Season 1, Episode 4
“It’s essential to accelerate the speed and time for gaps to identify them faster and proactively.”
In the context of risk adjustment gaps, there are two overarching goals—to identify and accurately predict member conditions. To do this, health plans are reliant on the amount and quality of the data they have—primarily sourced from clinical and claims data. Plans need this data to produce intelligence and directives to providers on how best to proceed on a proactive care path.
Due to COVID, plans experienced a decrease in access to data and that resulted in unique challenges for risk adjustment and their ability to supply predictive analytic recommendations to providers. As we return to normalcy, plans can expect a huge influx of data in 2022 and 2023 as members return to their normal care and procedures. This is going to be great for generating greater intelligence and actionable insights, but it’s also going to present some unique challenges and strain on some plan’s operational systems, especially if they’re manual.
For plans who have the technology and capacity to process large amounts of data, these coming years may be very fruitful for risk adjustment. The large data sets will provide a lot of intelligence for predicting member gaps and creating strong member segmentation models.
In this episode we'll discuss:
About Our Guest: Abe Chaaya is the Managing Director of Product Management for Analytics Products within Advantasure. Abe has over twenty years of experience as a global IT leader in QA, DevOps, and product management spanning several industries including manufacturing, payment systems, and healthcare IT. He holds a bachelor's degree in mechanical engineering from Rensselaer and an MBA in operations and technology from Bentley.
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This podcast is sponsored by Advantasure—managed service and technology solutions for government-sponsored health plans.