Veda Data

Veda Data sells a service to insurers that ensures physician directory accuracy using artificial intelligence and does that through a customizable subscription model for a data feed that is auto-updated every 24 hours. (Veda Data)

Anyone who follows healthcare knows that insurers—and their covered patients—suffer the perennial problem of inaccurate doctor directories.

It's a big problem: Those directories help patients figure out which doctors are in their network and which aren't but quickly become out-of-date as doctors retire or contracts change. Veda Data Solutions said it has created a fix for this problem, which it estimates is a $3-billion-a-year issue for the industry, using one of the buzziest forms of technology: artificial intelligence.

Veda sells the service to insurers through a customizable subscription model for a data feed that is auto-updated every 24 hours. They estimate their tool improves the data accuracy of a doctor directory by 400% and automates the work of one administrative employee over the course of a year down to three machine minutes.

The company has secured backing from the likes of New Dominion Angels, Great Falls, Virginia-based Riverbend Capital Advisors LLC and a collection of undisclosed investors.

Veda Data

The big idea: Automate healthcare data and save millions 

Headquarters: Washington, D.C.

CEO: Meghan Gaffney Buck

Launched: 2015

Funding raised to date: $3.2 million

Revenue: $550,000

Number of employees: 12

Fierce insights from Veda Data CEO Meghan Gaffney Buck 

FierceHealthcare: What is your best piece of advice for launching a healthcare company that challenges the status quo?

Meghan Gaffney Buck: Don’t be afraid to find unconventional partners. Veda was founded by an astrophysicist and a political entrepreneur. We have a unique ability to see where we can bring innovative ideas to healthcare because we aren’t limited by the status quo.

FH: What is the failure you’ve learned the best lesson from? 

MGB: We learned early the hard way that “AI” and “machine learning” mean very different things to different people. We focused too much on telling people how we could make their provider and claims data more accurate, but forgot to remind our customers why it mattered. Today, we talk less the mechanics of our system and focus on the transformative effect of cutting 50%+ in administrative overhead while increasing data accuracy.

FH: What is one change you predict in healthcare that people wouldn’t expect?

MGB: Five to 10 years from now, automation will allow providers to spend more time with patients, care will improve and provider and patient satisfaction will increase.

Veda Data

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