German Health Data Providers Compared: Claims Data Access, GDNG Compliance and RWE Fit for Pharma Research

This guide maps the main statutory, research-institute and commercial routes so that the data source can be selected around the research question rather than around provider familiarity.

This post provides a practical, evaluated comparison of the primary German health data providers available to pharmaceutical and MedTech teams — covering population coverage, data content, access model, timeline, GDNG and GDPR compliance architecture, regulatory acceptance track record, and the specific use cases each provider serves best. It is written from an advisory perspective: the goal is to help pharma teams understand where each provider’s genuine capabilities begin and end, not to advocate for any of them.

Why German Claims Data Matters

German statutory health insurance (SHI) generates a large and valuable source of routinely collected healthcare data. Because claims are created as part of the delivery and reimbursement of healthcare, they can provide a broad view of how patients use the healthcare system over time.

One of the key advantages of German claims data is its longitudinal nature. Rather than capturing a patient at a single point in time, claims records can allow researchers to follow patterns of healthcare utilisation across multiple periods, depending on the dataset and the available years of data.

This makes claims data particularly useful for understanding what happens to patients within the healthcare system. Depending on the dataset, researchers can analyse areas such as:

• Diagnoses recorded during healthcare encounters
• Hospital treatment and inpatient healthcare utilisation
• Prescriptions and pharmaceutical use where the relevant data are available
• Medical procedures and treatments
• Healthcare resource utilisation
• Healthcare costs where cost information is included in the dataset
• Treatment pathways and patterns of healthcare use over time

These characteristics make German claims data relevant to a wide range of research questions, including epidemiology, health economics and outcomes research (HEOR), real-world evidence (RWE), and health services research.

For example, longitudinal claims data can help researchers examine how frequently a particular condition is diagnosed, how patients move through different parts of the healthcare system, which treatments or procedures are used, and how healthcare utilisation changes over time. For health economics and outcomes research, claims data can also provide valuable evidence about resource use and healthcare costs where those data are available.

The value of a claims dataset therefore depends not only on the number of insured individuals it covers, but also on the data domains, level of detail, period covered, longitudinal structure and suitability for the specific research question.

What Claims Data Can – and Cannot – Tell You

Claims data is particularly strong when the research question concerns what healthcare was used, when it was used, and how healthcare utilisation changed over time. It can provide structured information about coded diagnoses, treatments and procedures, prescriptions, healthcare utilisation and, where available, associated healthcare costs.

Its longitudinal structure can also make it possible to examine patient pathways across different healthcare encounters. This can be particularly useful when studying patterns of treatment, healthcare resource utilisation, disease epidemiology or outcomes that can be observed through routinely collected healthcare records.

At the same time, claims data should not be treated as a complete clinical record. Information that is not required for healthcare reimbursement may not be captured, or may only be represented indirectly.

Depending on the dataset, claims data may therefore provide limited or no direct information about:

• Clinical measurements, such as laboratory values or other measurements that are not included in the claims data
• Patient-reported outcomes and patients’ own assessments of their health or quality of life
• Detailed clinical reasoning behind a physician’s treatment decision
• Disease severity where severity is not represented in the available claims variables
• Lifestyle information and other factors that are not routinely captured for claims purposes

This distinction matters when selecting a German claims dataset. A dataset can be extremely valuable for analysing healthcare utilisation and treatment pathways while still being unsuitable for a research question that depends on detailed clinical measurements, patient-reported outcomes or information about factors that are not routinely recorded in claims.

The right question is therefore not simply “How large is the database?” but “Does the data contain the information needed to answer the research question?”

For more detail on how different German claims data sources compare, see FDZ Gesundheit vs Commercial Claims Data Providers. For research questions involving health economics and outcomes research, see German Claims Data for HEOR and Market Access.

The German Health Data Landscape: Two Fundamentally Different Access Models

Before comparing individual providers, the most important structural distinction to understand is that German health data access operates through two fundamentally different models — and confusing them is the most common strategic error made by pharma teams approaching this market for the first time.

Model 1: Statutory/Regulatory Access through FDZ Gesundheit

Since the GDNG came into force in March 2024, the Forschungsdatenzentrum Gesundheit (FDZ Gesundheit) at the Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM) has been the central access point for pseudonymised SHI data held by German statutory health insurers. Access is application-based: organisations submit a formal research application to FDZ Gesundheit, which is evaluated against eligibility criteria defined in the GDNG. Approved applicants access data within a secure analysis environment rather than receiving a data delivery. This model offers the broadest population coverage and the strongest regulatory credibility — but the access timeline is measured in months, and the application process is demanding.

Model 2: Commercial Access through Research Institutes and Data Vendors

Several established organisations — including InGef, INSIGHT Health, and IQVIA Germany — offer access to German health data through commercial licensing arrangements. These datasets are derived from SHI claims data contributed voluntarily by participating health insurance funds, anonymised to GDPR standards, and made available for research and commercial analytics under contractual frameworks that do not require the formal GDNG application process. Coverage is narrower than the FDZ route — typically 8 to 15 million insured persons rather than 75 million — but access timelines are substantially faster and the commercial model is more familiar to pharma procurement teams.

Neither model is universally superior. The right choice depends on the specific research question, the required population coverage, the regulatory context of the intended use, and the available timeline. Understanding both is the prerequisite for evaluating any individual provider.

The right choice depends on the research question rather than simply on the size of the underlying database. A nationwide dataset can be particularly valuable for rare diseases or other research involving small patient populations, while a large commercial dataset may provide sufficient statistical power for many research questions with greater flexibility and a shorter path to results.

For a more detailed comparison of the two access models, see FDZ Gesundheit vs Commercial Claims Data Providers.

The GDNG Framework: What Changed in 2024 and Why It Matters

The Gesundheitsdatennutzungsgesetz enacted in March 2024 was the most significant legislative development in German health data access in a generation. Its practical implications for pharma and MedTech teams are worth understanding in concrete terms before evaluating individual providers.

What the GDNG established:

The GDNG created a legal framework under which pseudonymised SHI data from all statutory health insurers can be made available for approved research and public interest purposes through FDZ Gesundheit. Prior to the GDNG, health data research in Germany was primarily conducted through bilateral arrangements with individual health insurance funds — a fragmented process that produced geographically and demographically unrepresentative datasets. The GDNG replaced this with a centralised, population-representative access model.

Who can apply:

The GDNG permits access applications from a range of organisations including academic institutions, public health authorities, and — importantly for the pharma and MedTech industry — commercial organisations pursuing research in the public interest. Pharmaceutical companies can apply directly, provided the research question meets the eligibility criteria defined in the GDNG. Applications must document the specific research purpose, the data elements required, the analytical methodology, and the data security measures in place.

What data is accessible:

FDZ Gesundheit provides access to pseudonymised SHI data covering ambulatory diagnoses (ICD-10), hospital admissions and procedures (OPS codes), prescription data (ATC classification), and sociodemographic variables. Data model 4 — currently in development and not yet available for applications — will expand the scope further, incorporating data from the elektronische Patientenakte (ePA, the electronic patient record) once that system is sufficiently populated.

What the GDNG does not do:

The GDNG does not create a fast-track commercial data licensing route. Access through FDZ Gesundheit remains a formal regulatory process with application review timelines that are currently measured in months. It does not replace the commercial data access routes available through InGef, INSIGHT Health, or IQVIA for use cases where population representativeness is less critical than access speed. And it does not eliminate the need for a well-designed research protocol — applications that do not clearly articulate the research question, methodology, and data minimisation approach are not approved.

What the GDNG Means in Practice for Researchers

The Health Data Use Act (GDNG) has changed the framework for accessing and using health data in Germany. For researchers considering the FDZ Gesundheit, however, the practical question is not simply whether German claims data exists, but whether the research project meets the requirements for access and whether the FDZ’s research environment fits the project’s needs.

The FDZ provides access to statutory health insurance data in a controlled research environment. The available data covers the population of participating statutory health insurers and includes longitudinal information extending back to 2009, with higher granularity available from 2019 through the so-called Data Model 3.

Who Can Apply for Access?

Access is subject to specific eligibility requirements. The applicant institution must be registered, and requests can only be made by institutions located within the EU/EEA.

More importantly, the proposed research question must correspond to one of the legally permitted purposes for using the data. The research environment is intended to support research serving the public interest, so not every commercial or market-oriented research question automatically qualifies for access.

This distinction is particularly important for organisations considering the FDZ for projects involving market research or product development. These purposes are not included among the permitted purposes described in the FDZ access framework.

What Does the Access Process Involve?

Access to the FDZ is not simply a matter of purchasing a dataset and beginning an analysis. An institution must submit an application, have its research question assessed, and then wait for access to become available.

Researchers should therefore account for the application process and potential queue when planning a project. The FDZ operates as a structured research environment rather than an on-demand commercial data service.

Once access has been approved, researchers work within a secure server-based analysis environment. The analysis software is prescribed, with R, Python or SQL available for analysis.

Data Economy and Fixed Access Conditions

The FDZ applies a principle of data economy. Each application is provided with the smallest amount of data considered necessary for the approved research question.

This has an important practical consequence: the dataset cannot simply be expanded or redesigned during the analysis if the research team later discovers that additional information is needed. Changes to the data included in the analysis may require a new or modified application process.

Access also operates within fixed time windows. Researchers therefore need to plan their analysis around the available access period rather than assuming that the project timeline can be changed freely once access has been granted.

Restrictions on Results and Output

The FDZ also places controls on the results that can be taken out of the research environment. The supplied access framework specifies a limit of five tables and a maximum of 2,400 data cells, with results subject to review before release.

Access to released results is also time-limited. These conditions are designed to protect the data while allowing approved research to be conducted, but they should be considered when planning complex analyses or projects that are expected to produce multiple outputs.

Planning for Time and Cost

The practical cost of an FDZ project extends beyond a basic access fee. Costs can include a basic fee, reporting years, analysis environments and usage days, optional services and potentially training. The applicant institution also needs to account for its own internal personnel and resource requirements.

No standard cost estimate or quotation is provided in advance. Applicants therefore need to calculate the expected costs based on their planned use of the research environment.

Reduced fees may apply to accredited universities, public research institutions and other organisations covered by the relevant provisions of the Data Transparency Fee Ordinance (DaTraGebV). These reductions do not apply to the pharmaceutical industry.

When Does the FDZ Make Sense?

The FDZ can be particularly attractive when the research question benefits from the scale of its population coverage and the project can accommodate the statutory access process.

For example, nationwide coverage can be particularly valuable when studying rare diseases or other small patient populations, where the additional population size may provide an important advantage over a smaller dataset.

The FDZ may also be a good fit when the research question does not require extremely detailed data, the analysis is relatively well defined from the outset, and there is sufficient time to work through the application and access process.

For other projects, particularly those requiring rapid access, flexible study design or repeated extensions and updates, a commercial data provider may offer a more practical route.

For a detailed comparison of these two approaches, see FDZ Gesundheit vs Commercial Claims Data Providers.

For a step-by-step discussion of the access process, see How to Access German Claims Data under the GDNG.

Provider Profiles: Evaluated Comparisons

1. FDZ Gesundheit / BfArM

Provider type: Federal statutory research data centre
Website: forschungsdatenzentrum-gesundheit.de
Access model: Application-based, secure analysis environment (no data delivery)
Population coverage: Approximately 75 million GKV-insured persons — population-representative

Data content: Pseudonymised SHI claims data covering ambulatory diagnoses (AMBDIAG, ICD-10), hospital admissions and DRG codes, ambulatory procedures (EBM), prescription data (ATC classification), sick leave data, and sociodemographic variables. Data is provided as Scientific Use Files or Public Use Files depending on the research question and the level of access granted. Data from the elektronische Patientenakte (ePA) will be added in a future data model iteration.

Access timeline: Applications are reviewed by FDZ Gesundheit and a data access committee. Timeline from application submission to data access varies based on application complexity — the FDZ has published a complexity-based prioritisation framework. Applicants should plan for a timeline of several months from initial application to approved access. A webinar series and application consultation office hours are available to support applicants.

GDNG and GDPR compliance: FDZ Gesundheit operates under the GDNG framework and is the primary statutory implementation of that legislation. Data is pseudonymised and accessible only within a secure analysis environment (Analyseraum) — no data leaves FDZ infrastructure. Output disclosure controls apply: research results must pass an output checking process before they are released to applicants. Re-authentication via BundID is required every 90 days (daily for ePA data).

Regulatory acceptance track record: Strong and growing. Data from the FDZ statutory access route carries the highest regulatory credibility for German HEOR submissions and HTA processes because its population representativeness and data governance are fully documented. EMA and BfArM are both aware of and supportive of the FDZ access model. For regulatory submissions requiring demonstrable population-level representativeness, this is the strongest available data source in Germany.

Best suited for: Population-level epidemiology and disease prevalence studies, HEOR analyses requiring national representativeness, regulatory submissions and HTA dossiers where population coverage and data governance credibility are priorities, health services research, and academic or public interest research.

Limitations: Access timeline is the primary constraint for pharma teams with commercial deadlines. The secure analysis environment model means all analytical work must be conducted within FDZ infrastructure — teams cannot export raw data for analysis in their own systems. A technical error affecting the AMBDIAG (ambulatory diagnosis) table was disclosed by FDZ Gesundheit in mid-2026, affecting approximately 20% of the relevant data for four research users — the FDZ’s transparent disclosure of this issue is itself a positive signal for data governance quality. Output checking adds time to the analytical workflow. Applications require detailed research protocol documentation that demands upfront investment in study design.

2. InGef — Institut für angewandte Gesundheitsforschung Berlin

Provider type: Independent health research institute
Website: ingef.de
Access model: Research partnership — InGef typically conducts analyses on behalf of clients rather than providing raw data access
Population coverage: Approximately 8.8 million insured persons across 52 statutory health insurance funds

Data content: InGef’s research database contains anonymised claims information including sociodemographic data, drug prescriptions, and outpatient and inpatient treatments over six consecutive calendar years, longitudinally linked. The database covers pharmacoepidemiological research questions and health services research comprehensively. InGef became an official data partner in the European DARWIN EU network in April 2025 — the EMA’s real-world evidence network — which is a significant marker of data quality and methodological credibility for regulatory purposes.

Access model detail: InGef operates primarily as a research partner rather than a data licensor. Pharmaceutical companies and research organisations commission studies that InGef designs and executes using its research database. This model means analytical expertise is bundled with data access — which is an advantage for teams without internal pharmacoepidemiological capability, but a constraint for teams wanting to run their own analyses on the raw data.

GDPR compliance: Data is anonymised to GDPR Article 4(1) standards. InGef’s approach to anonymisation has been published and peer-reviewed, providing a documented compliance basis that many commercial data providers lack.

Regulatory acceptance track record: Among the strongest of any commercial German data provider. InGef’s OMOP CDM transformation project, presented at the OHDSI Europe Symposium 2025, aims to standardise research data to the international OMOP Common Data Model — which directly supports multi-national study designs and regulatory submissions requiring interoperability. InGef has an extensive published research track record in peer-reviewed pharmacoepidemiological and health services journals, which supports the use of its data in HTA submissions.

Best suited for: Pharmacoepidemiological studies, HEOR analyses, comparative effectiveness research for AMNOG submissions, multi-national studies requiring OMOP-standardised data, and any use case where peer-reviewed analytical credibility is a priority.

Limitations: Population coverage of 8.8 million is substantial but not population-representative at the national level — approximately 12% of the GKV population. For rare disease research where absolute patient counts in the dataset may be very small, this coverage limitation is material. The research partnership model means access to raw data for independent analysis is not available in the same way as commercial data licensing. Timelines depend on research project scoping and InGef’s capacity.

3. IQVIA Germany

Provider type: Global commercial health data and analytics provider
Website: iqvia.com
Access model: Commercial licensing — multiple data product types available
Population coverage: Varies significantly by product — see below

Data content: IQVIA Germany operates multiple distinct data products that serve different purposes and should not be conflated:

LRx (Longitudinal Prescription Database): Panel-based longitudinal prescription data tracking individual patient prescriptions over time through a sample of German pharmacies. Strong for treatment pattern analysis, therapy switching research, and commercial analytics. Not population-representative — a panel design.

Disease Analyzer: IQVIA’s Disease Analyzer database and German EMR data show the entire patient path, providing a clear understanding of treatment dynamics. GP panel-based EMR data covering diagnoses, prescriptions, and referrals. Strong for primary care research and treatment pathway analysis. Coverage approximately 3,000 GPs across Germany.

DDD (Drug Distribution Data): Pharmacy-level sales data measuring sell-in volumes. Useful for market sizing and commercial analytics but not patient-level data — no individual patient journey information.

SHI claims data partnerships: IQVIA Germany has developed specific health insurance data study capabilities, managing health insurance data studies and leading multi-country studies on statutory health insurance data. These are typically structured as bespoke research engagements rather than standardised data products.

GDPR compliance: IQVIA applies de-identification and anonymisation standards to its patient-level products. Specific documentation varies by product. Teams intending to use IQVIA data for regulatory submissions should request formal data provenance and anonymisation methodology documentation before contracting.

Regulatory acceptance track record: IQVIA data is widely used in pharma commercial analytics and market access research. For regulatory-grade submissions — particularly EMA HEOR submissions under AMNOG or EU HTA processes — the panel-based design of LRx and Disease Analyzer creates representativeness questions that need to be addressed in study protocols. IQVIA’s SHI claims data partnership model has stronger regulatory credentials than its panel products but requires bespoke engagement rather than off-the-shelf licensing.

Best suited for: Commercial analytics, competitive intelligence, treatment pattern analysis for brand strategy, market sizing, prescriber targeting, and any commercial use case where speed of access and analytical breadth are priorities over regulatory-grade population representativeness.

Limitations: Panel-based products are not population-representative and require careful characterisation in study protocols. The product portfolio’s breadth can create confusion — IQVIA’s German data capabilities vary substantially across product lines, and the right product for a specific research question is not always obvious from marketing materials alone. Cost is typically higher than research institute partnerships for equivalent analytical outputs. The vendor relationship — IQVIA has commercial interests in specific data partnerships — means their recommendations about which product to use should be evaluated with appropriate independent scrutiny.

4. INSIGHT Health

Provider type: German commercial health data provider
Website: insight-health.de
Access model: Commercial licensing
Population coverage: Prescription data covers approximately 80% of the German pharmacy market through panel design

Data content: INSIGHT Health’s primary product is longitudinal prescription data derived from German pharmacy point-of-sale records — covering approximately 80% of German pharmacies through a comprehensive panel. This provides near-complete market coverage for prescription monitoring, treatment pattern analysis, and competitive intelligence at the product and substance level. INSIGHT Health also offers hospital data products covering inpatient treatment and DRG coding.

Best suited for: Pharmaceutical commercial analytics, prescription market monitoring, competitive intelligence, launch tracking, and treatment pattern analysis at the brand or substance level. Strong for questions about what is being prescribed and where — weaker for questions about which patients are receiving which treatments and what outcomes they experience.

Limitations: Prescription data without linked patient-level diagnoses and outcomes is structurally limited for epidemiological research, HEOR, or regulatory submissions. Patient-level linkage across care settings is not available in INSIGHT Health’s standard products. For any research question requiring diagnosis data, treatment outcomes, or longitudinal patient follow-up, INSIGHT Health’s core products are insufficient on their own.

5. WIdO — Wissenschaftliches Institut der AOK

Provider type: Research institute of AOK statutory health insurance group
Website: wido.de
Access model: Research partnership
Population coverage: Approximately 27 million AOK-insured persons — Germany’s largest single health insurance fund group

Data content: WIdO operates comprehensive longitudinal claims data from AOK health insurance funds covering ambulatory and inpatient diagnoses, prescriptions, procedures, sick leave, and sociodemographic variables. Coverage of 27 million insured persons represents approximately 36% of the GKV population — substantially broader than InGef and with specific strengths in certain demographic segments where AOK has disproportionate market share (blue-collar workers, pensioners, and regional concentrations in certain German states).

Best suited for: Population health research, chronic disease epidemiology, health services research, and any study where the AOK demographic profile — which skews toward lower socioeconomic status and older age groups compared to the general GKV population — is analytically relevant or can be adequately characterised as a limitation.

Limitations: The AOK demographic profile creates representativeness questions for studies targeting the full GKV population. Access is research partnership-based — WIdO does not license raw data commercially. Commercial pharma engagement typically involves a formal research proposal and review process.

6. Zentralinstitut für die kassenärztliche Versorgung (Zi)

Provider type: Research institute of the National Association of Statutory Health Insurance Physicians
Website: zi.de
Access model: Research partnership
Population coverage: Ambulatory care data covering approximately 70–75 million GKV-insured persons

Data content: Zi operates one of the most comprehensive ambulatory care claims databases in Germany — derived from billing data submitted by panel physicians (Vertragsärzte) to the Kassenärztliche Vereinigungen. This covers virtually all outpatient physician contacts across Germany, including diagnoses, procedures, referrals, and prescriptions written in the ambulatory setting. Zi’s data is particularly strong for ambulatory diagnosis research, specialty referral pathway analysis, and studies of outpatient prescribing behaviour.

Best suited for: Ambulatory care research, outpatient treatment pathway analysis, disease prevalence studies, prescriber behaviour research, and any study where near-complete ambulatory care coverage is analytically important.

Limitations: Zi’s data is strong for ambulatory care but does not include the same depth of hospital and prescription data that a linked claims database provides. Commercial access requires a formal research partnership engagement. Access timelines and process requirements are similar to other research institute partnerships.

Comparison Across Key Evaluation Dimensions

The following framework maps each provider against the dimensions that determine analytical fit for pharmaceutical research purposes. This is an advisory assessment — not a definitive ranking — and specific capabilities may vary by product line and research engagement type.

Use Case Matching: Which Provider for Which Research Question

Population-level disease prevalence and diagnosed prevalence studies
Best fit: FDZ Gesundheit (population-representative, GDNG-compliant), with WIdO as a commercial alternative where AOK demographic profile is acceptable. InGef is adequate for studies where 8.8M coverage is sufficient for the condition’s prevalence.

HEOR analyses and AMNOG submissions
Best fit: FDZ Gesundheit for regulatory credibility; InGef for faster access with strong peer-reviewed track record. WIdO for studies where the AOK demographic profile is appropriate. All three are substantially stronger than panel-based commercial products for HTA submissions.

Comparative effectiveness research for regulatory submissions
Best fit: FDZ Gesundheit. For studies requiring EMA or BfArM regulatory acceptance, the GDNG statutory route provides the strongest data governance credentials. InGef’s DARWIN EU membership is an increasing advantage for multi-national regulatory evidence generation.

Prescription market analytics and competitive intelligence
Best fit: IQVIA LRx for breadth and speed; INSIGHT Health for pharmacy-level coverage. These are commercial analytics tools, not research databases — appropriate for brand strategy, launch tracking, and competitive intelligence but not for regulatory submissions.

Treatment pathway and patient journey analysis
Best fit: InGef or IQVIA SHI partnership for linked longitudinal data; FDZ Gesundheit for population-representative pathway studies. INSIGHT Health’s prescription data alone is insufficient for comprehensive patient journey mapping.

Rare disease cohort identification
Challenging in all commercial databases given coverage limitations — a condition affecting 1 in 10,000 patients represents approximately 880 cases in InGef’s 8.8M population, which may be adequate or insufficient depending on the specific analytical question. FDZ Gesundheit’s 75M coverage is the most viable option for very rare conditions. Phenotype validation in claims data is especially critical in rare disease contexts where misclassification has a disproportionate impact on analytical validity.

Multi-national studies requiring data interoperability
Best fit: InGef — its OMOP CDM transformation project enables standardised multi-national analyses within EMA’s DARWIN EU framework. IQVIA’s global OMOP infrastructure also supports multi-country study designs, though the German data component’s quality varies by product type.

GDPR Compliance: What to Verify Before Contracting with Any Provider

All German health data providers operating legally are GDPR-compliant — but GDPR compliance is not a binary property. The specific anonymisation methodology, the legal basis for processing, the data governance framework, and the contractual protections all vary significantly between providers and between product lines within the same provider. Teams procuring German health data for regulatory submissions or HEOR analyses should verify the following before entering any contract:

Anonymisation methodology documentation. Is the anonymisation approach published or at least formally documented? Can the provider supply a data provenance document describing how the original patient-level data was transformed into the anonymised product you are licensing? InGef’s published methodology is a positive benchmark. For providers where this documentation is not available, the compliance risk is higher than marketing materials typically indicate.

Legal basis for processing. Under GDPR Article 9, processing of health data requires a specific legal basis — typically scientific research under Article 9(2)(j), public interest under Article 9(2)(i), or explicit consent under Article 9(2)(a). For German SHI data, the GDNG provides the legal framework for FDZ Gesundheit’s statutory route. Commercial providers typically rely on anonymisation to remove the data from GDPR’s scope entirely — but the adequacy of that anonymisation is the critical question.

Cross-border transfer provisions. For teams based outside Germany conducting analyses on German health data, GDPR cross-border transfer requirements apply. Verify whether the provider’s data access model is structured to address this — FDZ Gesundheit’s Analyseraum model avoids data transfer entirely; commercial licensing models require explicit contractual provisions.

Data Processing Agreement. A GDPR-compliant DPA is required for any engagement involving health data. Review it carefully — standard commercial DPAs from large vendors frequently contain provisions that are inadequate for health data in ways that legal teams without health data experience may not identify. For guidance on what to look for in a claims data partner’s compliance documentation, see our post on pharma data procurement and GDPR compliance.

The Honest Advisory Assessment: What Most Teams Get Wrong

In our experience advising pharma and MedTech teams on German health data access, several misalignments between expectations and reality appear consistently:

Conflating prescription data with patient-level claims data. A team looking for treatment pattern and outcome data commissions INSIGHT Health prescription data and discovers months into the project that it cannot answer outcome questions. Prescription data and longitudinal patient-level claims data are fundamentally different products. The former tells you what was dispensed; the latter tells you what happened to the patient before, during, and after treatment.

Selecting a provider for name recognition rather than fit. IQVIA’s brand recognition in the global pharma market is substantial — and it creates a default selection bias that is not always analytically justified. For HEOR submissions and regulatory evidence generation in Germany specifically, InGef’s peer-reviewed track record and DARWIN EU membership represent credentials that IQVIA’s German panel products cannot match. Name recognition and analytical fitness for purpose are different things.

Underestimating FDZ Gesundheit’s timeline. Teams that identify FDZ Gesundheit as their preferred source — correctly, for use cases where population representativeness matters — then discover that the application process timeline is incompatible with their project schedule. FDZ access should be planned 12 to 18 months before the data is needed, not three months before. Teams with shorter timelines need a commercial provider alternative, which requires understanding the coverage and representativeness trade-offs that entails.

Overestimating InGef’s coverage for rare conditions. 8.8 million insured persons is a substantial research database by most international standards — but for conditions with a diagnosed prevalence below 1 in 10,000, the absolute patient count in InGef’s database may be too small for the intended analysis. For rare disease research specifically, FDZ Gesundheit’s 75 million coverage is often the only viable German data option.

Not verifying GDPR compliance documentation before contracting. The phrase “GDPR-compliant” in a vendor’s marketing materials is not a compliance verification. Legal teams with limited health data experience routinely approve contracts containing compliance gaps that become visible only when a regulatory reviewer or data protection authority asks questions. Verifying anonymisation methodology documentation, legal basis for processing, and DPA adequacy before signing is not optional for regulatory-grade research.

Learn More: German Claims Data Research

• FDZ Gesundheit vs Commercial Claims Data Providers — Compare the two main access models, including population coverage, timelines, flexibility, and research constraints.
• How to Access German Claims Data under the GDNG — Understand the application process, eligibility requirements, and practical considerations for accessing FDZ Gesundheit data.
• Rare Disease Research Using German Claims Data — Explore when nationwide coverage matters and how claims data can support rare disease research.
• Understanding Data Model 3 in the FDZ Gesundheit — Understand the increased granularity available in the FDZ data from 2019 onwards and its implications for research.
• German Claims Data for HEOR and Market Access — Examine how German claims data can support healthcare resource utilisation, outcomes research, and market access.
• EHDS and German Health Data: What’s Changing? — Understand how the European Health Data Space fits into Germany’s changing health data environment.
• Choosing Between Statutory and Commercial Data Sources — A practical framework for deciding which access route fits a particular research project.

Sources and References

1. Forschungsdatenzentrum Gesundheit (FDZ Gesundheit) at BfArM. Information for applicants. https://www.forschungsdatenzentrum-gesundheit.de/en/
2. Bundesministerium für Gesundheit (2024). Gesetz zur verbesserten Nutzung von Gesundheitsdaten (Gesundheitsdatennutzungsgesetz – GDNG). https://www.bundesgesundheitsministerium.de
3. InGef — Institut für angewandte Gesundheitsforschung Berlin. Research Database and Methods. https://www.ingef.de/en/health-research/methods/
4. InGef (2025). InGef is now an official data partner in the European network DARWIN EU. https://www.ingef.de/en/current-topics/news/
5. IQVIA (2025). Exploring Real World Data in Germany. https://www.iqvia.com
6. Ludwig, M., Enders, D., Basedow, M., Walker, J., & Jacob, J. (2020). Description of InGef database. Published in peer-reviewed literature — see InGef publications list at https://www.ingef.de/en/current-topics/publications/
7. European Medicines Agency. Data Analysis and Real-World Interrogation Network (DARWIN EU). https://www.ema.europa.eu
8. Benchimol, E. I., et al. (2015). The RECORD statement for observational routinely-collected health data studies. PLOS Medicine, 12(10). https://journals.plos.org/plosmedicine
9. U.S. Food and Drug Administration (2024). Real-world data: assessing electronic health records and medical claims data to support regulatory decision-making. https://www.fda.gov
10. European Medicines Agency (2025). Reflection paper on the use of real-world data in non-interventional studies to generate real-world evidence. https://www.ema.europa.eu