The AllazoEngine first incorporates claims data, patient demographics, coverage eligibility, and past intervention data. The system is able to leverage customer data as well as data from 3rd parties. Once the data is collected, the AllazoEngine then normalizes the data to enable the machine learning processes.
The AllazoEngine™ then runs proprietary algorithms to enrich the data by calculating hundreds of additional variables, such as the level of synchronization across multiple medications and complexity of dosing regimen – factors that have been proven to be predictive of medication adherence behaviors.
The machine learning models used in the AllazoEngine™ have been trained across millions of patients and hundreds of millions of data points to accurately and reliably correlate thousands of data variables with levels of adherence. Collectively these models provide a robust prediction for each individual patient’s risk of being non-adherent in the future. The AllazoEngineTM also predicts each patient’s likelihood of being influenced by various outreach channels and messages.
Based on these predictions, AllazoHealth prioritizes the patients who are both at risk of becoming non-adherent and whose behaviors can be changed through proactive interventions. This cuts out unnecessary and often burdensome patient outreach to streamline your intervention strategy. Furthermore, interventions are much more successful when intervening proactively with at-risk patients instead of waiting until patients become non-adherent.
While knowing who to intervene with is important, knowing how and when to intervene is just as critical. The AllazoEngine™ predicts the impact of multiple intervention channels and messages to select the most ideal combination for each individual patient.
With time, the AllazoEngine™ becomes more in-tune with and nuanced to the specific population of each client. In other words, as more interventions are delivered and patient behaviors analyzed, it becomes even better at delivering the right intervention, to the right patient, at the right time.