Data Quality ScoreCard and AI Tools for All Atropos Evidence Network Members

Sep 29, 2024 | Press Release

Data Quality ScoreCards are now available to all members of the Atropos Evidence™ Network, the largest federated healthcare data network with 300M+ patient records. Data contributors receive confidential, transparent, analytically driven feedback on their own data quality and strengths and access to the most advanced AI tools.

Read the full press release

Highlights:

  • The Data Quality ScoreCard enables data holders across the growing Atropos Evidence™ Network to confidentially measure, evaluate, compare, and benchmark their data quality based on objective criteria. The previously introduced Real World Data Score™ (RWDS) and Real World Fitness Score™ (RWFS) present the advantage of transparency as the metrics and weights used to calculate each score are available.

  • This capability comes native with the installation of Atropos Health’s GENEVA OS™, a cloud-based federated technology that can be installed atop existing healthcare data lakes.

  • Atropos Evidence Network members also enjoy other benefits including evidence generation tools ChatRWD™, Green Button™, Forge™ (fka Workbench), as well as Alexandria (a library of studies), clinical trial runs and vector databases. Benefits are based on membership packages. Potential members can select from Silver, Gold, and Platinum packages that are designed to meet a range of needs.

  •  Atropos Health recently announced the addition of Forian, Syndesis Health, and Norstella into the Atropos Evidence Network. The Atropos Evidence Network is built for the purpose of democratizing data for life science and healthcare organizations who are seeking evidence for decision-making in medicine. The Atropos Evidence Network already includes an impressive slate of leaders in the healthcare data ecosystem, including Arcadia.

    When it comes to data science and AI applications for clinical Q&A, Atropos Health answers clinical questions fast, transparently, and with previously unavailable information and data, enabling studies to be run on multiple datasets at once.

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