Applied Clinical Trials
Highlighting the six broad technology themes poised to transform future R&D.
This past year, SCORR Marketing and Applied Clinical Trials surveyed our audience on a number of topics around technology-Big Data, innovation, paperless, wearables and mobile health in clinical trials. (a full list of surveys is here. Instead of creating a new survey for insight into top technology innovations, SCORR analyzed existing data to inform the Technological Innovations Survey Report, which is available here.
The data showed various levels of acceptance of technologies, depending on the stakeholder (sponsor, CRO, site, service provider) vs. type (paperless, wearables, Big Data, etc.). But across the board, themes around barriers of adoption to new technology or processes emerged. Cost and skepticism about data quality are the leading barriers. Another theme, the fear of change and the reluctance to give up tried-and-true, even if cumbersome, processes or technologies. Next, the ever-present data security concern. Though not always the top concern, security was the most-identified choice resulting from a company’s adoption of paperless processes and the second most-identified concern coming from company’s use of Big Data.
While the above report focuses on current attitudes around technology innovation, Applied Clinical Trials assembled an expert group late last year to choose the top technology innovations affecting our industry-despite industry hesitation. The forward-looking panel included: Brian J. Chadwick, President, Bring Life Sciences Consulting, Inc.; David Evans, Managing Director, Accenture, LLP; Ken Getz, Director, Sponsored Research Programs, Tufts CSDD and Chairman, CISCRP; Wayne Kubick, CTO, HL7; Craig Lipset, Head of Clinical Innovation, Pfizer; and Alan Louie, Research Director, Life Sciences, IDC Health Insights.
What started as an attempt to winnow down a long list of technology innovations (nominated by our Editorial Advisory Board) to impact clinical research this year, became a robust discussion about technologies affecting research now, and those poised to ignite the “Clinical Trial of Tomorrow.” Rather than choose the top technologies, six broader themes emerged that encompassed many of the innovations.
This final mind map or matrix (click on graphic at right to view) was conceived because of the inter-relatedness of those themes. For most of the discussion, the “Engaged Patient” was the transformational game-changer-the one area that overarched all others, at the center of a hub-and-spoke model. However, ultimately, while a seismic shift in the way healthcare, clinical trials and clinical care is conducted, the Engaged Patient was placed within the bigger picture clinical trial of tomorrow. Many of the six topics and their listed sub-topics are inter-related, however, so as not to clutter and confuse, we decided to leave the dotted line relationships out.
What follows is the discussion of what constitutes the Clinical Trial of Tomorrow.
“My default is we are in the business of developing medicines, and anything is an enabler to that. Therefore, I shouldn’t be entirely dependent on any one technology,” said Lipset, shepherd of the sponsor viewpoint. This set the frame for the discussion. What are sponsors considering for clinical trial technology that will impact today, tomorrow, or trials starting now but not ending until five years from now? The technologies chosen are used in many industries-how they are applied or will be applied in clinical trials is how the committee ended up grouping the categories. Said Evans, “There are foundational technologies, but how will we use them to solve patient problems?”
From the vantage of an industry tech analyst, Louie said, “If you look at it from a tech-first perspective, IoT and wearables, while having a significant adoption on the consumer side-but still waiting to cross the chasm-hasn’t found its killer app in the life sciences. At some point down the road, there’s going to be a great new app that everyone is going to want to incorporate into a lot of major trials because it delivers something useful and valuable, which it’s not doing yet.” A valid point, you don’t know what you don’t know.
But what we do know is that while current wearables and devices may never pass the sniff-test for trials (or could get passed by with that killer app), their brethren in the Cognitive Computing/AI world-such as the artificial pancreas, which uses a “learned” glucometer and insulin pump that “make” the dosing decisions for patients based on their blood sugar levels-are just around the corner. Chadwick says AI will be big and is influencing current thinking in future clinical trials. Presently, “intelligent” devices are going through the 510k process, so their consideration is not far off. In another example, the FDA is exploring its RAPID system using NLP to identify potential public health outbreaks.
Getting to Cognitive Computing/AI is not an easy thing. Evans explained: “In machine learning, it has to have a gold standard of large volume and high quality endpoints to learn or determine decisions. You still have to build the Big Database using high-quality data in order for the machine to interpret what the unknown is so that it can learn.” Therein lies the inter-relatedness to “Intelligent Enterprise” and Big Data. Without large volumes of high-quality data in a consistent format, the pattern recognition, decision support, and signal detection is not achievable.
Evans believes that data can also come from the Connected Computing sector. “As the connected universe expands, you have to have large volumes of high quality data to go to Big Data, to inform analytics,” he said. “But currently, we have a lot of data, but not the quality,” Kubick agreed that Big Data may not be transformational in 2017, but the connected computing impact is now.
Getz said, “In some ways, the patient piece becomes an overarching principle that applies to all of these areas we are discussing. Connected computing creates a more convenient patient engagement, a more interactive patient engagement.” He continued, echoing Lipset’s point about technology’s effect on drug development. “At the very center, is the notion that all of these technologies ultimately serve the goal of assuring the drug development enterprise that its relationship with the patient is optimized. It becomes the most relevant, most informed, most convenient for our study volunteers, and most efficiently run that ultimately delivers the best therapies.”
Kubick said, “While I don’t think blockchain is going to be ready for primetime in our industry for several years, one of the compelling cases is having much more and control, and better control, over security and privacy in the hands of the patient. It is what’s critical to making patients more willing to share and exchange their data for research purposes.”
Kubick continued. “It basically means to secure their trust, so they feel confident that the data will be used for the right things.” Lipset said permissions that are baked into a chain of control closer to the patient is a great potential enabler for empowering tools for patients.
In addition to the trust-building that blockchain could offer, data privacy and clinical trial data disclosure-two additional choices on the original list-are also an exercise that help build patient trust. But here, the impetus for privacy and disclosure are regulatory authorities.
Evans said, “The regulators are a control. You can’t have any technology enabled to the patient without, first, having regulatory scrutiny. Even Google or Apple engage with FDA on a regular basis on how to innovate into that area and how they fit into the regulatory framework.”
And while some may see the FDA or other regulatory authorities as a hindrance to innovation or a drag on the pace of change, Evans believes the agency is moving faster than it ever has before in trying to address current tech innovations.
More controls in the Healthcare Environment were around the effects of population health decisions, payer decisions, care delivery, evidence-based medicine, and more. These entities may not be transformational, but their influence must be factored into R&D of tomorrow. Personalized medicine was placed in the Healthcare Environment because it is currently in use-and a control to how decisions are made. Information gained through personalized or precision medicine, including images, tumor markers, genome sequencing, and the ‘omics will feed and grow both cognitive computing and big data.
While it did not appear on the final matrix, personal health data sharing is a downstream use of HL7’s Fast Healthcare Interoperability Resources (FHIR) platform. It is the chance for patients to share data directly for research. Lipset explained: “It sits at the convergence of eSource and patient engagement, enabling patients to be the source of much more rich and diverse data.”
Another innovation on the original list was collaborative care networks. Chadwick, specifically, called out the Chronic Collaborative Care Networks (C3N). In this project, through objective communication and education, remission rates have gone from 50% to 75% for patients with inflammatory bowel disease.
The area of least interrelatedness to the Engaged Patient was the Master Data Management category. Louie suggested that some of the technologies on our original list-such as electronic trial master file (eTMF)-were strictly for the operational efficiencies they deliver. “They are operationally-based, and make the process more efficient and allow companies to respond to regulators; that’s not patient-related, but process-related,” he noted.
Getz agreed. “eTMF is one that is being rapidly adopted or more market-ready to adopt in 2017. It would deliver a higher level of efficiency, could contribute to faster cycle time, and provide more data to track what historically has been in documents that have been siloed. It’s a technology that will have a notable impact this year.” Making documents available in a single source is a plus for future uses, as are standards.
Louie added that the EMA’s approved implementation of ISO standards for the identification of medicinal products (IDMP)-a set of common, global standards for data elements, formats, and terminologies to uniquely identify and exchange information on medicines, is driving data access and standards.
Evans concluded that without standards, innovation is impossible. “There are maturing standards that have been driving that activity-across any of the categories. Without having the foundational standards in place, you are not going to be able to innovate or have widespread adoption of the innovation.”
The Intelligent Enterprise/Big Data theme encompassed the items listed on the original list, and were included as a source of data that can be mined to answer larger questions. For example, Chadwick noted pharmaceutical companies performing data mining on social media. “What pharma is doing with social media content is amazing. It is scraping data to get to the most subtle references out of social networking to find potential adverse events.”
As noted earlier, Big Data isn’t in the Impact stage, meaning it’s not here yet. But it is going to be evolutionary when it hits its stride. The panel mulled the term “Big Data” because it conjures the hype vs. reality factor that Lipset says exist with some technologies. But as Kubick pointed out, “it is a recognizable term, it is a theme in literature, and it can be applied to clinical trials and R&D.”
We hope the matrix helps apply what you do now in clinical trials to where the future of R&D is going, and shows the interrelatedness of the many technologies and stakeholders in the R&D space.
#1: Engaged Patient
What it is: Flip patient engagement to the engaged patient to more accurately represent the transformation taking place through them with technology. For the engaged patient, characteristics include interactive, more involved and more informed decision-making, convenience, improved comprehension, greater disclosure, and building trust, wrapped up with patients providing their own data.
Why it was Chosen: With almost all the technologies chosen, they ultimately served the goal of assuring the drug development enterprise that its relationship with the patient or patient community is optimized.
#2: Healthcare Environment
What it is: The larger environment that affects directly or indirectly technology, innovation, clinical care and clinical trial decisions.
Why it was Chosen: Each is a control that moves the technology needle, which without some kind of compliance involved, the market may not have adopted it. For example, regulators around the world are now demanding compliance with data transparency in clinical trials.
Impact: Current, influencer
#3: Cognitive Computing/AI
What it is: Artificial intelligence, natural language processing, learning healthcare, machine learning, intelligent devices-anything that pulls the data in the right way and applies it where it’s needed automatically, i.e., real-time data access and analysis.
Why it was Chosen: Cognitive Computing/AI was chosen for its downstream influence. While currently being used, it will not significantly impact clinical trials this year. But its applications deserve attention for the clinical trials of 2018 and beyond.
#4: Connected Computing
What it is: All the Internet of things, wearables, connections, smartphones, sensors, etc.,-those in use now and will be for the next few years-that offer real-time data everywhere.
Why it was Chosen: Connected computing creates a more convenient and interactive patient engagement experience. There are tangible opportunities in this category for clinical trials, but are limited by current challenges, mostly around regulatory-grade devices, validation and data quality.
#5: Master Data Management
What it is: Master Data Management refers to controlling the definition and use of data to improve quality, access, consistency or sharing for improved operations and efficiencies.
Why it was Chosen: Without a bigger picture toward and the incremental steps taken already to make data better or increase its usability, getting to the transformational clinical trial is impossible.
#6: Intelligent Enterprise/Big Data
What it is: Large volumes of easily accessible, high quality Big Data from multiple, disparate sources.
Why it was Chosen: The downstream effects of Big Data cannot be underestimated and will be evolutionary, as the foundations for Cognitive Computing/AI and the Healthcare Environment controls are built by high quality, high volume data.