While the increasing volume of clinical data is welcome in many respects, clinicians and trial managers often struggle with timely access to data held in multiple repositories, and face issues around quality, complexity, and format. To address the problem, the biopharmaceutical sector has invested large sums in data warehouses and data lakes, but timely access to analytics-ready data remains a serious challenge for non-specialists.
Some of the difficulties themselves stem from prior approaches. Manual solutions for data extraction, such as custom programs or scripts, can be costly to develop, deploy and maintain. A network of point-to-point data exchange services soon becomes inordinately complex as new sources are added, and the lack of centralised management can hinder or prevent team collaboration and efficiency.
For example, a cross-study analytics project to review drug safety information across a portfolio of oncology products would naturally need access to clinical data from multiple trials. The combination of different protocols, cohorts, data gathering, and formats soon leads to ballooning complexity as each trial is added to the project.
Solving or reducing data management challenges would immediately release time for better and deeper analytics, supporting the true purpose of a clinical trial, to discover the efficacy and efficiency of new therapies. Ultimately, the less time spent on wrangling data and the more time spent on investigation, the better.
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Bu hikaye BioSpectrum Asia dergisinin BioSpectrum Asia May2024 sayısından alınmıştır.
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