Use of health data

In the past, Switzerland has always been characterized by its ability to change. Now it is in danger of missing out on a decisive transformation. While many countries have recognized the opportunities of digitalization in the healthcare system, Switzerland is lagging behind in international comparison. To catch up, Switzerland must invest in the development of a networked healthcare data ecosystem and launch the DigiSanté program.

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Quality and standards

The responsible use of data requires common rules regarding the technical and quality standards according to which the data is collected, structured and stored. This is important for interoperability. If data is not collected in accordance with a common minimum quality standard and in compatible formats, it cannot be linked. In most cases, linking data is what makes it possible to gain insights from it.

Quality and standards

The responsible use of data requires common rules regarding the technical and quality standards according to which the data is collected, structured and stored. This is important for interoperability. If data is not collected in accordance with a common minimum quality standard and in compatible formats, it cannot be linked. In most cases, linking data is what makes it possible to gain insights from it.

Technology and infrastructure

Much like the transport system, the foundation of the health data ecosystem is its infrastructure. The data ecosystem also requires a basic infrastructure that enables the collection, sharing and use of data. This basic infrastructure has the character of a public good.

Technology and infrastructure

Much like the transport system, the foundation of the health data ecosystem is its infrastructure. The data ecosystem also requires a basic infrastructure that enables the collection, sharing and use of data. This basic infrastructure has the character of a public good.

Skilled workforce

The smooth functioning of the system requires skilled workers with the capabilities needed to maintain, operate and use the system. Data competence – the ability to work with data – must be firmly anchored in all apprenticeship and training curricula as a core competence.

Skilled workforce

The smooth functioning of the system requires skilled workers with the capabilities needed to maintain, operate and use the system. Data competence – the ability to work with data – must be firmly anchored in all apprenticeship and training curricula as a core competence.

Acceptance and involvement

The health data ecosystem requires the active involvement and trust of all involved parties in order to ensure that everyone benefits. Supporting this cultural transformation requires a comprehensive dialog with all of the players in the healthcare system.

Acceptance and involvement

The health data ecosystem requires the active involvement and trust of all involved parties in order to ensure that everyone benefits. Supporting this cultural transformation requires a comprehensive dialog with all of the players in the healthcare system.

Financing and investment

Building the infrastructures of the health data ecosystem requires investment by the Confederation and the cantons as well as the investments set forth in the DigiSanté program. In a dynamic health data ecosystem, sustainable financing can be secured though charges for secondary data users such as researchers, among other measures.

Financing and investment

Building the infrastructures of the health data ecosystem requires investment by the Confederation and the cantons as well as the investments set forth in the DigiSanté program. In a dynamic health data ecosystem, sustainable financing can be secured though charges for secondary data users such as researchers, among other measures.

Regulation and incentives

The use of data requires a supportive legal framework. It must be clearly established what data can be shared under which conditions. There is also a need for incentives for gathering and curating health data in a structured way as this is a very time-consuming task. So there is a need for new approaches to the compensation of the services of hospitals and doctors as this work is not adequately taken into account in the current billing practices.

Regulation and incentives

The use of data requires a supportive legal framework. It must be clearly established what data can be shared under which conditions. There is also a need for incentives for gathering and curating health data in a structured way as this is a very time-consuming task. So there is a need for new approaches to the compensation of the services of hospitals and doctors as this work is not adequately taken into account in the current billing practices.

Real World Data

The enormous progress that has been made in digitalisation has opened up access to medical data which, in contrast to data from conventional clinical trials, are generated in the patient’s everyday setting. This is known as real world data (RWD).

RWD have a wide range of uses, from assessing the safety and efficacy of therapies and risk-benefit assessments for certain diseases, to complex diagnoses and conclusions about patterns and striking features in specific patient groups.

The recruitment and participation of patients in clinical trials can also be facilitated by RWD. Hospitals can select suitable participants faster and better using their own patient data. Smartphone apps and wearables help patients to record and transmit their health data wherever they are, thus reducing the number of hospital visits.

Creating fair conditions

The use of RWD in research can only be beneficial, however, if the data are of high quality, obtained under defined conditions and evaluated carefully. Here, there is a need to create a framework that ensures fair compensation for the major effort involved in generating and curating this kind of data and for the innovations that RWD make possible. Data intended to promote health should be widely available. At the same time, compliance with data protection regulations must be ensured. The effort involved in generating, curating and making the data available must be rewarded and the necessary awareness of the different types of RWD must be created. On the one hand, there are RWD that are obtained almost as a by-product under uncontrolled conditions, and on the other, there are RWD that need to fulfil very high quality requirements. RWD obtained specifically for clinical authorisations should be accorded protection similar to that given to conventional clinical data.