LEVERAGING HEALTH INSURANCE DATA FOR ACCELERATED AND TARGETED RECRUITMENT IN CLINICAL TRIALS IN GERMANY
Author(s)
Mathias Flume, PhD1, Ulf Maywald, PhD2;
1pa-impact GmbH, Managing Director, Dortmund, Germany, 2patientshealth GmbH, Managing Director, Hamburg, Germany
1pa-impact GmbH, Managing Director, Dortmund, Germany, 2patientshealth GmbH, Managing Director, Hamburg, Germany
OBJECTIVES: Recruitment of suitable participants for RCTs remains one of the most critical bottlenecks in clinical research. Conventional recruitment accounts for 32-40% of study budgets, while delays of even one day can cost up to US$8 million and reduce the effective patent protection period. Increasingly specific eligibility criteria, smaller target populations, and high dropout rates (30-40%) further exacerbate these challenges. Patients are often unaware of clinical trial opportunities, while sickness funds lack transparency regarding options for trial participation. Especially for patients with no therapeutic options left, this situation is challenging and should be improved by structured solutions improving access to clinical trials.
METHODS: We implemented a novel recruitment model in cooperation with several German statutory health insurers (sickness funds), leveraging claims and diagnosis data covering over 10 million (>13%) insured individuals. Eligible patients are proactively contacted by their own insurer under a legally secured framework (§140a in conjunction with §68b SGB V) compliant to GDPR and German social law. This proactive 'push' model ensures precise patient identification, efficient outreach, and improved engagement. Patient retention is further supported through insurer-based communication and care coordination.
RESULTS: The model enables faster enrollment than traditional recruitment. Scalability to up to 73 million insured lives allows efficient identification of even niche populations. This approach reduces recruitment costs, improves retention, and enhances trial efficiency. Importantly, securing ≥5% German participation in the studies improves outcomes of AMNOG price negotiations.
CONCLUSIONS: Patients benefit from earlier access to innovative therapies and better care coordination. Sponsors and CROs achieve faster, more cost-efficient recruitment, reduced dropout, and stronger negotiation positions. Health insurers enhance patient services, contribute to innovation, and strengthen their competitive position. Policymakers gain a model that supports Germany’s role as an attractive location for clinical research while reducing system costs.
METHODS: We implemented a novel recruitment model in cooperation with several German statutory health insurers (sickness funds), leveraging claims and diagnosis data covering over 10 million (>13%) insured individuals. Eligible patients are proactively contacted by their own insurer under a legally secured framework (§140a in conjunction with §68b SGB V) compliant to GDPR and German social law. This proactive 'push' model ensures precise patient identification, efficient outreach, and improved engagement. Patient retention is further supported through insurer-based communication and care coordination.
RESULTS: The model enables faster enrollment than traditional recruitment. Scalability to up to 73 million insured lives allows efficient identification of even niche populations. This approach reduces recruitment costs, improves retention, and enhances trial efficiency. Importantly, securing ≥5% German participation in the studies improves outcomes of AMNOG price negotiations.
CONCLUSIONS: Patients benefit from earlier access to innovative therapies and better care coordination. Sponsors and CROs achieve faster, more cost-efficient recruitment, reduced dropout, and stronger negotiation positions. Health insurers enhance patient services, contribute to innovation, and strengthen their competitive position. Policymakers gain a model that supports Germany’s role as an attractive location for clinical research while reducing system costs.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
PCR160
Topic
Patient-Centered Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas