Harnessing Real-world Data to Inform Decision-making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)

Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). Methods: MS PATHS is being conducted in ten healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500; at least one Siemens 3T magnetic resonance imaging scanner; and willingness to standardize patient assessments, share standardized data for research, and offer universal enrollment to capture a representative sample. Eligible participants have diagnosis of MS including clinically isolated syndrome and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history; patient-reported outcomes; and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, patients contribute DNA, RNA, and serum for future research. Clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research. Results: As of August 5, 2019, MS PATHS enrollment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. Those with relapsing-remitting MS demonstrated more demographic heterogeneity than participants in six randomized phase 3 MS treatment trials. Across sites, significant variation was observed in follow-up frequency and patterns of disease-modifying therapy use. Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.