Use of health data

In the past, Switzerland has always distinguished itself by its ability to adapt. Now it is in danger of missing a decisive transformation.

Throughout the life cycle of a drug, data helps us to better understand diseases, develop more targeted therapies and provide individual support to patients. Drug approval, reimbursement and treatment decisions are based on data.

Data from healthcare providers makes it possible to gain a better understanding of the health, quality of care and treatment needs of the Swiss population. In addition to clinical results – such as blood values, imaging data, results of functional tests or complication rates – patient-reported outcome measures (PROMs) relating to the patient should also be collected. These record quality of life from the patient’s perspective. This data should mandatorily be recorded using standardized processes and instruments and integrated into the treatment plan in order to obtain a comprehensive overall picture. This information can contribute to the continuous and dynamic improvement of healthcare, in the spirit of a “learning” healthcare system.

What are the benefits of using health data?

For patients

• Overview of their own health data
• Gearing treatment to patient needs
• Early diagnosis of diseases
• Higher quality of treatment
• Cost reduction by reducing oversupply

For research and industry

• New hypotheses, more robust analyses
• Improved understanding of diseases
• Supports the research and development of drugs and vaccines
• Strengthens Switzerland’s position as a research location

For the healthcare system

• Improves the efficiency and quality of the system
• Transparency concerning costs and impact
• Basis for health policy decisions
• Strengthening digital technologies relieves the burden on skilled workers

Areas of action

For digital transformation to succeed, six key fields of action need to be addressed.

• 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.
• 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.
• 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.

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.