W-44: New Methods for Analyzing Clinical and Cost Outcomes in RA With Interactive Visual Analytics
Poster Presenter
Sharon Hensley Alford
Chief Information Officer
Cancer Insights United States
Objectives
We aimed to (1) identify the costs and outcomes associated with different patient sub-groups, including age, gender, prior treatment, co-morbidities, US region and urbanicity by approved treatments and (2) demonstrate ability to rapidly attain insights through the use of interactive visual analytics
Method
A disease-model for RA including patient cohort, patient characteristics, payer types, clinical characteristics, clinical and cost outcomes was built using FDA labels for approvals, professional society guidelines, clinical and economic literature review, and subject matter.
Results
The disease-model definitions were deployed on IBM MarketScan® Commercial and Medicare databases and visualized with an analytics engine. We visually explored and interacted with the data to identify variations in outcomes (emergency room visits, outpatient visits, hospitalizations) and costs (PPPM) for patients treated with targeted and conventional disease modifying agents therapies. The outcomes were further analyzed by patient and clinical characteristics to identify drivers of differences in outcomes. Notable differences in costs and outcomes based on treatment were identified over the last 12 months (ending 30 September 2017). Mean PPPM costs for RA ranged from approximately $2,500 on sulfasalazine to nearly $6,000 on adalimumab. Patients on anakinra were notably different from patients of other treatment groups. Women were hospitalized more than men as were patients 55-64 compared with other age groups. Patients with a Charlson Comorbidity Score (CCS) above 8 were not prescribed anakinra and rural patients were prescribed the drug less than urban patients. Interestingly, patients on leflunomide were more likely to be admitted to the hospital if their CCS was below 9.
Conclusion
Rheumatoid arthritis (RA) is a progressive and debilitating disease. While not life-threatening the condition has a recognized negative impact on quality of life. Understanding the costs and outcomes for all treatments by patient sub-groups is important for manufacturers and payers determining access and coverage. Traditional cost and comparative effectiveness studies provide results with narrow study criteria. As a result, manufacturers and payers are unable to compare clinical and cost outcomes across the disease to make informed access and coverage decisions. We show that insights on RA outcomes and costs can be easily assessed without the need for data science, clinical or programing knowledge when pre-built disease models and visual analytics are available. With our visual analytic approach, use real-world data can be included in discussions internally for value-driven decision making. In addition, organizations can use standardized approaches to disease-models and analytics to convey unbiased real-world evidence with stakeholders, including manufacturers and payers.
