I thought I knew a lot about clinical trials. I understood the structure, much of the science, and the business models. But when I became a patient, I learned how much I didn’t understand about being on “the other side” of a trial.

I sincerely wish to applaud clinical trial practitioners and this amazingly innovative industry. Each day, you do vital work designed to mitigate human suffering and prolong high quality of life. Truly, yours is a higher calling I’ve experienced firsthand.

I’d like to share some of my experiences as a patient in the hope practitioners, sponsors, and others can learn more about the patient perspective and make some critical adjustments I believe would increase clinical trial recruitment and retention.

I never expected to experience the clinical trial industry as a patient. But in late 2020 I was diagnosed with cancer. As you might imagine, the diagnosis was chilling for me and my family.

I’d spent my professional career in the healthcare and clinical development industry. First, as an analytical chemist with the Upjohn Company, then with Eli Lilly. Next, I worked with a few CROs leading their patient-centric initiatives.

I capped my full-time professional career as the executive director of the Association of Clinical Trial Professionals, the trade group representing people who execute protocols at the investigator site level.

After my cancer was discovered, my team of medical professionals put me through the standard regime of care for over a year. Unfortunately, it didn’t remove or stop the cancer.

I realized I needed to think about my treatment in new ways. And the most important and effective approach turned out to be getting involved in a clinical trial. However, even with my own knowledge base and network of friends and colleagues, it was very challenging to find a clinical trial. I can only imagine the difficulties faced by someone outside our industry.

I was fortunate to speak with people I knew in the industry, including my old friends Randy Hall and Craig Lipset, who helped connect me to a trial. However, when I look back at how lucky I was, and the haphazardness of my search, it frankly scares me to imagine how different my life might have been if I’d not found this trial thanks to the support of others.

We must do a better job explaining and promoting clinical trials to the general public. It’s a good start, but we cannot rely on ClinicalTrials.gov to do the job for us. It’s cumbersome, with too much jargon for the layperson, and not always easy to navigate online.

We need to ensure that the overall healthcare community considers clinical trials as a healthcare option and assists patients in finding them.

Once we identified the right clinical trial for me, a whole new journey began. And it was also sometimes challenging. For example, I benefited greatly from the services of a patient concierge who helped me understand some basic logistics, such as which building to go to when and at what time. It sounds simple, but for someone already worried about their health, the last thing they need when participating in a clinical trial is to drown in detail without help from someone who understands the most granular aspects of the process.

I also call on physicians and other practitioners to take the time to more fully explain how the trial will work. Spell out, in basic terms, the trial goals and machinations. I know people are busy, but helping patients better grasp their clinical trial will go a long way toward assuaging worry and improving retention.

We also need to think about patient compensation.I was fortunate in that the costs of travel, parking, and other issues weren’t too much of a burden. Plus, I was semi-retired at the time, so my schedule was relatively flexible. Unfortunately for many, such is not always the case. As an industry we need to find more effective ways to offset costs that are prohibitive for many. I’d even suggest we find ways to reimburse patients who miss work because of trial participation. 

My clinical trial has been integral to fighting my cancer. Today, I’m cancer free. Of course, follow up will be critical in the long run to make certain it stays that way but I’m far ahead of where I’d be without the benefits of the trial.

You have my deepest thanks and appreciation. I hope sharing my positive experience will further inspire you to even greater heights.

Articles

A Closing Thought

Jim Kremidas, former executive director, Association of Clinical Research Professionals, shares his experiences as a patient in clinical trials. 

Perspective and Hope From the ‘Other Side’

 in this series, published earlier this year, I introduced the idea that much of what challenges the clinical trials community can be overcome by applying best practices from cognitive science (how we think/decide), learning science (how we train/learn), and behavioral science (how we change/perform). These are lessons that are used in a wide variety of fields and an even wider variety of settings, but have seemingly gained little-to-no traction in clinical research. In the second 

 in this series, published in May, I introduced the Stages of Change model as one such, well-established best practice. Namely, change is not a simple yes/no binary event, instead each individual progresses on their own journey of change. As a result, enabling a specific behavior change often requires different interventions for different individuals at each different stage of change.

In this article I’d like to introduce a second, complementary model (or best practice) from behavioral science–one that I believe is the simplest and most actionable model for supporting specific behaviors to change–and one that should guide every step of what we do in clinical research, from protocol design, to site selection, to study site start-up, to patient recruitment. Remember: each step or requirement in a clinical trial should be seen as a classic example of behavior change.

First published in 2009, the Fogg Behavior Model (FBM) simplifies behavior change to three elements: motivation, ability, and prompts.

 All three elements must be sufficiently balanced for a change to occur (see Figure 1 below).

 is rather straight-forward, it is the general desire or drive that one has to make a change.

What’s important here is that the clinical research community has historically made some maverick miscalculations, overestimating the motivations of investigators, site staff, or patients. Instead, best practices from behavioral science suggest that stakeholder motivations should be carefully considered and monitored and that specific efforts should be implemented to ensure motivations are aligned and sufficient to empower sites, staff, and patients to execute trials and maintain compliance. This is one of the most important outcomes of effective trial-related training.

 is a bit more complex; but is actually quite intuitive. In the context of a clinical trial, the FBM suggests that there are four things that either enhance one’s ability to change (simplifying the target behavior) or undermine one’s ability to change (complicating the target behavior).

 If a target behavior requires time and you don’t have time, then the behavior is not simple. For example, if an informed consent workflow takes 45 minutes to complete, but could have been designed to be completed in 15 minutes, then the design of the trial lowered the ability for site staff to change.

 The more physical effort a target behavior requires, the more complicated it becomes. For example, if trial participation requires a patient to passively consume a static 30-page informed consent form, that could have been more effectively presented, then the design of the trial lowered the ability for a patient to engage.

If performing a target behavior requires us to think hard and apply knowledge that is not well developed, then the behavior is not simple. In essence, thinking deeply or thinking in new ways can be difficult. For example, if site training was implemented as a “check-the-box” experience or not informed by sound instructional design, then staff will lack the knowledge or competence (and motivation) required to perform their role on the study and the ability of site staff will be undermined.

 Finally, if a target behavior is unlike prior experience or behaviors, this novelty complicates the behavior and undermines one’s ability to change. In general, people tend to find behaviors simple if they are routine actions that they perform over and over again and/or are easily related to routine actions. While it’s to be expected that a trial of a new agent, new indication, or new device will require some new behaviors, the ability for site staff to effectively execute a trial protocol will be undermined if these new behaviors are not largely anchored to routine actions or staff are not provided robust support or training to practice and gain mastery.

In general, to increase an individual’s ability to change or perform: a) the individual must develop the knowledge and skill through effective training, and b) the target behavior itself should be ruthlessly simplified. Effective training and simplicity are the universal best practices to overcome the challenges of time, physical effort, cognitive load, and novelty.

are environmental influences that nudge one to make a change. The shape or form of a prompt doesn’t matter as long as it is available, effectively associated with a target behavior, and presented to users at a moment when the action should be taken. The FBM describes three types of prompts:

 is a prompt that enhances ability. When a behavior is challenging, a facilitator can make it easier. The most widely implemented example of a facilitator is pre-setting default options on a form.

is a prompt that enhances motivation. When a user lacks motivation, a spark can provide purpose. Examples of sparks range from warning labels that highlight risks, to brief patient/survivor videos that inspire hope.

is a prompt that simply serves as a reminder. When a behavior is easy and a user has motivation, a signal creates a mindful moment of action. Examples of a signal include designed learning moments in training experiences that (re)focus a learner’s attention and remind learners to reflect on critical elements of the training content.

The idea of prompts extends well beyond the FBM. In 2017 the Nobel Prize in Economics was awarded for the work of Richard Thaler on nudge theory and Thaler’s 2009 book, 

has sold over 2 million copies and given rise to more than 400 “nudge units” in governments and healthcare systems around the world.Regardless of what you call them, nearly two decades of research in behavior science have demonstrated that prompts (or nudges) are often the missing link allowing behavior change efforts to succeed.

The most important lesson from the FBM is that all three elements are necessary and critical, and it is the interplay of the elements that dictates whether change occurs. If principal investigator motivation is excessively low, no level of simplicity or number of prompts will allow change to happen. The same thing applies to other scenarios such as if trial protocols are excessively complex and training is inefficient or ineffective, or if prompts are missing or miss-designed.

Once you embrace the elements of the FBM, it should become increasingly clear that so much of what challenges complex contemporary clinical trials can be overcome by leveraging these best practices. And while they may be new to many of us, they have been successfully applied in other fields for decades. In the end, you might even boil all of this down to one singular root cause: behavior change is hard because we all want to believe that knowing equals doing–and simply put, it’s not that simple. 

Fogg, B.J. A Behavior Model for Persuasive Design. 

https://dl.acm.org/doi/10.1145/1541948.1541999

Feature Articles

Applying elements from the Fogg Behavioral Model to ease trial complexity.

Changing Behavior: Knowing Doesn’t Equal Doing

When it comes to human research, there is an inherent tension between enabling public access to data and protecting those who volunteer for research. Public discussions about how to balance this tension have evolved over the last decade and have informed both the public rulemaking process in the US and data stewardship practices within the medical research industry.

One of the first national policies regarding this was the National Institutes of Health (NIH) Genomic Data Sharing Policy enacted in 2014, which established the expectation of broad and responsible sharing of genomic research data. During the public comment period and subsequent publication of the final policy, the issue of adequate informed consent for this kind of far-reaching research was raised. This resulted in NIH recommending that investigators seek the broadest consent possible when first obtaining consent from participants. However, NIH recognized that in some cases, limits will still be required, and it allows for use of controlled access databases as a way to mitigate concerns.

The policy requires that participants provide consent for sharing their data, even after it is de-identified, in order for the data to be deposited in an accessible database. The policy also requires that institutions, usually by way of their institutional review boards (IRBs), confirm the data sharing is consistent with the informed consent of study participants, and that consideration was given to the risks to individual participants and their families as well as groups or populations associated with the data.

The most recent policy enacted in the US is the NIH publication of the Data Management and Sharing Policy, effective Jan. 25 of this year. NIH was given program-level funding for fiscal year 2023 of $49.123 billion.

 To maximize the value of these taxpayer dollars, NIH seeks to ensure that scientific data is broadly shared. Sharing data in an accessible way should accelerate medical discovery, enable validation of research results, and allow data to be reused in future studies.

 The policy requires a plan for data management and sharing that includes budgeting for the storage, maintenance, and sharing of data (including plans for protecting human participants’ data) at the time a research proposal is submitted. The policy does not include any specific new requirements regarding human participant data, but it notes that data management plans must both enable stewardship of scientific data and protect individual participants within existing laws and regulations.

In addition to concerns about adequate informed consent, issues were raised regarding the usability of data. The scale of current scientific data accumulation is truly staggering. According to the National Human Genome Research Institute, genomic research is predicted to generate two to 40 exabytes of data within the next 10 years.

 An exabyte is one billion gigabytes, enough to fill two million average desktop computers. How should this ever-increasing and complex data be stored, curated, and annotated to facilitate responsible sharing?

In 2016, the FAIR Guiding Principles for scientific data management and stewardship were published.

These principles direct that data
should be:

Findable (metadata and data should be easy for both humans and machines to find).

Accessible (users need to be able to access the data with authorization).

Interoperable (data needs to operate with various applications for storage and analysis).

Reusable (metadata and data are annotated to allow for replication).

Since the publication of the FAIR Guiding Principles, various stakeholders, including NIH and companies in the medical research industry, have considered FAIR to be an enabler of digital transformation. They have implemented the principles in crafting their data management strategies in order to realize the promise of real-world data (RWD) to generate real-world evidence (RWE) in healthcare, which requires linking of disparate data sets.

Outside of NIH, the 2018 revised Common Rule public comment period similarly included extensive discussion around consent and the secondary use of data. The preamble to the final rule lays out the various ways in which advances in technology and the research enterprise led to the need for updated rules around how to protect human participants’ research data. While the original Common Rule focused on physical risks and harms to participants, research stakeholders recognize that due to advances in generating and sharing data, informational harms to participants must also be considered.

One important question addressed is whether, given current data-mining technology, data can truly be de-identified. The final rule did not go so far as to require consent for future research with de-identified data. It did, however, include a change to the definition of human subject by including “using, studying, or analyzing individuals’ information or biospecimens or generating identifiable private information or identifiable biospecimens”in the definition of human subject research.

It also expanded the definition of “identifiable” to include “identifiable private information is private information for which the identity of the subject is or may readily be ascertained by the investigator or associated with the information.” The revised Common Rule also requires federal departments to review the concept of “readily identifiable” periodically in recognition of the fact that technological and scientific advances may change the nature of what is readily identifiable.

Specifically, regarding consent, the revised Common Rule requires that consent forms contain one of two statements:

Identifiers might be removed from the identifiable private information or identifiable biospecimens and that, after such removal, the information or biospecimens could be used for future research studies or distributed to another investigator for future research studies without additional informed consent from the subject.

The subject’s information or biospecimens collected as part of the research, even if identifiers are removed, will not be used or distributed for future research studies.

Likewise, the 2022 notice of proposed rulemaking to harmonize FDA regulations with the revised Common Rule includes adding a basic element of informed consent that would require a description of how information or biospecimens may be used for future research or distributed for future research. However, it would not be limited to the two options available under the revised Common Rule.

Furthering scientific and medical knowledge

Technology for mining and linking patient data is advancing at a rapid pace. FAIR principles seek to enhance the ability of both machines and humans to use this data to further scientific and medical knowledge. These increasing opportunities to use new forms of data for research could reduce the need for some placebo-controlled studies that can be ethically challenging if scientifically ideal. At the same time, as data analysis techniques grow more sophisticated, identifying the risks and benefits to participants and explaining them in a comprehensible way becomes more challenging.

The policies and rules enacted since 2014 attempt to improve informed consent for use of data, but it continues to be a challenge to obtain consent for future research use from participants. It is often impossible to conceive of all future uses when consent is being sought. But participants are engaging in an altruistic act when they agree to enroll in a research study, and they deserve to know how their data will be used. Giving permission to link their research data to future non-research data sets enhances the value of their participation, but it may also increase the risk that their personal data is disclosed or that their data is used to drive conclusions they do not support.

When and how should meaningful consent for future use be sought? If consent is obtained upfront, information may be limited about what types of sharing and linking will be done in the future, and broad access will need to be obtained. Some potential participants could reject this without more specific information. If consent is not obtained upfront and decisions to use data in ways that require consent are made later, participants may be lost to follow-up, obtaining consent would be more difficult, and the value of the data may be lost.

To maximize the benefits of data from research without compromising the protection of those who volunteer for research, care must be taken to ensure appropriate consent language is used to explain how data can be shared and linked and how far-reaching that can be. Best practices should include a clear explanation of whether and how data can be re-identified and how future research using the data will be controlled. If there are limits to a participant’s ability to withdraw their data, this should also be clearly explained. Researchers should think expansively about future uses of data when designing studies to ensure appropriate consent is obtained and that their plans for data stewardship are robust. 

https://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-124.html

National Institutes of Health (NIH) Finding: FY1996-FY2023. Congressional Research Service. May 17, 2023. 

Data Management and Sharing Policy. NIH Scientific Data Sharing. 

https://sharing.nih.gov/data-management-and-sharing-policy

Genomic Data Science. National Human Genome Research Institute. 

https://www.genome.gov/about-genomics/fact-sheets/Genomic-Data-Science

Wise, J.; de Barron, A.G.; Splendiani, A.; et al. Implementation and Relevance of FAIR Data Principles in Biopharmaceutical R&D. 

https://www.sciencedirect.com/science/article/pii/S1359644618303039?via%3Dihub

Federal Policy for the Protection of Human Subjects 

https://www.govinfo.gov/content/pkg/FR-2017-01-19/pdf/2017-01058.pdf

Protection of Human Subjects and Institutional Review Boards 

https://www.federalregister.gov/documents/2022/09/28/2022-21088/protection-of-human-subjects-and-institutional-review-boards

Best practices in data re-identification and control when designing future trials.

Obtaining Informed Consent for Future Reuse of Patient Data

There have been a number of occurrences of late signaling that the adoption of decentralized clinical trial (DCT) technologies is far from a fait accompli. To name a few:

A February 2023 assessment of ClinicalTrials.gov listings conducted by IQVIA found that only 1% of industry-sponsored clinical trial starts are using DCT solutions.

This spring, DCT providers reported stalling revenue, cited market resistance, and announced layoffs and restructurings.

A recent Tufts Center for the Study of Drug Development (Tufts CSDD) study found that sponsor-reported use of virtual and remote solutions in active clinical trials had fallen well below the levels reported by sponsors during the first year of the pandemic.

Lengthy and bumpy timelines associated with the adoption of any innovation supporting clinical trial execution are not new. Tufts CSDD research has shown that the typical adoption cycle for a single company takes a full six years; 20+ years for the drug development industry to achieve wide-spread adoption, defined as 67% or more of companies routinely using an innovative solution.

This article reflects on Tufts CSDD’s research characterizing the innovation adoption process in drug development to shed light on and share insights into what we can more realistically expect for DCT solutions adoption moving forward.

A pair of conceptual frameworks—Everett Roger’s Innovation Diffusion Model and the Gartner Group’s Hype Cycle—set the stage for understanding the dynamic nature of innovation adoption. The former, introduced in 1962, suggested that within companies and across an industry, there are varying levels of opportunistic behavior and aversion to risk.

Individuals and organizations that are highly receptive to risk, keenly aware of and willing to act quickly on the need to innovate are called “pioneers” and “early adopters.” Those organizations that are highly risk averse, lacking awareness and slow to act are characterized as “late majority” and “laggards.”

The Gartner Hype Cycle, introduced in 1995, focused on the evolution of expectations associated with a given innovation. Individuals and companies first look at an innovation with unrealistic and exuberant expectations that contribute to initial adoption perceptions. As organizations gain real-world experience with an innovation, their expectations plateau at the “peak of inflated expectation” and fall precipitously into the “trough of disillusionment” where they must temper and revise their expectations. Over a protracted period of time, expectations align with reality when organizations begin to realize the true benefits and value of an innovation.

Drug development professionals have noted the unprecedented—and, for some,—irrational —level of excitement that followed early use of virtual and remote solutions at the beginning of the COVID-19 pandemic.

In 2022, Tufts CSDD undertook an empirical study to gather granular data on innovations supporting all aspects of clinical trial execution, including protocol planning and design; investigative site selection and management; study initiation, ongoing trial management and closeout; patient screening, enrollment and retention; administration of protocol procedures; and data collection, management, analysis, and reporting. Innovations in biomedical science and pharmacology fell outside the scope of this study as they occur largely within scientific functions and do not require cross-functional changes, integration, and coordination in operating practices, procedures, and processes.

A working group of 17 companies provided input into defining areas of focus; designing an interview guide and survey instrument; and discussing preliminary study results and their implications. Tufts CSDD conducted a global survey that yielded 631 total responses from approximately 225 distinct companies—90% biopharma; 10% contract research organizations(CROs). Among respondents, there was good representation by company size, geographic location of company headquarters, and therapeutic area(s) of focus (e.g., 62% focus on oncology; 25% on immunology and infectious diseases, 16% on cardiovascular diseases, 14% on neurological disorders, and 7% on rare diseases). Nearly half of respondents indicated having a role in a clinical operations function. The highest percentage (54%) operates within a large pharmaceutical company, 28% in mid-sized, and 18% in small companies. Half (49%) are based in Europe and 39% are based in North America.

The overall time to adopt an innovation supporting clinical trial execution is 70 months, on average, or 5.8 years. Approximately 20% of the total time—13.8 months—is spent in the initiation stage where organizations are identifying and characterizing an operating need, gauging organizational interest and initiating plans to assess an innovation. One-out-of-10 respondents rates this as the most difficult stage, citing major challenges associated with building cross-functional support and coordination and the absence of regulatory clarity. Another challenge cited is the misalignment of incentives that dissuade personnel and functions from embracing risk and novel solutions.

In the next stage—evaluation—organizations identify, qualify, and engage solution providers; and pilot and evaluate various innovative solutions. This stage takes, on average, 15.7 months, or 23% of the total adoption cycle. A similar one-out-of-10 rates this stage as the most difficult. Poorly designed and executed pilots, the failure to gather sufficient evidence to assess and compare innovative solutions, and the inability to generalize across the portfolio and demonstrate ROI are cited as the largest challenges in this stage.

Industry observers and insiders have noted that the rapid adoption of DCT tools necessitated by the pandemic substantially compressed the initiation stage and invariably skipped the evaluation stage. In the wake of the pandemic, organizations have regrouped and are looking to conduct considerably more thorough evaluation and ROI assessment.

The third stage involves making the decision to move forward and fully adopt an innovative solution. In the adoption decision stage, organizations review their pilot experience, build internal consensus to move forward with, and announce an enterprise-wide implementation. Arriving at an adoption decision takes nearly 17 months, or one-quarter of the total innovation adoption cycle time. One-third of respondents believe that this is the most difficult stage, citing uncertainty and internal dynamics and politics associated with having insufficient “evidence” to support management’s decision to implement.

The final stage—full implementation—takes, on average, two years and represents one-third of the total time to adopt an innovative solution. This stage entails implementation planning, roll-out, communication, training, monitoring, and continuous improvement. Nearly half (47%) of all respondents rate this stage as the most difficult, citing several major challenges, including the lack of senior management and cross-functional support and engagement; poor regulatory clarity and support resulting in substantial concern and resistance from regulatory and legal affairs functions; the absence or late preparation of a comprehensive change management plan to guide the organization through full implementation; and failure to provide the necessary investment of time and resources to fully support the transition from adoption decision to full implementation.

Mid-size companies take the longest time—on average, 6.6 years—to complete the innovation adoption process. Small companies are nine months faster than larger companies and nearly two years faster (21 months) than mid-size companies. CROs are able to complete the innovation adoption process in nearly half the time—3.1 years compared to 5.8 years for pharma companies (see Table 1 below). Indeed, a significant speed advantage and much lower variation around mean durations are observed among CROs at each innovation adoption stage compared to that of pharma companies.

Small companies—and most notably CROs —bring speed advantages to the innovation adoption process but for different reasons. They are generally more nimble than their larger counterparts with less siloed functional relationships and with personnel often responsible for cross-functional activities. With more constrained capital and resources, and pressure from outside investors, smaller companies must often take more risks and arrive at decisions faster. For CROs, executional innovation is central to their ability to differentiate their services and capabilities and retain their clients. 

A much higher proportion of mid-size and small companies rates the initiation and evaluation stages as “most difficult.” Mid-size companies in particular are more likely to positively rate their ability to complete each stage in the process.

Whereas 10 years ago a larger proportion of companies centralized their innovation adoption functions, only 15% of companies today report using a centralized, dedicated function to drive innovation adoption. The majority (85%) reports using a decentralized approach today, relying on individual functional areas to promote, pilot, and evaluate new operating innovations.

Lastly, most companies are harsh self-critics of their ability to adopt innovative solutions supporting clinical trial execution. Approximately one-third (31%) rate the overall adoption process within their organization as “poor” or “very poor.” Nearly 80% perceives that the process takes “somewhat’ or “much” longer than expected and 61% believes that the process for their organization takes longer than it does for their peers. Larger companies are more likely to perceive this.

The drug development industry has long placed a premium on innovations that drive differentiation and competitive operating and performance advantages. As stakeholders throughout the clinical research enterprise look in earnest to accelerate drug development timelines, the ability to quickly, efficiently, and effectively adopt novel solutions supporting clinical trial execution and improve patient engagement is paramount. Although the stages in the adoption process —from initiation to full implementation—are rational and necessary, the relative speed of small pharma and CRO companies offers insight and indicates opportunities to optimize the innovation adoption process.

Clinical Trial Insights

Study uncovers realistic goals in efforts to embrace decentralized clinical trials.

Optimizing the Process for Adopting DCT Solutions

What follows is a bulleted list that outlines why pharmaceutical company sponsors should not consider returning clinical trial data to patient participants. In short, it will:

In other words, the effort would cost more money, and demand a whole new way of thinking.

What follows is why sponsors should return clinical trial data to patient participants:

This slowly emerging idea of returning trial participants their own data is not ephemeral or whimsical. A few pharma houses are seriously considering eschewing the top four bullets in favor of that stand-alone bullet. One, Pfizer, is just about ready to roll out its own return-the-data program. Another, Janssen, is “actively pursuing” its own version of a patient data-return, a spokesperson said. A few others, all members of the nonprofit collaborative TransCelerate BioPharma, are seriously talking about the matter.

“It is a natural next step for a clinical trial patient,” says Sylvia Baedorf Kassis, program director, the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard. Patients, she adds, will want to know, ‘“what can I learn from participating in this study?”’

Sponsors are becoming more comfortable with the idea, according Baedorf Kassis, and for some people, it will make all the difference.

However, she cautioned—like others interviewed for this story—every patient is different, every patient’s need is different, every trial is different. So along with the possible regulatory hurdles that await attention and the significant buy-in from pharma that needs attainment, the data-return supporters have continued working on tools for sponsors to use. The MRCT Center has produced case studies and a patient-friendly glossary of clinical research terms; TransCelerate has made publicly available its resource pack to advise sponsors on returning data; a European consortium has its own project. And, yes, these groups cooperate with one another. (Baedorf Kassis said the glossary has been adopted as a global Clinical Data Interchange Standards Consortium standard.)

A potential selling point to pharma is possible improvement in the historically low trial participant recruitment and retention numbers. In its 2023 omnibus bill, Congress told FDA it wants a diversity action plan with each trial submitted for its review.

 Those interviewed said data return would help in these areas.

At one time, says Christine Lemke, cofounder and CEO, Evidation, “I used to think [the idea] was paternalistic but now I think it is the right decision. Returning the data will take more time.” She adds doing so won’t be easy, but believes it is worth it. “The participants deserve respect.”

“It was maybe 10 years ago when we started saying there’s something here we have to think about, even though it’s not required” by regulators, Jean Sposaro, director, Global Clinical Trial Industry Collaborations at Bristol Myers Squibb (BMS), tells 

. However, not all pharma companies are ready to “take the leap” due to the lack of those requirements.

So why the effort now to create patient data-return plans? “Patient participants are louder than ever,” says Baedorf Kassis. Providing participants with the aggregated results from their trial has not been enough, she argues.

The COVID-19 pandemic, adds Sposaro, put a light on an industry; up until then, pharma had been working behind a curtain, in essence. “Now we’re not,” points out Sposaro. “People know what pharmaceutical companies do and they know that we can’t do it without patients.”

While there always has been a handful of trial participants who wanted their data, “there has absolutely been an uptick in requests for data since the pandemic,” adds Sposaro. “The group of people that I meet with a couple of times a week [regarding patient data return] are all saying the same thing.”

Besides the patients’ desire and appreciation for the value of patient data, “We have the digital capabilities we didn’t have before,” says Sposaro. “So, do we just ignore it or do we start to plan and think ahead?”

While the temptation to ask “what happens if this patient wants this or if a genomic test shows that?” is ever present, an interviewer soon learns those kinds of questions are moot. “We’re just beginning to really scratch the surface,” says Sposaro.

“We haven’t gotten there yet, it is so specific,” adds Baedorf Kassis. The MRCT Center, and other data-return advocates, are going with or recommending a phased-in approach, starting with the easiest data to return and thereby gaining experience with the process.

A ‘what happens if’ scenario is discussed anyway. Imagine, says Lemke, that a patient-participant experiences unintended consequences of a data return. Someone needs to walk through the results with them. “You wouldn’t want someone to get that without context,” she notes.

That scenario is exactly what happened to a patient advocate in an MRCT Center case study.

 The woman, a patient with multiple sclerosis, learned that she was at high risk of developing Alzheimer’s disease, but no genomic counselor was provided to help her, and the institutional review board (IRB) said the clinical trial researchers met their contractual obligations.

At this point, these groups are taking the 30,000-feet view. TransCelerate is creating a patient infographic for patient advocacy groups. It is also creating a sponsor-directed considerations guide that will detail “why they should and how that patient data-return could happen,” says Sposaro, explaining that pharma executives aren’t convinced that patients want their data. “What I get a lot from senior leaders is, ‘Do you think patients really want their data?’”

In Europe, a group called Facilitate–which lists 29 pharma houses, academic institutions, patient groups as well third parties–has formed to devise a model plan for patient data return and for the reuse of study data. No two organizations, says Sposaro, have the same goals.

The plan at Pfizer, according to a MRCT hosted webinar

, is to allow patients to receive their data at the end of the trial, which, of course, could take years. The program will begin in US-centered trials for now. Participants interested in getting some data–no imaging files or genomic data just yet–will create an account via a portal.

A few years ago, says Sposaro, BMS ran a pilot study on data return, but at a specific point–when patients failed admission to the trial.

Those failures still result in a lot of data, including blood draws, potential specimens, or imaging results. BMS gave the data back to those patients. She says BMS is now reviewing the outcomes of that data give-back.

Sposaro counted off the benefits to these patients: It lets them move to the next place for their treatment options.

Pharma reduces the burden on the patient, the participant, “but we also reduce the burden on the healthcare system because somebody has to pay for this,” she says. “For me, this is one of the low-hanging fruits. I’m not telling you pharma is doing it yet. I can tell you that we have started to think about it and people are starting to pilot it because that’s just one example of ‘why not.’”

Lauren Wu, head of privacy and senior director of legal for regulatory and compliance, Evidation, says sponsors who are interested in returning patient data need to move cautiously from a regulatory perspective. “I would be concerned how this would be interpreted by regulators,” Wu tells 

Despite the good intentions, regulatory agencies might consider the data returns when accompanied with insights that would make the information understandable to patients as getting “close to practice of medicine considerations or CLIA testing requirements,” says Wu. There are, the attorney adds, “juxtaposed insights, considerations, and conflicts.”

The discussion to return patient data follows a long history of similar efforts by regulators, the government, and others, to share data that emerges from interventional clinical data. At the NIH, its final policy on data sharing among scientists went into effect in January.

The agency wants to see a data-sharing plan before it considers extending a grant–including from its own investigators.

For the past five years, authors of manuscripts reporting clinical trial results who submitted to a member journal of the International Committee of Medical Journal Editors have been required to include a data-sharing statement.

But pharma hasn’t exactly embraced the “let’s-share” momentum. One study published last year found that of 61 published trials, nine were considered eligible for sharing data–two years after publication. A study was considered ineligible if it lacked a data-sharing policy or involved an ongoing investigation.

As for who owns clinical trials data, that argument has multiple sides. It seems that cases can be made for those who funded it, and also for those who provided it.

“Ultimately,” wrote the National Academies of Sciences in 2015, “the question of who owns the data is less important than the question of the rights and responsibilities of data holders.”

Sposaro says data-return advocates aren’t looking for regulators to tell them what to do, “but what we would like to know is what are the areas that they’d be concerned about.”

Lemke highlighted a study Evidation was part of in 2014 with Brigham Women’s Hospital and Atrius Health that demonstrated the good and the questionable regarding patients’ access to their data. The two-arm study involved 411 patients at risk or already diagnosed with high blood pressure.

 Patients used a blood pressure cuff at home. Researchers wanted to see how adherent they would be in taking their medications and using a smartphone app to record their numbers. The upshot: there was some improvement among those patients using the app. These patients were consistent witnesses to their data.

“This is part of the trickiness of sharing,” Lemke tells 

. Sharing outcome results could be a confounder that contributes to the
actual intervention.

Other complications, she adds, would be sharing data after an observation period. Those results need to be stored away until they are analyzed.

“People want to lock up data until it is time for statistical analysis and in the hands of regulators for decision-making,” asserts Lemke. “These are complications that pharma hasn’t worked out in order to make [data sharing] a fluid process.”

The public is of two minds regarding their data. A 2018 

 study found that of 771 former and current trial participants, fewer than 8% thought the negative reasons of data sharing with scientists outweighed the good reasons.

 The main concerns with sharing data with professionals, the respondents said, was that it would discourage would-be participants in joining a trial (37% “very’ or “somewhat” concerned) and would be used for marketing purposes (34%.)

On the other hand, when the UK’s National Health Service proposed in 2021 that its members’ personal data be shared among various healthcare providers, more than one million people said,
no thanks.

Returning patient data, says Sposaro, is trust-building. How, she questioned, can industry refer to itself as patient-centered if it doesn’t return the data generated by those people who are helping in the first place?

“We know big pharma is a machine,” she notes. “We’re asking the machine to do something very different.”

 is a  freelance writer for medical, clinical trials, and pharma information

Baumann J. Diversity in Clinical Trials at FDA Gets a Boost from New Law. 

https://news.bloomberglaw.com/pharma-and-life-sciences/diversity-in-clinical-trials-at-fda-gets-a-boost-from-new-law

Case Study: Supporting Participant Decision-Making in Genetic Testing Studies. MRCT Center. 2023. 

https://mrctcenter.org/return-of-individual-results/wp-content/uploads/sites/9/2023/05/IRR-Case-Study-1-with-CC-license.pdf

Returning Individual Research Results and Data to Participants: Experience from the Field (Webinar). Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard. May 2, 2023. 

https://mrctcenter.org/blog/resources/returning-individual-research-results-and-data-to-participants-experience-from-the-field-webinar/

Final NIH Policy for Data Management and Sharing 

https://grants.nih.gov/grants/guide/notice-files/not-od-21-013.html

Taichman, D.B.; Sahni, P.; Peiperl, L.; et al. Data Sharing Statements for Clinical trials. 

https://www.bmj.com/content/357/bmj.j2372

Siebert, M.; Gaba, J;, Renault, A; et al. Data-sharing and Re-analysis for Main Studies Assessed by the European Medicines Agency—a Cross-sectional Study on European Public Assessment Reports. 

https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-022-02377-2

Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. Committee on Strategies for Responsible Sharing of Clinical Trial Data; Board on Health Sciences Policy; Institute of Medicine. Washington (DC): National Academies Press (US). April 20, 2015. 

https://pubmed.ncbi.nlm.nih.gov/25590113/

Morawski, K; Ghazinouri, R; Krumme, A; et al. Association of a Smartphone Application With Medication Adherence and Blood Pressure Control: The MedISAFE-BP Randomized Clinical Trial. 

https://pubmed.ncbi.nlm.nih.gov/29710289/

Mello, M.M.; Van Lieou, B.S.; Goodman, S.N. Clinical Trial Participants’ Views of the Risks and Benefits of Data Sharing. 

https://www.nejm.org/doi/full/10.1056/NEJMsa1713258

Devaney, S. Research Shows Patients Want More Control Over How their Health Data are Shared. University of Manchester press release. February 2, 2023. 

https://www.manchester.ac.uk/discover/news/research-shows-patients-want-more-control-over-how-their-health-data-are-shared/

Experts explore potential paths to achieving buy-in from pharma and regulators.

Patient Data Return: Do the Benefits Outweigh the Barriers?

IQVIA’s Patient Centered Solutions (PCS) team is distributing print copies of a newly released book, 

Using Patient Experience Data to Evaluate Medical Interventions. Generating, Understanding and Using Patient Experience Data Within and Alongside Clinical Trials,

 at the DIA meeting in Boston at the end of this month. If you are not on hand to pick up a complimentary copy, it will also be available for purchase at the Kindle store as an eBook.

Edited by Matt Reaney, (PhD, CPsychol, CSci, MSc), head, science and analytics, PCS, at IQVIA, the content is timely and the company believes it has the potential to become the authoritative text on patient-experience data (PED) in drug development. It was created with all levels and roles of clinical research in mind, with the intent to use the content to facilitate their work in bringing their products to the market.

“Patient experience data (PED) in intervention development – what it is, why you should care, and how this book can help?”

 He writes, “The relevance of PED extends beyond patient­level healthcare and into the development of new interventions. Indeed, understanding PED in intervention development is an important precursor to patient-focused healthcare provision. The goal of intervention development has long been to maximize health and well-being, but it is only recently that developers and sponsors of intervention development have recognized the importance of the patient experience in achieving this goal.”

He continues to outline the reasons why PED could be considered confusing, why it is becoming more important and how it can be used effectively. For example, PED can help answer the following questions:

What signs and symptoms are most bothersome to patients?

How are patients' lives affected by disease and treatment?

How can we best understand treatment benefit?

Do patient preferences align with clinical priorities?

How well do typical trial endpoints align with outcomes that matter most to patients?

How do attitudes toward interventions vary by patient subgroup?

The book is broken down into four important sections of PED—generating, understanding, using, and developing a PED strategy. IQVIA contributing authors range the professional functions within the organization from PCS, to statistics, research, endpoints, and many more to reflect their expertise to the included sections.

, editor-in-chief, Applied Clinical Trials

Newly released book edited by Matt Reaney, (PhD, CPsychol, CSci, MSc), head, science and analytics, PCS, at IQVIA focuses on patient experience data with all levels of clinical research in mind. 

Applied Clinical Trials-06-01-2023

Perspective and Hope From the ‘Other Side’

Changing Behavior: Knowing Doesn’t Equal Doing

Obtaining Informed Consent for Future Reuse of Patient Data

Optimizing the Process for Adopting DCT Solutions

Patient Data Return: Do the Benefits Outweigh the Barriers?

Patient Experience Data: Get Your Copy at DIA

An eCOA Lexicon: The Quest for Single, Trusted Source of Terminology

From 'Brave Spaces' to Battling Bias in Patient Engagement

Industry Trends: Cycle Time to Resolve Risk Signals

A Business Case to Bridge Trials and Care

ASCO, Clinical Trials for Life

European Legislative Proposals Presage New Rules for Clinical Trials

eConsent is Not What You Think

FDA Eyes New Strategies to Spur Gene Therapy Development

Advancing ICU Care with eConsent