AI Wins For The Health Payer, Provider and The Person

Like any digital innovation, the insertion of artificial intelligence (AI) and machine learning (ML) into the healthcare ecosystem requires a more thoughtful and measured build than other industries.? From enhancing diagnostic markers & accuracy to streamlining administrative processes, AI & ML are reshaping how healthcare payers and providers operate, clinicians work & treat and the overall member journey. In particular, the application of AI and ML is proving invaluable in breaking down language barriers, improving claims adjudication accuracy, and facilitating preventative and post acute care.

Without argument, language barrier is a thorn in the side of healthcare equity. ?Language barriers can often lead to misunderstandings and decreased patient engagement. ?At the California Department of Public Health, there is a focus on generative AI (Gen AI) solutions to increase the speed, efficiency, and accuracy of patient language translations, so the nearly 20% of Californians with limited English proficiency can more easily learn how to access the health care and social services they need.

From enrollment to access, the inability to shift to an interaction that not only provides the most authentically digestible information to the member/patient but will also loop back their inputs/communication to the payer rep or clinician can have a significant effect on all outcomes.

The capacity of a Natural Language Processing (a subset of AI/ML) creates such “loop” via text, video or audio that gives lift to the enrollment process, adherence to care plans, quality of patient inputs for diagnostics and on and on.? A hurdle to healthcare payer & provider how the NLP is structured specific to health & medical specific definitions, descriptors and cultural influence on these specific topics.? Working with a team of engineers that have a sound knowledge of Natural Language Processing libraries is paramount. These libraries help in breaking the text down into its grammar, extracting key phrases, and deleting unnecessary words, among other things.

Accurate medical documentation is vital for patient care and billing. NLP can transcribe and translate medical notes, ensuring that all relevant information is captured accurately, regardless of the language in which it was originally documented. Mark the win for clinical and one for claims as well.

The lifeblood of the payer data model is the claim - AI and ML are revolutionizing the claims adjudication process by increasing accuracy, reducing costs, and speeding up the adjudication cycle. With the use of AI, automating healthcare transactions, including benefit verification, referrals and claim inquiries, has saved the healthcare industry?$187 billion annually. ?AI can extract relevant data from medical claims and electronic health records (EHRs) efficiently, therein, reducing manual errors and ensuring that all necessary information is considered during the adjudication process.? Further, given the wealth of historical data that can be leveraged into the model, AI can add value across the entire payer value chain -

• Fraud Detection: Some estimates show that more than $100 million dollars is lost every year to FWA.? Machine learning algorithms can analyze patterns in claims data to identify potential fraud or abuse that aren’t as easily detected by legacy systems. By flagging suspicious claims, these systems help payers prevent financial losses and ensure that legitimate claims are processed promptly.
• Predictive Analytics: AI can predict the likelihood of claim approval based on historical data, helping providers submit cleaner claims and reducing the number of denied or delayed claims. ?This predictive capability improves cash flow and reduces administrative burdens.

With the backing of all that rich historical data, AI and ML provide tools that enable healthcare providers to predict and prevent health issues with a preventative approach.

Machine learning algorithms can analyze patient data to identify individuals at high risk for specific conditions. By stratifying patients based on their risk profiles, providers can target preventative interventions more effectively but more importantly, focus on the specific needs of the individual.?

From another angle, AI systems can analyze medical images and EHRs to detect early signs of diseases such as cancer, diabetes, and cardiovascular conditions. Or, in the instance of chronic medical management, AI-driven tools can help manage treatment plans & communications by monitoring patient data and providing real-time feedback. For example, the brains of a wearable devices that can track vital signs and alert patients and providers to any concerning changes, allowing for timely interventions is rooted in AI.

To boil it down, AI/ML are tools of equity for the payer & provider. ?AI can analyze genetic, environmental, and lifestyle data to provide personalized treatment recommendations. The cohesive and historical wealth of information in our EHRs, claims and patient surveys just waiting for the correct conduit that will put this diagnostic gold into the clinicians' hands.

The benefits of AI and ML in healthcare create lift to the entire member patient/journey while also giving back precious treatment time to the clinician and creating operational efficiencies (and savings!) to the payer. While the quick wins for the industry lie in breaking down language barriers, harnessing the power of historical data and creating accuracy & efficiency to the administrative processes, there are countless more applications available to the industry. ?

One note to bear in mind across the board – It’s not just about the destination but how you get there.? Meaning, careful consideration of the partners and resources a payer or provider uses can make or break an initiative.? “A careful, measured approach is needed to what type of value are we looking for and what types of tools can we trust and provide the resources to implement successfully… What works in one site or in one population may not in another, and as I noted, what works today may not next year.”? - Tom Hallisey, Heathcare IT News. Nov 30, 23

For healthcare payers and providers, embracing AI and ML is not just about adopting new technologies but also about fundamentally improving the quality, efficiency, and accessibility of care. As these technologies continue to evolve, their integration into healthcare systems will likely bring even more profound benefits, driving the industry toward a future where healthcare is more personalized, proactive, and patient-centered.