Advancements
How Advancements in Data Intelligence Will Transform Co-Pay
Along with education and support for co-pay stakeholders, data collection has been a cornerstone of effective co-pay programs since their inception. That data is getting exponentially better.
For the first time, insights based on predictive analytics using machine learning and artificial intelligence allow companies to stay ahead of payment trends rather than simply react to them. In the current environment of uncertainty and rapid change, this model can give brands a major advantage.
These highly intelligent data models have the capacity to quickly discover patterns and derive their own rules to craft programs that align with brand objectives and more closely mirror the nuances of patient experiences and demographics, as well as the needs of prescribers and other stakeholders.
Biopharma companies must move strategically now to create smarter affordability programs that include continuing education, support and conversation with patients, employers and providers.
Based on a careful analysis of RxCrossroads by McKesson data and trends, we believe that the insights gleaned from highly intelligent predictive-modeling strategies offer companies an effective pathway for developing and funding co-pay programs in the current market.
How to Respond to Affordability Challenges
RxCrossroads by McKesson recommends that biopharma companies align with patients and providers to do the following:
Educate employers and patients prior to health plan enrollment to help them understand the potential impact co-pay accumulator programs may have and what to look for during the plan-selection process.
Use data to forecast and budget for co-pay-assistance programs and provide alternative means of support to the growing number of patients who will be impacted by co-pay accumulator programs.
Stay informed about health insurance trends and tactics, with an emphasis on understanding how they impact patients.
Create a board of patient advocates to share knowledge of the impacts they experience.
The Predictive Modeling Approach to Co-Pay
A predictive modeling and machine learning approach to co-pay-program development leverages data intelligence as a foundational component, not merely as value-added support.
Program development under this model moves through iterative cycles of clearly defined processes:
Starting at the top and moving clockwise around the circle are the following cycles:
- Identification of machine learning objectives
- Data preparation and preprocessing
- Data modeling to identify trends and predict changes and needs
- Developing and evaluating scoring metrics to assess the solution on a continuous basis
- Tweaking the model to improve and optimize results
The Benefits of Intelligent Co-Pay
An intelligent data strategy modeled on predictive analytics can give companies a powerful and adaptable springboard from which to develop and modify programs and solutions that tackle a host of complex challenges around affordability, access and adherence.
Strategic Program Design
Forecast the potential impact of accumulators across various chronic and specialty therapy categories in a manner that becomes more intuitive over time and allows companies to provide smarter patient support.
Test benefit designs by leveraging historic claims data to apply “what if” scenarios and model how those factors will affect retention and budget.
Define patient-journey objectives and assess the effectiveness of patient-support tools with a higher level of specificity.
Detect trends and patterns in patient behaviors across therapeutic classes with a high degree of accuracy that becomes increasingly specific.
Quantify claims impacted by co-pay programs and break down claims by program volumes, therapeutic category and financial impact.
Increased Patient Access and Affordability
Drive access to patients and prescribers by designing a co-pay-program launch strategy to ensure the program’s optimal reach.
Integrate controls within the point-of-sale adjudication process by providing insight for patients into their financial responsibility at each transaction.
Tailored Patient Support
Tailor specific co-pay strategies that more closely meet the needs of various prescriber groups and therapeutic areas.
Reduce claim denials and, by extension, prescription abandonment, by electronically connecting co-pay card utilization with prior authorization challenges.
Identify patients in accumulator plans and devise strategies to support them by leveraging patient-claims data and predictive tools.
Standardized Controls
Detect cash-discount cards by leveraging data intelligence and advanced analytics and limit application of cards when a claim should be adjudicated by commercial insurance.
Identify percentages of claims going through pharmacies in which co-pay utilization is not consistent.
Reduce the likelihood of patient misuse of co-pay assistance and enable brands to ensure support is provided only for their medication.