A recent Kaiser Family Foundation survey found that nearly half of all Americans say they or someone they live with has delayed care since COVID started. The why behind this is two-fold – fear and costs. Deferred care, now more than ever, is something payers need to strategize around and prepare for. Once the floodgates of pandemic-related delayed care open, payers are going to be left to sink or swim. Sheer numbers and overdue treatment aside, they likely will find that this unexpected disruption in care has rendered previously relied-upon techniques and tools of little use in the post-COVID environment.
Let’s take Diabetes as an example. According to the CDC, 1 in 4 adults with diabetes don’t even know they have the condition.1 Regular doctor visits can hopefully surface a person’s risk sooner than later – but what if they are not going to the doctor? According to the American Diabetes Association, if people don’t get the care, they need, it can lead to unnecessary cost and utilization. The Association estimates that 30% of diabetic emergency department visits are avoidable – leading to $8.3 billion in unnecessary costs to the industry.2
It’s one thing to manage the health of the known diabetics in your population – but what of those that your traditional risk stratification analytics haven’t picked up on yet? Diabetes is just one example – chronic diseases affect approximately 133 million Americans, representing more than 40% of the total population of this country.3 Payers need to know who to focus on and with less and less encounter data to feed their risk stratification analytics – the question is how will they get there?
Here are three things payers should keep in mind:
#1) Traditional predictive analytics won’t do.
Until recently, payers could pinpoint risk using historical data to spot patterns, flag at-risk patients and populations, and predict trends. But with encounters on the decline, this no longer is possible, as predictive analytics require a steady state, and there is nothing steady about the state of the healthcare system in 2020.
The truth is that these techniques were already losing value before the pandemic, most often producing known risk targets but little direction as to how to mitigate that risk. Payer resources are already stretched thin, and individual member needs can get lost in the data dump. This is where clinical artificial intelligence comes in.
Clinical artificial intelligence can help payers get to the populations and individual members that may not fall into high-risk bands today but are on an accelerated trajectory because of deferred care. It can do this because of its ability to look at quadrillions of risk dimensions and hundreds of thousands of data features, including social determinants of health.
#2) You can’t help someone you can’t reach.
Jvion’s clinical artificial intelligence approach factors in the best method for effectively engaging a high-risk individual. Let’s take me for example, I don’t answer calls from unknown numbers. I do, however, read all my text messages. If I show up on a prioritized list of high-risk members for a specific health condition, the best way to get me to respond immediately is to send me a text. Beyond knowing they “who” it’s equally important to know the “how” if outreach efforts are to be effective.
#3) No two people are the same – neither are the paths that lead to better outcomes.
People are dynamic. Lives are dynamic. The question then is are the strategies a payer deploys to manage member risk dynamic? Do they account for the unique aspects of a member’s clinical condition and socioeconomic environment to drive action? Most predictive analytics and risk stratification techniques stop at identifying risk. Jvion’s validated clinical artificial intelligence goes beyond that – aggregating and understanding the most relevant correlations within large data sets to make inferences that lead to personalized and prioritized recommendations. A one size fits all approach in this new environment will not work.
Times have changed. Drastically. And health plans must change with them. A clear clinical artificial intelligence strategy is paramount to surviving the current risk landscape.
Jvion is here to help lead the way – contact us to learn more.
 New CDC report: More than 100 million Americans have diabetes or prediabetes (https://www.cdc.gov/media/releases/2017/p0718-diabetes-report.html)
 Modern Healthcare: Unnecessary ED visits from chronically ill patients cost $8.3 billion (https://www.modernhealthcare.com/article/20190207/TRANSFORMATION03/190209949/unnecessary-ed-visits-from-chronically-ill-patients-cost-8-3-billion)
 Google search (https://www.google.com/search?rlz=1C1GCEA_enUS880US880&sxsrf=ALeKk03ersUJLbcgteVqLorMTNy4RPvnSg:1596461349608&q=chronic+disease+statistics+2019&sa=X&ved=2ahUKEwj2-I_9kf_qAhUMU98KHVVjA7sQ1QIoAHoECAwQAQ&biw=1280&bih=610&dpr=1.5)