Atropos Health Redefines Data Quality With Two Scoring Metrics, Bridging The Gap For Transparent Evaluation of Data Use In Evidence and Prediction Generation

Real World Data Score™ and Real World Fitness Score™ set the standard for data grading, enabling the precise transparent evaluations of the quality for evidence generation and use in predictive modeling

PALO ALTO, Calif.--(BUSINESS WIRE)--Today, Atropos Health, a pioneer in translating real-world clinical data into personalized, real-world evidence and insights, published a whitepaper outlining how its Real World Data Score (RWDS) and Real World Fitness Score (RWFS) also referred to as “Fitness Quotient” or “FitQ,” solve for the two most significant gaps that currently exist for real-world evidence in healthcare—quantitative evaluation metrics for datasets and the ability to evaluate fitness-for-purpose assessments.

About the publication:

• Title: Quantifying Fit-for-Purpose in Real World Data: Data Grading and FitQ Scores
• Real-world evidence (RWE) derived from analysis of real world data (RWD) is increasingly used to guide decisions in drug development, regulatory oversight, and clinical decision-making. Using the dataset that is best suited to answer a clinical question is increasingly important. Existing literature identifies two gaps in data grading: the need for quantitative data grading scores and scoring mechanisms that can be run in an automated fashion and at scale. Atropos Health solves this with its transparent methodology:
  • The RWDS rates a dataset's overall quality and completeness across a range of metrics.
  • The RWFS or FitQ grades how well a specific data source is suited for a specific query.

These tools give producers and consumers of datasets the objective tools to assess the quality consistently and accurately. You can read the publication here.

RWDS and RWFS address the missing pieces cited in published literature, providing quantitative metrics to evaluate a dataset’s overall quality and fitness for any queries. Users can quickly characterize the overall quality of data sources for their research. Atropos Health provides both RWDS and RWFS/FitQ to guide users to appropriate data sources for analysis.

“As Artificial Intelligence (AI) and Large Language Models (LLM) use in healthcare accelerates, it is critical for clinicians and researchers to understand the quality, appropriateness, and bias that may exist in the underlying data used,” said Brigham Hyde, Ph.D., CEO and Co-Founder of Atropos Health. “The critical innovation here is that the grading and quality evaluation of data must be context-specific to the question being asked or event being predicted. RWFS enables this transparently so the clinicians can understand, ‘was this data appropriate for use in this instance?’ It also helps researchers and healthcare institutions evaluate differences between datasets, selecting the right one that is fit-for-purpose, and understand bias where that exists.”

The rise of networks linking many RWD sources from multiple providers is creating new paradigms for healthcare research and drug safety surveillance. RWDS and RWFS/FitQ present the advantage of transparency as the metrics and weights used to calculate each score can be available along with the final scores themselves. This capability now 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. Across the growing Atropos Evidence™ Network, users can now evaluate which dataset may be most appropriate for their given query. By making the data grading process clear and reproducible, Atropos Health takes the guesswork out of selecting appropriate healthcare data for clinical or research inquiries or even for training predictive AI models.

Today’s announcement of the publication comes on the heels of Atropos Health launching GENEVA™ OS (Generative Evidence Acceleration Operating System) with Google Cloud's healthcare offerings, including Google Cloud’s Healthcare Data Engine (HDE), leveraging HDE application programming interfaces and BigQuery. Earlier this year, the nonprofit Every Cure partnered with Atropos Health to serve its mission of unlocking the full potential of existing medicines by leveraging Atropos Health’s GENEVA OS and Atropos Evidence™ Network to advance the discovery and development of therapies for underserved populations.

About Atropos Health

Atropos Health is the developer of GENEVA™OS, the operating system for rapid healthcare evidence across a robust network of real-world data. Healthcare and life science organizations work with Atropos Health to close evidence gaps from bench to bedside, improve individual patient outcomes with data-driven care, expedite research that advances the field of medicine, and more. We aim to transform healthcare with timely, relevant, real-world evidence.

To learn more about Atropos Health, visit www.atroposhealth.com, connect through LinkedIn, or follow on X @AtroposHealth.