August 2026
Real-world data (RWD) increasingly informs regulatory decisions, reimbursement determinations, and clinical guidelines. However, because RWD is collected for clinical care and billing rather than research, it can vary widely in format, frequency, quality, and purpose. Algorithms are essential tools for turning this fragmented data into consistent classifications to generate interpretable real-world evidence (RWE). Rigorous, multidisciplinary approaches to algorithm development and validation ensure that RWE can reliably support healthcare decisions across the product lifecycle.
This white paper describes how algorithm (phenotype) quality impacts the reliability and comparability of RWE and introduces a framework for scientifically robust algorithm design, development, and validation.