Practical Research that Identifies and Illuminates

[R1] Using Rapid Cycle Analytics of Real-World Data to Manage High Risk, High Cost Patients

ACPE UAN: 0233-0000-19-021-L04-P | Contact Hours: 1.25 | Activity Type: Knowledge-based

Faculty(s)

• 

  Sara Eapen, PhD

  Vice President
  Aetion
  Boston, Massachusetts
• 

  Saira A. Jan, MS, PharmD

  Director / Professor
  Horizon BCBSNJ / Rutgers
  Newark, New Jersey
• 

  Sebastian Schneeweiss, MD, ScD

  Professor of Medicine and Epidemiology
  Harvard Medical School and Brigham and Women's Hospital
  Boston, Massachusetts

Moderator(s)

• CW

  Carol L. White, PharmD, BPCS, BCGP, CPHIMS

  Manager, Ambulatory Pharmacy Informatics & Medication Management
  HCA Healthcare
  Nashville, Tennessee

It’s a well-established fact that a small percentage of patients contributes to the largest proportion of health care spending in the United States. These individuals are likely to be long-term medication utilizers, especially if they have common co-morbidities such as diabetes. Many managed care organizations are interested in identifying such high-risk, high-cost patients to implement targeted interventions that can improve their outcomes and decrease overall health-related spending.

One promising approach involves rapid-cycle analytics of real-world data. How can this be confidently used by payers? What high-risk populations would benefit from such targeted interventions? How is this done across multiple stakeholders? What drugs or drug classes impact drivers of high costs and may reduce overall costs? How can actionable analytics be completed in a rapid, scalable way across many indications?

This session will explore these questions and with case examples, demonstrate an approach of using rapid-cycle and real-world evidence to improve care, decrease costs and possibly assist in the development of value-based models.

Learning Objectives:

At the completion of this activity, participants should be able to:

1. Describe the rationale to identify high-risk, high-cost patients and understand challenges faced by payers.
2. Define the key characteristics required of analytical platforms capable of providing rapid-cycle analytics and valid real-world evidence that is actionable by payers for cost-conscientious population management.
3. Describe the methods used to identify cost drivers of medical and pharmacy costs, understand care pathways, and compare treatment effectiveness and costs to inform managed care interventions unique to a specific population and describe case examples illustrating this approach.
4. Describe how payers can collaborate with stakeholders such as manufacturers and integrated health systems to define data-driven interventions based on results from advanced rapid-cycle analytics of real-world data.