  We develop a remote patient monitoring (RPM) service architecture, which has
two tiers of monitoring: ordinary and intensive. The patient's health state
improves or worsens in each time period according to certain probabilities,
which depend on the monitoring tier. The patient incurs a "loss of quality of
life" cost or an "invasiveness" cost, which is higher under intensive
monitoring than under ordinary. On the other hand, their health improves faster
under intensive monitoring than under ordinary. In each period, the service
decides which monitoring tier to use, based on the health of the patient. We
investigate the optimal policy for making that choice by formulating the
problem using dynamic programming. We first provide analytic conditions for
selecting ordinary vs intensive monitoring in the asymptotic regime where the
number of health states is large. In the general case, we investigate the
optimal policy numerically. We observe a threshold behavior, that is, when the
patient's health drops below a certain threshold the service switches them to
intensive monitoring, while ordinary monitoring is used during adequately good
health states of the patient. The modeling and analysis provides a general
framework for managing RPM services for various health conditions with
medically/clinically defined system parameters.
