By 2025, it’s been estimated that healthcare data will represent 36% of the world’s data volume.
While the industry has leveraged data to make significant advances – predicting disease, understanding trends and outcomes to improve care delivery – not enough data is being viewed and acted upon in real-time. In short, we are looking in the rearview mirror while attempting to drive the car forward.
Healthcare systems are challenged to use more real-time data to make care decisions. Similarly, most health insurers have built robust data analytics capabilities and are increasingly working to use that data for the benefit of their members. However, the status quo for exchanging information between payers and providers’ clinical and administrative teams has centered around faxing or online portals to share clinical information and determine medical necessity.
When alignment isn’t achieved, manual processes to gather and share more data, including scheduling peer-to-peer discussions, all require additional time. These processes are inconsistent, subjective and inefficient at best, exacerbating the billions of dollars in waste, friction, error and delay in our healthcare system at worst.
A recent report revealed that Medicare Advantage plans overturned 75% of their own denials over the course of a three-year period, when appealed. Further, the report said health insurers typically approved more than 90% of all services which require authorization. This begs the question: why, then, is there so much administrative time spent on these processes? Given our industry’s current staffing and revenue challenges, surely there is a better way.
A big culprit of current inefficiencies is that healthcare does not fully utilize real-time patient data or apply objective data analysis to make medical necessity determinations. With the rise of predictive analytics, machine learning and artificial intelligence, healthcare is poised for real collaboration (and efficiency) across and between health plans and systems.
There are some early benefits observed when payers and providers leverage real-time clinical data sharing and predictive analytics to align on medical necessity decisions more quickly and objectively. Such an approach doesn’t benefit one party over the other; the shared value is in achieving alignment more quickly, with reduced friction.
“[Since deploying the shared platform] … we work collaboratively, and it feels like we’re on the same team,” said Alana Llewellyn, concurrent review nurse manager at Coordinated Care, about their improved provider relations.
A recent Chilmark Research case study provides another example. When using a shared platform with real-time data and automation capabilities, the average time for approvals was reduced from 37 minutes (traditional EMR workflows) to 9 minutes – 76% faster. The study also found that first-touch patient status determinations were achieved 66% of the time by utilization management teams.
When payer and provider teams work collaboratively using real-time clinical data sharing coupled with the application of highly accurate data science, they work more efficiently, spending less time on cases where they will ultimately agree. With today’s limited resources, this offers a new path forward to achieve joint efficiencies and ensure the future viability of our healthcare system.
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