Basys.ai receives $2.4 million for its prior authorization tech
Basys.ai, a firm that assists health plans and health systems, is implementing value-based care beginning with prior authorization. The startup debuted after receiving $2.4 million in pre-seed investment. Nina Capital led the investment. Eli Lilly (Lilly Ventures), Mayo Clinic, Two Lanterns Venture Partners, Asset Management Ventures, and Chaac Ventures also participated.
Prior Authorization Necessities and Complexities
Prior authorization is a very laborious process. It requires obtaining clearance from your health insurance company before performing a medical operation. It includes several processes, evaluations, and individuals collaborating. Prior authorization is essential to avoid unneeded operations and keep healthcare expenses low. However, the long process of prior authorization sometimes leads to delayed or even abandoned therapy. Administrative costs represent 20% to 34% of total healthcare spending in the United States.
In an effort to remedy this, the Centres for Medicare & Medicaid Services (CMS) presented a proposal in February. It aims at digitalizing how burdensome prior authorization is in the healthcare system. According to some experts, the CMS plan opens the door for technology businesses. Furthermore, it will develop solutions that will ultimately improve the way healthcare data is used. A recently emerging company capitalizing on this opportunity is Basys.ai.
Features of Basys.ai
Basys.ai uses generative AI and deep machine learning power which can accurately automate up to 90% of prior permission requests for medications and procedures. Moreover, the platform does not require sensitive data from insurance companies or physicians, cutting the average integration period from up to a year to weeks.
In addition, Basys.ai can construct timelines with health insurers up to nine months faster than most of its competitors. The platform does not require sensitive data from insurance companies or physicians, cutting the average integration period from up to a year to weeks.
“The engine is trained on extensive Joslin Diabetes Center and Mayo Clinic’s longitudinal data of more than 10 million patients,” CEO Amber Nigam said. “This translates to flattening the cost curve for patients and reducing administrative burden by leveraging AI.”
Current Practices and Future Goals:
Basys.ai began selling to providers which was making money. However, it has subsequently shifted its business strategy to selling to health insurance companies. According to Nigam, it is currently initiating pilots with two big payers in Massachusetts and Minnesota. The organization is also concentrating on tracking patient outcomes by lowering readmission rates and detecting if the patient’s illness progression has stopped or reduced.
“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.”
