With the growing popularity of mobile phone technology, new opportunities have arisen for real-time adaptive medical intervention. The simultaneous growth of multiple "big data" sources (e.g., mobile health data, electronic health records, lab test results, genomic data) allows for the development of personalized recommendations. This award supports initiation of a collaborative research project that will generate a new mathematical model for changes in subjective pain over time in patients with chronic conditions. The model will be combined with statistical techniques to ultimately obtain optimized, continuously-updated treatment plans balancing competing demands of pain reduction and medication minimization. Those resulting personalized treatment plans will be incorporated into a currently active pilot study on mobile intervention in patients living with chronic pain due to sickle cell disease (SCD). Since nearly a quarter of patient visits to the emergency room are for conditions that could have been managed as outpatients, it is crucial to improve mobile health technologies to allow these patients to quickly recognize and receive appropriate health care information.
There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for chronic conditions like pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. The proposed model will consist of a dynamical systems approach using differential equations to forecast future pain levels, as well as a statistical approach tying system parameters to patient data (including reported pain levels, medication history, personal characteristics and other health records). A third key component will be the development and pilot study of a new control and optimization strategy to balance the competing demands of pain reduction and drug dosage minimization. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.
