SPHN Archives - European Industrial Pharmacists Group (EIPG)

The Pact for Research and Innovation in Europe


by Giuliana Miglierini The roadmap to support the implementation of the new vision of the European Research Area (ERA) made a concrete step forward on 16 July 2021, with the adoption by the European Commission of the proposal for a Read more

The opportunity for repurposing of oncology medicines


by Giuliana Miglierini Rare cancers, which account for approx. 22% of new cases in Europe, represent an area of low business interest for the pharmaceutical industry, due to the limited number of patients compared to the very high costs to Read more

A golden era for UK’s life sciences and a new Code of practice for its pharmaceutical industry


by Giuliana Miglierini Less than a year has gone since the Brexit, and the UK innovation landscape is experiencing a new, vivid era of expansion under the stimulus of a strong demand from global investors. According to recent data of Read more

The Swiss interoperable national eHealth infrastructure

, , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

The new model of a personalised and interconnected healthcare asks for the interoperability of data in order to precisely access all the information needed to make the correct diagnosis and decide the most appropriate treatment for each patient.

Interoperability is at the core of the new Swiss strategy used to build the national eHealth infrastructure; the strategy has been developed by a team of scientists from the University of Geneva (UNIGE) and the University Hospitals of Geneva (HUG), in collaboration with the Swiss Institute of Bioinformatics (SIB) and the Lausanne University Hospital (CHUV), under the auspices of the Swiss Personalized Health Network (SPHN) and in close collaboration with representatives from all five Swiss university hospitals and eHealth Suisse.

A journey started in 2015

The new national infrastructure strategy will be adopted by all Swiss university hospitals and academic institutions. The announcement of the new strategy follows a long-lasting work to adequate the Swiss legislation, started in 2015 with the approval of the new federal law on patients’ electronic health records (EHR) (see more on Health Policy).

According to the Swiss law (entered into force in April 2017), adoption of the interoperable infrastructure is voluntary for ambulatories and private practices. In the same year, the Swiss Personalized Health Network (SPHN) also created by the government, an initiative led by the Swiss Academy of Medical Sciences in collaboration with the SIB.

Despite major investments over the past decade, there are still major disparities”, says Christian Lovis, director of the Department of Radiology and Medical Informatics at the UNIGE Faculty of Medicine and head of the Division of Medical Information Sciences at the HUG. “This is why we wanted, with our partners and the SPHN, to propose a strategy and common standards that are flexible enough to accommodate all kinds of current and future databases.”

A semantic framework integrating with the existing standards

The new infrastructure will be implemented to complement the existing tools already used by the Swiss eHealth community. Synergy and flexibility are the principles that inspired its development, which is based on a common semantic framework that does not aim to replace existing standards. The final target is to make a step forward towards the application of personalized medicine, so to better respond to the needs of both patients and the Swiss healthcare system. The new infrastructure has been officially presented by an article published in the JMIR Medical Informatics.

Its stepwise implementation has already started at mid-2019, within the framework of the Swiss Personalized Health Network. “Swiss university hospitals are already following the proposed strategy to share interoperable data for all multicentric research projects funded by the SPHN initiative”, reports Katrin Crameri, director of the Personalized Health Informatics Group at SIB in charge of the SPHN Data Coordination Centre. Some hospitals are also starting to implement this strategy beyond the SPHN initiative.

In the JMIR Medical Informatics article, the authors describe the process that led to the new strategy, starting from the deep analysis of various approaches to interoperability, including Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE). Several domains have been also addressed, including regulatory agencies (e.g. Clinical Data Interchange Standards Consortium [CDISC]), and research communities (e.g. Observational Medical Outcome Partnership [OMOP]).

The semantics of the infrastructure was mapped according to different existing standards, such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), the Logical Observation Identifiers Names and Codes (LOINC), and the International Classification of Diseases (ICD).

A resource description framework (RDF) allows for the storing and transportation of data, and for their integration from different sources. Data transformers based on SPARQL query language were implemented to convert RDF representations to the required data models.

A common semantic approach

The three pillars on which is built the new infrastructure reflect the three essential components of communication: the commonly shared meaning we give to things, a technical standard producing the “sound” and the organisation of the meaning and sound with sentences and grammar so that communication becomes intelligible. The same occurs with data, where the agreed semantic significant is used to represent conceptually what has to be communicated. “Then we need a compositional language to combine these meanings with all the freedom required to express everything that needs to be expressed. And finally, depending on the projects and research communities involved, this will be ‘translated’ as needed into data models, which are as numerous as the languages spoken in the world”, explains Christophe Gaudet-Blavignac, a researcher in the UNIGE team.

Unification of vocabularies instead of creation of new ones has been a major target for scientists involved in the effort; this new common vocabulary will be now used to communicate within any type of grammar, without need to learn a ‘new language’. “In this sense, the Swiss federalism is a huge advantage: it has forced us to imagine a decentralised strategy, which can be applied everywhere. The constraint has therefore created the opportunity to develop a system that works despite local languages, cultures and regulations” says Christian Lovis.

This approach is expected to provide a robust guarantee of mutual understanding and significant time savings for researchers called to prepare relevant documentation, as specific data models will be applied only as the last step of the procedure. The chosen modalities shall provide the needed flexibility to adapt to the formats required by a particular project, for example those typical of the FDA in the case of collaboration with an American team.

The challenges of interoperability

The new infrastructure takes also into due account the many challenges related to the sharing of data. Instruments that create interoperability and their implementation have to face the regulatory framework that governs data accessibility and protection, for example with reference to the GDPR regulation on personal data. “The banking world, for example, has long since adopted global interoperability standards, – comments Christophe Gaudet-Blavignac. – A simple IBAN can be used to transfer money from any account to any other. However, this does not mean that anyone, be they individuals, private organisations or governments, can know what is in these accounts without a strict legal framework

Interoperability is even more a challenging goal to be achieved in the biomedical field, due to the very great heterogeneity of data involved in the diagnosis and treatment of a certain disease, and the consequent need to interconnect and integrate many different systems to achieve a robust communication. This issue has been made fully explicit during the pandemic, when a huge amount of data of different types were produced: even if lifting all technical, legal and ethical constraints to their interoperable use, the data remain difficult to analyse because of semantic ambiguities, notes the Swiss scientists.

Big data and new technologies

The digital opportunity in the Swiss healthcare system has been also examined by PricewaterhouseCoopers (PwC) in a report of February 2019. Many new informatics technologies may prove useful to boost the eHealth Swiss landscape, suggest the analysts, from the use of big data and data management to the spreading of wearable devices and sensors among patients.

According to PwC, the first ones are expected to transform the diagnosis process from a subjective experience to an objective, data-driven process. This would allow also to improve its transparency, providing a rationale for the choice and effectiveness of treatments.

Wearables and sensors are expected to further expand this vision to self-diagnosis, monitoring and remote treatment, thus supporting the transition towards a prevention-based healthcare industry pursuing very early-stage identification of pathologies and related therapeutic interventions.

The PwC’s study – comprehensive of 38 interviews with patients and industry experts – ran in collaboration with the University of St Gallen. Six different categories of patients were identified: the Health enthusiast, the Sceptic, the Healthy Family, the Chronic, the Frail elderly and the Mentally stressed. For each of them, a map identifying pain points along the patient journey were also derived in relation to the domains of Time, Emotions, Information and Resources.

Lack of trust in the healthcare system, insufficient availability and accuracy of resources and the time is spent in waiting rooms are among the main issues experienced by Swiss patients, according to PwC. All of them can be tackled using the new digital technologies, including big data, wearables and sensors, artificial intelligence, robotics, telemedicine and mobile health, digital simulation, body augmentation and remediation.