The importance of health data
The relevance of health data extends beyond individual patient care and plays a key role in various aspects of healthcare:
Data types
Health data includes all information about you that is collected and used in the course of medical examinations and treatments.
This may include information about your medical history, blood pressure readings or x-rays. The data is collected by your doctor or hospital, for example, in your electronic health record.
Health data is very diverse and includes a wide range of information, from personal details about your general health to technical measurements. Careful categorization makes it easier to understand and more secure to handle. As part of the Data Sharing Cockpit, different health information is grouped into clusters based on internationally accepted classification systems for health data.
Data categories | Description | Comments / Examples |
---|---|---|
Allergies & Vaccinations | Structured medical information regarding documents allergies, sensitivities, vaccination and immunization the patient received in the past | e.g., Penicillin Allergy, HepB vaccination |
Tests & Labs | Structured medical information regarding the different clinical tests and results from clinical laboratories, tests and measurements that affect medical risk-factors and treament options, including infectious pathogen data | Measurements (e.g., Height, Weight, Blood Pressure, etc.) Labs tests & results (e.g. Blood [CBC, troponin, TSH, etc.…) Tests (e.g. EKG, EMG) Risk Scores |
Medications | Structured medical information regarding the different medication treatment recommended to the patient, including the relevant dates and prescription parameters (dosage, regimen, form, route of administration) | e.g., TAB Simvastatin 20mg PO, once a day |
Conditions & Procedures | Structured medical information regarding the different medical conditions (chronic and acute) the patient has suffered from in the past (sometimes refered to as "Problem list"), procedures & surgeries the patient went through, and meta-data regarding medical encounters with clinical teams and the treating healthcare professionals | e.g. Tonsillectomy (1995), UTI (2005), data on urgent care visits for Ischemic Heart Disease (2020) |
Genetic Risk Factors | Structured list of genetic markers and genetic predispositions to develop specific diseases | Results from Genetic analysis: e.g., BRCA1 |
Demographics | Structured list of potential demographic risk factors including: demographics, family history of chronic disease, and lifestyle | Demographics (e.g., Age, Sex) Lifestyle (e.g., smoking, occupation, diet, physical activity) Family History (e.g., Mother with DM2) |
Wearables & Wellness data | Data collected using personal wearable devices and wellness application (e.g., fitbit, google fit, apple health kit, samsung health, garmin etc) | e.g., number of daily steps, REM sleep quality |
Unstructured text | Medical notes and reports containing free text without coding | e.g., physicians' free-text reports |
Imaging data | Raw data from different imaging studies | e.g., MRI, CT, PET CT, X-RAY |
Full Genome / Biomics data | Raw data from genetic, genomic, proteomic & other biomic studies | e.g., Genome seq data |
For maximum transparency, you can explicitly see which data fields are currently being collected and which data types they are assigned to.
Personal data
In most research and development projects, it is not necessary to link data to a specific person. Health data, in particular, requires a high level of confidentiality, as it contains highly sensitive information. For this reason, health data is typically pseudonymized or anonymized. Below is an example of how the level of personal identification in data can vary.

Display of aggregated statistical data
Aggregated data is consolidated and anonymized data that originates from a large number of individual data sets. This combination makes it possible to identify general trends, patterns and insights without revealing the identity of any single person.

Evaluation in a secure processing environment
The datasets are not made directly available to data users. Technical measures are in place to allow clinics, scientists and technology providers to process the data for specific research questions, but not to view, copy or download individual data. The data therefore remains the responsibility of the operator of that processing environment at all times.

Transmission of individual datasets
In the context of health data, these are anonymized datasets that contain information about an individual patient, such as medical examination results, diagnoses or treatment histories. The handling of individual data records requires special attention to privacy and security to ensure the confidentiality and integrity of personal information.