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Electronic Health Record systems for Clinical Research (EHR4CR)
Start date: Mar 1, 2011, End date: Feb 29, 2016 PROJECT  FINISHED 

Current medical needs, the growth of targeted therapies and personalized medicines, and escalating R&D costs result in formidable cost pressures on healthcare systems and the pharmaceutical industry. Clinical research is also growing in complexity, labour intensity and cost. There is a growing realization that the development and integration of Electronic Health Record systems (EHRs) for medical research can enable substantial efficiency gains, make Europe more attractive for R&D investment and provide patients better access to innovative medicines and improved health outcomes.EHRs can now be designed to seamlessly integrate with existing research platforms and healthcare networks to create opportunities for many stakeholders, including the pharmaceutical and bio-pharma industries. However, key challenges are compliance with various ethical, legal and privacy requirements (and acceptance by the general public, patients, and medical professionals), providing a platform that works across many EHR systems and is sustainable within a scalable business model.A 4-year project, EHR4CR will involve a team of recognised European academic and industrial partners. The project will build a platform to enable the use of EHR for more efficient medical research and run pilots (on interoperability, security, data quality, data storage solutions, organisational issues, accreditation and certification, etc) to demonstrate the viability and scalability of an EHR4CR business model.The EHR4CR project supports the IMI strategic agenda with an information gateway solution to enhance clinical research efficiency and innovation. A key IMI aspect is the development of a knowledge management capability that can, for example, provide information management support for other research on personalized medicines, now an IMI 2010 call topic. EHR4CR also supports other IMI R&D projects by enabling the use (and reuse) of large amounts of health data – in an ethical and cost-effective way.

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