  Nearly a quarter of visits to the Emergency Department are for conditions
that could have been managed via outpatient treatment; improvements that allow
patients to quickly recognize and receive appropriate treatment are crucial.
The growing popularity of mobile technology creates new opportunities for
real-time adaptive medical intervention, and the simultaneous growth of big
data sources allows for preparation of personalized recommendations. Here we
focus on the reduction of chronic suffering in the sickle cell disease
community. Sickle cell disease is a chronic blood disorder in which pain is the
most frequent complication. There currently is no standard algorithm or
analytical method for real-time adaptive treatment recommendations for pain.
Furthermore, current state-of-the-art methods have difficulty in handling
continuous-time decision optimization using big data. Facing these challenges,
in this study we aim to develop new mathematical tools for incorporating mobile
technology into personalized treatment plans for pain. We present a new hybrid
model for the dynamics of subjective pain that consists of a dynamical systems
approach using differential equations to predict future pain levels, as well as
a statistical approach tying system parameters to patient data (both personal
characteristics and medication response history). Pilot testing of our approach
suggests that it has significant potential to predict pain dynamics given
patients' reported pain levels and medication usages. With more abundant data,
our hybrid approach should allow physicians to make personalized, data driven
recommendations for treating chronic pain.
