The European Commission has long recognised the need for addressing the multiple levels and complex challenges of interoperability of eHealth solutions. The roots of policy efforts to improve interoperability are grounded in the European eHealth Action Plan of 2004. These are followed by a number of joint activities with Member States and relevant stakeholders, supported by European projects.
4 key priority areas are defined:
- Patient care: patient safety; dissemination of good practice, integration of education and care; connecting multiple locations for collaborative care delivery (at local, regional, national and international levels); empowerment of citizens (patient centred healthcare)
- Public health: international statistics; comparative outcome assessment; pharmacovigilance; coordination of risk assessment, management and surveillance of large-scale adverse health events, population health research
- Research and translational medicine: multi-centre studies and trials, health data repositories, bio- and tissue-banks, development of personalised medicine based on genetic and genomic analyses
- Support for diverse markets: identification of solutions with superior benefit/cost ratios; enabling plug-and play best of breed, encouraging industry involvement, especially SMEs; stimulating innovations by health service providers and involving clinicians, harmonizing legal and regulatory frameworks.
Furthermore, the European Health Data Space (EHDS) wants to foster the exchange, and sharing of different kinds of health data . These include:
- electronic health records
This should support the delivery of primary care as well as the development of new treatments, medicines, medical devices and services.
Why is this not achieved yet, in an ever more technological advanced world we live in today?
This article was first published on https://www.healthcarebusinesstech.com/achieve-ehr-interoperability/ 
Too many silos exist, preventing the vision of true interoperability.
On average, hospitals use 16 distinct EHR platforms, according to a recent report by Health Information and Management Systems Society (HIMSS) Analytics. And 62% aren’t using patient information outside their own EHRs because external providers’ data isn’t available within their workflows, a recent Black Book Research survey reported.
Moreover, 57% of hospital network physician practices operating on assorted EHRs reported they continue to lack the financial and technical expertise to adopt complex interoperability, which are “compulsory to attain higher reimbursements built into value-based care initiatives by both public and private payers,” said Black Book Managing Partner Doug Brown.
EHRs key to addressing interoperability
Despite use of multiple EHRs, Black Book’s survey reported 85% of network physicians are looking to their core health system’s EHR to enable interoperability among integrated healthcare delivery providers and support the variety of data-intensive initiatives the industry must address.
As administrators reevaluate their IT resources to meet the requirements for value-based care, they should examine interoperability on three levels – foundational, structural or semantic interoperability.
- Foundational interoperability enables data exchange without requiring IT systems to interpret data. This generally means two systems can exchange data, but a person is needed to interpret that information and what it means to a patient’s care.
- Structural defines syntax of the data exchange, allowing systems to interpret data at the data field level, requiring less human interpretation. Much of the data that’s received can be reconciled into a patient’s healthcare record without extensive manual effort or data entry, enabling faster, more responsive and efficient care.
- Semantic or advanced interoperability – the highest level – supports meaningful exchange of information among disparate systems. In the context of health care, this means providers will have the most relevant clinical data to a patient’s care depending on the setting of care and a patient’s condition.
In order for this to be achieved, interoperable systems need to have data that has:
- Consistency: the receiving system must be able to recognise what has been sent, so it is a prime requirement for machine-machine communications and dictates the need for unambiguous identifiers.
- Understandability: humans can tolerate considerable ambiguity, but tend to focus too narrowly, so that the requirements are almost the reverse as for automated support. It is limited by the trust that the information is valid, especially with aggregated population data where the aggregation process may result in loss of information.
- Reproducibility: addresses the question of inter-individual reliability when data are collected or encoded. This is true both for individual and aggregated data.
By recognizing the varying types or levels of interoperability and the way humans understand and use data, practice administrators can identify their workflow requirements to make sure the data exchange works within their practice.
With enhanced data, practices can make better decisions for patients, ultimately improving visibility across the continuum of care.