The healthcare industry is complicated and fragmented, frequently with a lack of coordination or mismatched incentives that results in an inefficient allocation of resources. The results of patients, expenses, and the effectiveness of therapies are all negatively impacted by fragmentation.
Data can be utilized to improve fragmented system and lower healthcare costs
Jana Eggers, CEO of Nara Logics, and Sachin Joshi, SVP, data and analytics engineering at Evernorth (a Cigna subsidiary), discussed how Cigna is using data to improve fragmented care and lower healthcare costs during VentureBeat’s Transform 2022 virtual conference. Jana Eggers has 30 years of experience in the AI industry. We also discussed the future of healthcare data security 2 months ago. Data is crucial for the healthcare sector. Did you know AI can tell what doctors can’t? Now it can determine the race!
In order to deploy interventions specific to a patient’s clinical conditions and engagement preferences, Cigna has developed a platform that enables the business to automatically identify situations requiring clinical attention. Additionally, the platform offers a 360-degree view of patient activity, creating the best care coordination program.
The World Health Organization (WHO) asserts that the efficiency, effectiveness, and patient fitness of medical care are all impacted by the delivery system used in the healthcare industry. When picking an outcomes-based care model that guarantees clinical targets and connects a patient’s touch points across the care continuum, patients frequently make the right choice.
However, Eggers pointed out that many individuals have run into difficulty in the healthcare industry. She encouraged Joshi to consider healthcare issues from the viewpoint of the healthcare system rather than from the perspective of the patient. Joshi agreed that the main issue is this internal fragmentation.
“I know we as consumers feel that every day when we go to get our labs done, go to our primary care physician… go to get our prescriptions filled. Imagine trying to pull together that information to create better outcomes for our patients so that they’re not having to navigate all of those various channels without having the feeling that, one, they’re being intentionally priced gouged and, two, [wondering if] their whole health is being considered with all those different channels,” explained Joshi.
Joshi claims that individuals who are privy to this know there is a way to put it all together and provide solutions.
Joshi clarified that one technique used to alleviate some of those challenges is coordination management. According to Joshi, Evernorth’s Health Connect 360 is made to consider the entire population from the standpoint of patient management. Customers get a macro perspective as a result.
“Then at a micro level we can look at an outcomes-phased approach that delivers better patient outcomes from a health perspective by reducing costs,” said Joshi.
From a data viewpoint, the issue with Health Connect 360 is to be able to offer predictive modeling that enables Evernorth to focus on and enhance those patient outcomes.
Joshi continued, saying that in this case, it’s crucial to be able to provide each patient with a unique treatment strategy. He offers a customized approach for clients that enables them to work with the business, evaluate the degree of commitment and expense paid to service their patients, and compare it to the results they were previously experiencing.
“It’s something that is common in the population, but maybe more common in certain employer segments versus others. So, what we’re able to do from our total population perspective is analyze that data across medical pharmaceuticals, third-party data, claims data, which is our own. By integrating that data with third parties as well as digital devices, Evernorth is able to generate data at a macro level,” Joshi stated when using diabetes as an example.
“Are we seeing that they’re filing less pharmaceutical [prescription] claims and purchasing less? Because we have that information, we’re able to then distinguish in that population, do outreach that then intervenes to ensure that adherence to medications or [that] prescriber directives are properly carried out to create a preventative sort of use case where we do outreach around glucose monitoring,” he added.
According to predictive modeling and machine learning concepts, the company is able to reach out to patients directly or through the providers, if they are at risk for pre-diabetes or diabetes so that they can take action before they end up in the emergency room, which has much more serious consequences. Joshi claims that this enhances patient outcomes while also lowering long-term costs for their consumers.
“Huge amounts of data have been the major challenge of the tech audience, as they’re very fragmented, they don’t line up well together. The audience is working through their own challenges about how to power that data. I know there’s some power there, but I get stuck in the analytics or something,” explained Eggers. AI can tell what doctors can’t: Now it can determine the race
To address that problem, Eggers reiterated Joshi’s advice, telling the tech audience to “get to an MVP you know [and] make sure you stay focused on the experience. Then make sure your team feels that experience that you’re trying to drive. Make sure that they know to be vulnerable and to try things and it’s OK to go with that middle ground because you’re not always going to get perfection.” Did you know that Neural network-based visual stimuli classification paves the way for early Alzheimer’s diagnosis?
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