Patients can experience unexpected access and affordability barriers that physicians lack visibility into, but by searching for the early indicators of non-adherence, physicians could act to help patients stay on their treatment plans.

Rochester, Michigan-based OptimizeRx, which provides care-focused engagement throughout patient journeys and connects more than 60% of U.S. healthcare providers to their patients, launched a pharmaceutical pilot to identify doctors whose patient treatment plans are at risk of lapsing due to costs.

The company found that machine learning was able to accurately predict healthcare providers with at-risk patients due to loss of insurance coverage, including Medicare coverage gaps.

The evidence-based physician-engagement program used deidentified third-party datasets to learn what is happening in real time at the point of care. Based on the predictive algorithm, the program sends the healthcare providers targeted financial resources information to share with the at-risk patients.

"HCPs are struggling with the lack of visibility to their patients' full health stories," Steve Silvestro, OptimizeRx’s chief commercial officer, said in a prepared statement.

By using artificial intelligence, OptimizeRx increased the number of identified healthcare providers with at-risk patients in the pilot by more than 200%. 

More than 46,000 scripts triggered a need to send healthcare providers resources on the pharmaceutical company's affordability information program, and 27% of identified healthcare providers enrolled patients into the program for the first time. 

"We are able to leverage real-time data across all care delivery settings to empower our manufacturer clients to deliver financial support at the point of care during critical moments of patient need. This is the direction we hope to drive our industry: to stay one step ahead of the patient's next care milestone by identifying latent opportunities to drive positive health outcomes," Silvestro added.