Health Execs Get Huge Payout When AI is Applied to This Core Function

September 11, 2018

New Accenture research reveals how artificial intelligence can net health insurers big bucks.

Health insurers can unlock billions of dollars in total value-in just a year-and-a-half-by applying artificial intelligence (AI) to core administrative functions, according to new research.

According to Accenture’s findings, healthcare payers can unlock 10% to 15% of operating expenses ($7 billion in total value) in about 18 months by automating and streamlining their core functions, such as claims, enrollment/billing, and customer service through AI and machine learning technologies.

For an individual health plan, this calculation equates to $1.5 million in total value for every 100 full-time employees (FTEs), by the end of the next calendar year, as a result of automating core administrative functions using AI, according to the research.

“By starting at the core, health insurers not only modernize processes, but develop a data-driven foundation that’s enabled by AI that is tried and tested across industries, removing uncertainties associated with implementation and enabling enterprises to realize the significant long-term potential of improving clinical outcomes,” says Rich Birhanzel, managing director of Accenture’s payer practice.

The research also highlight the immense pressure insurers face as the industry continues to change and consumer demand evolves. Out-of-pocket costs continue to rise, with 63% of employee single-coverage deductibles rising from 2011 to 2016. Also, demand continues to evolve with consumers seeking out personally-tailored services over value, as 37% of millennials cite service as a primary driver of switching health plans.

Accenture found 72% of payer executives say within the year, AI will be in the top three strategic priorities for their organization.

“We believe there’s significant opportunity for health insurers to apply artificial intelligence (AI) to core administrative functions to quickly generate value for achieving growth,” says Birhanzel. “We see this value being generated from six areas that align to an insurer’s operating model.”

  • Customer interactions ($2.1 billion): Apply “intelligence” to effectively anticipate and respond to customer demands;
  • Membership and billing ($1.4 billion): Accelerate onboarding of customers and members while enhancing ability to advance product design;
  • Reimbursement ($1.1 billion): Automate and redefine claims processing and reviews;
  •  Provider network management ($1 billion): Accurately streamline processes associated to network management;
  • Health management ($0.9 billion): Elevate ability to engage members and improve outcomes; and
  • Quality improvement and compliance ($0.5 billion): Automated reporting and regulatory updates to ensure quality and compliance.

“Healthcare executives are currently pressed to find the necessary capital for navigating imminent industry change as traditional sources of unlocking new value continue plateauing,” says Birhanzel. “The question becomes: how can payers quickly generate new value so that they can navigate a strategic course to growth? That answer is why AI is increasingly a top-level priority among U.S. health insurers.”

According to Birhanzel, AI and machine learning technologies can enable change in healthcare organizational performance in a multitude of ways, particularly through a range of customer service and administrative functions.

“The top three key areas for health insurers targeting near-term value are addressing customer questions, benefit-capture, and accelerating claims processing, such as prior authorization and clinical review,” he says.

For example, in an effort to anticipate and resolve customer questions, applying advanced call analytics with automated communications, can deflect the potential influx of avoidable calls while improving overall satisfaction by anticipating the needs of customers and members, he says.

“Similarly, as the benefit-capture process is traditionally cumbersome and redundant, a benefits-capture utility can streamline the process by using natural language processing (NLP) and robotic process automation (RPA) to simplify and validate benefits data entry in the field that then informs the structure required in the claims system through integration,” according to Birhanzel. “Payers can accelerate prior authorization and claims review by applying intelligent automation and virtual agents to streamline the intake of information associated with initial steps of eligibility/prior authorization requests, allowing agents to focus on more complex cases.”