“Patients are invaluable to the clinical trial process,” writes Julia Lakeland for Clinical Informatics News. And, of course, she’s right.
Lakeland goes on to point out that one of the major challenges of clinical research is to solve patient recruitment and retention. As a result, many managing researchers are looking for an innovative approach that allows a direct-to-patient solution.
This is the direction technology is heading. Jennifer Peters, Bracket’s senior VP for scientific services, puts it unequivocally: “Digitization of clinical trial processes is the driving force behind real ROI and its demonstrated results are increasing the adoption rate among sites and sponsors.”
In other words, technology is now a critical aspect of clinical research success. In a recent Wall Street Journal article, Deloitte vice chairman Greg Reh agreed. Reh points out that technology can solve the challenge of productivity during the clinical trial stage of drug development. At the same time, he notes that few clinical trials are incorporating the digital tools they have at their disposal.
So, what does the digitization of engagement and adherence look like?
Engagement goes beyond response rates and adherence. It means zeroing in on patient experience. As PwC’s Matt Rich, TimPantello and Brian Slizgi write, “In order to successfully execute clinical trials, pharma companies must amplify their focus on the patient experience and aim to incorporate the patient voice.”
Focusing in on these elements of engagement and adherence requires a level of personalization not usually found in clinical research. As Dr. Jorg Schickert notes for Hogan Lovells, most clinical trials still rely heavily on paper documents. Thankfully, there are a host of tech tools that can help clinical trial managers simultaneously increase patient engagement and improve patient experience. The two go hand in hand.
These tech tools position pharmaceutical companies and CROs alike to not just stay on top of clinical trials, but transform their approach.
“Patient engagement and site adoption are two obstacles already addressed with new technologies,” writes Melissa Fassbender, US editor at Outsourcing-Pharma.com. The examples she cites include clinical grade wearables, smartphone applications and cloud computing. Still more advanced applications include data analytics, machine learning and automation. These are some of the tech tools and advancements we touch on here.
More specifically, there are two areas that will be particularly helpful in helping clinical trial managers increase engagement:
Beyond these two areas, we also dive into how the rise of technology-enabled clinical research has taken shape, and the specific technology approaches that are making a difference in engagement and adherence.
Increasing engagement can have two big impacts: improving connections with patients and decreasing the burdens of trial participation requirements on those patients. Social and mobile tools can help in both of these areas.
Let’s start with how using tech tools for engagement can decrease the burden on patients. The lower the burden, the more likely they are to adhere to the trial.
Validic CEO Drew Schiller summarizes this implementation: “Simple online assessments, data capture by wearable devices, remote monitoring, and virtual clinical visits offer additional advantages in terms of reducing patient burden.” For example, digital tools make the possibility of eConsent and eMonitoring relatively straightforward, increasing adherence.
But the role of these tech tools is not simply to reduce the negative. The other motivation of this integration of social and mobile tools into clinical trials is the desire to put patients front and center during the process. It’s about engagement.
As clinical strategist Moe Alsumidaie writes for Applied Clinical Trials, “According to some patients, patient centricity is about empowering patients by treating them as a partner.” This translates into incorporating patient perspectives into the design and implementation stages of a clinical trial. Alsumidaie notes that this should attract, engage and retain patients for the study duration.
This is also where social and mobile tools can come into play.
“As new tools for interacting with patients become available, patient engagement in both health care and clinical trials has shifted from sending messages to having conversations,” writes Chris Dailey, global head of technology at Cenduit. Clinical research managers can choose to utilize social platforms to have these conversations with patients.
This can take a few different forms, from dedicated platforms to existing social and mobile tools. We’ll also jump into a few specific tools below.
Of course, all of this is a process — we can’t expect overnight change.
For example, Bray Patrick-Lake of the Duke Clinical Research Institute breaks using digital tools for patient engagement down into six stages, from pre-discovery to outcome stages. The goals range from gauging the interest of the trial from the patient community to providing feedback on how the patient community views results.
Another example comes from mProve CEO Jeff Lee, who points out that one crucial step is to achieve buy-in from both study teams and executives before using new tools.
Aligning a study with the patient’s voice means hearing from the patients, which in turn means opening direct communication with patients. eConsent and eMonitoring can certainly increase efficiency, but using these tools for engagement goes beyond.
Finally, these dynamic tools can help research managers and entire organizations engage with patient advocacy groups. From Facebook groups to qualitative surveys or remote focus groups, digital tools translate into connection. As Barbara Zupancic at Worldwide Clinical Trials makes clear, taking the time to hear from these groups can dramatically improve one’s patient recruitment and engagement strategy. It can also help clinical trial managers understand what leads to lower rates of adherence to specific trials.
Here’s the thing: Implementing a few simple social and mobile tools means more than simply improving engagement today. It can also prepare clinical trial managers and entire research organizations to move toward remote patient research. This is simply the first step along the path that THREAD Chief Product Officer John Reites calls the crawl, walk and run of remote patient research.
For Lee at mProve, that path opens up all kinds of possibilities to bring whole new populations of patients into trials. “The vast majority of patients who benefit from clinical research studies don’t live near enough to an investigator site, and therefore can’t participate in the study,” Lee tells us.
“Telehealth, as well as decentralized access points (like pharmacies, or lab centers from providers like LabCorp or Quest) can bring the study to the patient. We don’t even need to do entirely site-less studies in order to support this model. Pharma companies routinely fly patients to research visits. If the study sponsor uses telehealth/remote labs to find a patient who appears to be qualified for the study, they can fly them to the site for the initial randomization visit.
“From there, most studies could offload the remainder of activity to home-based procedures/assessments and/or clinician interactions at places like LabCorp phlebotomy centers. So, by ‘placeshifting’ the clinical research study, we can open up clinical research to many more individuals.”
Further, Lee notes, the same tools that “placeshift” trial can also “timeshift” trials, or make the data-collection aspects of the research fit around participants’ own busy schedules, not vice versa.
The relationship between big data and patient engagement or adherence may not be immediately apparent. After all, we just talked about personal connection with patients.
In contrast, big data utilizes large data sets to find trends. But research leaders can’t miss the forest for the trees. Big data can increase adherence rates by taking a broad view of the trends that influence adherence the most. Dr. Ram Kumar Mishra, analytics manager for Tata Consultancy Services, points out that this level of data can even serve as the basis of AI-based apps designed to increase both engagement and adherence.
Writing for BioMedCentral, Lars G. Hemkens and Kimberly Alba McCord drive home just how useful the use of big data can be for large-scale clinical trials. “We can use the routine data collected in daily care to measure the outcomes, avoiding cumbersome and costly follow-up visits and avoiding an artificial situation,” they write.
Instead of becoming more costly, the use a single database with all the routinely collected data makes larger clinical trials more effective and more insightful. By way of evidence, the researchers cite a study they recently completed with more than 10 million patient contacts in Switzerland.
At the same time, implementing big data as a norm can increase the standardization of clinical trials, benefiting their usefulness over time. One study of the benefits and challenges of big data initiatives, headed by Dr. Charles Mayo, concluded: “Increased standardization of common data elements and nomenclature should assist in streamlined trial design and exchange of data… [and] will allow easier multi-study analysis.”
In other words, the promise of big data goes beyond any single clinical trial or manager.
Comprehend Systems echoes this sentiment, showing that big data can solve the issues related to the expanding size, complexity and cost of clinical trials. Another piece on Pharmaceutical Executive from Andrew Griffiths, James P. Angus and Andy N. Brown reaches the same conclusion. They make it clear that real-time data capture (and subsequent analysis) can improve the timing, cost and quality of data collection.
A major boon to the use of big data is the proliferation of cloud computing. As Rick Morrison writes at Applied Clinical Trials, cloud data has improved data collection, analysis tools, data standards and even the communication of tools. “Today’s cloud-based technologies are designed to enable these sought-after improvements,” Morrison concludes.
Craig Morgan at goBalto gives a fantastic example of big data at work. He cites the use of a standardized, industry-wide dashboard for site studies. The aim was to increase data transparency across sites, highlight study-specific trends, benchmark sites against others and enable sites to access performance data. While this may not be directly related to patient engagement, the end result was better tracking of retention rate, randomized subjects, response time, query rate and more.
In a phrase, the dashboard put big data front and center to simultaneously improve patient experience and efficiency. It’s just one of many examples of how technology can help clinical trial managers increase engagement and improve adherence.
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