

Specialty manufacturers often develop their own strategies and analytics to identify at-risk patients. But because patient monitoring, in many cases, is still performed using traditional patient activity assessments and patient status aging, recognizing combinations of attributes that lead to non-adherence is time-consuming if not impossible, especially when performed using basic foundational analytics tools. Ultimately, this means identifying the appropriate interventions and quantifying successes is not only challenging, but if done incorrectly, can lead to repeated mistakes.