Atropos Health Redefines Data Quality With Two Scoring Metrics
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.
Read the full press release here
PALO ALTO, Calif.—Feb 13, 2024— 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.
“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. 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.”
Real World Data Score is a general dataset evaluation. It looks at dataset size, completeness, and patient timelines within. This gives potential users a quick quality reading and tells a data owner how they might complement and/or refine their data assets with alternatives/additions and gives potential users a quick quality reading. Each dataset in the Atropos Evidence™ Network is assigned one Real World Data Score which is rescored after a dataset refresh. This confidential data evaluation ranks data quality on usability factors beyond size. In summary, the RWDS is a quick, confidential quality heuristic to help data holders understand their assets’ usability across the Evidence Network.
Real-World Fitness Score is a fit-for-purpose assessment of the appropriateness of a data source to the question at hand. Designed as an attribute score, Real World Fitness Score is based on a proprietary algorithm that accounts for how well question criteria are represented within the dataset. For users, this gives clarity on how well data sources truly answer the questions they have posed. It also helps them compare and contrast results run on more than one data source. The higher fitness scores indicate more trustworthy results that data is more appropriate for answering that particular research question. The RWFS changes based on each question submitted.
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.
About Atropos Health
Atropos Health is the developer of GENEVA OS, the operating system for rapid healthcare evidence across a vast network of real world data. Health systems and life science companies work with Atropos to close evidence gaps from bench to bedside, elevate clinical outcomes with data-driven care, expedite research, and more. Our solutions offerings are based on many peer-reviewed publications, thousands of active users over the past decade, and on-staff clinical expertise. We aim to transform healthcare with timely, relevant real-world evidence.
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