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Today’s market pressures are making it more critical than ever for health plans to deliver medical cost reductions and better outcomes for their members. But just knowing which populations to target is a long way from delivering sustained and proven results. How do you get your teams to work together to drive member behavior change and measure the impacts of their efforts?

NextHealth is the first solution that orchestrates the entire process from end-to-end in one scalable platform. Focusing on your most important use cases, the system delivers who to target, which programs to offer to whom for the best outcome, and importantly, measures what’s working (and what isn’t) – all in one place. 

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.

Member engagement begins within 90 days of data receipt. Outcomes are measured with every data refresh, starting 30 days from deployment. Want to learn more? Contact us.

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.

WHO

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.

WHAT

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.

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HOW WELL

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.

4 Platform Advantages

That drive outcomes.

Automate Insights
NextInsight™ automates predictive analytics in order to reduce many of the labor intensive, trial-and-error tasks formerly required to segment customers and analyze KPI’s.  Attribute correlation, importance, and clustering are automatically generated and displayed to reveal key drivers and member characteristics of customer subpopulations.  Analytics teams can use this information to inform targeted campaign designs or other strategic outreach initiatives.

Supercharge Data
The NextHealth platform merges and transforms data from many sources – claims, programs, providers, demographics, etc. – to create a Master Member Dataset (MMD) that can drive insight generation, prediction, and optimization algorithms across multiple KPI’s.  The resulting data structure is completely extensible and flexible to accommodate new data sources and new use cases.  We can even include outputs of your own prediction and risk scoring algorithms to create more powerful, hybrid predictions.  The platform utilizes automated, proprietary algorithms that restructure data types, impute missing values, and eliminate redundant predictors to maximize predictive strength and information content.

Measure Outcomes
NextLift™ statistically estimates both the lift and its significance for each intervention or “nudge”. By using randomized controlled trials – often seen in other industries but unique in healthcare analytics – it is possible to isolate the change due to the nudge itself versus some other systematic factor that may be operating upon your target populations. By considering significance, you can know with a high degree of confidence that the measured lift is caused by your intervention, and is not just simply a reflection of random variations in the data.

Learn and Optimize
NextLift™ uses the current outcome measurements to adjust the next round of assigning members to campaigns.  Our unique simulation-optimization approach is able to determine the optimal size of each campaign to maximize expected future lift without exceeding campaign resource constraints.  Prediction algorithms feed the members at highest risk into these campaigns while also considering the campaign to which the individual member will best respond.