Prescription Non Adherence

Prescription Adherence

Identifying patients who are at risk of dropping their medications, and taking pre-emptive & preventive measures.

Problem Statement

One of the biggest challenges in the US Pharama market is that patients stop taking their medications midway or do not start them at all. This can be due to multiple reasons – lack of affordability, side-effects from the drug, demographic factors, etc. The objective is to use AI-driven insights to identify patients at risk of abandonment / non-adherence and to try and mitigate the risk by determining its cause.
28% increase in revenue achieved by recommendation engines for some dealers

Integrate data from various sources in the healthcare value chain to carefully curate dataset for modelling and predictions

Accurately identify patients with a high risk of prescription abandonment / non-adherence

Determine factors responsible for the patient being at high risk, and thus give manufacturers actionable insights

Predict chances of non-adherence for every refill of the prescription at the time of request generation

Continuous learning over time through reinforcement techniques and feedbacks to improve the accuracy of predictions

Prescription Non Adherence
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