top of page Secures $2.4 Mn to Revolutionize Prior Authorization with Mayo Clinic's Data-Powered Tech

August 10th, 2023

In the complex world of healthcare, prior authorization, a procedure where a medical practitioner seeks approval from a health insurance company before performing a medical treatment, often involves a labor-intensive process with multiple stages, reviews, and coordination among various parties.

While seeking insurance company approval serves the purpose of preventing unnecessary medical procedures and managing healthcare costs, the extensive nature of the prior authorization process frequently results in delayed or abandoned patient care. Administrative costs linked to this process contribute to a staggering 20% to 34% of total U.S. healthcare expenditures.

Recognizing the need for reform, the Centers for Medicare & Medicaid Services (CMS) proposed measures in February to address the cumbersome nature of prior authorization within the healthcare system, aiming to digitize this process.

Industry experts have suggested that the CMS proposal presents an opportunity for technology companies to introduce innovative solutions that can ultimately enhance the utilization of healthcare data.

One such emerging player is, a company dedicated to aiding health plans and health systems in transitioning to value-based care, starting with the transformation of the prior authorization process. Founded in early 2022 by Amber Nigam and Jie Sun, who initially crossed paths in Harvard's health data science program, is now capitalizing on advancements in artificial intelligence and deep learning to streamline its "engine." This engine has the capacity to automate nearly 90% of prior authorization requests for drugs and medical procedures with a high level of accuracy, explained Amber Nigam to TechCrunch. Remarkably, the platform does not require sensitive data from insurance companies or medical professionals, thereby shrinking the integration timeline from potentially a year down to mere weeks.

Nigam stated, "The engine is trained on extensive Joslin Diabetes Center and Mayo Clinic’s longitudinal data of more than 10 million patients. This translates to flattening the cost curve for patients and reducing administrative burden by leveraging AI."

In addition to automating payer policy encoding, can expedite the creation of timelines for health plans by up to nine months compared to most competitors, a significant achievement highlighted by Nigam, which includes entities like Cohere Health.

The company officially launches commercially, fortified by $2.4 million in pre-seed funding. The funding round was spearheaded by Nina Capital, drawing participation from a consortium of investors including Eli Lilly (Lilly Ventures), Mayo Clinic, Two Lanterns Venture Partners, Asset Management Ventures, and Chaac Ventures.

While Basys initially catered to healthcare providers and generated revenue, it has since shifted its business model to serve health insurance companies. Currently, Basys is conducting pilot programs with two prominent payers based in Massachusetts and Minnesota, according to Nigam.

In a continuous effort to enhance patient outcomes, Basys is actively working on capturing data related to patient readmission rates and evaluating whether the progression of diseases has been slowed or halted.

“We also make sure we have a lot of information about the patients,” Nigam said. “Sometimes when you make decisions, it is not entirely based on one or two attributes; it’s based on hundreds or thousands of attributes along with the understanding of the insurance company’s policies. Once you match these policies with the patient information, then resolving a prior authorization request is more nuanced.”

Source: TechCrunch

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