2013 Innovation Awards: Population Risk Scorecard

PMSI INC.

POPULATION RISK SCORECARD

www.pmsionline.com/RI-MAY.aspx

A new predictive model developed by Tampa, Fla.-based PMSI Inc. is targeting workers compensation claims that could go awry due to prescription problems.

PMSI, a workers comp pharmacy benefit manager, has been using Population Risk Scorecard for more than one year, according to Maria Sciame, vice president of clinical services for PMSI. The model analyzes a company's comp pharmacy claims to determine how to improve care and reduce drug spend for claimants that are receiving inefficient medical treatment.

This capability earned the product a 2013 Innovation Award from Business Insurance.

“We help identify who is the actual claimant within your population that is highest risk and what type of clinical intervention can you, the client, engage ... to help manage that risk,” Ms. Sciame said.

The model is part of PMSI's MedAssess Risk Intelligence System, a suite of clinical services that aims to provide proper medications for injuries and lower drug risks for patients that need long-term care.

Claims that would be flagged by Population Risk Scorecard include those that have long-term prescriptions for opioids that are intended for short-term use, or patients who are receiving higher-than-recommended medication dosages. Such claims would receive assistance, such as outreach to treating physicians to help guide their prescribing strategies, or drug testing and monitoring for patients.

While the model focuses on prescription data, PMSI believes Population Risk Scorecard can help improve overall care for workers comp claimants, Ms. Sciame said.

“We know, as clinicians, that when you manage a medication-related issue, you're often preventing clinical issues down the line — things such as emergency room benefits, hospitalizations, even deaths,” she said.

PMSI is evaluating how much money Population Risk Scorecard has been able to save clients so far, Ms. Sciame said. She notes that interventions recommended by the model have already helped clients reduce their pharmacy costs in the past.