Large Pharma Chases Elusive Triple Aim with Implementation of Value-Based Insurance Design

The Challenge

A self-insured large pharma company needed to improve members’ health outcomes and enhance care experiences, while also reducing costs. This Triple Aim challenge is nothing new in the healthcare industry, as it has loomed over organizations since the Institute for Healthcare Improvement introduced the concept in 2007.1 Though, leaders at the pharma organization realized that they might be able to meet the hard-to-conquer challenge with a new strategy: Value-Based Insurance Design (V-BID).

V-BID is built on the principle of lowering or removing financial barriers to essential, high-value clinical services, according to the Center for Value-Based Insurance Design at the University of Michigan. V-BID aligns patients’ out-of-pocket costs, such as copayments and deductibles, with the value of services.2

As such, the pharma organization had to find a way to eliminate waste and move away from low value and toward high-value services for its covered employees and dependents. The organization turned to EXL Health for help in this mission.

Solution

The pharma organization specifically worked with EXL Health to implement advanced patient analytics, a longitudinal analysis service that sheds light on members with a variety of health conditions. The analysis compared EXL benchmark data to the pharma’s own data rom 2017 to present. Overall, the organization found the EXLVANTAGETM data and analytics foundation to be very powerful, as it offered descriptive, predictive and perspective analytics for the analysis.

By relying on advanced patient analytics, human resources leaders at the employer group were able to make informed plan coverage and benefits design decisions that would ultimately support a V-BID model. More specifically, the analysis of patient level medical and pharmacy claims data helped the employer group develop V-BID plans by zeroing in on where it was providing low-value services and where it was offering high-value services.

In addition, the organization leveraged the analysis to identify the patients who were at risk for specific health conditions. For these specific health conditions, the organization had higher prevalence of ~48% versus the benchmark of ~39%. The members with these specific conditions accounted for 10% of total health spending.

The organization then worked with EXL to ascertain which services were resulting in high-value care and which were providing low-value for these patient populations. And, finally, the analysis empowered the organization to assess the level of utilization of high-value services, such as preventive vaccinations or tests, versus the level of utilization of low-value services such as steerage from hospital to an office setting.

Results

EXL was able to identify a potential for ~15% savings for the large self insured pharma organization in specific condition categories.

Unnecessary treatments based on various ‘standards of care’

Unnecessary treatments based on various ‘standards of care’

Cost Inefficiencies, through a comparison of costs to benchmark data

Cost Inefficiencies, through a comparison of costs to benchmark data

Exactly where interventions can have the greatest effect

Exactly where interventions can have the greatest effect

Screening tests or treatments that lead to better financial and clinical outcomes

Screening tests or treatments that lead to better financial and clinical outcomes

Clinical pathways that result in lower adverse events such as inpatient admissions, emergency room visits and readmissions

Clinical pathways that result in lower adverse events such as inpatient admissions, emergency room visits and readmissions

The impact of member co-payments on the use of high-value services vs. low-value services

The impact of member co-payments on the use of high-value services vs. low-value services

Members recommended for early detection, cost efficiencies and clinical pathway conversations

Members recommended for early detection, cost efficiencies and clinical pathway conversations

Key drivers or barriers for improving outcomes based on lifestyle factors, social determinants of health and geographic locations

Key drivers or barriers for improving outcomes based on lifestyle factors, social determinants of health and geographic locations

Reference

1. Lewis, N. A Primer on Defining the Triple Aim. http://www.ihi.org/communities/blogs/a-primer-on-defining-the-triple-aim

2. University of Michigan V-BID Center. Frequently Asked Questions. https://vbidcenter.org/frequently-asked-questions/