Prior authorization, a complex term referring to the requirement for a physician to gain permission from a health insurance company before procee
Prior authorization, a complex term referring to the requirement for a physician to gain permission from a health insurance company before proceeding with a medical procedure, has traditionally involved a labor-intensive process that includes multiple stages, assessments, and collaborative efforts.
The objective behind seeking approval from insurance companies is to prevent unnecessary medical procedures and control healthcare expenses. However, the protracted nature of the prior authorization process frequently results in delayed or even abandoned medical treatment. Furthermore, the administrative expenses linked to this process contribute to a significant portion of healthcare expenditures in the United States, ranging from 20% to 34%.
In an attempt to address this issue, the Centers for Medicare & Medicaid Services (CMS) unveiled a proposal in February with the intention of alleviating the burdensome nature of the prior authorization process in the healthcare system by transitioning it into the digital sphere.
According to some experts, the CMS proposal lays the groundwork for technology companies to introduce their solutions that can ultimately enhance the utilization of healthcare data.
One emerging company capitalizing on this opportunity is Basys.ai, which specializes in aiding health plans and health systems in implementing value-based care, beginning with the streamlining of prior authorization procedures. Established in early 2022 by Amber Nigam and Jie Sun, who crossed paths during Harvard’s health data science program, Basys.ai is focused on reshaping the utilization of healthcare data.
Was this response better or worse?
Basys employs a blend of generative artificial intelligence and deep learning to drive its core “engine.” This engine has the capability to automate as much as 90% of the prior authorization requests for medications and medical procedures with a high level of precision, as explained by Nigam to TechCrunch. Notably, the platform does not necessitate the use of sensitive information from insurance companies or medical practitioners, resulting in a substantial reduction of the usual integration timeline from potentially up to a year down to a matter of weeks.
“The engine has undergone extensive training using longitudinal data from the Joslin Diabetes Center and Mayo Clinic, encompassing more than 10 million patients,” Nigam explained. “This leads to a reduction in patient costs and alleviates administrative burdens by harnessing the power of AI.”
Furthermore, by automating the interpretation of payer policies, Basys is able to rapidly establish timelines with health plans, significantly outpacing most of its competitors by up to nine months. Nigam noted that this list of competitors includes companies like Cohere Health.
Backed by $2.4 million in pre-seed funding, the company is launching its commercial operations today. The funding round was led by Nina Capital and featured participation from various investors, including Eli Lilly (Lilly Ventures), Mayo Clinic, Two Lanterns Venture Partners, Asset Management Ventures, and Chaac Ventures.
Initially targeting providers, Basys has shifted its business model to focus on selling to health insurance companies. Presently, it is commencing pilot programs with two major payers in Massachusetts and Minnesota, Nigam revealed.
Basys.ai is also actively involved in tracking patient outcomes by diminishing readmission rates and assessing whether the advancement of a patient’s illness has been curtailed or decelerated.
“We also ensure a comprehensive dataset about the patients,” Nigam explained. “At times, decisions are not solely reliant on just a couple of factors; they rely on numerous attributes, potentially numbering in the hundreds or thousands. This is coupled with a deep comprehension of the insurance company’s regulations. When these policies are aligned with patient information, the resolution of a prior authorization request becomes more intricate and nuanced.”
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