Clinical Data Science in 2025

john87833
Jun 24
2 min read

Rohit Singh, a physician with 10+ years of experience in clinical data science and its management.

Overview

Many biopharma and biotechnology organizations struggle with clinical data science, especially those dealing with complex, fragmented data and limited technical resources. Detailed reasons can include data integrity, it was difficult to integrate, it was under regulatory scrutiny, or the clinical trials failed. Success in this area requires the right data and process culture, and strategic investment in technology, skilled personnel, and operational change.

Common Challenges with Clinical Data Science

Data Silos and Integration: Clinical data is often stuck in systems that don't work together (like EHRs, lab systems, imaging, etc.) and that use different formats and terms. This makes it hard to integrate and standardize the data.
Quality and Curation of Data: Models and statistical analyses are harder to make when data is missing, inconsistent, or wrong. Manual curation takes a lot of time and costs a lot of money.
Privacy and Compliance: Sharing and reusing data is against strict rules like HIPAA and GDPR. It's hard to put in place protocols for de-identification and safe data access.
Lack of Talent: There aren't enough data scientists and engineers who know both clinical and technical skills, which makes it hard for businesses to put together good teams.
Old IT systems and infrastructure: Old IT systems can't handle the size and complexity of modern data science.
Change Management and Culture: Resistance to data-driven decision-making among clinicians and executives can slow adoption of analytics and AI solutions.

Some Important Examples

Big Pharma talked about the problems they had with making global clinical trial data consistent and adding AI to research workflows in public.

Small biotech companies don't have the internal resources to build integrated data science platforms and instead hire CROs or consultants, which slows down innovation.

Hospital Systems: They have a hard time digitizing and combining old records, which makes it harder to do advanced analytics.

Trends in the Industry

Increased investment in data engineering, interoperability platforms (e.g., FHIR), and cloud-based analytics. Partnerships with specialized data science vendors or consulting firms. Adoption of federated learning and privacy-preserving analytics to enable insights while protecting patient privacy.

Therapeutic Area of Neurology

Because of the complexity of disease mechanisms, inconsistent or non-validated biomarkers, challenging endpoints, and trial logistics, Neurology poses some of the most difficult problems in clinical science data.

Clinical trials in neurology face many obstacles, such as high failure rates and operational difficulties, but continued advancements in therapeutic and diagnostic techniques give hope for better results. The advancement of neurological research and treatment will depend on sustained attention to data integrity, patient recruitment, and regulatory navigation.

Rohit Singh Atwal

MHSA MBBS

Consultant, Clinical Data Science Visioneer