Predicting Patient Discontinuation for an Oral Drug Using Specialty Pharmacy Data

The Challenge

A pharmaceutical company sought to improve patient adherence and reduce patient discontinuation for their oral drug. The client wanted to leverage the rich data from specialty pharmacies to gain specific, actionable insights for their field teams to address and mitigate patient discontinuations.

ProcDNA's Solution

Prediction Algorithm

The team created a prediction algorithm that estimated the risk of patient discontinuation based on several factors, including physician and patient profiles, dose modification behavior, and fulfillment trends observed in the specialty pharmacy data.

Decision Tree Models

ProcDNA developed logistic regression and decision tree regression models using historical data, with patient discontinuation as the dependent variable and various influencing factors as independent variables.

Risk Score Development

A risk score system (ranging from 1 to 5, with 5 representing high risk) was developed for all patients in the specialty pharmacy data. These scores were shared with the field teams to provide actionable insights.

Impact

Real-Time Guidance

The prediction model provided real-time guidance to the field teams, enabling them to prioritize physicians with high-risk patients for discontinuation, thereby improving patient management.

Measurable Success

The model resulted in a 14% reduction in patient discontinuations, significantly enhancing patient adherence and improving overall business outcomes.