  Spinal cord stimulation (SCS) is a therapeutic approach used for the
management of chronic pain. It involves the delivery of electrical impulses to
the spinal cord via an implanted device, which when given suitable stimulus
parameters can mask or block pain signals. Selection of optimal stimulation
parameters usually happens in the clinic under the care of a provider whereas
at-home SCS optimization is managed by the patient. In this paper, we propose a
recommender system for the management of pain in chronic pain patients
undergoing SCS. In particular, we use a contextual multi-armed bandit (CMAB)
approach to develop a system that recommends SCS settings to patients with the
aim of improving their condition. These recommendations, sent directly to
patients though a digital health ecosystem, combined with a patient monitoring
system closes the therapeutic loop around a chronic pain patient over their
entire patient journey. We evaluated the system in a cohort of SCS-implanted
ENVISION study subjects (Clinicaltrials.gov ID: NCT03240588) using a
combination of quality of life metrics and Patient States (PS), a novel measure
of holistic outcomes. SCS recommendations provided statistically significant
improvement in clinical outcomes (pain and/or QoL) in 85\% of all subjects
(N=21). Among subjects in moderate PS (N=7) prior to receiving recommendations,
100\% showed statistically significant improvements and 5/7 had improved PS
dwell time. This analysis suggests SCS patients may benefit from SCS
recommendations, resulting in additional clinical improvement on top of
benefits already received from SCS therapy.
