Q-REC - European Quality Labelling and Certification of Electronic Health Record Systems

European Institute for Health RecordsThe Q-REC project entitled "European Quality Labelling and Certification of Electronic Health Record systems (EHRs)" is a Specific Support Action for the strategic objective 2.4.11 "Integrated BioMedical Information for better Health" as addressed in Call 4 of the Information Society Technologies Work Programme.

The project relates to the Action Plan of the eHealth Communication COM (2004)356 by supporting mainly "4.2.5 Conformity testing and accreditation for an eHealth market" but also "4.2.2.2: Interoperability of Electronic Health records". The scope of Q-REC thus neither extends to the national roadmaps nor the "overall" eHealth interoperability issues but is restricted to interoperability among Electronic Health Record systems, with as its principal focus, Conformance Testing and Certification. Q-REC is not a co-ordination project but a Specific Support Action (SSA) which aims at complementing (bottom-up wise) the existing e-Health ERA Co-ordination Project "Towards the establishment of a European e Health Research Area", which main goal is to coordinate the planning of eHealth R&D and coherent national roadmaps in Europe.

The main objective of Q-REC is to create an efficient, credible and sustainable mechanism for the certification of EHR systems in Europe by addressing mainly:

  • EHR Systems Quality Labelling and Certification Development, thereby:
    • producing a State of the Art Report on EHR-Certification Schemas as already implemented in at least three European countries;
    • performing a Pan-European Requirements Assay;
    • proposing a Labelling Terminology and Functional Profiles for EHRs to be certified;
    • comparing and harmonising the EHR-Certification Procedures at a European level;
    • drafting Model Certification Guidelines and Procedures;
    • planning the Validation of the Guidelines.
  • Resources for EHR Interoperability, including:
    • the register of Conformance Criteria and Guidance Documents for obtaining EHR Certification;
    • an inventory and guidelines for EHR Archetypes;
    • the registration of Coding Schemes in Europe (as mandated by CEN/TC 251);
    • an inventory of relevant EHR related standards;
    • a register of XML Schemas and Open Source components for EHRs.
  • Benchmarking Services:
    • Benchmarking Services Manual for Quality Labelling and Certification;
    • preparing the Business Plan for new EHR-Certification related Services.

The co-ordinating partner is the EuroRec Institute, which is the overarching network of already existing national ProRec centres. EuroRec's main mission is to promote high quality Electronic Health Record systems (EHRs) throughout Europe. The network and its centres are platforms wherein a wide variety of stakeholders are involved. The coordination with healthcare authorities will be done through the collaboration with the eHealth ERA consortium and its European Health Care Authorities (HCA) / Ministries groups. Both platforms (EuroRec and eHealth ERA) will assure the necessary bottom-up and top-down approaches for the adequate assessment of needs and for the optimal choice of methods for quality labelling and certification of EHRs in Europe.

For further information, please visit:
http://www.eurorec.org

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...