As clinical trial complexity, duration, and cost continue to rise, life sciences companies are turning to data to help them make better decisions and extract efficiency. Managing extensive amounts of data, both internal and external, is a challenge encountered by the industry, including sponsors and contract research organizations (CROs).
There is also pressure to leverage artificial intelligence (AI) and machine learning (ML), which requires a complex data integration and standardization plan, while being cautious of outputs and interpretation.
Biopharmaceutical companies who specialize in benchmarking now strive to create clear performance indicators to provide clients a roadmap: a way to prioritize what to measure, when, how, and why.
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