Mendel Team
July 7, 2020

Here is how Mendel transforms EMR data and clinical literature.

Mendel's CEO Karim Galil was a guest in Hospital-to-Home by UCSF Health Hub.

Paul Grand: (...) We want to talk about companies that are validated. Companies that are out there on the market that have been there in the trenches, and that have some stories to share with you.

So we've got three that we're going to be focusing on today. They're each going to give you a short presentation.  (...)  so Karim, I'll let you take it away.

Dr. Karim Galil: Hi everyone. My name is Karim. I'm the co-founder and CEO of Mendel.ai. We are a technology company that enables you to glean and unravel knowledge from any type of clinical, written language. We trained the computer how to read clinical language in EMRs and in scholarly articles.

We convert that into an analytics and search ready format. All of our clients think of us as being Flatiron 3.0, which, in many ways, has some truth to it. If you want a computer to answer a question, you need the data to look like that:

It needs to be clean and in a tabular format.

But the reality is data looks like this in healthcare:

We're still one of the very few industries that use faxes, so you get a lot of those PDFs. I believe 80 to 90% of the data is in formats that the computers cannot read. Your options today is: either to use structured data, use claims data (10%), or you hire an army of human abstractors who sit down and clean the data for you, or  you use an AI that requires your help. It's an AI that renders low quality and needs someone to prove and do a lot of QC on top of it.

What we have done here at Mendel is:

We are the first company to build a set of AI tools that can truly help the human.

It helps humans to unravel the unstructured data. So what we do is we're able to ingest all types of data, whether it's a doctor note, whether it's a pathology report, whether it is a fax or a scanned record.

It goes through several tools. To identify, to change all scan records into text and finally to do fact extraction or organize the data into a format that is readable and analyzable by the computers.

One of the biggest problems that we have seen since we started the company is that the common perception is: "when it comes to quality, human abstraction is the standard. You have to sacrifice speed and scale because if you want quality, humans are the way to go. AI is fast, it's scalable, but it doesn't render good quality."

With Mendel, that's essentially not true. This is a graph that shows an error metrics comparing our AI against a gold standard.

We ingested several medical records and we can compare the performance of our AI against the gold standard. We actually scored higher than human beings in quality.

We were able to be in the upper nineties and when it comes to accuracy. That was one of the challenges that we had to fix: how can you have an AI that renders quality that a physician and a researcher can trust?

The other big problem that we had to tackle was a PHI. We've built an engine that is able to curate data and is able to mine knowledge out of it. But nobody is willing to give you data to begin with – because of PHI, because of HIPAA compliance.

So what we have done is we've built a very interesting AI tool that can scrub any personal information from any type of record, whether it's a scan record, doctor note, whatever it is. And we were able to get a third party to statistically verify the accuracy.

So the HIPAA threshold is at least 99% accurate in scrubbing data to be qualified. We scored 99.8%. To our knowledge today, we are the only company that has an AI technology that was able to get a third party statistical verification for the de-identification. So quality and data privacy were two main problems that we had to fix.

Just to put things in context, I would like to share this case study with you: we got a client that came to us, with a cervical cancer, retrospective study. They had records of 50,000 patients, which translated to around 600,000 documents. The client, (because of  COVID) had to deliver the project really fast with some cost savings.

Using their traditional methods, they estimated 27,000 hours of human abstraction. That's going to cost them in the upper hundreds of thousands of dollars. And they were only able to extract a certain number of end-points or certain number of facts. Using our system, we were able to do the whole fact extraction in 15 minutes, and changed the $ to ¢.

The interesting thing (as my background is medicine) was the agility. The researchers in this study were able to change the endpoints 11 times in five days.

So the ability to change and architect your experiment and be super agile while the machine is able to deliver back as fast, allowed them to change the whole hypothesis and change the whole design of the trial. The other interesting thing was they were able to successfully do an FDA submission, which was a great testament to the quality of our AI technology today.

Again, to our knowledge, we are one of very few, if only, AI companies that was able to curate the dataset and help a client to get an FDA submission for it.

When the COVID started, we found that there's tons of literature out there, and it's very hard to glean knowledge out of that literature. And we decided to take on that challenge. The company was built around oncology, and we had to scale. We repurposed our technology and we were able to build a search engine that can sit on top of most of the medical literature that exists today about about COVID.

It's a very advanced search engine that is able to understand context. So if you search for something like "potential therapy", it is able to glean different types of therapies from those scholarly articles without having to use keyword search.

Mark Goldstein: So, Karim, if you were to say: COVID changed you guys, you were focusing down the cancer route and COVID, opened up an opportunity where your technology was applied. You know, well beyond just cancer and you really made a difference with a number of studies.

Dr. Karim Galil: Yes. And using that search engine, one of the pharmaceutical companies were able to find out that they have a calcium channel blocker hypertension drug that actually had some antiviral effect that was proved in preclinical studies, is available in the literature, and they never heard of that experiment before.

Mark Goldstein: That's great. But let's wrap it up in 10, 15 seconds. What would you say?

Dr. Karim Galil: I would say that COVID changed Mendel from being a vitamin, to being a painkiller. Today, using our technology, we're able to render clinical data trials faster and significantly less expensive than the standards today.

Every company is trying to cut costs. And we're very excited about the opportunities that are coming to us during the COVID. Thank you.

What the Experts are Saying

Enterprise validated de-identification

Enterprise validated de-identification

Mendel has validated its clinical AI technology with third party statistical verification.

Mendel is the only company to validate outcomes with AI in a clinical setting

Mendel is the only company to validate outcomes with AI in a clinical setting

Mendel is the only company to validate outcomes with AI in a clinical setting.

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Mendel.ai is improving identification of suitable cancer patients and matching to clinical trials. Mendel.ai, a US-based company founded in 2016, uses AI tools to analyse clinical data, including medical history and genetic analysis, from cancer patients. Its goal is to facilitate clinical trials in oncology research by organising and analysing RWD, and matching patients to the right clinical trials.45 They take medical data from many structured and unstructured data sets and turn these into data digitised patient journeys aligned to specific treatments. Having collected data on over 1 million journeys, its algorithm can segment patient populations by their demographic, such as how African American women with breast cancer respond to a particular drug. In essence creating in silico or patient-less trials. The AI algorithm built on these data helps trial recruitment by finding eligible patients.

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