Profiling remotely monitored patients with adherence parameters

Remote patient monitoring systems provide plenty of structured information about the monitored health variables. We propose a set of parameters that exploits the remotely monitored data to describe patient's monitoring adherence from different viewpoints. The parameters were applied in self-monitored weight and blood pressure data sets of 30 heart failure patients inherited from a clinical trial setting. With the help of the extracted adherence parameters the thirty heart failure patients were clustered into two subgroups with different monitoring profiles: a group of well-performing subjects (n=22) and a group of low-performing subjects (n=8). Beneficial effects of active monitoring were reflected as positive changes in clinical health outcomes and lowered burdening of health care providers among the group of well-performing subjects