data that help to answer this question needs to be captured, recorded, and analyzed.

According to Heywood, patient-reported data can help improve the relevance of medical research to patients. He provided a brief overview of the PatientsLikeMe (PLM) online platform, and how it enables patients to share their data and learn from others. Patients create profiles on PLM which detail personal information, medical history, treatment history, and track functional status over time (using accepted patient reported outcome measures). This allows other patients on the site to find individuals similar to them, and learn from their experiences.

Despite some concerns over the perceived quality of patient reported data, Heywood provided an example of how patient-reported data can answer some of the same questions that traditional clinical outcomes research methods are used for. Since patients with amyotrophic lateral sclerosis (ALS) comprise one of the largest groups on PLM, he detailed the use of patient-reported data to assess the efficacy of lithium in slowing the progression of ALS. In 2008, the results of a clinical trial were published showing that lithium significantly slowed the progression of ALS symptoms. Using the PLM platform, researchers were able to test this same treatment in the PLM population. They used an algorithm to match ALS patients being treated with lithium to similar patients who were not undergoing lithium treatment. The variety of demographic and physiologic variables recorded on PLM profiles allowed for each patient to be matched to an individual control, rather than pairing groups. No change in the progression of ALS symptoms was observed in the population being treated with lithium. The same results were later found in four clinical trials stopped early for futility.

The benefit of routinely collecting patient-reported data through a platform like PLM is that it greatly speeds up the assessment process for interventions. Since data are already in place, conducting clinical research does not require building new infrastructure nor collecting new data. According to Heywood, this allowed the researchers at PLM to conduct their study of lithium efficacy in ALS patients in a fraction of the time, and at a fraction of the cost, of the follow-up clinical trials to the 2008 study.

After focusing on the ALS case study, Heywood broadened his discussion to consider the transformation necessary to use data—regardless of source—to improve the health system. He returned to the center question patients value most: Given my status, what is the best outcome I could hope to achieve and how do I get there? The path to answering this question, he suggested, is building learning mechanisms, such as predictive models, into the system to speed discovery, assessment, and implementation. If done effectively, this would converge clinical research and clinical care into one model on a common platform. Heywood proposed that if this is done within the context of what the patient perceives as valuable, and keeps



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