Industry experts shared key Insights during our session at both the 2025 MAPS Americas and MAPS EMEA conferences.

The sessions were presented at:
MAPS Americas (March 23-26, 2025), New Orleans
– Cerise James, MD, MPH, Regional Medical Director, Cardio Dx
– Anna Newton, Senior Director, BD, RWE, Lumanity
– Ann Hartry, AVP, Value, Evidence, Outcomes, Neuroscience, Lilly

MAPS EMEA (May 12-14, 2025), London
– Meena Venkatachalam, Global Head, RWE, Lumanity
– Jetty Overbeek, Head of PHARMO Research, PD Groep
– Judith Nelissen, PhD, Head, Global HEOR/Medical Affairs, Astellas

Key session takeaways

1. Enhancing retrospective data with patient-finding algorithms

  • Lumanity showcased how patient-finding algorithms and predictive models can significantly boost the usability of existing secondary retrospective databases. The session provided a comprehensive guide on the step-by-step process for developing these algorithms within secondary data. 

2. The critical role of data feasibility

  • The importance of identifying suitable real world data was a key focus. Data feasibility is essential for the success of patient-finding algorithms, encompassing factors such as the country of interest, transferability of data, variable capture, sample size, costs, timelines, and applicability to external stakeholders.

3. Insights from the PHARMO Data Network

  • The session included patient finding algorithm case studies utilizing the PHARMO Data Network, which offers anonymized real world data from Dutch primary and secondary care settings. These case studies demonstrated the practical application and benefits of using patient finding algorithm in post-launch evidence generation scenarios.

4. Clinical and patient validation – a crucial step

  • Emphasizing the need for appropriate clinical opinion leaders in the development, validation, and deployment of patient-finding algorithms, the session also highlighted the growing importance of involving patient advocacy groups in this process. This collaborative approach ensures the algorithms are both effective and aligned with patient needs.