For diagnostics and clinical-stage organizations, integrating AI into clinical studies has the potential to fast-track time-to-market for new products by up to 4x. However, one of the biggest barriers to realizing this potential is delays in data readiness—driven by fragmented systems, inconsistent formats, and manual data wrangling.
Clinical operations teams frequently spend months reconciling and integrating data across EHRs, lab systems, imaging platforms, and clinical trial data. This process often involves repetitive manual mapping, inconsistent coding standards, and ad hoc pipelines that break when data models or dictionaries change. The result? Slower AI model development, higher costs, and missed opportunities for accelerated insights.
In this session, we’ll discuss how top organizations are unifying data and code with real-time, scalable, and accurate clinical data pipelines that transform raw, siloed data into governed, AI-ready data products. This shift enables faster decision-making, reduced development timelines, and maintains data integrity across the entire product lifecycle—from discovery to clinical validation and launch.
For diagnostics and clinical-stage organizations, integrating AI into clinical studies has the potential to fast-track time-to-market for new products by up to 4x. However, one of the biggest barriers to realizing this potential is delays in data readiness—driven by fragmented systems, inconsistent formats, and manual data wrangling.
Clinical operations teams frequently spend months reconciling and integrating data across EHRs, lab systems, imaging platforms, and clinical trial data. This process often involves repetitive manual mapping, inconsistent coding standards, and ad hoc pipelines that break when data models or dictionaries change. The result? Slower AI model development, higher costs, and missed opportunities for accelerated insights.
In this session, we’ll discuss how top organizations are unifying data and code with real-time, scalable, and accurate clinical data pipelines that transform raw, siloed data into governed, AI-ready data products. This shift enables faster decision-making, reduced development timelines, and maintains data integrity across the entire product lifecycle—from discovery to clinical validation and launch.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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