Artificial Intelligence Based Healthcare Payer Analytics Would Help All Stakeholders Involved


The healthcare industry in the United States is a complex web of regulation, bureaucracy, red tape, and lots of chances for litigation for providers and payers. Every time a provider needs to get paid, the organization or practice submits a claim. The insurance payer processes the claim and accepts it, denies it, or rejects based on certain criteria. The workflow around processing claims forms the cornerstone of an insurance payer’s operations. It takes up the most resources and requires experienced administration.


An insurance company, like every organization in every industry, generates business data through everyday operations. And like every industry, analyzing this data helps to gain a bird’s eye view of what’s been happening. In other words, healthcare payer analytics provides a means for payer organizations to gain insights into their organization’s operations. By analyzing historical payer data about providers, claims, reimbursements, health plans, and patients, insurance companies gain the ability to identify patterns that help them operate with greater efficiency.


Healthcare payer analytics is a rather broad term. It might appear to generalize or simplify many things. Although analytics refers to data analysis when applied to healthcare payer services, it would encompass the analysis of data from many sources, such as patients’ health history, providers’ credentialing, claims history from different providers, and reimbursements paid to providers, and so forth. 


One of the most promising applications of healthcare payer analytics solutions is the assessment of population health. It is no secret that there is racial inequity in the United States especially when it comes to healthcare. By analyzing various data points about the population, insurance payers can pinpoint parts of the population that are at higher risk and also know more about their medical and wellness requirements. The information for this comes from medical data gleaned from claims, health plans, history of illnesses, and demographic information about average life expectancy, quality of life, employment, and so forth.


When solutions for healthcare provider analytics target population health, they can identify which ethnic group might be at greater risk of certain diseases, whose healthcare needs are not being met, infant mortality, lifestyles, access to care, availability of nutritious food, and so on. Based on these parameters, the payers can devise suitable health plans and accommodate corresponding providers into their network. Such a predictive approach to population health can have a significant impact on the clinical outcomes and patient experiences in underserved minority groups.


Another application of AI-based health care payer analytics is assessing providers. In the insurance industry, provider analytics highlights revealing patterns about practices, bundled payments, risk assessments, services provided, and so forth. This information enables payers to identify high-value providers to be partnered with and also goes a long way in helping to streamline credentialing processes. Moreover, in the wake of the pandemic, many people, payers, as well as policymakers are pushing for a switch from a fee-for-service model of reimbursements to a value-based model. The new model would base provider reimbursements on the clinical outcomes they can achieve for their patients. AI-based analytics for providers could help payers collaborate with them better to ease the transition to a value-based system. 

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