Predictive analytics uncover the populations that can most impact the targeted use case based on a rich data set. The platform pinpoints avoidable and impactable behavior vs. just finding the highest risk patients. This approach means you can focus resources on fewer total members while concurrently driving better (and bigger) outcomes.
Prescriptive analytics define the best outreach strategy based on the unique attributes of each population and the available programs - the “next best action”. The consumer engagement element in the platform manages the messaging and execution of interventions across all channels, including call center, direct mail, email, SMS texts, and websites.
On the platform dashboard, dive into the details of each campaign to gauge what worked for which members, how well your delivery channels performed, and where your ROI was greatest. Machine learning optimizes subsequent campaigns to deliver only those interventions that worked well to the populations most likely to respond to them, saving you time and money - and maximizing outcomes.
Frequently Asked Questions
NextHealth has delivered groundbreaking outcomes such as savings of $144 PMPY in medical costs and 26% reductions in avoidable ER usage in targeted commercial, Medicaid, and ASO populations. We’d love to customize a cost savings calculation for you. Contact us to start the discussion.
NextHealth supports millions of members on its platform and a myriad of national and regional health plans. We’d love to talk with you about how we can reduce medical costs for your plan. Please contact us for more information.
You can choose to deploy using the SaaS platform or with a managed services offering. If you choose a managed services model, NextHealth offers a shared savings model based on causal claims savings. To learn more, contact us.
Machine learning refers to algorithms that find patterns in data. These algorithms are typically “trained” on large amounts of input data and then apply that knowledge to new, but similar data for inference and prediction. NextLift™ and NextInsight™ use several algorithms from machine learning to predict KPI’s for members from large amounts of historical member attributes. NextLift™ also incorporates an adaptive learning cycle where results from previous deployment iterations feed back into the platform, and new deployments are re-optimized according to the latest results. However, this cycle is not considered “machine learning” in the traditional sense.
We randomly assign members to trial and control groups during the nudge assignment algorithm via computer generated random numbers. There is no human intervention, and randomization occurs independently of any attributes other than membership within a population. The randomization is generally set to generate an average of 1 control member for every 2 trial members.