>> 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)

>> Live from the MIT campus in Cambridge, Massachusetts, it's the Cube, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of MITCDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're joined by Dr. Santikary, he is the vice-president and chief data officer at ERT. Thanks so much for coming on the show. >> Thanks for inviting me. >> We're going to call you Santi, that's what you go by. So, start by telling our viewers a little bit about ERT. What you do, and what kind of products you deliver to clients. >> I'll be happy to do that. The ERT is a clinical trial small company and we are a global data and technology company that minimizes risks and uncertainties within clinical trials for our customers. Our customers are top pharma companies, biotechnologic companies, medical device companies and they trust us to run their clinical trials so that they can bring their life-saving drugs to the market on time and every time. So we have a huge responsibility in that regard, because they put their trust in us, so we serve as their custodians of data and the processes, and the therapeutic experience that you bring to the table as well as compliance-related expertise that we have. So not only do we provide data and technology expertise, we also provide science expertise, regulatory expertise, so that's one of the reasons they trust us. And we also have been around since 1977, so it's almost over 50 years, so we have this collective wisdom that we have gathered over the years. And we have really earned trust in this past and because we deal with safety and efficacy of drugs and these are the two big components that help MDA, or any regulatory authority for that matter, to approve the drugs. So we have a huge responsibility in this regard, as well. In terms of product, as I said, we are in the safety and efficacy side of the clinical trial process, and as part of that, we have multiple product lines. We have respiratory product lines, we have cardiac safety product lines, we have imaging. As you know, imaging is becoming more and more so important for every clinical trial and particularly on oncology space for sure. To measure the growth of the tumor and that kind of things. So we have a business that focuses exclusively on the imaging side. And then we have data and analytics side of the house, because we provide real-time information about the trial itself, so that our customers can really measure risks and uncertainties before they become a problem. >> At this symposium, you're going to be giving a talk about clinical trials and the problems of, the missteps that can happen when the data is not accurate. Lay out the problem for our viewers, and then we're going to talk about the best practices that have emerged. >> I think that clinical trial space is very complex by its own nature, and the process itself is very lengthy. If you know one of the statistics, for example, it takes about 10 to 15 years to really develop and commercialize a drug. And it usually costs about $2.5 to 3 billion. Per drug. So think about the enormity of this. So the challenges are too many. One is data collection itself. Your clinical trials are becoming more and more complex. Becoming more and more global. Getting patients to the sites is another problem. Patient selection and retention, another one. Regulatory guidelines is another big issue because not every regulated authority follows the same sets of rules and regulations. And cost. Cost is a big imperative to the whole thing, because the development life-cycle of a drug is so lengthy. And as I said, it takes about $3 billion to commercialize a drug and that cost comes down to the consumers. That means patients. So the cost of the health care is growing, is sky-rocketing. And in terms of data collection, there are lots of devices in the field, as you know. Wearables, mobile helds, so the data volume is a tremendous problem. And the vendors. Each pharmaceutical companies use so many vendors to run their trials. CRO's. The Clinical Research Organizations. They have EDC systems, they can have labs. You name it. So they outsource all these to different vendors. Now, how do you coordinate and how do you make them to collaborate? And that's where the data plays a big role because now the data is everywhere across different systems, and those systems don't talk to each other. So how do you really make real-time decisioning when you don't know where your data is? And data is the primary ingredient that you use to make decisions? So that's where data and analytics, and bringing that data in real-time, is a very, very critical service that we provide to our customers. >> When you look at medicine, obviously, the whole notion of evidence-based medicine has been around for 15 years now, and it's becoming a seminal feature of how we think about the process of delivering medical services and ultimately paying it forward to everything else, and partly that's because doctors are scientists and they have an affinity for data. But if we think about going forward, it seems to me as though learning more about the genome and genomics is catalyzing additional need and additional understanding of the role that drugs play in the human body and it almost becomes an information problem, where the drug, I don't want to say that a drug is software, but a drug is delivering something that, ultimately, is going to get known at a genomic level. So does that catalyze additional need for data? is that changing the way we think about clinical trials? Especially when we think about, as you said, it's getting more complex because we have to make sure that a drug has the desired effect with men and women, with people from here, people from there. Are we going to push the data envelope even harder over the next few years? >> Oh, you bet. And that's where the real world evidence is playing a big role. So, instead of patients coming to the clinical trials, clinical trial is going to the patient. It is becoming more and more patient-centric. >> Interesting. >> And the early part of protocol design, for example, the study design, that is step one. So more and more the real world evidence data is being used to design the protocol. The very first stage of the clinical trial. Another thing that is pushing the envelope is artificial intelligence and other data mining techniques and now people can be used to really mine that data, the MAR data, prescription data, claims data. Those are real evidence data coming from the real patients. So now you can use these artificial intelligence and mission learning techniques to mine that data then to really design the protocol and the study design instead of flipping through the year MAR data manually. So patient collection, for example, is no patients, no trials, right? So gathering patients, and the right set of patients, is one of the big problems. It takes a lot of that time to bring those patients and even more troublesome is to retain those patients over time. These, too, are big, big things that take a long time and site selection, as well. Which site is going to really be able to bring the right patients for the right trials? >> So, two quick comments on that. One of the things, when you say the patients, when someone has a chronic problem, a chronic disease, when they start to feel better as a consequence of taking the drug, they tend to not take the drug anymore. And that creates this ongoing cycle. But going back to what you're saying, does it also mean that clinical trial processes, because we can gather data more successfully over time, it used to be really segmented. We did the clinical trial and it stopped. Then the drug went into production and maybe we caught some data. But now because we can do a better job with data, the clinical trial concept can be sustained a little bit more. That data becomes even more valuable over time and we can add additional volumes of data back in, to improve the process. >> Is that shortening clinical trials? Tell us a little bit about that. >> Yes, as I said, it takes 10 to 15 years if we follow the current process, like Phase One, Phase Two, Phase Three. And then post-marketing, that is Phase Four. I'm not taking the pre-clinical side of these trials in the the picture. That's about 10 to 15 years, about $3 billion kind of thing. So when you use these kind of AI techniques and the real world evidence data and all this, the projection is that it will reduce the cycle by 60 to 70%. >> Wow. >> The whole study, beginning to end time. >> So from 15 down to four or five? >> Exactly. So think about, there are two advantages. One is obviously, you are creating efficiency within the system, and this drug industry and drug discovery industry is rife for disruption. Because it has been using that same process over and over for a long time. It's like, it is working, so why fix it? But unfortunately, it's not working. Because the health care cost has sky-rocketed. So these inefficiencies are going to get solved when we employ real world evidencing into the mixture. Real-time decision making. Risks analysis before they become risks. Instead of spending one year to recruit patients, you use AI techniques to get to the right patients in minutes, so think about the efficiency again. And also, the home monitoring, or mHealth type of program, where the patients don't need to come to the sites, the clinical sites, for check-up anymore. You can wear wearables that are MDA regulated and approved and then, they're going to do all the work from within the comfort of their home. So think about that. And the other thing is, very, terminally sick patients, for example. They don't have time, nor do they have the energy, to come to the clinical site for check-up. Because every day is important to them. So, this is the paradigm shift that is going on. Instead of patients coming to the clinical trials, clinical trials are coming to the patients. And that shift, that's a paradigm shift and that is happening because of these AI techniques. Blockchain. Precision Medicine is another one. You don't run a big clinical trial anymore. You just go micro-trial, you just group small number of patients. You don't run a trial on breast cancer anymore, you just say, breast cancer for these patients, so it's micro-trials. And that needs -- >> Well that can still be aggregated. >> Exactly. It still needs to be aggregated, but you can get the RTD's quickly, so that you can decide whether you need to keep investing in that trial, or not. Instead of waiting 10 years, only to find out that your trial is going to fail. So you are wasting not only your time, but also preventing patients from getting the right medicine on time. So you have that responsibility as a pharmaceutical company, as well. So yes, it is a paradigm shift and this whole industry is rife for disruption and ERT is right at the center. We have not only data and technology experience, but as I said, we have deep domain experience within the clinical domain as well as regulatory and compliance experience. You need all these to navigate through this turbulent water of clinical research. >> Revolutionary changes taking place. >> It is and the satisfaction is, you are really helping the patients. You know? >> And helping the doctor. >> Helping the doctors. >> At the end of the day, the drug company does not supply the drug. >> Exactly. >> The doctor is prescribing, based on knowledge that she has about that patient and that drug and how they're going to work together. >> And out of the good statistics, in 2017, just last year, 60% of the MDA approved drugs were supported through our platform. 60 percent. So there were, I think, 60 drugs got approved? I think 30 or 35 of them used our platform to run their clinical trial, so think about the satisfaction that we have. >> A job well done. >> Exactly. >> Well, thank you for coming on the show Santi, it's been really great having you on. >> Thank you very much. >> Yes. >> Thank you. >> I'm Rebecca Knight. For Peter Burris, we will have more from MITCDOIQ, and the Cube's coverage of it. just after this. (techno music)