Abstract 19778: Reframing Clinical Neurorehabilitation in Individuals After Stroke Using a Big-Data Approach

Background and Objective: Advances in connected health delivery systems to provide communication rehabilitation have provided a unique opportunity to maximize intervention effectiveness while collecting large sets of data to facilitate clinical decision making. Such data can be vastly insightful to neurorehabilitation in general where the evidence for gains in chronic stroke patients is weak. Methods: Over a span of 18 months (2013-2014), data was anonymously aggregated and analyzed from over 2,500 patients with post-stroke aphasia. Data was collected using a mobile therapy platform, Constant Therapy. The program was used by patients in the clinic with clinicians, at home as homework, and independently if they were not currently receiving therapy. This program was administered in a uniform way across patients but also individualized for each patient and dynamically adapted to each patient’s progress. Patients who completed between 3 and 1000 treatment sessions of at least 15 items or more were analyzed in a case-mix adjusted way controlling for intensity of practice and initial severity to determine which tasks demonstrated statistically significant improvement. Results: Our analyses take into account the number of patients who completed a specific task and show a significant change (p-values at least Conclusion: These results show that all patients, including the most severe, can make progress in their rehabilitation. Analysis of large data sets can be used to inform neurorehabilitation by highlighting therapies that are effective by taking into account etiology and individual performance variability.