Patients

There are diverse reasons for why this happens. Today we assume there are several influencing factors. They include - among other things - differences and changes

• in genetics and epigenetics
• in the immune system
• in the microbiome (bacteria that live in the gut as symbionts)
• in lifestyle
• in the environment

In addition, different illnesses can sometimes exhibit similar or identical symptoms and are not therefore recognized as separate. More information can be found at: Precision Medicine

Enormous progress has been made in analytical procedures in the past decade. It was only these discoveries that made it possible in the first place to examine some causes of illnesses. Such innovations include Next Generation Sequencing (NGS) in the analysis of the human genome with its roughly 20,000 genes. The dramatically enhanced power of bioinformatical approaches (deep learning, neuronal networks) has also enabled research to grasp the complexity of individual factors in their entirety or to analyze their interrelationships.

What patients and healthy people have in common and what divides them plays a crucial role in many of these analyses. Comparing a large number of patients leads to answers to the question of the causes of an illness as well as clues for treating it successfully.

One decisive prerequisite is therefore growing in importance: it requires the willingness of many people to make themselves, their samples and data available for such comparative analyses. Such samples and data are collected anonymously in clinical data registries or biobanks. Clinical data registries and biobanks represent the hopes and the crucial foundation in research's fight against the causes of many diseases.

Clinical data registries and biobanks are subject to very tough data protection requirements which often even exceed the high level already applicable to hospitals.

1. If a patient agrees, the hospital erases all information on their identity from their medical records. The anonymized data go to a so-called Trust Center that only knows the treating facility but not the patient.
2. The Trust Center in turn passes the data on to clinical data registries or biobanks. However, in the process, the Center does not reveal their origin, i.e. the hospital where treatment is being administered.
3. In the end, the clinical data registry or biobank knows neither the identity of the patient nor the facility where the patient was treated.

As a result of these steps, it is impossible to trace the data to the patient. What happens, however, if the clinical data registry gains insights in the course of its analyses which might be of help to individual patients? Even then, the new insights cannot be relayed back to the patient or the people treating them. Anonymization is complete and cannot be reversed.

For this reason, many patients decide against absolute anonymity. Instead the opt for a so-called double pseudonymization. In this process, the hospital communicates an additional patient ID to the Trust Center. The Trust Center in turn passes on its own, second ID to the clinical data registry. This leads to the following differences:

1. The clinical data registry still knows neither the identity of the patient nor the treating facility. However, in the event of new insights, it can pass specific information to the Trust Center.
2. The Trust Center still does not know the patient. However, if it is notified by the clinical data registry, it can use the ID to pass on the information to the hospital.

The hospital – and still only the hospital – can then make contact with its patient.