interoperability Archives - European Industrial Pharmacists Group (EIPG)

ACT EU’s Workplan 2022-2026


by Giuliana Miglierini The implementation phase of the Accelerating Clinical Trials in the EU (ACT EU) initiative, launched in January 2022 by the European Commission, started with the publication of the2022-2026 Workplan jointly drafted by the Commission, the European Medicines Read more

EFPIA’s Annual Report on the Pharmaceutical industry 2022


by Giuliana Miglierini “In the 21 years from 2000 to 2021 – in which time we’ve come through the Global Financial Crisis and a pandemic – EFPIA companies have more than doubled production, increased exports by a factor of six, Read more

Webinar: Contamination Control Strategy, an Implementation Roadmap


The next EIPG webinar will be held in conjunction with PIER and University College Cork on Friday 23rd September 2022 (16.00 CEST), on the implementation roadmap of Contamination Control Strategy (CCS). This presentation is given by Walid El Azab, Read more

Draft topics for the first IHI calls for proposals

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by Giuliana Miglierini

The Innovative Health Initiative (IHI) published on its website the first draft topics which may be part of the first two calls for proposals, scheduled in June 2022. Interested parties may start the activities needed to build and formalise the research consortia, taking into consideration that the announced topics are draft, pending their approval by the IHI Governing Board and their final version may differ from the drafts.

IHI call 1 shall focus on innovative technologies for the development of decision-support system for improved care pathways, next generation imaging, personalised oncology and access and integration of heterogeneous health data in areas of high unmet public health need.

IHI call 2 shall address cardiovascular diseases and the development of a harmonised methodology to promote early feasibility studies.

The draft topics of IHI call 1

Innovative decision-support systems are important to make available improved care pathways for patients with neurodegenerative diseases and comorbidities. The actions to be undertaken include the enhanced cross-sectoral and sustainable collaboration between healthcare industries, academia and other stakeholders in order to exchange data (through the new European Health Data Space), analytical tools and material for training and professional development of personnel.

Earlier diagnosis should lead to more clinically effective interventions and reduced hospitalisation and facilitate the adherence to therapy. Clinical outcomes are expected to support a better patient stratification, which is needed to develop more patient-adapted interventions, therapeutics and cost-effective pathways for the management of neurodegenerative diseases. Among the expected outcomes is the development of a re-usable, interoperable, easily adaptable, and scalable digital platform, initially targeted to support patients in this therapeutic area, but further expandable in the future to other areas of interest. The action should also involve the development of agreed standards and guidelines to support data collection and operational features of the digital platform. New algorithms may provide (near) real time feedback on health interventions and support the constant monitoring of the patient’s status.

The second topic is focused on the development of high-quality tools, high-quality data, advanced patient imaging and image-guided technologies and processes for improved early diagnosis, prognosis, staging, intervention planning, therapy and management of cancer. Imaging can be part of new combined cancer therapies (e.g., theranostics, chemotherapy, targeted therapy including immunotherapy, radiotherapy and/or surgery). The call should also include the development of improved validation and evaluation methodologies specific to artificial intelligence (AI)and machine learning (ML), with a particular attention to the creation of new solutions that automatically link images to clinical data. This could be applied, for example, to develop minimally invasive interventions guided by medical imaging, or image-driven planning and predictive tools.

The third topic is also aimed to tackle cancer through the development of personalised interventions. This action should contribute to break down silos that are often still characterizing medicine and technological areas. The availability of harmonised approaches should lead to safe and effective innovative health technologies, to the integration of future products, services and tools and the development of more patient-centred tools. Here again, an expected outcome is represented by a dynamic platform for R&I collaboration across different sectors and stakeholders, focusing on the early stages of applied clinical research on cancer and on the testing and validation of multi-modal therapeutic approaches, including novel or emerging technical and clinical concepts and the possible contribution arising from in vitro diagnostics.

Topic 4 of IHI 1 addresses the integration of future products, services and tools along the healthcare pathway to better respond to specific patients’ needs. The availability of interoperable, quality data which reflect the FAIR principles (Findability, Accessibility, Interoperability, Reusability) is central to this action, as well as the development of advanced analytics/artificial intelligence supporting health R&I. Among the main expected outcomes is the long-term access to diverse types of data enabled by the linkage and integration of novel and cross-sectoral sources. Access to interoperable tools should also become possible for citizens and patients to support the self-management of health and the joint decision making process between healthcare professionals and patients.

The draft topics of IHI call 2

Cardiovascular diseases (CDV) remain one of the main causes of death; the development of new tools for the primary and secondary prevention of CDV is the main focus of Topic 1, to be pursued by the identification of existing comprehensive CVD and heart failure (HF) patient datasets, in order to facilitate the diagnosis of atherosclerosis and HF. These data shall be also integrated with those captured by diagnostic tools (e.g., wearables, imaging devices, bio samples/biopsies).

Classical diagnostic screening, in-vitro- diagnostics, ‘multi-omic’ platforms (e.g. genomic, transcriptomic, proteomic and multimodality imaging data), continuous glucose monitoring (CGM) data, continuous electrocardiogram (ECG) from wearable, HF and activity data, wearable devices and digital health applications are all possible sources for the data. Projects may also leverage data in currently available IMI federated databases in compliance with the GDPR regulation governing protection of personal data.

The utility of already existing or new biomarker combinations shall be assessed to detect patients at risk, also making use of AI models to analyse data. Validated data referred to patient reported outcome and experience measure (PROMs and PREMs) may also be considered for use in the clinical setting.

The second draft topic is targeted to establish a harmonised methodology to promote the diffusion of Early Feasibility Studies (EFS) among healthcare professionals. Once again, the availability of digital technologies easily accessible by patients shall be key to this action. Among expected outcomes are the improvement of the quality of clinical evidence on health technology innovation generated through earlier clinical experience, together with the increase of the attractiveness of clinical research for healthcare technologies in the EU.

These activities are essential to enable the fast translation of innovation into the clinical practice, improving access to patients especially where there are only limited or no alternative therapeutic options. This approach to the development of innovative technologies may also benefit regulators and health technologies assessment (HTA) bodies, as well as notified bodies. All stakeholders involved in clinical practice and research may contribute to the early generation of quality data, so to achieve a better understanding of diseases management and treatment options and to support the future development of new medical guidelines.

The creation of hubs of clinical excellence to attract investment may also be considered under this topic, with involvement of developers of medical devices, drug-device combination products, imaging equipment, in-vitro diagnostics, and SMEs.


EMA’s OMS has turned mandatory for centrally authorised products

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by Giuliana Miglierini

Since November 1st, 2021, the use of the Organisation Management Service (OMS) became mandatory for all Centrally Authorised Products (CAPs). The European Medicines Agency (EMA) has published a Questions & Answers document to better explain the new procedures, that will impact the source of data to be used to exactly identify the organisations filing CAP procedures with EMA.

The progression in the implementation of the new provisions

The use of the OMS system is now compulsory for all organisations filing CAP submissions, with the final goal to improve the interoperability of data and the overall efficiency of the regulatory process. Should applicants lack to use OMS data, the relevant applications will be filtered out of the EMA’a validation procedure and sent back to the applicant for remedial action.

The OMS data management service was launched in 2015, and applied to electronic application forms (eAFs) since 2017, and then to many other types of procedures. The availability of OMS data may prove critical to allow the smooth implementation, in early 2022, of the new Clinical Trial Information System (CTIS) and of the Clinical Trial application procedure; during the next year, EMA plans to integrate the OMS also with the Union Product Database (UPD), Variation applications (via DADI project) and Manufacturing/Importers Authorisations (MIAs), Good Manufacturing Practice (GMP) inspections and Wholesale distribution authorisations (via EudraGMDP).

Validated OMS data also need to be used with reference to the “applicant” and “contact person affiliated organisation” sections of pre-submission applications. With the new eAF release (eAF V.1.25.0.0) for Medical Devices, the compulsory use of OMS data will also refer to the “Device Manufacturer”, “Notified Body” and “Companion diagnostic” sections.

Remediation in case of lack to use OMS data includes the insertion of all relevant information in the OMS database before updating and re-submitting the application form. Should applicants not provide sufficient responses, the application may be completely or partially invalidated.

Discussions are undergoing to further extend the use of OMS data also to National Procedures (NP); according to EMA, this may be turn inevitable in the next couple of years, as current eAF forms will be progressively replaced by web-based application forms (through the DADI project), being the latter the same for centrally and nationally authorised products by design.

Any question on the use of the OMS can be sent to EMA’s e-mail addresses specified in the Q&As document.

What is new for applicants

The use of OMS master data (the so-called “OMS Dictionary”) is now mandatory for both Human and Veterinary centralised procedures, namely those making use of eAFs (initial marketing authorization applications, variations applications, and renewals) and well as other procedures (see the Q&A document for more detail). The name and contact details of the contact person are not OMS data, and do not need to be registered with the system; historical organisational data do not have to be registered as well.

To manage a CAP procedure, applicants now need to first register their organisation data with the OMS, or request the update of data already registered by submitting a “Change Request” before filing of the regulatory application.

All requests will be assessed by EMA OMS Data stewards, that will also update data in the systems if the requirements are met. This validation step is fundamental to avoid duplication of data, as all information is checked against the same reference sources (i.e. national business registry, DUNS and/or GMP/MIA certificates) and standardised according to the OMS rules agreed with the Network. The Service Level Agreement provide for EMA to process 75% of OMS requests within five working days and 90% within ten working days. Changes will become visible in the eAF the day after they had been processed, and only upon active refresh of the relevant lists.

The business process which makes use of OMS data is usually responsible to submit such a request, but it can arise also form other parties. More specifically, EMA advises the user who needs to use the data should take the lead in updating it. This may prove relevant, for example, to ensure all manufacturer organisations are included in the OMS Dictionary as needed.

EMA warns applicants to consider the turnaround time for processing the OMS change request when planning to submit applications: even if the application forms will not immediately change and everything may appear as usual, the background process has been now modified and may need additional activities to validate the change requests.

Changes in the eAF templates are planned to remove the free text fields for CAP applications, but until the new models will be available, the free text field for “organisations” should not be used. Planned availability and entry into force of the new versions are December 2021 for Human procedures (v1.26.0.0) and January 28th, 2022 for Veterinary procedures (in line with the veterinary regulation).

How to access the OMS

EMA’s data management system refers to four different domains of data, including the substance, the product, the organisation and referential (SPOR) master data in pharmaceutical regulatory processes.

The SPOR portal provides access to the respective four specific areas of service (e.g. SMS for substances, PMS for products, OMS for organisations and RMS for referential). SPOR is the mechanism used by EMA to implement the ISO IDMP standards, as required by articles 25 and 26 of the Commission Implementing Regulation (EU) No. 520/2012. Organisation master data, even if not covered by ISO IDMP, have been considered by EMA, National Competent Authorities and Industry in Europe to be essential in order to make the master data operating model work.

Applicants need to create an EMA account with SPOR user roles to conduct additional tasks, such as requesting changes to data, translating data or managing user preferences. Already granted credentials to access other active accounts for any EMA-hosted website or online application can also be used. OMS data can now no longer be captured in other EMA databases.

OMS master data include the organisation name and address, labelled by mean of unique identities (ID) (i.e. ‘Organisation_ID’ and ‘Location_ID’). Different categories of organisations are possible (i.e. ‘Industry’, ‘Regulatory Authority’ or ‘Educational Institution’), and of different size (i.e. ‘Micro’, ‘Small’, or ‘Medium’). The role played by a certain organisation is context-specific and cannot be defined within the OMS.


The Swiss interoperable national eHealth infrastructure

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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.