Across the country, a growing number of payer organizations are transforming the way they engage with both their members, and the providers their members seek services from.
Payers are investing more in innovation, with one of the core focus areas being artificial intelligence and machine learning. Ultimately, their goal is to take advantage of the digital transformation these and other technologies are enabling to improve and personalize member experience dramatically while creating greater value and driving operational efficiencies.
In this article, we will look at some of the specific ways in which payers are using machine learning and artificial intelligence to do that and more.
Improving Outcomes for Everyone
At a time when customers have come to expect the kinds of easy, always-on, and personalized experiences they’re used to getting in every other aspect of their lives, member experience is critical.
We all know that the traditional healthcare experience is not the same experience that you may get in other personalized industries in your life today. To deliver better experiences, payers have recognized the need to focus on two key areas.
The first is ease of use, which includes things like ensuring customers always understand what’s in network and what’s not, or what the status of a claim is. The second is ensuring their services are available on demand so that customers have around-the-clock access to the systems, people, and processes they need. Underlying both of these expectations is a need for data-driven insights that create value.
When payers take advantage of machine learning and artificial intelligence, they’re not only able to meet their customers’ growing needs and expectations, but also deliver better outcomes for providers and patients alike. That’s critical and just one of the reasons why 54 percent of healthcare professionals expect the widespread adoption of AI across the industry by 2023.
To understand how payers are using machine learning and artificial intelligence to increase engagement and drive better outcomes, let’s take a look at some real-life examples of how this technology is being put to use:
Reducing hospital readmission rates
Some payers are using machine learning to look at claims data across multiple electronic medical records to try to help providers reduce hospital readmissions. Reducing the rate of infection following surgery is a good real-life example. Savvy payers are using machine learning to analyze vast claims data sets to see when issues like inflammation and blood clotting occur most following surgery. They can then see which medications were used to treat those issues and if they were effective. Ultimately, they can use this data to predict whether or not a patient is likely to have to get re-admitted to the hospital following surgery.
By delivering those insights to providers, they’re not only demonstrating greater value, but also empowering providers to take the steps necessary to try to prevent any hospital readmissions that can be avoided. In doing so, the patient, the provider, and the payer all benefit.
Revenue cycle management
Some payers are using machine learning to figure out how soon a hospital will get paid for services rendered based on a particular diagnosis. In other words, they’re determining how quickly patients will receive a letter in the mail that says what their financial responsibility is, and how quickly they’ll actually pay it. Payers are also using machine learning to help providers understand their patients better, including which ones are most likely to be no shows and which are likely to receive services and not pay for them.
Improving clinical efficiency
Merging the clinical and financial, healthcare payers are also focusing on improving clinical efficiency and outcomes. Here they’re using machine learning to look at which treatments deliver the most clinically effective outcome at the lowest cost.
Fraud detection is another use case for payers. At a time of ever-escalating medical identity theft, it’s important for them to know if the person submitting a claim is in fact who she says she is and actually received the surgery or treatment she’s claiming to have. With real-time authorizations that work in a way similar to the banking industry model, payers can ensure the authenticity of a claim.
Machine Learning Gets Results
Although healthcare organizations haven’t typically been among the early adopters of new technologies, this risk-averse group is finding security in the public cloud, often by partnering with third parties. Doing so is allowing payers to explore the potential that machine learning and artificial intelligence bring to improve outcomes across their member base and within their own financial centers. In fact, these days, payers are among the growing number of healthcare organizations using machine learning and artificial intelligence to change almost every aspect of the healthcare industry. Importantly, they’re doing so to not only engage providers and patients and deliver a better overall experience and better outcomes, but also to drive efficiencies and lower their costs.
While some of the ways in which payers are already leveraging machine learning and artificial intelligence include reducing hospital admissions, managing revenue cycles, and improving clinical efficiency, that’s just the tip of the iceberg in terms of what’s possible. In the not–too–distant future, we expect to see major changes as healthcare increasingly happens through mobile interfaces and machine learning enables data-driven diagnoses.
For payers more specifically, the ongoing adoption of machine learning and artificial intelligence will mean that it becomes much easier for customers to achieve better health outcomes as well as understand things like if their treatments are in network or not, or how much they’ll need to pay out of pocket.
In the process, it will also reduce the amount of time it takes for claims to get processed dramatically.
Ultimately, these and other benefits of AI and machine learning are a win for payers, providers, and patients alike.