Researchers at the Johns Hopkins Bloomberg School of Public Health have developed Johns Hopkins ACG Rx-PM - new predictive modeling software based on retail pharmacy claims information.
ACG Rx-PM can help health plans, state Medicaid agencies and pharmacy benefit managers reduce costs and improve quality by predicting the need for future health care services. The developers see advantages to the new predictive modeling software because pharmacy data is timelier, more complete, and communicates more clinical information than traditional prediction methods, which are based on age, gender and prior cost. ACG Rx-PM also provides accurate forecast support for Medicare Part D pharmacy prescription drug plans.
"Rx-PM is a powerful complement to the ACG System," said Jonathan Weiner, DrPH, co-developer of the original ACG System and leader of the ACG research and development team at the Johns Hopkins Bloomberg School of Public Health. "It allows us to integrate new data sources into our predictive models for greater statistical performance and more clinical texture. Combining pharmacy-based predictive models with diagnosis-based predictive models will lead to even greater insights into patient quality and population health."
ACG Rx-PM is the latest addition to the suite of ACG Predictive Models developed by the Bloomberg School Public Health. ACG Rx-PM also complements the previously released ACG Dx-PM predictive modeling software based on diagnosis information from medical claims.