>> Live, from Boston, it's theCUBE, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Dr. Prakriteswar Santikary known as Dr Santi. He is the Vice President and Global Chief Data Officer at eResearch Technology. Thank you so much for coming back on theCUBE. >> Yeah, thank you for inviting me. >> So Dr Santi tell our viewers a little bit about eResearch Technology. You're based in Marlborough... >> Yeah, so we're in Boston, but ERT has been around since 1977 and we are a data and technology company that minimizes risks and uncertainties within clinical trial space and our customers are pharmaceutical companies, biotechnology companies, medical device companies, and where they really trust us in terms of running their clinical trials on our platform. So we have been around over 40 years, so we have seen a thing or two in the space. It's a very complex domain a very highly regulated as you know, because it's dealing with patients lives. So we take huge pride in what we do. >> We know how involved clinical trials can be long, very expensive, how are the new tools, big data impacting the cost? >> Well, that has been an age old problem within the clinical trials, usually a drug takes about eight to 12 years and costs about $2 billion from start to commercialization. So it's a very lengthy, manual and arduous process. So there are lots going on in this clinical trial domain that's tries to shorten the timeline and employing of big data technologies, modern data platform to expedite data processing, data collection from mobile devices and health technologies and all these. Artificial intelligence is playing a big role in terms of disrupting some of these domains, particularly if you see the protocol development down to patient selection, down to study design, then study monitoring. So you need to do all those things and each takes long long long time, so AI with the big data technologies is they're really making a difference. >> In what ways? >> For example, patient selection is one of the huge pin points in any clinical trial, because without patients there are no clinical trials. Particularly when you try to launch a drug, you will have to identify the patients, select the patients and not only select the patients, you have to make sure those patients stay with the clinical trials throughout the duration of the trial. So patient engagement is also a big deal. So with these big data technologies, like now you can see all this mobile health devices that patients are wearing using which you can monitor them. You can remind, send them a reminder, take your drug or you can send a text saying that there will be a clinical visit at that site come at seven o'clock, don't come at nine o'clock. So these kind of encouragement and constant feedback loop is really helping patients stay engaged. That is critical. Then matching patients with the given clinical trials is a very manual and arduous process, so that's where the algorithms is helping. So they are just cranking up real world evidence data for example claims data, prescription data and other type of genomic data and they're matching patients and the clinical trial needs. Instead of just fishing around in a big pond and find out, okay I need three patients. So go and fish around the world to get the three patients. That's why current process is very manual and these AI techniques and behind technologies and big data technologies are really disrupting this industry. >> So are the pharmaceutical companies finding that clinical trials are better today because patients are more engaged and they are getting as you said this constant reminder, take your drug, stay with us. Do you think that they are, in fact, giving them better insights into the efficacy of the drug? >> Yes because you will see their compliance rate is increasing, so because remember when they have to fill out all these diaries, like morning diaries evening diaries, when they are taking which medicine, when they are not taking. It used to be all manual paper driven, so they would forget and particularly think about a terminally ill patient, each day is so critical for them. So they don't have patience, nor do they have time to really maintain a manual diary. >> Nor do their caregivers have the time. Right. >> So this kind of automation is really helping and that is also encouraging them as well, that yeah somebody is really caring about me. We are not just a number, patient is not a number that somebody is really relating to them. So patient engagement, we have a product that specifically focuses around patient engagement. So we do all these phase one through phase four trials, one, two, three, four and then forced marketing, obviously, but through the entire process, we also do patient engagement, so that we help our customers like pharmaceutical companies and biotechnology companies so that they can run their trials with confidence. >> How about analyzing the data that you collect from the trials, are you using new techniques to gain insights more quickly? >> Yes, we are. We just recently launched a modern data platform, a data lake while we are consolidating all the data and anonymizing it and then really applying AI techniques on top of it and also it is giving us real time information for study monitoring. Like which side is not complying, with patients or not complying, so if the data quality is a big deal in clinical trials, because if the quality is good, then FDA approval, there is a chance that FDA may approve, but if the data quality is bad, forget about it, so that's why I think the quality of the data and monitoring of that trial real time to minimize any risks before they become risks. So you have to be preempted, so that's why this predictive algorithms are really helping, so that you can monitor the site, you can monitor individual patient through mHealth devices and all these and really pinpoint that, hey, your clinical trials are not going to end on time nor on budget. Because here you see the actual situation here, so, do something instead of waiting 10 years to find that out. So huge cost saving and efficiency gain. >> I want to ask about data in healthcare in general because one of the big tensions that we've talked about today is sort of what the data is saying versus what people's gut is saying and then in industry, it's the business person's gut but in healthcare it is the doctor, the caregivers' gut. So how are you, how have you seen data or how is data perceived and is that changing in terms of what the data shows that the physician about the patient's condition and what the patient needs right then and there, versus what the doctors gut is telling him that the patient needs? >> Yeah and that's where that augmentation and complementary nature, right? So AI and doctors, they're like complementing each other, So predictive algorithm is not replacing doctors the expertise, so you still need that. What AI and predictive algorithm is playing a big role is in expediting that process, so instead of sifting through manual document so sifting through this much amount of document, they would only need to do this much of document. So then that way it's minimizing that time horizon. It's all about efficiency again, so AI is not going to be replacing doctors anytime soon. We still need doctors, because remember a site is run by a primary investigator and primary investigator owns that site. That's the doctor, that's not a machine. That's not an AI algorithm, so his or her approval is the final approval. But it's all about efficiency cost cutting and bringing the drugs to the market faster. If you can cut down these 12 years by half, think about that not only are you saving lots of money, you are also helping patients because those drugs are going to get to the market six year earlier. So you're saving lots of patients in that regard as well. >> One thing that technologies like Watson can do is sort through, read millions of documents lab reports and medical journals and derive insights from them, is that helping in the process of perhaps avoiding some clinical trials or anticipating outputs earlier? >> Yes, because if you see Watson run a clinical study with Cleveland Clinic recently or Mayo Clinic I think or maybe both. While they reduce the patient recruitment time by 80%, 80%. >> How so? >> Because they sweep through all those documents, EMR results, claims data, all this data they combined-- >> Filter down-- >> Filter down and then say, for this clinical trial, here are the 10 patients you need. It's not going to recommend to who those 10 patients are but it will just tell you that, the goal is the average locations, this that, so that you just focus on getting those 10 patients quickly instead of wasting nine months to research on those 10 patients and that's a huge, huge deal. >> And how can you trust that, that is right? I mean I think that's another question that we have here, it's a big challenge. >> It is a challenge because AI is all about math and algorithm, right? So when you, so it's like, input black box, output. So that output may be more accurate than what you perceive it to be. >> But that black box is what is tripping me up here. >> So what is happening is sometimes, oftentimes, if it is a deep learning technique, so that kind of lower level AI techniques. It's very hard to interpret that results, so people will keep coming back to you and say, how did you arrive at that results? And that's where most of the, there are techniques like Machine Learning techniques that are easily interpretable. So you can convince FDA folks or other folks that here is how we've got to it, but there are a deep learning techniques that Watson uses for example, people will come and, how did you, how did you arrive at that? And it's very hard because those neural networks are multi-layers and all about math, but as I said, output may be way more accurate, but it's very hard to decipher. >> Right, exactly. >> That's the challenge. So that's a trust issue in that regard. >> Right, well, Dr. Santi, thank you so much for coming on theCUBE. It was great talking to you. >> Okay, thank you very much. Thanks for inviting. >> I'm Rebecca Knight for Paul Gillin we will have more from the IBM CDO Summit in just a little bit. (upbeat music)