The different kinds of health data and what aspects of alignment are needed to achieve interoperability

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:

  1. 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)
  2. Public health: international statistics; comparative outcome assessment; pharmacovigilance; coordination of risk assessment, management and surveillance of large-scale adverse health events, population health research
  3. 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
  4. 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 [1]. These include:

  • electronic health records
  • genomics
  • registries
  • etc.

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/ [2]

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.

Click here to find out what is needed in practice to achieve true interoperability

Examples of standards in terms of what they achieve

There are many standards in the world that all have a different objective in terms of what they want to achieve. We can put them into 3 different categories:

  1. For businesses: Standards are important to the bottom line of every organisation. Successful companies recognise standards as business tools that should be managed alongside quality, safety, intellectual property, and environmental policies. Standardisation leads to lower costs by reducing redundancy, minimising errors or recalls, and reducing time to market.
  2. For the global economy: Businesses and organisations complying to quality standards helps products, services, and personnel cross borders and also ensures that products manufactured in one country can be sold and used in another.
  3. For consumers: Many quality management standards provide safeguards for users of products and services, but standardisation can also make consumers’ lives simpler. A product or service based on an international standard will be compatible with more products or services worldwide, which increases the number of choices available across the globe.

Here are some examples:

Learning standards are concise, written descriptions of what students are expected to know and be able to do at a specific stage of their education. Learning standards describe educational objectives—i.e., what students should have learned by the end of a course, grade level, or grade span—but they do not describe or mandate any particular teaching practice, curriculum, or assessment method (although this is a source of ongoing confusion and debate).

Students understand and interpret written and spoken language on a variety of topics.

Students demonstrate an understanding of the relationship between the practices and perspectives of the culture studied.

To be able to compare the financial situation of companies, standards in accounting are internationally agreed upon.

Quality standards are defined as documents that provide requirements, specifications, guidelines, or characteristics that can be used consistently to ensure that materials, products, processes, and services are fit for their purpose.

Standards provide organisations with the shared vision, understanding, procedures, and vocabulary needed to meet the expectations of their stakeholders. Because standards present precise descriptions and terminology, they offer an objective and authoritative basis for organizations and consumers around the world to communicate and conduct business.

Examples include: ASQ Quality standards like ISO 9001, ISO 14000, ISO IDMP

Case studies of interoperability

Here are some examples of successful interoperability standards and what benefits they deliver to patients:

The International Patient Summary is a minimal and non-exhaustive Patient Summary, specialty-agnostic, condition-independent, but readily usable by all clinicians for the unscheduled (cross-border) care of a patient.

The European EHR Exchange Format aims to allow EU citizens to access securely their health records across Member States more easily. In particular it aims at the creation of a European format that will allow electronic health records being shared in a secure manner at the same time as adhering to data protection rules.

The European Medicines Agency (EMA) implemented the standards developed by the International Organization for Standardization (ISO) for the identification of medicinal products (IDMP).

  • The ISO IDMP standards specify the use of standardised definitions for the identification and description of medicinal products for human use.
  • Their purpose is to facilitate the reliable exchange of medicinal product information in a robust and consistent manner
  • They help to ensure wide interoperability across global regulatory and healthcare communities, which is critical in ensuring accurate analysis and unambiguous communication across jurisdictions.

In essence this sounds logical and easy to do, but… In order to reuse EHR’s for clinical research some important aspects have to be taken into consideration:

  • Compliance with data protection legislation, at a European level and across all European Member States needs to be in place. There have to be consistent information governance practices and expectations across all European countries.
  • Good practices for governing the use of health data needs to be put in place to make it societally acceptable
  • A state of the art privacy protection and information security has to be implemented
  • Greater confidence and reduced risk for those providing data for research use e.g. hospitals, GPs, patients
  • Greater confidence and reduced risk for those performing the research, managing the data or sponsoring the research
  • Greater societal endorsement of public health and research uses of health data
  • A scaling up of learning from health data, leading to more rapid innovation in treatments, and accelerated health system transformation towards better health outcomes