Below, you will find a detailed explanation of the purposes of use defined in the law. All applications submitted to the Health Data Lab (HDL) must specify a purpose of use. HDL staff will review whether the stated purpose aligns with the application and the requested data subset.
What this means:
The "collective agreement partners" refer to the Associations of Statutory Health Insurance Physicians and the statutory health insurance funds. Together, they ensure outpatient medical and dental care for individuals with statutory health insurance in Germany. To fulfill this responsibility, they have various planning and steering tasks, primarily:
To carry out these tasks, the collective agreement partners require a solid data foundation.
Example:
Using data from electronic patient records (ePA) related to e-prescriptions stored at the Health Data Lab (HDL), they can identify regional differences in medication prescriptions. This can help reveal gaps in healthcare provision in certain areas.
In short:
The collective agreement partners jointly ensure outpatient care for individuals with statutory health insurance by fulfilling legally mandated responsibilities. The data at the HDL provides the necessary foundation to identify, for example, gaps in care, allowing the collective agreement partners to respond appropriately.
What this means:
A key requirement for an efficient healthcare system is quality assurance, particularly in medical and nursing care, as well as patient safety. To ensure high-quality care, the current situation must first be assessed. Then, an evaluation is made to determine which aspects should be maintained or improved. The fundamental requirements for quality assurance are legally regulated.
Binding and concrete regulations for outpatient care and hospitals are primarily established by the Federal Joint Committee (G-BA), supported by the Institute for Quality Assurance and Transparency in Healthcare (IQTIG) and the Institute for Quality and Efficiency in Healthcare (IQWiG).
Example:
The data at the HDL can make significant contributions to these tasks, particularly by assessing the need for and effectiveness of quality assurance measures. The HDL data also complements the data collected directly in care settings for quality assurance.
For example, data can be used to evaluate the quality of care for diabetics. HDL data can show whether individuals with diabetes frequently develop complications such as nerve damage or experience increased hospital admissions. This allows for an evaluation and assessment of the care situation. Another example is analyzing healthcare quality across different regions, such as comparing stroke treatment in rural areas versus large cities.
In short:
A good healthcare system is not only about availability but also about high-quality care. This is why it is important to continuously assess the quality of medical and nursing services. A comprehensive assessment requires as much data as possible about these services. The data at the HDL can help evaluate safety standards and quality in healthcare.
What this means:
Efficient and needs-based healthcare and long-term care require careful planning of resources within the healthcare system. This includes hospital planning (e.g., determining the number of available beds and the specialties offered) and long-term care planning.
Example:
A concrete example is hospital bed occupancy planning. The data at the HDL can be used to analyze how many patients with specific conditions were hospitalized and for how long. By analyzing this occupancy, future resource planning can be improved. This ensures that healthcare resources are effectively utilized and that the best possible healthcare and long-term care continue to be available in Germany.
In short:
In our healthcare system, resources such as personnel or hospital beds are limited. It is crucial that these resources are fairly distributed. To do this effectively, the real demand must first be understood—where is demand highest? The data at the HDL enables such an assessment.
What this means:
Scientific research is essential for gaining new insights into health and nursing care and for analyzing the healthcare system. Research in life sciences contributes to discovering the causes of diseases and developing innovative treatment methods.
Example:
The data at the HDL is particularly valuable for researching chronic diseases because they allow for long-term studies. One example is osteoarthritis research: HDL data can help analyze the progression of the disease, the effectiveness of treatments such as physiotherapy or pain medication, and the demand for surgeries such as joint replacements. These research findings improve healthcare and prevention.
Similarly, chronic back pain can be studied over time to assess the long-term effects of physiotherapy, pain medication, or surgical interventions (e.g., for herniated discs).
In short:
Scientific research generates new insights in many areas, from discovering disease causes to developing new medications and treatments. The data at the HDL allows researchers to study disease progressions over long periods, making it possible to uncover previously unknown correlations.
What this means:
Data and analyses are essential for making informed political decisions. A solid data foundation helps policymakers better understand the healthcare landscape in Germany and leads to improved decisions.
Example:
After a successful application, relevant authorities, the scientific service of the German Bundestag, or a state parliament can analyze aggregated data at the HDL to prepare key decisions or legislation.
For instance, if the data indicate a high prevalence of health issues among elderly individuals during heatwaves, targeted protective measures can be implemented. This ensures that political decisions are based on high-quality, representative data.
In short:
Political decisions should be based on the real state of healthcare. The data at the HDL helps authorities better assess the current situation and make evidence-based decisions.
What this means:
Cross-sectoral healthcare models involve the transition between different types of care, such as from a hospital to a general practitioner’s office. Analyses based on HDL data in this area provide valuable insights into patient care and the effectiveness of different healthcare approaches.
Example:
One example is the treatment of heart attack patients. Using HDL data, various aspects can be examined, such as:
These analyses help identify areas where the transition between different healthcare sectors can be improved without identifying individual patients or healthcare providers.
In short:
Cross-sectoral healthcare occurs when a patient is treated in different settings, such as first in a hospital and then in a general practitioner’s office. The HDL contains data from multiple healthcare sectors, allowing for the analysis of whether patients receive optimal care when transitioning from one sector to another.
What this means:
Health reporting and official statistics are essential tools for documenting public health trends. These reports track the development of diseases in Germany and recommend measures for treatment and prevention.
Example:
Health reports collect information on the prevalence of conditions such as cardiovascular diseases in Germany. Often, these reports compare different regions or age groups to identify systematic differences. HDL data provides an excellent foundation for these reports because it contains long-term information on all individuals with statutory health insurance.
In short:
Health reports help document the state of public health and provide recommendations for treatment and disease prevention. The HDL contains comprehensive data from across Germany over multiple years, making it a valuable resource for analyzing regional disease patterns and producing accurate, up-to-date health reports.
What this means:
Public health tasks include those carried out by health authorities in Germany, such as monitoring reportable infectious diseases and implementing infection control measures. Many health offices also provide information and testing services for infectious diseases.
Given the emergence of new diseases and the urgent need to respond to transmissible conditions, these responsibilities are becoming increasingly important.
HDL data can provide insights into infection trends and vaccination rates across different population groups and regions in Germany. These insights can inform targeted interventions.
Example:
For public education and vaccination campaigns, this data is crucial in shaping content, format, and identifying specific needs. Additionally, the data can help determine:
A concrete example is the spread of measles. Public health authorities track the number of infections and how they spread across Germany. Based on this data, new vaccination and awareness campaigns may be recommended. Similar insights can be gained using HDL data to inform targeted disease prevention strategies.
In short:
When infectious diseases spread, it is important to first understand the situation. Only then can effective containment measures be identified. The HDL contains diagnostic data on various diseases, making it possible to detect unusual outbreaks early and implement timely countermeasures.
What this means:
This purpose of use is primarily aimed at developing safe and effective healthcare products and methods, including:
To develop and improve these products, reliable real-world data on healthcare practices in Germany is needed. The safety, effectiveness, and performance of these products must be continuously monitored. Additionally, these products and methods must be regularly updated to protect the population, as not all side effects can always be anticipated through clinical trials alone.
Example:
HDL data can be used to precisely track which medicines were prescribed and whether subsequent diagnoses indicate potential side effects (e.g., identifying an increased occurrence of bleeding after the use of blood thinners). This allows for continuous safety monitoring of medications.
Another important area is the development of artificial intelligence (AI) systems. Training AI models requires large datasets.
For example, a hospital developing an AI tool to detect lung cancer in X-ray images needs real patient images that indicate whether cancer is present or not. The quality and integrity of the training data is crucial for the AI’s reliability. HDL data provides real-world cases and information, making it possible to train, test, and validate AI models on high-quality, representative data for Germany. This improves diagnostic accuracy and speeds up medical decision-making.
In short:
Medications, medical devices, and AI-based healthcare technologies must be both safe and effective. Continuous monitoring is essential—not just during approval but throughout their use. HDL data can help identify safety concerns and effectiveness by tracking treatment outcomes (e.g., whether a blood thinner led to unexpected side effects). This supports ongoing improvements in healthcare products.
What this means:
Newly approved medicines are not only assessed for safety but also for their benefits to patients. Compared to established treatments, a new medicine should ideally:
In addition to clinical trials and observational studies, real-world data provides the advantage of analyzing an entire population. This allows researchers to gain further insights that contribute to safer and better healthcare. It is also important to ensure that medicatin is available at a reasonable cost.
Example:
Some medication, initially approved for one condition, later proves to be effective for other uses.
For example, a drug originally approved for type 2 diabetes might be found to be highly effective for obesity treatment. HDL data can help systematically analyze these alternative applications and provide a comprehensive basis for benefit assessments.
In short:
This purpose of use focuses on newly approved treatments and healthcare products. It is crucial to continuously evaluate whether new medication is indeed safer or more effective than existing ones. Additionally, over time, some medication may be found to be beneficial for other conditions. HDL data can provide insights into these new treatment possibilities.