Big Data and Clinical Research: Trends, Issues and Considerations
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Delivered by Zaher El-Assi, Merge Healthcare eClinical president, this presentation provided an overview of how researchers can leverage advanced information technology and strengthen their ability to meet the demands of increasingly complex studies and larger data sets – all while remaining compliant within an ever-changing regulatory landscape. Presentation highlights included the following: - Increasing cost, time and regulatory pressures are challenging clinical researchers to seek innovative methods and systems to capture and analyze clinical data. - Recent policy changes at the FDA in the United States suggest a growing emphasis on quality over compliance with greater involvement the research process by patients and disease advocacy groups. - There is a shift from population-based medicine to a more personalized approach fueled by advances in genomics and new technologies to monitor individual health. - The four characteristics of Big Data are: volume, variety, velocity and veracity. - The advent of Big Data in clinical research requires improved data collection and management systems, improved analytical tools and processes, and improvements in researchers’ ability to respond to rapidly changing circumstances in data outcomes. - Mobile apps that enable researchers to manage critical study functions such as unblinding, randomization and inventory tracking will become more prevalent. - The Big Data movement provides opportunities to: - Strengthen health economics and outcomes research - Target drugs at specific patient populations - Accelerate drug and device development - Improve patient recruitment and retention - Clinical researchers will face more privacy, liability and technology challenges because of Big Data. Zaher El-Assi is president of Merge eClinicalOS, a division of Merge Healthcare (NASDAQ: MRGE) and the fastest-growing software solution company in the clinical research industry. Prior to joining Merge Healthcare, Mr. El-Assi served as the vice president of Global Sales and Strategic Partnerships for Boston-based KIKA Clinical Solutions. At KIKA, he was responsible for establishing and developing global partnerships and building upon the company’s diverse client base. Among other achievements, Mr. El-Assi led the successful sale of KIKA Clinical Solutions to Merge Healthcare in 2010.
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Big Data and Clinical Research: Trends, Issues and Considerations
- 1. South African Clinical Research Association | June 2015 | 1 Zaher El-Assi President Big Data and Clinical Research: Trends, Issues and Considerations SACRA | June 2015
- 2. South African Clinical Research Association | June 2015 | 2 Four game-changing moments in clinical trial history
- 3. South African Clinical Research Association | June 2015 | 3 King Nebuchadnezzar First trial 600 BCE James Lind, M.D. 1747First controlled trial Sir Geoffrey Marshall 1946 First controlled randomized trial Big Data 2015First trial on steroids
- 4. South African Clinical Research Association | June 2015 | 4 Today’s clinical trial landscape (What a mess . . .)
- 5. South African Clinical Research Association | June 2015 | 5 30-fold increase since 2000 $8 million lost per day of delay $1 billion to develop a drug 170,000+ registered trials ~75% delayed by one month or more 94% Experience delays
- 6. South African Clinical Research Association | June 2015 | 6 Key Trends Regulatory Shifts
- 7. South African Clinical Research Association | June 2015 | 7 Richard Moscicki, M.D. Deputy Director, Science Operations Center for Drug Evaluation & Research, FDA A bellwether?
- 8. South African Clinical Research Association | June 2015 | 8 “Quality over compliance” Office of Pharmaceutical Quality (OPQ) One voice Integrate review and inspection across lifecycle Patient-focused drug development Apply risk-based approaches The changing face of the US regulatory environment
- 9. South African Clinical Research Association | June 2015 | 9 Speed to Market Monitor Assess Pivot Adaptive Trial Design
- 10. South African Clinical Research Association | June 2015 | 10 Key Trends Technology Shifts
- 11. South African Clinical Research Association | June 2015 | 11 Population-based
- 12. South African Clinical Research Association | June 2015 | 12 Wearable technologies (biometric apparel) PART I
- 13. South African Clinical Research Association | June 2015 | 13 Source: Scanadu; NASA Ames Research Center Star Trek-Style, Non-Invasive Tricorder (scanning + mobile data capture)
- 14. South African Clinical Research Association | June 2015 | 14 Source: Athos Apparel – Biosignals to Mobile Devices via Bluetooth
- 15. South African Clinical Research Association | June 2015 | 15 Source: Stanford University, MendlesPod.com, Bloomberg Business, Apple Inc. Apple Watch + Research Kit (open source platform) Signed up 11,000 people in less than 24 hours (contrasted against a year’s time and 50 research sites nationwide) Stanford Cardiovascular Study: Signed up 7,406 patients for a Parkinson’s Disease trial in 6 hours (greatest prior study participation was 1,000 subjects) Sage Bionetworks:
- 16. South African Clinical Research Association | June 2015 | 16 Source: Google and Novartis Google + Novartis – Smart Contact Lenses
- 17. South African Clinical Research Association | June 2015 | 17 Implantables: The ultimate inside edge PART II
- 18. South African Clinical Research Association | June 2015 | 18 Source: Texas A&M + MIT Under-Skin (dermally implantable) Sensors
- 19. South African Clinical Research Association | June 2015 | 19 Source: University of Illinois at Urbana-Champaign Straight-to-Skin Printed Sensors
- 20. South African Clinical Research Association | June 2015 | 20 Source: Proteus Digital Health Digital Pills
- 21. South African Clinical Research Association | June 2015 | 21 All data, all the time 24/7/365 (or 366)
- 22. South African Clinical Research Association | June 2015 | 22 It means trying to control clinical trials these days is kind of like . . .
- 23. South African Clinical Research Association | June 2015 | 23 Enter “Big Data”
- 24. South African Clinical Research Association | June 2015 | 24
- 25. South African Clinical Research Association | June 2015 | 25 The Four V’s of Big Data Volume Variety Velocity Veracity
- 26. South African Clinical Research Association | June 2015 | 26 Volume
- 27. South African Clinical Research Association | June 2015 | 27 Volume • Paper records • Electronic medical records • Radiology images • Clinical trial data • FDA submissions • Population data genomic sequences • Personal genomic data • 3D imaging • Biometric sensors • Social media • Videography • (to name a few…)
- 28. South African Clinical Research Association | June 2015 | 28
- 29. South African Clinical Research Association | June 2015 | 29
- 30. South African Clinical Research Association | June 2015 | 30 Variety
- 31. South African Clinical Research Association | June 2015 | 31 Variety • Structured data • Unstructured data • Semi-structured data
- 32. South African Clinical Research Association | June 2015 | 32 Velocity
- 33. South African Clinical Research Association | June 2015 | 33 Velocity • Collection • Organization • Analysis • Action
- 34. South African Clinical Research Association | June 2015 | 34 Veracity
- 35. South African Clinical Research Association | June 2015 | 35 Veracity • Assumes simultaneous scaling up in granularity and performance • Architectures, platforms, algorithms, methodologies and tools • “Divide and process” across multiple servers
- 36. South African Clinical Research Association | June 2015 | 36 More data More questions More inputs require Improved analysis Improved action Improved systems
- 37. South African Clinical Research Association | June 2015 | 37
- 38. South African Clinical Research Association | June 2015 | 38 The Opportunities • Strengthening health economics and outcomes research • Targeting drugs at specific patient populations • Accelerating drug and device development • Improving patient recruitment and retention
- 39. South African Clinical Research Association | June 2015 | 39 However…
- 40. South African Clinical Research Association | June 2015 | 40 However…
- 41. South African Clinical Research Association | June 2015 | 41
- 42. South African Clinical Research Association | June 2015 | 42 Looking Forward “Data and information technology... are the least regulated part of the market, so this area has the most opportunity for really innovative ways in which we can change the environment.” – Dr. Kevin Schulman
- 43. South African Clinical Research Association | June 2015 | 43 Q & A
- 44. South African Clinical Research Association | June 2015 | 44 King Nebuchadnezzar James Lind, M.D. Sir Geoffrey Marshall You
- 45. South African Clinical Research Association | June 2015 | 45 Zaher El-Assi zaher@eclinicalos.com +1 919.653.3420
Editor's Notes
- Thank you for that kind introduction – honored to be here, etc.
- King Nebuchdanezzar conducted the first-ever recorded clinical trial in the seventh century BCE. In short, it examined the effect of vegetables on a warrior’s health and fighting ability. Some 2300 years later, British-based Dr. James Lind held the first controlled trial in 1747 in which he explored scurvy Two centuries later, Sir Geoffrey Marshall conducted the world’s first controlled randomized study. And in 2015, we are seeing the emergence major game-changer: Big Data. Where do we go from here? In large measure, the answer is up to you and me to figure out together.
- Before we look ahead, though, let’s first take a look around at the state of our industry today.
- Numbers that are likely familiar to most of us, but they bear repeating in context of this presentation.
- A few weeks ago, I had the pleasure of hearing Dr. Richard Moscicki (“Moe-Sick-Ee”), who works in the Center for Drug Evaluation and Research at the FDA, speak at a conference. Dr. Moscicki’s presentation focused on the changing face of drug research and evaluation. In short, Dr. Moscicki profiled the top 10 changes at the FDA and CDER that will affect the life sciences industry in 2015. I should add he said he had culled this list of 10 from another speech he had given the week before that enumerated more than 50 changes the FDA is implementing this year alone. I’d be happy to send you a link to Dr. Moscicki’s presentation. Just give me your card after this presentation or email me at zaher@eclinicalos.com.
- Key changes to be coordinated under the Office of Pharmaceutical Quality, which has been recently reorganized. Wants to speak with One Voice and be clearer in its directions regarding drug development and approvals Goal is to integrate the review and inspection process across the development lifecycle to ensure more consistent, thorough and cohesive drug development from bench to bedside. A major change in emphasis at the OPQ this year will be a shift towards more patient-focused development – working more in concert with patient- and disease-based advocacy groups to help bring promising compounds and therapies to the patients who need them more quickly. Related to this idea of patient-focused drug development, Dr. Moscicki also noted the FDA will delve deeper into the application of risk-based approaches in research. According to Dr. Moscicki, the underlying theme for the FDA from now on will be: “Quality over compliance.”
- When used to the full extent of its powers, sophisticated data management software combined with an adaptive trial design can accelerate speed to market because sponsors can pivot more deftly and more quickly when the data indicate. In this way, advanced IT can combine with innovative approaches to clinical research to fuel quantum leaps in the quality of research organizations’ work and, in turn, the quality of the therapies and devices we bring to patients in need. By providing unparalleled control over and visibility into the mountains of data collected during a trial, EDC tools help researchers monitor, measure, evaluate and respond to key trends and results in real time. As never before, finely detailed metrics can be set and acted upon immediately so compounds that show promise can move forward and those that don’t can be identified earlier in the process. This capability - to act on data in real-time – can help reduce the risk associated with responding to and managing adverse events.
- I want to share a story I read last month on CNN.com. In 2013, an Illinois man convinced several investors to fund a revolutionary medical device, to the tune of over $25 million. He called it the "McCoy Home Health Tablet", and promised it would instantly deliver patient data to doctors. In other words, he was pitching the legendary Tricorder from Star Trek, even naming it after Dr. Leonard "Bones" McCoy, the blue-clad, very irritable medical officer from the 1960s show. It was a scam. The device didn't exist, and the man was caught and convicted for his crimes. But as a testimony to how quickly reality catches up with fantasy nowadays, less than two years later the Tricorder does exist. And it works. This is the Scanadu Scout. You hold it to your forehead and it measures heart rate, temperature, blood pressure, oxygen level and provides a complete ECG reading. It then transmits that data to a mobile device or laptop via Bluetooth. http://inhabitat.com/scanadu-creates-worlds-first-star-trek-style-medical-tricorder/
- Or this – clothes you can wear that monitor and transmit data automatically. http://www.wearable-gadgets.net/wearable-technology-fitness-athos-biometric-apparel/
- And while the jury is still out on how big the Apple Watch and Apple’s open source Research Kit software will be, the results from these two studies suggest strongly the watch – and similar wearable devices – will be a huge boon to trial recruitment and monitoring. http://mendelspod.com/podcasts/gene-and-tonic-iwatch-and-research-kit-23andme-goes-big-time-no-spaceship/ http://www.bloomberg.com/news/articles/2015-03-11/apple-researchkit-sees-thousands-sign-up-amid-bias-criticism
- And finally, the fact that we can now monitor glucose levels via a contact lens that has within it not only a sensor but also an antenna and a chip to receive power should tell us that we very clearly are no longer in Kansas (no offense intended to any Kansas natives here, by the way). http://techcrunch.com/2014/01/16/google-shows-off-smart-contact-lens-that-lets-diabetics-measure-their-glucose-levels/
- But wearable biometric devices aren’t the only technology that is continuously evolving. At the risk of incurring a collective groan at this very intentional pun, let’s take a deeper look inside implantable devices.
- At Texas A&M University, researches have developed a tattoo that, in short, monitors glucose levels for diabetics. Once implanted, the tattoos can be read externally by a device that provides immediate data that can be collected, analyzed and acted upon wirelessly. http://today.tamu.edu/2012/05/11/texas-am-research-makes-monitoring-glucose-painless/ http://biomed.tamu.edu/BioSyM/dermal_implants.html
- And this one I love. John Rogers, a materials scientist at the University of Illinois at Urbana-Champaign has developed so-called “epidermal electronics.” These devices consist of ultrathin electrodes, sensors and wireless power and communication systems that can attach to the skin and record and transmit electrophysiological measurements. The early versions, which were applied to a thin, soft elastomer backing, were fine for use in an office environment, but that was about it. Recently, Rogers and his team figured out how to print the electronics right on the skin, making the device more durable and rugged so you can take showers, work out or go swimming with one. http://www.technologyreview.com/news/512061/electronic-sensors-printed-directly-on-the-skin/
- And then, of course, there are these for all of us who have ever had or will one day need a colonoscopy, endoscopy or similar procedure. Beyond that application, though, digital pills offer a host of advantages for researchers and medical professionals. Back in 2012, Proteus became the first company to have an ingestible device approved by the FDA. The sensor consists of a tiny silicon chip containing trace amounts of magnesium and copper. When swallowed, it generates a slight voltage in response to digestive juices, which conveys a signal to the surface of a person’s skin where a patch then relays the information to a mobile phone belonging to a healthcare provider. Proponents of these medical devices predict that they will provide alternatives to doctor visits, blood tests, MRIs and CAT scans. Other gadgets in the pipeline include implantable devices that wirelessly inject drugs at pre-specified times, and sensors that deliver a person’s electrocardiogram to their smartphone. Clearly, the implications for how, when and where researchers collect data from clinical subjects – not to mention the accuracy of that data – are huge. http://blogs.nature.com/news/2012/07/digital-pills-make-their-way-to-market.html
- It means trying to control clinical trials these days is kind of like trying to get a firm grip on sand.
- Big Data” is a moving term coined to mean a voluminous amount of unstructured, semi-structured and unstructured data, a data set that is so large and complex that it cannot be processed using database management techniques, but requires more sophisticated processing tools. The sources of “Big Data” are wide ranging, from internet search data to social media postings to data collected by cameras, mobile phones, radio-frequency identification and by monitoring devices worn by patients in clinical trials Potential of Big Data lies in the combination of traditional data with new forms of data, both individually and on a population level. Not many companies are using Big Data (DNA, sensor, genomic data, etc.) – not there yet. As EDC platforms continue to evolve with additional functionalities, better interoperability and lower costs, the barrier to entry is lowered and the power of Big Data to improve how - and how well - clinical trials can be conducted, not to mention their efficiency and resource usage, increases.
- Example of Big Data
- Here are some of the sources today – we can count on this list tripling within the next 18 months. How much data are we talking about here when we say the volume is increasing? A lot. And it’s getting bigger. For example, one of our clients at Merge eClinical is using our eClinicalOS platform in a study with 22 Trillion – that’s trillion with a “T” – data points. As far as we know, that is the largest dataset ever managed in a clinical trial And clearly, a study like this would have been impossible before EDC.
- “A pharmaceutical company’s value in the future is going to be its data...This is what the technologies conceivably do.” – David B. Agus, M.D., Professor of Medicine at the University of Southern California Already we are seeing a shift in the clinical research industry as companies are sharing data sets more freely. GSK and several other pharmaceutical manufacturers, including Bayer, Sanofi, Roche, and Lilly, have collaborated to upload patient-level data collected by clinical trials to a web portal call Clinical Study Data Request where researchers can submit proposals and request anonymized data from certain studies. Also, Thomson Reuters recently announced the availability of data on more than 180,000 clinical trials through its Cortellis Clinical Trials Intelligence program, including data on drugs, biologics, biomarkers, diagnostics, and medical devices. The data can be accessed through analytics tools within the Cortellis platform or through an API embedded in other software applications, widening the possibilities for clinical integration and research substantially.
- Also, a consortium of pharma companies focused in cancer research have formed a not-for-profit effort, Project Data Sphere, to share and analyze de- identified, patient-level data from late-stage comparative studies to be analyzed. The hope is that with access to historical clinical trial data more efficient clinical trials can be designed, reducing the cost and accelerating the speed of finding meaningful treatment. Leveraging this dormant data is one example of how the clinical research industry can use the powerful techniques used in Big Data analytics to actively address the safe acceleration of clinical trials.
- Potential of Big Data lies in the combination of traditional data with new forms of data, both individually and on a population level. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. It includes clinical data from CPOE and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called tweets), blogs, status updates on Facebook and other platforms, and web pages; and less patient-specific information, including emergency care data, news feeds, and articles in medical journals.
- The wealth of historical data being collected every day – often long before a patient will ever engage in a clinical trial – could be as powerful as EMR data for patients that diligently track health status. For patients in a clinical trial, the potential to capture nearly unlimited data about their mood or daily food intake during the study by having the user snap a quick picture of each meal changes the landscape of data analysis for clinical trials – pushing the envelope of Big Data significantly over the next several years. When there are more channels that collect and transmit more data, they, in turn, generate more questions that researchers must consider. To do that on the scale of a clinical trial, though – especially with the stakes as high as they are – requires that we constantly seek better systems that can help us analyze and understand that data more efficiently and accurately so that we then have a higher likelihood of making better decisions and taking better actions.
- An example of the kind of technology platforms that can help researchers manage studies more efficiently when the data are coming in around the clock is a recent update to our Study Connect mobile app. Seamlessly integrated with eCOS, Study Connect™ enables researchers to: Manage randomization of subject assignments. Coordinate dispensing and inventory management. Set up customized alerts for specific events. Unblind individual subjects, as circumstances warrant. Stay connected with real-time customized notifications such as adverse events, enrolled patients and site status changes. Share top-level statistics for each study with key stakeholders using the study dashboard. Expedite approval response times for critical study decisions such as patient study eligibility. Monitor enrollment and establish milestone alerts. Maintain security with the ability to set customized role-based permissions for individual studies. This kind of technology helps MITIGATE RISK. We’re pushing the boundaries of what’s possible in a mobile environment for clinical researchers. While Merge eClinical is exceptionally proud to be among the first EDC providers to offer true mobile study management capabilities, we’ve only scratched the surface of what’s possible.
- The evolving healthcare space provides us with a few unique opportunities where organizations can leverage healthcare big data to drive greater efficiencies in quality and cost. Strengthening health economics and outcomes research (HEOR) Health Economics and Outcomes Research (HEOR), which makes use of real world data, can find great applications of, and consequently a lot of value in, using big data technologies. While HEOR used to be a supporting discipline, it is now becoming an important independent process in pharmaceutical and medical device organizations. For effective health economics, epidemiologists and statisticians should be able to mine real world data, such as EHR and claims databases, and derive valuable insights to establish product value. Targeting drugs at specific patient populations Life sciences and pharmaceutical companies can use big data technologies to aggregate and analyze clinical data from provider applications, registries and public health studies. The aggregated data can be used to identify and classify specific groups of patients based on their conditions, clinical processes and health outcomes. Using big data technologies on this real-world data could help accurately define these groups, as well as effective treatment patterns. Combining clinical data with drug data (e.g. dosage, adverse effects and safety information) can unveil interesting trends in the effectiveness of products and services in certain sub-populations that show a superior or unexpected response. Big data analytics could be used to identify underserved patient populations and also assess the effectiveness of drugs already in use across different patient groups. Accelerating development of new drugs Drug repositioning is emerging as a big opportunity to reduce the time to market for drugs. Combining publically available big datasets and sponsors’ internal databases can help with targeting drugs for new indications or patient populations. This whole process can save a lot of time and money used for proving the safety and efficiency of the drug candidate. Improving patient recruitment and retention in clinical studies Big data analytics can be used on a de-identified patient and investigator data to create meaningful insights for researchers. Analytics can also help identify various problem areas such as study design, trial dropouts’ protocol adherence, etc. Researchers can also use analytics to identify hidden patterns in patient data, assess risks and influence the success rate of clinical studies through timely interventions. For example, estimating the minimum number of patients required to meet the trial’s statistical end-points or analyzing similar protocols used in earlier studies to predict future patient retention rates. At the same time, using big data effectively comes with its own set of challenges, including data diversity, accessibility, aggregation, standardization, storage and security across multiple data types, sources and models
- Since the data in question here is patient and health information, it is bound by security and privacy concerns. Healthcare data is bound by HIPAA regulations, so accessing, storing and converting this data for analysis remains a challenge. Although data can be anonymized by removing patient names and other personally identifiable information, it is still debatable what the minimum personal information required to generate relevant insights for clinical research happens to be. More to the point, clinical research organizations as never before will be targets for Hackers, raising a key challenge for all of us: Potential liability challenges: We may be entering a new era of liability concerns for clinical researchers – informed consent may or may not provide protection. And as trials become border neutral, the application of local laws beyond the conduct of the trials themselves will get more and more complicated. And coming from a country that is hailed as the lawsuit capital of the world, this is a very real issue for us.
- As life sciences and pharmaceutical companies start investing in technology and tools for big data processing and analytics, they will also need to build strong capabilities across a large number of technology areas such as Hadoop, MapReduce, EDW, ETL, visualization tools, machine learning, Natural Language Processing, etc. They will also need to find professionals with a combination of healthcare domain knowledge (processes, standards, clinical data) and statistical techniques and tools (e.g. data mining, data science, information architecture and predictive models). And there are a host of other issues to consider: Integrating disparate sources of data through seamless interfaces - Interoperability Implementing technology and processes to ensure data is pristine and uncompromised Creating technology and analytics systems that can translate data into usable knowledge Implementing that knowledge in real time at the point of care.
- But when the use of Big Data works, it can work very well. A good example of how large volumes of non-traditional data can be used in real time to improve healthcare occurred here on the African continent. This case involved the use of anonymised data on a wide scale to during the Ebola breakout. Anonymised text and voice data from mobile phones was collated by a telecommunications company located in one of the African countries hit by the virus to allow a non-profit organisation to produce maps of the populated areas in the region, allowing authorities to then work out where to locate treatment centres and how they may be able to contain the disease by restricting travel. In addition, mobile phone mast activity information from mobile operators was collected by the US Centers for Disease Control and Prevention to work out where most calls to helplines were being made from and thus where resources should be allocated. While this process did not treat existing Ebola patients, of course, it did help stem the tide of transmission, provide quicker access to medical help and build a greater understanding of how such outbreaks can be better managed in the future.
- The potential for clinical researchers to leverage the power of Big Data in the coming years is boundless. It will call for Innovative Thinking: Kevin Schulman, M.D., M.B.A., Associate Director of the Duke Clinical Research Institute at Duke School of Medicine in my home state of North Carolina spoke at an industry roundtable regarding the potential for innovation in the area of big data technology: “Data and information technology... are the least regulated part of the market, so this area has the most opportunity for really innovative ways in which we can change the environment.” We can do this together – sponsors, CROs, AROs, sites, study management companies and others in collaboration.
- I’ll be happy to answer any questions you might have.
- I’ve circled back to where we started to emphasize the point that, just like these historic figures behind me, each of us has the ability to help reach new milestones in clinical research. As they sought to use the knowledge and technology that was available to them in their time to understand better how to improve the human condition, so, too, do we have that opportunity in our time. At least from where I’m standing, I think a number of your faces would look good alongside Nebuchdanezzar’s up here.