence powered health technologies) in order to facilitate assessment of their added value at individual, health system and society levels and facilitate the cross-border deployment of digital health services within the EU. Existing Health Technology Assessment (HTA) methodology is well developed for health technologies such as medicinal products, but also for some categories of medical devices; however digitalisation raises new methodological challenges to the standardisation of assessment criteria such as privacy, cybersecurity, data storage and handling, interoperability, usability etc. Also including aspects like learning curves, iterative development of innovations, variability between settings, determining optimal timing of evaluations in the development process (maturity) are not yet solved.

Proposals are expected to build on existing frameworks such as (but not restricted to) 'Model for Assessment of Telemedicine' (MAST framework - Kidholm et al., 2012) and the results of previous EU-funded projects in particular (but not restricted to) COMED, project that already identified HTA challenges of telehealth and mhealth, and mHealth hub.

Proposals should consider involving the JRC to take advantage of its expertise on assessment frameworks of innovative health technologies and its activities at the interface between research and regulatory aspects and/or in translating assessment results into best practice recommendations anchored in EU policies. In that respect, the JRC is open to collaborate with any successful proposal after its approval.

Opening date: 12 January 2023

Deadline: 13 April 2023 17:00:00 Brussels time

Deadline Model: single-stage

Type of action: HORIZON-RIA HORIZON Research and Innovation Actions

For topic conditions, documents and submission service, please visit:
https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-hlth-2023-ind-06-07

Most Popular Now

AI System Helps Doctors Identify Patient…

A new study from Vanderbilt University Medical Center shows that clinical alerts driven by artificial intelligence (AI) can help doctors identify patients at risk for suicide, potentially improving prevention efforts...

Smartphone App can Help Reduce Opioid Us…

Patients with opioid use disorder can reduce their days of opioid use and stay in treatment longer when using a smartphone app as supportive therapy in combination with medication, a...

AI's New Move: Transforming Skin Ca…

Pioneering research has unveiled a powerful new tool in the fight against skin cancer, combining cutting-edge artificial intelligence (AI) with deep learning to enhance the precision of skin lesion classification...

Leveraging AI to Assist Clinicians with …

Physical examinations are important diagnostic tools that can reveal critical insights into a patient's health, but complex conditions may be overlooked if a clinician lacks specialized training in that area...

AI can Improve Ovarian Cancer Diagnoses

A new international study led by researchers at Karolinska Institutet in Sweden shows that AI-based models can outperform human experts at identifying ovarian cancer in ultrasound images. The study is...

Predicting the Progression of Autoimmune…

Autoimmune diseases, where the immune system mistakenly attacks the body's own healthy cells and tissues, often have a preclinical stage before diagnosis that’s characterized by mild symptoms or certain antibodies...

Major EU Project to Investigate Societal…

A new €3 million EU research project led by University College Dublin (UCD) Centre for Digital Policy will explore the benefits and risks of Artificial Intelligence (AI) from a societal...

Using AI to Uncover Hospital Patients�…

Across the United States, no hospital is the same. Equipment, staffing, technical capabilities, and patient populations can all differ. So, while the profiles developed for people with common conditions may...

New AI Tool Uses Routine Blood Tests to …

Doctors around the world may soon have access to a new tool that could better predict whether individual cancer patients will benefit from immune checkpoint inhibitors - a type of...

New Method Tracks the 'Learning Cur…

Introducing Annotatability - a powerful new framework to address a major challenge in biological research by examining how artificial neural networks learn to label genomic data. Genomic datasets often contain...

From Text to Structured Information Secu…

Artificial intelligence (AI) and above all large language models (LLMs), which also form the basis for ChatGPT, are increasingly in demand in hospitals. However, patient data must always be protected...

Picking the Right Doctor? AI could Help

Years ago, as she sat in waiting rooms, Maytal Saar-Tsechansky began to wonder how people chose a good doctor when they had no way of knowing a doctor's track record...