Adaptive symptom reporting for mobile patient-reported disability assessment

Mobile symptom reporting apps can conveniently gather health-related information at low cost from day to day, fundamentally altering the relationship between patients, health data, and care providers. However, current mobile systems face a difficult trade-off between the quality of the information they collect and the burden placed on patients. In this paper, we propose an algorithm for adaptive system reporting designed for mobile platforms. This algorithm uses personalization, domain-specific knowledge, and Bayesian reasoning to reduce the number of questions required for accurate disability assessment, substantially decreasing demands placed on the patient. Following development of the algorithm, we validate it retrospectively using responses to the 12-item multiple sclerosis walking scale collected from 31 subjects with multiple sclerosis. Trade-offs between accuracy and response quantity are explored in detail. In this dataset, a 42% reduction in the median number of patient prompts was achieved without causing a single clinically relevant estimation error. A 75% reduction was associated with 4.45% clinically relevant estimation error. Given these promising results, future work will focus on prospective validation in multiple sclerosis and other clinical populations.