Transitioning to a Learning Healthcare System: The Importance of High-Quality Data and Real-World Evidence

Healthcare systems typically rely on data from small-scale, costly trials to make decisions about patient care, but this approach can be slow and often insufficient. However, with the introduction of real-world evidence, which has become more credible due to the rise of AI, robust health data, and cloud, healthcare could potentially improve and become more efficient. This introduction of RWE would favor individualized care, with decisions based on routine care practices and patient uniqueness. Yet, this process carries a risk: poor data quality could lead to learning the wrong lessons, resulting in detrimental treatment decisions. For example, this error has occurred in past mistakes with postmenopausal women and COVID-19 data handling.

The FDA is developing a data quality standard to ensure accurate and safe outcomes, including accuracy, completeness, and traceability. Companies now focus on providing quality data to advance these changes, emphasizing that transitioning to a learning healthcare system is pivotal for the industry’s future if executed correctly.

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