A changing world brings data to the forefront, but how do we manage it all to make the biggest impact?
The life sciences industry has been fundamentally altered in recent years. Diseases that were once considered life threatening and terminal are now being managed as chronic conditions. Previous chronic illnesses are treatable and curable, while other diseases have been reduced to irritations or consigned to the history books.
Clearly, there is much to celebrate, but there is still much to do. Advanced solutions are needed to treat such conditions as multiple cancers, heart disease, obesity, Alzheimer’s, and Parkinson’s, to name a few. The speed of innovation and the acceleration of new solutions to market will be increasingly important.
Within pharmaceutical companies, the quest for understanding has accelerated, leading to the establishment of a knowledge-based economy with data as the currency. The more we know, the more we can develop these advanced solutions to not only meet, but get ahead of expectations.
Scientific breakthroughs will continue the more we understand the human body—not just the biochemical pathways, systems, and organs, but also the inter- and intrapersonal behaviors that form our very makeup. This process will accumulate vast quantities of data, especially in clinical trials.
As the volume of clinical data rises, the ability to turn those data into quick decisions is limited by today’s technology approaches, including electronic data capture (EDC) systems. Consequently, sponsors and sites are not equipped to support new and innovative trial designs, such as adaptive clinical trials.
Making complete and accurate data available will enable life sciences researchers to finally run the trials they want, not the trials today’s EDC systems allow. If clinical researchers can have their data in real time, the life sciences industry can better address the problems that are leading to distress, illness, and even death. It is the accumulation and conversion of these data into actionable insights that will drive the era of personalized or precision medicine.
Major Shifts Impacting the Industry
The Rise of Personalized Medicine
The industry has long discussed the end of the blockbuster era, which raises the impending need for life sciences companies to find avenues for bringing products to market other than through a narrow focus on potential mega-selling therapies. In January 2017, ClinicalTrials.gov showed that since 2014, the number of registered clinical studies has increased by almost 50% (see Figure 1*). Likewise, new records for U.S. Food and Drug Administration product approvals were consecutively set in 2014 and 2015.
It is easy to conclude that more trials, reaching more patients, and generating more data are resulting in more products to market, but in 2016 that trend was reversed (see Figure 2*). This speaks to a rapidly changing environment, and highlights the need for yet more innovation.
Further exacerbating the challenge for manufacturers are looming patent cliffs for many of their top products; in 2016, several high-profile, brand name products were slated to lose patent protection. Patent expirations for highly prescribed medicines will continue to influence healthcare spending as lower cost generics are allowed to compete in the larger marketplace and drive down costs. Although it depends on the type of treatment, the average price of a generic can be as much as 85% lower than its patented brand name counterpart.1 In fact, between 2009 and 2014, more than $120 billion in pharmaceutical sales was lost to patent expirations.2
An Increased Focus on Patient Outcomes
Another major change is linked to consumption models and patients. With every new scientific development, there is renewed expectation of long-term benefit. Armed with heightened anticipation, patients don’t buy drugs anymore, they buy outcomes. This means manufacturers will see their reimbursement strategies set on a value-based principle, which depends not only on direct therapeutic effect, but also on patient compliance and adherence.
Insights gained from a better understanding of patient behavior will be vitally important—serving not only as validations for, but also playing a key role in, a treatment regimen itself. Exercise, mobility, social interactions, and behavioral patterns will play a greater role in determining whether patients perceive a sense of well-being, as opposed to just being told they are getting better. Understanding the mode of action at a chemical level is crucial, but understanding human nature and human behavior is often the key to determining in what situations a new treatment will actually work.
Patient outcomes combine collective and individual experiences, enabling clinicians to fast-track conclusions in the lab into everyday clinical life. Companies will take this even further through accelerations in personalized medicine, recognizing that all human beings are different and that their characteristics, behaviors, and experiences shape well-being.
Data Currency in Clinical Trials
While clinical research continues to advance, the demand for better, faster, more effective treatments shows no sign of slowing. The kinds of scientific advancements that once took 10 years to reach the mainstream could soon take less than two years. To sustain this quest for better knowledge and more effective treatments, however, researchers need to better understand myriad in vivo and in vitro biochemical processes. That means connecting individuals across the globe—from patient to caregiver, from life sciences to healthcare organizations, and beyond—into the regulatory landscape. Data represent the unit of intelligence, currency, and commodity all wrapped into one neat package.
To establish a free-flowing data stream is difficult enough, but data alone do not deliver the end result. Data are ubiquitous and come in a wide range of volumes, varieties, and velocities. To successfully operate in a knowledge-based economy, data must be consumable and available in real time to derive actionable insights.
Today, the medical development industry is investigating the use of wearable devices, such as FitBit, Garmin, and many others—each capable of generating tens of millions of data points daily. Imagine combining those data with real-time observations, clinical assessments, long-term medical histories, financial data, behavioral data, and even social data. Exploring and characterizing patients from so many dimensions would enable clinicians to create a picture of each individual on a macro and micro level, from cradle to grave, from their very genetic beginnings to their current day experiences.
However, the data are not enough, nor is reviewing the data sources in isolation or combining the data into a periodic data set. Companies will need to create a complete picture of the individual, refreshed every time a new data point is generated or recorded, in order to turn raw data into actionable insights and decisions. A direct line must be drawn between decision making and continuous improvement in patient well-being.
Pulling together this vision of an individual has another acute benefit. By sharing the outcomes and the characteristics of that individual, the industry can connect caregivers and patients across the globe, adding to the knowledge pool and advancing research in unimaginable ways. By mining data confidently, companies can find patterns and draw conclusions that have always evaded researchers until the very end of a clinical study, enabling real-time course corrections that reduce exposure to unnecessary treatments and redirect efforts to the best options available.
In parallel with clinical results, companies will also be able to seek operational patterns and identify problems, challenges, and obstacles faster. Many clinical trials still rely on manual, paper-based, or obsolete systems to collect, manage, and report clinical trial data. Time from event to analysis is still measured in weeks and months, when the need is for minutes and seconds. The application of first-generation eClinical platforms has been heralded as a big achievement, but these efforts have yet to accelerate clinical research or reduce the costs of research in any significant way.
In order to achieve a state of complete and concurrent data—with data equaling knowledge and knowledge leading to better decisions—data should be managed with a single software platform that empowers participants to optimize their contributions in the data value chain. The platform needs to create a coherent and contiguous environment for management of patient data, enabling research in all of its formats, through all of the contributors and consumers of those data.
A Better Way is Needed
EDC systems were first introduced 40 years ago for clinical data management, but really took off at the turn of the century. However, today’s EDC is arguable still not a central, critical part of the clinical trial process. More often than not, clinical investigators still turn to paper and pen before EDC; while clinical trials are getting increasingly more complicated, technology is not being leveraged to simplify this complexity. If anything, it is common to find investigators bypass technology completely in favor of manual data capture and then input the data into EDC systems as an afterthought. Does this actually render today’s EDC as unfit for purpose? Let’s explore that last question for a few moments, and consider the following stumbling blocks to widespread EDC adoption and making it core to clinical trials:
E for Electronic: Many EDC solutions are still reliant on the traditional paper-based processes, and most patient visits are recorded using paper and pen. These manual steps expose the entire clinical trial process to unnecessary risk and inefficiency.
D for Data: EDC Solutions are really electronic case report form (eCRF) tools that fail to address total data needs. In fact, eCRF data can easily represent less than 20% of study data, according to various estimates.
C for Capture: If all your EDC solution does is enable data capture, what about data management, monitoring, and reporting?
For clinical trial solutions to be classed as “fit-for-purpose,” all of the incoming data must first be accessible in real-time and in one place. This provides a complete and concurrent view of data that is very specific to every patient, effectively creating a patient passport. A real-time window into patients’ own worlds can deliver a better understanding of their symptoms, behaviors, and actions. Consolidating data not only advances the patient cause, but also improves the likelihood of success. Trials become faster, better informed, more knowledgeable, and better placed to react to whatever events arise.
To be fit for purpose, a data management tool (perhaps EDC) needs to address each and every data type plus the “four Vs” of data (see Figure 3*):
- Volume: Managing vast quantities of data (structured and unstructured) without system performance degradation or financial loss. Today’s EDC and eCRF solutions are designed to just manage data entered at the site, which is typically just a fraction of the total data in a study, according to various estimates.
- Variety: Managing data from a variety of sources, in differing formats and data types. Many EDC or eCRF solutions are designed to manage structured data, in limited format types.
- Velocity: Managing data in real time and consuming and supplying data with simplicity and elegance. EDC or eCRF solutions often are not designed to handle large volumes of data, so adding significant volumes causes severe performance delays.
- Veracity: Recognizing that not all data are born equal and that different strategies may be required for each data point (in essence, a risk-based data strategy). EDC and eCRF solutions are designed to manage data by type, and therefore need external assistance to drive more varied strategies.
Advanced, fit-for-purpose EDC solutions will address the needs for volume, variety, velocity, and veracity, and will lead to a full value assessment that aids study design, execution, and conclusion.
Realizing the Clinical Trials of the Future, Today
The life sciences industry will, very soon, be able to eliminate the need for paper in a clinical trial setting. However, companies need to not just eliminate paper, but completely redefine user experiences to be paperless—electronic systems will no longer be designed to look and behave as pieces of paper. This will result in user interfaces that are far more intuitive and that have advanced functionality, such as search and automatic grouping, designed into the system.
Real progress will also come from tackling data at the source. More often than not, source data are still recorded on paper manually with a pen or a paper-like format (using Microsoft Excel or Word). The resultant need for source data verification has a significant negative impact on the ability to reduce trial time or cost, and has been subject to many recent reviews that highlight only minimal quality advances.
As patients record more of their own data, paper is still the preferred solution. This only exacerbates and extends traditional challenges. For example, the “car park syndrome” is well documented, with patients who forget to fill out their diaries or questionnaires trying to recreate their experiences and symptoms as they sit in their cars just before they walk in to see their doctors. If company leaders tackle the source data challenge correctly, they not only advance clinical research, they also create a path to better, faster, long-term medical records that facilitate data sharing across multiple solutions (i.e., EDC and electronic health records).
While cloud-based technology and Big Data management have delivered proven results across the board in all industries, the life sciences industry has been slow to adopt a true cloud-based solution capable of delivering on global usage, minimizing costs, and handling data. Mainly, this is due to the lack of a true cloud solution that addresses these issues to date; when software doesn’t work, it makes routine tasks and processes more difficult.
The Next Wave of Innovation in Clinical Data Management
Clinical trials are a very patient-centric, patient-driven process. Someday soon, patients will have complete control of their data. Personalized medicine is designed to ensure that our research delivers medical solutions that are better defined and that increase an individual’s likelihood of responding. To understand individuals, each patient must be closely examined, including through the use of data that haven’t yet been considered for clinical trials. The true fit-for-purpose EDC solution will handle all of the data a patient can generate, and use those data to derive real-time decisions for patients and caregivers.
Clinical research can become truly global, connecting patients and caregivers across the globe. This opens up new vistas for clinical data capture and management to bring the trial to the patient. The Internet-of-things (the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data), for example, has increased the ability for data to be shared in real time and opened up the possibility of integrated data from varied sources flowing into clinical trial management. With it, the industry can take clinical research to previously improbable, if not impossible, places. Consider, for instance, rather than sites finding only patients who live or can temporarily stay nearby, how it may soon be a routine situation in which patients can remotely find trials based anywhere through smart devices and the experimental drugs can be administered and monitored from thousands of miles away.
Further, while succeeding quickly is a critical goal, failing early in trials is also vitally important for both financial and patient well-being reasons. Not every new clinical solution will drive benefit, therefore, there is opportunity to redirect money, resources, and patients.
Cloud computing, mobile health technology, Big Data, and the Internet of things hold immense potential when it comes to transforming clinical trials, especially ones that span geographic boundaries. A global, cloud-based solution for clinical data management makes installation, ongoing maintenance, and performance inherently easy, while managing cost, time, and resources. The true, fit-for-purpose EDC solution will work anywhere, anytime, and enable life science companies to design and execute the trial that they want, not the trial that is limited by technology today.
As important, this type of Internet-enabled cloud solution will increasingly support a global economy, including emerging and developing countries where 54% of adults identified themselves as Internet users in 2015.3 Of course, the digital divide remains a challenge, but as more tech giants like Google (with its “Project Loon” initiative) and Facebook (with its “Internet.org” initiative) drive innovations forward to bring the Internet to more people, this challenge will slowly, but certainly, diminish.4
The life sciences industry is at an inflection point where the drive for patients, treatments, and research is increasingly global, medicine is becoming personalized, and there is a growing demand for new drugs to reach the market faster. With these trends in mind, a true cloud-based clinical data management solution that delivers global clinical trials and incorporates a high variety, volume, and velocity of data into personalized clinical trials is needed. This system will go far beyond the EDC solutions of today, which have not delivered on innovation in well over a decade, as well as beyond the eCRF limitations that have historically governed clinical trial processes. Clinical trials are still largely paper-based undertakings, and EDC systems serve largely as data entry systems.
The next generation of EDC solutions will combine data from every source in real time, present those data to all consumers, and facilitate clinical trials. This will mean embedding technology across the clinical trials process—from patient to regulator—ensuring that every observation, result, and event is captured as it occurs. Currently it is typical for data to be recorded on paper first and entered many days later. Ideally, however, data will be digitized at the source precisely when a patient event is happening, anywhere in the world, at any time, and will become a part of the global dataset immediately, not days or weeks later. Learning, patient management, and ability to address challenges will all happen in real time.
Technology will not only support the clinical trial, but the wider healthcare systems, feeding data into the patient’s long-term medical records. The benefits of harmonizing across life sciences and healthcare will reap huge rewards, and will ultimately save the need for some research altogether. Still, the life sciences industry has a long way to go when it comes to leveraging technology to transform clinical data management. Industry is moving fast toward digitalizing clinical trials on a global scale, and the life sciences companies that are not quick to ride this change will soon be left behind with insurmountable costs, unable to keep up with the changing economy.
To change clinical research is to change patients’ lives, and the power to do this comes from encouraging fresh innovations and identifying the barriers that stop our advancement. Data and knowledge help us to learn, and it is through learning that we can make real change.
- U.S. Food and Drug Administration. Understanding Generic Drugs—Facts About Generic Drugs. www.fda.gov/Drugs/ResourcesForYou/Consumers/BuyingUsingMedicineSafely/UnderstandingGenericDrugs/ucm167991.htm
- Drugs.com. Looking ahead: pharma projections for 2016 – & beyond. https://www.drugs.com/slideshow/looking-aheadpharma-projections-for-2016-and-beyond-1230
- Poushter J. 2016. Pew Research Center. Smartphone ownership and internet usage continues to climb in emerging economies. www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
- Glick H. 2015. Global Citizen. Google wants to deliver internet to the developing world—via balloon. https://www.globalcitizen.org/en/content/google-wants-to-deliver-internet-to-the-developing/
Richard Young (firstname.lastname@example.org) is vice president for Vault EDC with Veeva Systems and a former vice president of global consulting partners for Medidata Solutions.
To see all figures and/or tables published originally in this article, please visit the full-issue PDF of the April 2017 Clinical Researcher.