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Improved health risk modeling—improved member engagement results
Technology Category
- Analytics & Modeling - Data Mining
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Predictive Maintenance
- Regulatory Compliance Monitoring
- Remote Patient Monitoring
Services
- Data Science Services
- System Integration
- Training
The Challenge
In the face of rising medical claims costs, payers are looking to improve member engagement in their own health and minimize unnecessary utilization of the health care system. However, the Affordable Care Act (ACA) and the creation of Health Insurance Exchanges have led to millions of consumers accessing health benefits for the first time and possessing little or no historical, clinical or claims data. The lack of medical history makes risk modeling and effective member engagement particularly difficult. This Case Study seeks to answer the question, “Can socioeconomic data be used to help predict member health risk and inform improved member engagement strategies?” EveryMove (and their payer clients) need a comprehensive picture to determine population health risk and the ability to precisely target high-risk members with the appropriate engagement incentives. Always seeking to optimize the timing and accuracy of its member interventions, EveryMove places an extremely high value on predictive intelligence that provides insight into the health status and potential health risks of individuals. With so many new consumers lacking traditional data, like claims and clinical records, EveryMove chose to test and measure the ability of non-medical, socioeconomic data to fill in major gaps in member health profiles, and to accurately predict health risks.
About The Customer
Founded in 2011, EveryMove offers strategies, products and services designed to get people to take the right actions for their health and—in a win-win for patients and health plans—spend less time in the health care system. EveryMove is a leader in tailoring the way health care payers engage members on an individual level to increase retention, minimize clinical and financial risk and improve member satisfaction. EveryMove specializes in member engagement, focusing on optimizing the timing and accuracy of its member interventions. The company places a high value on predictive intelligence that provides insight into the health status and potential health risks of individuals. EveryMove's goal is to create a comprehensive picture to determine population health risk and the ability to precisely target high-risk members with the appropriate engagement incentives.
The Solution
EveryMove began the search for a data partner with the understanding that some supplemental data providers are less reliable than others. Data that is limited in scope, incomplete, outdated and/or inaccurate offers limited potential for improving model accuracy or identifying additional costs and risks. The key to effectively integrating socioeconomic data to aid in predicting health outcomes is knowing which datasets enhance a model—and which simply add noise. That’s the expertise-and-experience sweet spot that LexisNexis® alone occupies. LexisNexis data scientists have examined a vast inventory of socioeconomic indicators to ascertain the potential each has to impact member health. After a thorough testing and refining process, their team has developed sophisticated prediction techniques that are independent of traditional health care data, and capable of revealing a picture of future risk that would otherwise go undiscovered. LexisNexis simply runs a health plan’s member file against the model to receive a score delivered at the individual member level. That score indicates the level of health risk that member poses over the next 12 months—and it can be used alone or in conjunction with other modeling or population platforms to identify candidates for proactive intervention, such as wellness programs and case management.
Operational Impact
Quantitative Benefit
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