Open Call SC1-HCO-18-2020: Developing Methodological Approaches for Improved Clinical Investigation and Evaluation of High-Risk Medical Devices

European Commission
  • In May 2017, a new Regulation on medical devices, Regulation (EU) 2017/745 entered into force that will come into effect in spring 2020. This new Regulation sets forth reinforced rules for the generation of clinical evidence: for instance, clinical investigations for high-risk devices will be compulsory and the requirements regarding the clinical evaluation throughout the product lifetime are more stringent.
  • Medical devices have particularities that make the conduct of clinical investigations difficult. Taking into account these particularities, there is a need for methodologies that enable to generate improved clinical evidence. New developments in medical technologies such as mHealth, artificial intelligence, and combination products, pose additional challenges and opportunities for developers[5] to generate high-quality clinical evidence.
  • Owing to rapid scientific progress and lack of knowledge on the regulatory frameworks among the scientific community, there is a need to raise awareness on new regulatory requirements in terms of clinical evidence. It is important to ensure a smooth transition from the former directive to the new regulatory framework, especially with regard to clinical evidence, by informing stakeholders involved in the clinical evaluation of high-risk medical devices (e.g. academic researchers, clinicians, manufacturers, notified bodies, contract research organisations).

These challenges can be addressed by developing and promoting methodological approaches, including alternative statistical methodologies, adapted to the specificities of high-risk medical devices. These methodological approaches will improve the robustness of clinical data needed at different phases of the product's lifetime, such as conformity assessment, post-market clinical follow-up, continuous clinical evaluation, post-market surveillance, and potentially relative effectiveness assessment.

To address these challenges, the proposals should focus on i) methods to generate clinical data both within the context of a clinical investigation and in daily practice (i.e. real-world data) so that robust clinical evidence is available for high-risk medical devices, and ii) aggregation methods that will allow to make optimal use of all available data taking into account its heterogeneity (e.g. meta-analysis methods using different statistical approaches, methods to combine data from different types of sources) and iii) promote exchange of best-practices and support network activities among developers. Proposals should in particular:

  • Analyse the particularities of high-risk medical devices and the potential resulting problems with regard to clinical evaluation, carry out a review of the currently used clinical investigation designs for the evaluation of such devices, provide a hierarchy of these approaches, identify gaps to be filled (in particular in view of new developments like e.g. mHealth, artificial intelligence, and combined products) and derive recommendations for the choice of clinical investigation methodology to obtain sufficient evidence.
  • Develop methodologies for generating clinical data on high-risk medical devices enabling to collect sound data and to use data from different sources including real-world data. These methodologies should be adapted for the needs of conformity assessment and for continuous clinical evaluation throughout the lifetime of the device. Proposals should take into account the various specificities of high-risk medical devices and therapeutic areas if relevant.
  • Contribute to the exchange of best practices among notified bodies with regard to the assessment of clinical data as provided by developers of high-risk medical devices.
  • Support networking activities among developers and in particular academic centres with regard to regulatory requirements for assessing high-risk medical devices and foster a pool of scientific expertise on clinical evaluation of high-risk medical devices.

Applicant consortia should bring together partners with relevant expertise from e.g. academia, competent national authorities, centres of expertise for clinical research and care, scientific and medical learned societies. The consortium should also seek input from relevant stakeholders such as technology developers, healthcare providers, health technology assessment agencies and patients, with special regard to endpoints that are relevant for patients. The composition of the consortium should ensure a broad geographical representation of European countries. Sex and gender aspects should be taken into account in carrying out the relevant activities.

Proposals should complement or build on existing work, including results of EU-funded research projects and Joint Actions in the field of medical devices evaluation, and related activities, like e.g. those of the Competent Authorities for Medical Devices (CAMD) and the successor Medical Devices Coordination Group (MDCG).

Proposals could consider the involvement of The European Commission Joint Research Centre (JRC) to provide added value regarding aspects like interfacing among the different stakeholders (e.g. developers and regulatory bodies) or contributing to European and international harmonization. In this respect the JRC is open to collaborate with any successful proposal.

The Commission considers that proposals requesting a contribution from the EU of between EUR 1 and 2 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amount.

Opening date: 04 July 2019

Deadline: 07 April 2020 17:00:00 Brussels time

Deadline Model: single-stage

Type of action: Coordination and Support Action (CSA)

For topic conditions, documents and submission service, please visit:
https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/sc1-hco-18-2020

PS: Find your partners or consortia preparing a project proposal
If you need help to identify a potential partner with particular competences, facilities or experience, please join and explore (HEALTH IT) SPACE www.healthitspace.eu.

Most Popular Now

AI Helps Physicians Better Assess the Ef…

In a small but multi-institutional study, an artificial intelligence (AI)-based system improved providers' assessments of whether patients with bladder cancer had complete response to chemotherapy before a radical cystectomy (bladder...

Smartwatches and Fitness Bands Reveal In…

A new digital health study by researchers at Scripps Research shows how data from wearable sensors, such as smartwatches and fitness bands, can track a person’s physiological response to the...

AI may Detect Earliest Signs of Pancreat…

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed...

Open Call U4H-2022-PJ2: Call for Proposa…

The Ukraine crisis has an unprecedented impact on the mental health of the displaced people in the EU coming from Ukraine. The conflict and experiences of people in war zones...

AI Reduces Miss Rate of Precancerous Pol…

Artificial intelligence reduced by twofold the rate at which precancerous polyps were missed in colorectal cancer screening, reported a team of international researchers led by Mayo Clinic. The study is...

Medical Valley EMN & Volitan Global …

The two healthcare innovation experts Medical Valley EMN and Volitan Global strengthen their existing inbound- and outbound activities through a strategic partnership. The aim is to offer companies access to...

DMEA - Connecting Digital Health Opens w…

26 - 28 April 2022, Berlin, Germany. What plans does the new federal government have concerning the digital transformation of the healthcare sector? What are the initial experiences of doctors regarding...

AI can Predict Probability of COVID-19 v…

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities...

Using AI to Detect Cancer from Patient D…

A new way of using artificial intelligence to predict cancer from patient data without putting personal information at risk has been developed by a team including University of Leeds medical...

Oulu University Hospital Expands Partner…

Siemens Healthineers and Oulu University Hospital in Finland have entered a strategic partnership for the next ten years, adding to an existing radiotherapy collaboration to jointly expand and modernize the...

Positive Conclusion to DMEA - Connecting…

26 - 28 April 2022, Berlin, Germany. After three days DMEA, Europe's leading digital health event, came to a successful conclusion - with around 11,000 visitors, more than 500 exhibitors and...

AI-Enabled ECGs may Identify Patients at…

Atrial fibrillation, the most common cardiac rhythm abnormality, has been linked to one-third of ischemic strokes, the most common type of stroke. But atrial fibrillation is underdiagnosed, partly because many...