Real-world evidence can usefully support precision medicine and faster regulatory approvals. The use of statistical methods to develop better case-identifying algorithms enables use of claims databases to study rare diseases and other targeted indications as well as more valid outcome ascertainment for safety and effectiveness research.

Machine learning combines advanced statistical methods with the results from validation studies to construct new case-identifying algorithms. We have found these methods to offer marked improvement in the accuracy with which we can identify patients of interest. These methods not only reduce bias from misclassification, which translates into more valid research, but we have also been able to identify patients with conditions for which there are no diagnosis codes in claims, such as cancer stage or biomarker status.

As these various efforts to address the growing problem of prescription drug abuse and misuse play out, it will be important to monitor the degree to which they succeed in the real world. Safety and Epidemiology research is providing insights into the effectiveness of these efforts.