Have you experienced the nightmare of finding good doctors? How about getting reimbursed for a telemedicine consultation by your insurance company? This conversation deals with all those issues.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as CareDash.
Ted Chan: CareDash is the fastest-growing doctor review website on the internet. We’ve made it our mission to help patients make the right decisions about their healthcare providers and services. One of the things that we do that’s really different is we collect every doctor review behind a registered login.
We use artificial intelligence and machine learning to stamp out review frauds. There’s a lot of review fraud. We have about 175,000 reviews now that we’ve accepted. We’ve rejected well over 20,000.
Sramana Mitra: What is the call of review frauds?
Ted Chan: It’s similar to something you might see with Yelp. Providers who want to boost their reputation but use tactics that aren’t above board. It’s fairly easy to detect a great deal of the fraud. Unfortunately, we see a lot of market leaders who will reject the reviews and they’ll be very obvious rejects.
These are cases where 100 reviews come in from foreign IP addresses within a couple of hours but we’ll see those reviews posted elsewhere. We know that patients are unfortunately making decisions based on that data. It was my experience that made me start this company. My wife had some health issues. We struggled to find good information using the existing sites to make our decisions.
Sramana Mitra: Is that the only trigger in terms of what chunk of reviews coming from a particular foreign destination or an IP address and generating reviews, or there are other triggers that your system picks up as red flags?
Ted Chan: There’s quite a lot of different angles to it. I shouldn’t say just foreign because many online reputation managers that use deceptive processes are actually US-based. Some of them can be quite clever. We see negative review attacks. Somebody might leave a negative review for every provider in the area so that the one who they represents looks superior. That can be hard to detect initially.
There’s a lot that goes into it in terms of e-mail addresses, identity, the type of text, and the patterns whether they create. They do it off one account or they create hundreds of accounts. There are lots of different topics to try to get under the pass with different algorithms and different sites used to do it. We’ve got a couple of legacy competitors in this space.
Sramana Mitra: What is your business model?
Ted Chan: Our business model is similar to TripAdvisor. My co-founder David Blundin was a seed stage investor in TripAdvisor. In any given area, if you’re searching for a hotel in Orlando, you might see 20 or 30 four or five-star hotels and pick across them based on their profiles, their features, and whether or not they have a pool.
With health care providers, you should be able to see your options geographically and then be shown a full list of providers based on that. What we do is we’ll show a sponsored listing at the top and that’s our monetization model. But no matter what, we won’t remove a negative review if we think it should be there. You can sponsor but you won’t get any patients.