The multidecade rise in healthcare costs isn’t expected to reverse course any time soon. In search of a fix, Adun Akanni and TJ Ademiluyi co-founded Alaffia Health in 2020, one of the startups participating in the TechCrunch Disrupt Battlefield 200. The health tech company uses machine learning to try to identify fraud, waste and abuse in healthcare claims.
“We leveraged key insights from our family’s medical billing company in founding Alaffia,” Ademiluyi told TechCrunch in an interview. “We determined that the majority of the waste in the system results from natural human error, lack of transparency in claims processing, and misaligned incentives between healthcare providers and payers. We founded Alaffia to tackle these issues using nascent machine learning and AI, built on top of deep healthcare domain expertise.”
Alaffia sells services primarily to health insurance payers and enterprises that provide their employees health coverage. Using AI to extract and standardize data from hospital bills, including various medical billing procedure codes and dates of service, the platform aims to reduce payers’ spending by finding errors and overcharges within the bills sent by healthcare providers.
The causes of medical billing errors are myriad but often arise from double billing, missing the payer submission deadline and a failure to capture patient information. Non-specific diagnostic codes are another common issue, leading to instances of upcoding and undercoding. Upcoding is when a coder reports a higher-level service than patients received or never had performed, while undercoding is when billing codes don’t capture the full scope of work performed by a physician.
Medical expenses are expected to grow by an average of 5.1% from 2021 to 2030, reaching $6.8 trillion, according to the Centers for Medicare and Medicaid Services — and a significant portion of those expenditures are derived from errors in health insurance claims. It’s estimated that about 80% of claims in the U.S. contain at least one medical billing error and that as much as $300 billion is lost to provider fraud, waste and abuse each year.
Image Credits: Alaffia Health
“This is a quite challenging technical problem due to the lack of data standardization in the healthcare system, so we’ve rigorously trained machine learning models using training data generated by our in-house annotation team,” Ademiluyi said.
Alaffia reviews facility bills for errors such as “unbundling” — i.e., using multiple codes for individual parts of a procedure — while checking the accuracy of more complex claims like implants and surgeries. The company says it taps registered nurses, certified coders and certified billers to cross-reference the AI’s findings, as well as a clinical review team that examines each claim and corresponding medical record.
When asked about competitors, Ademiluyi says he sees “legacy industry participants” who manually process and review claims as Alaffia’s principal rivals. But Alaffia isn’t the only startup attempting to tackle the medical billing error problem with AI. Anomaly, which works with insurance companies and providers, offers an AI-driven platform designed to detect irregularities in medical bills. There’s also Nym, whose technology converts medical charts and electronic medical records from physician consultations into auditable billing codes automatically.
Alaffia has managed to gain traction in the space, however — and funding. Ademiluyi claims the company’s services currently cover over 300,000 health plan members in aggregate. To date, Alaffia has raised $6.6 million in venture capital from backers including Anthemis, 1984 Ventures, Aperture Venture Capital, Tau Ventures, Twine Ventures, Plug and Play Ventures and ERA’s Remarkable Ventures Fund.
Ademiluyi says that 2022 revenue is on pace to more than double year over year. The near-term plan is to expand Alaffia’s commercial footprint and product offerings, he added, starting with hospital bill review services direct to patients. The company currently employs “just over” 20 people and expects to hire five more by the end of the year.
“Fortunately, we operate in an industry resistant to recession. Regardless of pandemics, macro trends or the outlook for interest rates, people will still visit the doctor to receive care,” Ademiluyi said. “When patients receive care, it leads to further healthcare spending, which benefits our business as we review generated hospital bills for errors. As we move into a slowdown in the market, large enterprises — both health insurance institutions and employers whom we support are actually looking at ways to lower their expenses, which we directly support by reducing healthcare spending. As such, we believe the pandemic and current slowdown in the economy to be a net positive for the business.”
Alaffia Health taps AI to detect errors in hospital bills by Kyle Wiggers originally published on TechCrunch
