The Michael J. Fox Foundation for Parkinson’s Research (MJFF) aims to speed clinical research by removing obstacles that stand in the way of drug development. In pursuit of this mission, the Foundation gathers insights from a wide range of stakeholders and uses these perspectives to enhance clinical trial processes from start to finish. In 

 Eye on Patient Advocacy series, we will share best practices and lessons learned from the field of Parkinson’s research that can be applied to clinical trials across disease states. In our latest column in this series, we discuss the dearth of research connecting social determinants to mistrust and distrust of clinical trials, and one study’s efforts to connect these dots.

Marginalized groups in the US, such as Black, Indigenous, Latinx, and transgender persons, have been historically neglected and systematically excluded in clinical trial participant recruitment. This exclusion is despite federal regulations that mandate the inclusion of women and other minorities in clinical trials.

 Recruitment strategies rarely prioritize the aforementioned marginalized groups in clinical trials despite evidence-based approaches such as sustained community education and engagement.

 Instead, researchers have continued to attribute the lack of diversity in clinical trials to the mistrust of marginalized communities.

 Mistrust is the belief that an individual from a marginalized group will not receive quality or fair treatment based on a history of maltreatment toward marginalized communities.

 However, interdisciplinary literature suggests social determinants—including poverty, segregation, food insecurity, and structural racism—not only produce poor experiences of health, but also a distrust of health research in general.

 Distrust refers to the suspicion that a specific provider, researcher, or institution can poorly treat or harm an individual from a marginalized group based on specific examples of discrimination or misconduct.

 Recent discussions on vaccine hesitancy have rightfully directed attention toward historic examples of misconduct toward Black communities. There are also historic reasons for distrust in Indigenous, Latinx, and transgender communities, which include imperialization, forced sterilization, and poor healthcare delivery.

 Hence, there is reason to believe that social determinants drive inclusion in clinical trials as well as trust.

Some literature suggests discriminatory experiences with other institutions—including law enforcement, housing, and schools—contribute to mistrust and distrust.

 For instance, as incarceration rates indicate, the burden of social injustice disproportionately impacts Black, Latinx, and transgender communities.

 Similarly, the recent forced sterilization of Latinx persons at the US/Mexico border presents another example of structural racism and institutional complicity.

 Interestingly, while there is strong evidence that mistrust and distrust contribute to lack of clinical trial participation for Black, Indigenous, Latinx, and transgender persons, there is also a dearth of research connecting mistrust, distrust, and clinical trial participation to social determinants. Additionally, there has not been an exploration of how Black, Indigenous, Latinx, and transgender persons, who have participated in clinical trials, have navigated barriers like social determinants and trust.

Trials and Trust/Investigación y Confianza 

study seeks to connect the dots from social determinants to clinical trial participation using narrative storytelling. 

Trials and Trust/Investigación y Confianza

 is a qualitative study collecting 40 narratives of Black, Indigenous, Latinx, and transgender persons currently living in the US and its territories who successfully completed clinical trials between 2010-2020. Participants are recruited through long-standing relationships the research team has with Black, Indigenous, Latinx, and transgender communities, as well as disease-specific research centers, community neurology centers, community health centers, foundations, support groups, adult day cares, nursing homes, and community-based organizations. The study is advertised in both English and Spanish using culturally-responsive content and media. Using semi-structured in-depth interviews, participants share the barriers to clinical trial participation that they faced and key facilitators that supported their completion. To explore the potential intersecting role of disease prognosis in motivation to participate in a clinical trial, the 

 study focuses on neurological conditions without effective treatments such as Parkinson’s disease, Alzheimer’s disease, and related dementias. The expected findings of this exploratory research include (1) a description of the impact of social determinants over the life course on mistrust, distrust, and clinical trial participation, (2) an understanding of the facilitators of clinical trial participation for individuals impacted by social determinants, and (3) a preliminary idea of how disease prognosis plays a role in the decision to participate in a clinical trial.

uniquely takes an emic approach to research by centering the voices of marginalized communities. 

 utilizes a novel approach to research by focusing on the successful completion of clinical trials by marginalized group members, further emphasizing the importance of diverse perspectives in clinical research. In addition, by engaging community stakeholders and returning the findings to communities, this study ensures the bidirectionality of research benefit for scientific and lay community alike. Lastly, by identifying barriers and key facilitators to clinical trial participation, The 

Trials and Trust/Investigación y Confianza Study

 will not only contribute to delineating the pathway to clinical trial participation for Black, Indigenous, Latinx, and transgender persons; findings from this study will also assist the research community with addressing social determinant barriers for future studies as this may increase diverse participation in health research.

, contact the study’s principal investigator, Dr. Paris Adkins-Jackson, at padkins-jackson@mgh.harvard.edu, and co-Investigator, Dr. Angie Sanchez, at asanchez27@mgh.harvard.edu.

Agarwal P, Ray S, Burdick D, Griffith A, Madan A. Optimizing Recruitment of Racial and Ethnic minority population in Clinical Trials in Parkinson’s Disease: Potential Barriers and Prospective Strategies. Neurology. 2017; 88(16 Suppl.): P4.010.

Jackson JD, Sanchez A, Ison J, Hemley H, Siddiqi B. Importance of Diversity in Parkinson’s Research. 

https://www.appliedclinicaltrialsonline.com/view/importance-of-diversity-in-parkinson-s-research

Freedman LS, Simon R, Foulkes MA, et al. Inclusion of women and minorities in clinical trials and the NIH Revitalization Act of 1993—the perspective of NIH clinical trialists. Control Clin Trials. 1995;16(5):277–285.

Jackson JD, Moy B, Evans MK. The Elimination of Cancer Health Disparities: Are We Ready to Do the Heavy Lifting? 

. 2016;21:1–3. doi:10.1634/theoncologist.2016-0327

Griffith DM, Bergner EM, Fair AS, Wilkins CH. Using Mistrust, Distrust, and Low Trust Precisely in Medical Care and Medical Research Advances Health Equity. 

e. 2020;1-4. doi:10.1016/j.amepre.2020.08.019

Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389:1453–63.

Asare M, Flannery M, Kamen C. Social Determinants of Health: A Framework for Studying Cancer Health Disparities and Minority Participation in Research. 

. 2017;44(1):20-23. doi:10.1188/17.ONF.20-23

Jaiswal J, LoSchiavo C, Perlman DC. Disinformation, Misinformation and Inequality-Driven Mistrust in the Time of COVID-19: Lessons Unlearned from AIDS Denialism. 

2020;24(10):2776-2780. doi:10.1007/s10461-020-02925-y

Hodge FS. No Meaningful Apology for American Indian Unethical Research Abuses. 

 2012;22(6):431-444. doi:10.1080/10508422.2012.730788

Stern AM. Sterilized in the name of public health: race, immigration, and reproductive control in modern California. 

. 2005;95(7):1128–1138. doi:10.2105/AJPH.2004.041608

Cahill SR, Keatley J, Taylor SW, Sevelius J, Elsesser SA, Geffen SR, Wang T, Mayer KH. “Some of us, we don’t know where we’re going to be tomorrow.” Contextual factors affecting PrEP use and adherence among a diverse sample of transgender women in San Francisco. 

. 2020;32(5):585-593. doi:10.1080/09540121.2019.1659912

Schneider MG, Swearingen CJ, Shulman LM, Ye J, Baumgarten M, Tilley BC. Minority enrollment in Parkinson’s disease clinical trials. 

. 2009;15(4):258-262. doi:10.1016/j.parkreldis.2008.06.005

Tilley BC, Mainous AG, Elm JJ, et al. A randomized recruitment intervention trial in Parkinson’s disease to increase participant diversity: Early stopping for lack of efficacy. 

. 2012;9(2):188-197. doi:10.1177/1740774512436881

Curry L (Ed.), Jackson J (Ed.). Science of Inclusion: 

Recruiting and Retaining Racial and Ethnic Elders in Health Research

https://onlinedb.geron.org/cvweb/gsa_live/cgi-bin/msascartdll.dll/ProductInfo?productcd=GSA_Inclusion

Alang S, McAlpine DD, Hardeman R. Police Brutality and Mistrust in Medical Institutions.

 2020;7:760–768. doi:10.1007/s40615-020-00706-w

Loeb TB, Adkins-Jackson PB, Brown AF. No internet, no vaccine: How lack of internet access has limited vaccine availability for racial and ethnic minorities. 

https://theconversation.com/no-internet-no-vaccine-how-lack-of-internet-access-has-limited-vaccine-availability-for-racial-and-ethnic-minorities-154063

. Published February 8, 2021. Accessed Feb. 13, 2021.

Hughto JMW, Reisner SL, Kershaw TS, Altice FL, Biello KB, Mimiaga MJ, Garofalo R, Kuhns LM, Pachankis JE. A multisite, longitudinal study of risk factors for incarceration and impact on mental health and substance use among young transgender women in the USA. 

. 2019; 41(1): 100–109. doi:10.1093/pubmed/fdy031

Wildeman C, Wang EA. Mass incarceration, public health, and widening inequality in the USA. 

. 2017;389(10077):1464-1474. doi:10.1016/S0140-6736(17)30259-3

Hernández L, Upton S. Transgender Migrant Rights, Reproductive Justice, and the Mexico–US Border in the Time of COVID-19. QED: 

. 2020;7(3):142-150. doi:10.14321/qed.7.3.0142

Adkins-Jackson PB, Legha RK, Jones KA. Naming IT: How to measure racism in academic health centers. AMA Journal of Ethics. 2021; 23(2): E144-149.


; all with CARE Research Center-Massachusetts General Hospital; and 

This study is supported by The Michael J. Fox Foundation, Genentech, a member of the Roche group, and Boston University’s Alzheimer’s Disease Research Center.

The MJFF Research Engagement Team includes 

, Research Partnerships Officer at The Michael J. Fox Foundation in New York, NY. To contact the MJFF Research Engagement Team, email: 

The Systemic Synuclein Sampling Study (S4) was a multicenter, cross-sectional, observational study sponsored by MJFF. The primary objective of this study was to better understand the progression of Parkinson’s disease (PD) by identifying the optimal biofluids and tissues for measuring the protein alpha-synuclein outside of the brain as a potential biomarker in individuals with PD. The secondary objective of the study was to create standard operating procedures for the collection and assessment of multiple tissues and biofluids to better understand alpha-synuclein’s potential as a biomarker for PD.

In January 2017, MJFF launched a survey to assess the need for transportation infrastructure at clinical trial sites. Data from the survey was used to inform the design of a transportation intervention that was tested via the Parkinson’s Disease Trial Recruitment Innovation (PD-TRI) project, an MJFF-funded pilot study aimed at reducing barriers and enhancing facilitators to clinical trial recruitment.

Patient retention is an important element of the Parkinson’s Progression Markers Initiative (PPMI), a landmark, longitudinal, observational study sponsored by MJFF. PPMI aims to find reliable and consistent biomarkers for PD progression by studying cohorts of Parkinson’s patients, populations at risk for PD, and controls without PD.

Although the barriers to PD clinical research among underrepresented groups (URGs) have been well identified, it remains unclear whether suggested interventions will improve engagement of traditionally underrepresented populations in PD research. For these reasons, MJFF sponsored the Fostering Inclusivity in Research Engagement for Underrepresented Populations in Parkinson’s Disease (FIRE-UP PD) Study to 1) explore whether it is possible to identify best practices to engage URGs in PD clinical research, 2) to assess knowledge and attitudes toward clinical research designs, and 3) to increase participation of URGs to Fox Insight.

Articles

Feature Articles

Understanding the role of social determinants on clinical trial participation and trust.

Clinical Trials and Trust

Effective and efficient clinical data collection and management is one of the key factors affecting clinical trial success and is of heightened importance during the COVID-19 pandemic.

 Not only have the scope and complexity of clinical trials continued to increase over the past decade, but the volume and diversity of clinical study data grows ever larger. As routine drug development cycle times continue to get longer, the challenges of attempting to accelerate study start up and database lock and reporting while maintaining quality has become harder.

Researchers have accelerated the development of vaccines and therapeutics for COVID-19 as evidenced by the 4,846 trials found on 

 The COVID-19 pandemic presents a unique opportunity for understanding technologies that can enable trial data management and their effects on streamlining and expediting clinical trial design and implementation.

This study aims to evaluate how a clinical data management system (CDMS) supported COVID-19 vaccine and therapeutic drug clinical trials based on the experiences of a contract research organization (CRO) during the pandemic.

The study objectives were to evaluate how the CDMS was used during the conduct of two COVID-19 trials and identify any potential impact on clinical trial efficiency and/or CDMS usability and workflow. We examined data management processes and explored how the CDMS supported optimizing workflow in clinical trials during the COVID-19 pandemic and periods of remote work; identified perspectives regarding data management challenges either specific to or exacerbated by the COVID-19 pandemic; and measured usability, acceptability, and satisfaction.

We evaluated the two COVID-19 clinical trials conducted by the contract research organization (CRO), Veristat, as of the date of this research. IBM Clinical Development (ICD) was the data management system utilized. Project-level retrospective and prospective internal usage and administrative data, routinely collected to monitor utilization of the CDMS, were analyzed for each clinical trial. Internal data from usage reports included dates for key data management events such as database release and lock, module usage, changes to the database after its release (referred to as mid-study updates), and database downtime. The CRO provided additional operational and study management metrics including data management information, outlined in Table 1 below, to supplement CDMS usage data.

Specific measurements evaluated in this study were: database build efficiency, velocity of data collection, frequency of protocol amendments on the database, and the time impact of mid-study updates to the database. The cycle time to design and release the study database in the CDMS was used as an indicator for efficiency in database build. Specifically, this was defined as the number of weekdays between the date of the final approved protocol to the date of the first database “Go Live.” The average cycle time to build these COVID-19 study databases was compared to both the CRO’s and published industry baselines prior to the pandemic to establish study efficiency. Velocity of data collection was measured as the number of data points collected over time after the database was released.

Data managers completed a brief form on each protocol amendment and mid-study update to the database, providing the date and background information for the event, the reason for the protocol amendment, and an indication of whether a protocol amendment and mid-study update were related. Average database downtime was used to indicate impact of mid-study updates on the trial timeline. Database downtime refers to the number of days the database was “Locked” and no data collection could take place.

A survey was designed to investigate users’ attitudes, experiences (e.g., impact on cycle times, efficiency and cost) and satisfaction with the CDMS’s ability to meet the COVID-19 clinical trials’ needs. A web-based survey composed of 37 questions divided into seven sections was used to collect survey data. These sections included questions covering the System Usability Scale (SUS), a Net Promoter Score (NPS), general clinical trial data management, general use of the CDMS before and during the COVID-19 pandemic, specific use related to the COVID-19 trial being run in the CDMS, and demographics, such as years of experience in the field and years of experience using CDMS tools. SUS is a simple, ten-item attitude Likert scale that provides a subjective assessment of usability.

 NPS is a well-established measure to assess customer satisfaction on a scale from 1 (not at all likely) to 10 (extremely likely) by asking the single question “How likely is it that you would recommend our system to a friend or colleague?”

 The survey was distributed to the CRO’s data managers and was open from July 27 to August 7, 2020. Descriptive methods for analyzing the objective data were a five-point Likert scale and cause-and-effect relationships.

Interviews were conducted with the lead data managers and CDMS users from each clinical trial in order to highlight unique challenges or approaches for their specific COVID-19 clinical trial. Interviews were conducted via a secure online video communication instrument, used an approved interview guide, and each lasted approximately 60 minutes. Using an open coding approach, interviews were recorded, transcribed, and analyzed. Thematic analysis was defined by a combination of deductive review of the direct responses to interview questions and inductive analysis to identify emerging patterns. The interview guide is given in the Supplement. The study was submitted to the Western Institutional Review Board for regulatory review and received an exempt status determination. Participants provided informed consent for both the survey and interview.

Two COVID-19 clinical trials were included in this study: a therapeutic drug and a vaccine. The vaccine trial is a Phase I open-label, non-randomized study evaluating the safety, tolerability, and immunogenicity of the intervention in 120 healthy volunteers across three US sites. The therapeutic drug trial is a Phase II open label, randomized control, proof-of-concept study enrolling 50 patients hospitalized with COVID-19 across eight US sites.

Figure 1 below depicts key clinical data management milestones and the volume of data over time for the two COVID-19 clinical trials. The average time to design and release the study database for a COVID-19 trial was 11 business days. This was a 63.3-72.5% reduction compared to the CRO’s typical 30-40 day timeline and an 83.9% reduction compared to an industry benchmark of 68.3 days for a non-COVID trial.

 Mid-study updates to the database after first release were primarily a result of protocol amendments. Both trials had three mid-study updates during the period evaluated. On average, the database was only locked for less than one hour per mid-study update in each trial. Data managers classified all protocol amendments and mid-study updates as “completely unavoidable.”

Four (100%) clinical data managers and data management professionals were contacted, and all completed the survey; 75% were 25 to 34 years old. Each participant managed three to four trials simultaneously and used the CDMS on average 25.75 hours/week (range 20 – 30). Years of experience using the CDMS were one (50%), two (25%), and five (25%). The average SUS score was 80.6 (range 77.5 to 85.0 and considered excellent. NPS was 75% with 0% detractors, which is also considered excellent.

Table 2 below reports how much time the CDMS saves performing various tasks compared to other solutions used where 100% of participants believed the CDMS either somewhat or very much saved time for study startup, data collection, study closeout, and overall. All (100%) survey respondents agree or strongly agree that the CDMS integrates with workflow, increases productivity, is interoperable, and research studies are easy to set up and start up in the CDMS, while 75% think the CDMS is flexible and customizable and reduces manual errors. Of the survey respondents, 100% thought the CDMS somewhat or quite a bit helped to avoid cost, reduce manual errors, and decrease delays from mid-study updates. The most commonly reported causes for delays when building a clinical trial database were protocol changes (50%) and user acceptance testing (50%).

Interviews highlighted two specific functions as the main factors contributing to the rapid launch of study databases. First, the system easily allows reuse of code for database creation. The users created and routinely consulted a template library to generate new customized databases more efficiently. Bypassing de novo coding for both common and specialized database creation allowed users to focus efforts on the specific customizations required by the study sponsor.

Second, the ability to execute mid-study updates or post-go live changes with minimal to no downtime (< one hour) allowed the users and sponsors to work in parallel rather than in serial fashion, speeding up trial start dates and implementation of protocol amendments, and accommodating adaptive COVID-19 trial design. The ability to copy portions or the entirety of a database in a single environment permits users to ‘split’ the release of the trial database and allow the sponsor to initiate trial startup (e.g., screening and randomization) while the user completed the database without risk of error or corruption of data already collected. This is a direct attribute of having a single codebase for all applications of the CDMS.

Furthermore, the mid-study update feature continued to be critical to the flexibility of the database after study startup. Participants reported decreased downtime for mid study updates. In fact, usage data revealed that databases in these trials were only locked on average for about one hour in order to execute a mid-study update.

The sudden advent of the COVID-19 pandemic caused unprecedented stress to global health systems due to the novelty of the disease, rapid societal transmission, and absence of established therapies or vaccines. This global exigency prompted pharmaceutical companies and sponsors to fast-track the clinical trial clock to expediently bring effective therapies and vaccines to patients. One integral component of accelerating the clinical trial timeline is deployment of an efficient data management system. Utilization of an end-to-end solution with multiple embedded functionalities built on a single code base, including randomization, endpoint adjudication, electronic clinical outcome assessment, inventory management and medical coding within one platform was one supporting factor to the rapid execution of the trials in this study. In addition, the flexibility of the EDC platform facilitated efficient mid-study updates and post-go-live protocol amendments which are inherent in an adaptative clinical trial. The CDMS also enabled seamless transition to remote work during the pandemic without disruption to data entry and patient enrollment. Users highlighted that CDMS compartmentalization facilitated quick study launch and enabled database splitting, which also allowed parallel processing of downstream workflows to further accelerate study start-up.

This study is limited by the small sample size of survey respondents and clinical trials assessed. Many other factors in addition to the specific data management functionalities and practices utilized in the trials could have contributed to compressing COVID-19 clinical trial timelines; these include speed up in regulatory review, efficient protocols, reallocation of organization priorities and resources, exceptional efforts from the research community, and extraordinary awareness and participation from subjects. Evaluation of additional COVID-19 trials and CDMS users is needed to clarify the contribution of each of these factors.

World Health Organization. Coronavirus disease (COVID-19) situation reports. 

https://clinicaltrials.gov/ct2/results?cond=COVID-19

Brooke J. SUS: a “quick and dirty” usability scale. In P. W. Jordan; B. Thomas; B. A. Weerdmeester; A. L. McClelland (eds.). Usability Evaluation in Industry. London: Taylor and Francis. 1986.

Reichheld F.F. The one number you need to grow. Harvard Business Review. 2003;81(12):46–55.

Wilkinson M, Young R, Harper B, Machion B, Getz K. Baseline Assessment of the Evolving 2017 eClinical Landscape. Ther Innov Regul Sci. 2019 Jan;53(1):71-80. doi: 10.1177/2168479018769292. Epub 2018 Apr 26. PMID: 29714600.


, MHS is a Biomedical Program Manager in the Center for Artificial Intelligence, Research and Evaluation at IBM Watson Health, 

, PhD, Chief of Staff and Center Program Manager, Center for Artificial Intelligence, Research and Evaluation, IBM Watson Health, 

, MA, Medical Ethnographer, Center for Artificial Intelligence, Research and Evaluation, IBM Watson Health, 

, MS, is Associate Director Data Management, Veristat, 

, PhD is a Senior Biomedical Writer in the Center for Artificial Intelligence, Research and Evaluation at IBM Watson Health, 

, MD, PhD is the Chief Data Scientist and Vice President in the Center for Artificial Intelligence, Research and Evaluation at IBM Watson Health, 

 is Executive Vice President, Strategic Development, at Veristat, 

, PhD is a Genomics Expert in the Center for Artificial Intelligence, Research and Evaluation at IBM Watson Health, 

, PharmD, is a Deputy Chief Health Officer at IBM Watson Health, 

, PhD, FAMIA is Associate Chief Health Officer in the Center for Artificial Intelligence, Research and Evaluation at IBM Watson Health and an IBM Industry

Peer Reviewed

Fully-integrated, component-based CDMS offers flexibility, customization, and efficiency.

Accelerating Clinical Trial Design and Operations 

The eClinical market is on the rise. Research released in December 2020 estimated it would surpass $16.36b by 2027.

 But what do we understand by ‘eClinical’ and what innovations may this growth lead to and how will the industry make use of them?

eClinical is a term that has evolved over time and in definition, just as the industry has shifted and developed. What may have started as trial automation to streamline particular function areas has expanded, as the number of applications and disruptive technologies increase, bringing with it its own problems of duplication of data and redundancy in process.

As technologies integrate, improved usability is the operative characteristic for an efficient eClinical approach. And this not only to improve productivity, but also increase collaboration to the benefit of all. And central to this new approach is drawing from patients’ experiences and perspectives and developing next generation tools driven by patients.

At the same time, whilst decentralized/mobile trials focus on the patient, new tools to support the clinician, investigator, medical device companies, third party patient services and broader healthcare networks are emerging, to create a more accurate holistic view, and consolidate operations and efficiencies to develop faster therapeutic solutions.

The ultimate endgame is technology that works across all data types, protocols, sources, and formats for research and care coordination across all therapies which can:

So, what new developments are shaping today’s eClinical evolution?

While some have tried with blockchain, there is a lack of fully distributed electronic health records (EHRs) where all participants can contribute–family doctors, specialists, hospitals, nurses, and the patient. There are a number of technical challenges to consider. Who can modify and access data? Is the data valid? What data is from a health professional and what data is from the patient? There are also questions about how EHR can be used for research beyond the obvious use for care.

However, there are solutions which can be utilized to allow EHRs to reach their full potential, such as marking every value in every data field with a unique ID, date-time stamp and source. Every piece of data is therefore uniquely identified and there can never be a ‘data conflict’, even if the data is modified at various places at the same time by multiple actors. This is critical for transparency and traceability. It also means everyone reading the health record will know where the data comes from. This allows data to be graded based on whether it has come from a patient, healthcare professional, consumer grade or medical grade devices or a laboratory system.

Informed consent and having the right processes in place to achieve it, has long been a challenge for clinical research and treatment. Recent guidance suggests the best way to meet the legal requirements of informed consent at treatment level is to develop a consistent practice of involving patients, even if that involvement may occasionally be limited.

 eConsent can help increase this involvement, as well as aiding understanding for clinical trial participants, for a number of reasons. It can be adapted and provided in different formats to meet diverse learning styles and levels of health literacy. eConsent also offers the opportunity to test and reinforce comprehension, widen the patient recruitment process, and reduce drop-out rates. Crucially, when well designed, it allows participants to give feedback quickly and easily on how consent materials could be improved. It must be very simple to use, very clear, with no hidden ‘small print’ and, with GDPR right to be forgotten, it must also enable consent to be removed. However, a scoping review published in May 2020

 identified a need for researchers and commercial software vendors to create a unified approach to truly harness its potential.

The costs of medicines non-adherence, both personal and economic, are well reported. Common reasons for unintentional non-adherence include difficulties recalling or understanding instructions or simply forgetting to take the medication. eClinical offers several solutions to this hugely costly issue. Apps can be used to distribute medicines schedules to all patients, reminders can be sent, and concomitant drugs can be managed using Personal Health Records (PHRs). If consent, data collection (questionnaires) and adherence can all be within the same tool, it can simplify life for the patient and keep them better engaged and informed. Where adherence modules feature in mobile systems, these can also capture intake information to feed a study.

eClinical is about more than just helping patients manage their health and clinical trials. It can also help medical staff on a day-to-day basis. Simple alert systems can give data on biometrics including oxygen saturation, heart rate, respiratory rate, and temperature. Such deep integration between data collection and alerting allows for real time, or near real time alerting, which can be crucial.

There is, however, a need to ensure systems work with, and for, doctors rather than increasing their burden. A 2020 study examining perceived EHR usability as a predictor of task load and burnout among US physicians, found more favorable usability was associated with less workload. Researchers concluded improving EHR usability while decreasing task load has the potential to allow practicing physicians more working memory for medical decision making and patient communication.

 It is therefore vital clinicians are involved in development.

Incorporating PROs into platforms provides access to real-world data which, in the long-term, could have a significant impact on development decisions and improve care. Recent research has found both clinicians and patients find value in electronic real-time symptom reporting.

But to maximize their potential, PEPs should be informed by a full personal health record (PHR) and PROs providing rich data. They must be able to handle multiple types of documents, including lab results, pictures, and specialist reports. For example, one ePRO tool for rheumatoid arthritis includes a sleep tracker, pedometer, the ability to upload pictures, documents, X-rays and lab results and questionnaires.

The ‘data gold rush’ driving eClinical approaches can result in forced data sharing or lack of real comprehension regarding data collection.

By reversing the role completely and enabling patients to be in control of their data, empowering them to contribute to research and to the wider common good, is proving to be a great incentive and generating significant return.

A full personal health record stored on a smartphone, available for use even outside a trial for wider healthcare benefit, puts patient centricity at the next level.

Bridging the gap between research and care

The inclusion of a full PHR into advanced platforms is transforming trials and RWE studies as the range and scale of data is much greater. It is also of a much higher quality when acknowledging that up to 30% of hospital EHR data is wrong

 and easily rectifiable if patients can access and correct this.

Giving each patient a global view and access to all their health data which they can continually collect, share and control will bring a future of cheaper and faster research and more targeted treatments and therapy innovations.

Next generation mobile technologies and applications are revolutionizing the way we think about clinical research. But so much more can be done to better integrate all the relevant stakeholders across the health ecosystem, bringing real eClinical ‘intelligence’ and a holistic approach to clinical research, patient care, and therapeutic innovation.

https://www.precedenceresearch.com/eclinical-solutions-market

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307558/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233043/

https://www.eurekalert.org/pub_releases/2020-10/ulcc-cpc103020.php

https://ehrintelligence.com/news/diagnostic-medication-errors-rank-among-most-common-ehr-related-issues

New technological solutions for both patients and HCPs create a well-rounded eClinical approach

Taking eClinical to the Next Level

ePRO (electronic patient-reported outcome) approaches in clinical trials have been around for quite some time but it has taken a pandemic, with time of the essence, to give a boost to wider deployment. The proliferation and acceptance of digital devices and wearables by regulators, clinicians and patients has helped to fuel this opportunity for remote monitoring and will help to bring clinical trials further into the 21st century.

An increased focus on patient engagement and the 21st Century Cures Act, regulatory authorities, i.e., the FDA, are embracing the need for more patient-centric drug development and wider access to assure accurate data collection as trials become more decentralized.

For example, one of our partners, a market leader, is currently running 17 COVID studies with ePRO. These studies are being conducted in more than 38 countries with sites of up to 400 and patient counts of up to the tens of thousands. Another partner is conducting 15 COVID studies with ePRO, indicating that the market is confident in providing more patient-centric alternatives to the traditional trial model.

ePRO and other eCOA (electronic clinical outcome assessment) data has thus taken a higher priority in assuring accurate patient experiences are collected. However, this data goes through a process of validation measures in order to be used as a primary endpoint in drug development and trial evaluations.

With the continuous mutations of SARS CoV-2, drug developers are met with the challenges of determining the efficacy of current vaccines against the emerging strains. Along with embracing new technologies, there is a need to adapt trials to accommodate social distancing and to keep patients safe while still collecting necessary information.

The focus on improved visibility and oversight of data collection, faster trial implementation, sharing of real-time data and patient comfort and collaboration has led to a variety of eClinical applications. ePRO and other eCOA approaches can transform trials to make them more pragmatic, patient-centric and efficient by maximizing the potential to quickly access data through electronic health records, and especially to assist trial managers to make reliable data-driven decisions, and to mitigate risks.

The technology used to gather ePRO can be a downloadable app configurable to any digital device. The app provides the ability to record responses to trial questions as well as create photos or videos. With provisioned devices, the technology is usually preloaded. The technology may also include features to help improve patient compliance, such as reminder alarms.

Supporting these opportunities are the introduction of wearable devices like fitness trackers bringing, yet again, another opportunity to expand patient engagement in clinical trials. As wearables can be considered a BYOD (bring your own device), there is, as of yet, an unclear path as to which is the sponsor’s preferred method, i.e., BYOD versus the provisioning of a device. A number of factors can contribute to this depending on the trial, cost implication and privacy/security concerns. There could be potential issues with regards to carrying an additional device or using data from an individual’s personal data plan—all important considerations.

Data science teams have developed dashboards, visualizations and analyses to enable effective safety and trial risk management. This alleviates some of the concerns associated with BYOD where participants are able to turn off or change their notification settings, which could lead to missing data points. These tools. i.e. dashboards, would reassure sponsors that sites can track compliance and potentially improve outcomes by reminding participants via telephone contact or text messages to improve participant engagement.

Some sponsors have opted for a partially decentralized eCOA approach where the patient will visit a central site for major assessments but will also have home visits performed by trained nurses. This limits the number of visits to the central site and helps ensure patients are complying with protocols and are comfortable using the digital device whether it be a wearable device or more traditional ePRO device. This option is enabled by making the most of ePRO and other eCOA applications. One of the major upsides to monitoring in hybrid or remote trials is the opportunity to engage wider global patient participation, ensuring the trials are representative of a larger and more diverse population. Either way, mitigation or even elimination of travel can be a boon for both patients, who can self-monitor in a comfortable setting, and trial monitors, who are often on the road covering vast geographic areas.

As beneficial as these new technologies are becoming to streamline trial data integrity, privacy and security are all of paramount priority.

Clinical Data Management (CDM) teams have a crucial role in a successful clinical trial–a role that must ensure the production of quality, accurate and comprehensive clinical data to meet safety and efficacy standards to pass review by regulators. Modern techniques are emerging in the application of data science tools and technologies to support data validation and reporting, and these are proving very effective in identifying any untruths in that data. One of these tools is the application of artificial intelligence (AI) and the development of visualizations to deliver insights, drive efficiencies and add substantial value to the clinical trial process. The CDM approach considers five essential data elements: data volume, variety, velocity, veracity and value. The data science techniques support CDM in these areas via the exploration, application and development of different technologies and approaches.

Artificial Intelligence and Machine Learning (ML) inspire real data-driven decisions. For the trial sponsor, the application of AI/ML techniques uses historical data that can be easily factored into data moving forward. For example, there are algorithms, which can help to identify profiles, for example gender, race, ethnicity, of the different types of participants in the trial and use existing data to engage the patient to exhibit the correct behavior, i.e., follow procedures. ePRO, too offers a huge bank of data and some creative trial managers have even applied gaming techniques to inspire patients to stay engaged. This can help with both current participant behavior as well as for the future participant. Currently historical data is being used to help future participant behavior. As the amount of data increases over time in trials for specific indications, then the algorithms can generate much better insight to aid current participant behavior, as close to real-time as possible.

After identifying variables and other data, AI can identify common issues where automation could benefit the data cleaning process and with a rule-based approach, identify potential data quality issues. Data science methods, such as AI, can auto-detect potential hot-spots of data inconsistencies and potential anomalies. Working with experts, a rule-based approach can be applied to these hot-spots to highlight potential data anomalies, driving efficiencies in the overall data cleaning process.

AI/ML techniques are already in existence but generally pharma companies have been slow to embrace them for a variety of reasons, including concerns about regulatory compliance. The pandemic, however, has accelerated their adoption. In the area of event prediction, a trial manager can look into a company’s historical clinical trial data and provide data guidance when for example, writing new protocols, i.e., for dosages that may need to be increased/reduced for trials in different geographic areas or age groups. At an organizational level, advice can be given to the sponsor based on a virtual trial conducted with historical data. At the patient level, these techniques can predict adverse events causality–before they happen.

Remote, risk-based monitoring has increased and shows a great deal of success with these strategies, particularly if there is a remote monitoring platform integrated into a visualization platform to verify what is critical and what is not. Customized visualization platforms improvedata integrity and security for each trial.

Clinical trial costs are highly dependent on a long list of factors that can vary substantially but it has been noted that late-stage clinical trials can sometimes comprise as much as 50% or more of the cost of bringing a drug to market. At the highest levels, factors can include the geographic expanse of trial locations as well as the complexities of the drug engaged in the trial. Hold-ups due to data reconciliation, common mistakes in data input and reporting errors, quality and regulatory actions that could put a trial on hold, can certainly impact the success of a trial.

It makes good sense for sponsors to continue to embrace and invest in existing and new technologies that bring cost-saving potential and an opportunity for wider patient engagement. The future is bright for ePRO data collection and it may have taken a pandemic to bring new ways to ensure its ongoing success.

Push for accurate data collection during pandemic led by increased focus on patient engagement.

ePRO and Ensuring Data Integrity

A wealth of information is available regarding decentralized clinical trials and the technology solutions available for implementation of eConsent, eCOA, and everything “e.” These technologies are essential and allow clinical trials to be patient centric, as well as provide easier access for patient participation.

However, what is less often discussed is how the ‘e’ data is incorporated into the full clinical database and how we ensure that the data is meeting regulatory expectations and trial integrity requirements for FDA approval. In this interview, Heather Kopetskie, Director of Biostatistics for Rho, discusses some points related to proactively managing the many “e”s in data collection.

How is ‘e’ data incorporated into the full clinical database? Is this an easy or complex process given the various data sources?

 Incorporating all ‘e’ data into the full clinical database, means bringing all the ‘e’ data together into one central location. Preferably, early on in the trial and often. The complexity of this process is highly dependent on the process of receiving data from the various data sources. If it is data that can be downloaded on-demand from a cloud-based server, then they will be easy to obtain. If the data aren’t available in a central location and need to be accessed from each subject individually via a central site, then it can be more complex to obtain the data quickly and often.

Who is involved in mapping out a data flow process? How often do you find this map changes?

 The clinical data manager, programmer, and representatives from any third-party vendors are essential in this mapping. However, project managers, the clinical operations lead, and sponsor representatives are also critical depending on who holds the contract with the vendors and depending on how the flow of data works. Similar to above, if data can be accessed via a cloud, the clinical data manager and third-party vendor can determine most of the process. If the sites are involved the project manager or clinical operations lead are important in those conversations.

With data collected directly from the patient, “source data verification has been removed.” Why is this and how do we ensure confidence in this data and what quality and statistical controls should be put into place to ensure data integrity?

 What is meant by ‘when data are collected directly from the patient, source data verification has been removed’ is there is no paper source for a clinical monitor to verify against since the patient directly entered information into an application. In this situation, central monitoring is essential to ensure that following accuracy of the data. A few things that can be checked are that the data are being collected at the appropriate time and are not missing; participants are appropriately understanding scoring (e.g. on a 0-5 scale); variability in the data is appropriate (i.e. lack of variability is concerning); and reviewing distributions of the data and trends over time.

In this interview, Heather Kopetskie, Director of Biostatistics for Rho, discusses some points related to proactively managing the many “e”s in data collection. 

Plan the Flow of Data Early for Maximum Effect

Issue PDF

Click the title above for a link to open the Applied Clinical Trials March 2021 issue in an interactive PDF format.

Applied Clinical Trials March 2021 Issue (PDF)

As I mentioned last month, we debuted our first ever 

. And because of our speakers (side note, they were all women!) and our audience involvement, it was a success. We had over 1,300 registrants over the two-day event, and you can still register to watch on-demand. “Clinical Trials Operational Successes Post COVID-19” focused on two topic areas: Patient Participation and Technology. And each of the speakers brought their expertise to their topic, and that experience over COVID. The intention, to apply what was learned to next generation clinical trials. Some of those highlights are listed below.

This has been on the radar for clinical trials for some time, but clearly a necessary solution for COVID. There remains a lot of questions around what you can and can’t do according to local regulatory or governments, as well as how it works operationally. Dig in, plan ahead, and work through the issues with both the home nursing provider and the sites.

Clinical trial participation and diversity.

 With underrepresented populations an ongoing industry topic, COVID brought the specter of health inequities, as well as the need for what patients want in clinical trials. Joan Chambers, Senior Director, Marketing & Outreach, Center for Information & Study on Clinical Research Participation (CISCRP), brought research and insights from CISCRP, which based on audience comments, should be largely shared in the industry.

Pivoting and implementing new processes. 

From Honor Health Research, the speakers described how they worked to advance COVID-19 research quickly in their local hospital community. Try new things, communicate constantly and implement eConsent were keys to their success.

We’ve talked about these before, but NORD described the use of patient communities to inform and educate patients during COVID, as well as patient registries as a source for RWE.

Takeaways from technology day…decentralized trials and direct-to-patient (or family) trials are the way to the future, important for patients, as well as data integration. Remote monitoring and its possibilities made many sponsors and sites unaware at the beginning of the pandemic. Now, more than ever, the industry needs to look at building these capabilities into their protocol designs as back up or because it works well. And finally, Kimberly Ray presented ACRP data and support that technology is not going to replace anyone’s job at the site. Comforting data for everyone in the midst of change.

 is Editor-in-Chief of Applied Clinical Trials. She can be reached at 

From the Editor

Applying a year's worth of lessons to next generation clinical trials.

A Bevy of COVID-Tested Successes

Before we discuss how some seriously smart people have been working to improve clinical trial efficiency, it might make sense to nail down some definitions, namely, eSource and eClinical.

But nails can’t be used here; tacks are more appropriate, since definitions differ slightly, depending on the stakeholder who’s speaking. FDA, which coined the term a few years ago, puts the definition bluntly: Electronic Source Data—Data initially recorded in an electronic format. Trans Celerate BioPharma, a non-profit consortium whose members provide and share data, divides eSource into four groups: non-case report form; devices and apps; direct digital entry at the trial site; and patient-sourced EHRs.

A more-to-the-point way of defining eSource and eClinical: eSource is part and parcel of eClinical, said Craig Lipset, adviser and founder of Clinical Innovation Partners. Trials require data, regardless of how the information is recorded, accessed and delivered. The data that live in hospitals, urgent care centers, doctor’s offices and elsewhere are coveted by trial sponsors. They want it standardized, in a cloud, available from EHRs everywhere.

To standardization pioneers, how that information is recorded, accessed and delivered matters immensely.

In the eSource world, the concern is identifying the data’s headwaters—which lab, which hospital, which clinic. It concerns standardizing these data —this lab result has a blood glucose level, but the script said get an HbA1c. And it’s about establishing a common entry language—milliliters or microliters.

Data aggregation is a means to an end, not the other way around. ESource has been around a long time, said Lipset. “The new hook is, can I get it out of an EHR system, and that is the challenge.”

In the eClinical space, the path back to the EHR bedside with new treatments involves trekking through aggregated, standardized streams of data to find the right patients; have them e-sign their consent; visit trial sites, maybe in virtual mode, with updates; and include all other pertinent information, including from labs, pharmacies and
digital devices.

eClinical, eSource and EHRs is the journey to the holy grail of data efficiency in clinical trials.

Applied Clinical Trials-03-01-2021

Clinical Trials and Trust

Accelerating Clinical Trial Design and Operations

Taking eClinical to the Next Level

ePRO and Ensuring Data Integrity

Plan the Flow of Data Early for Maximum Effect

Applied Clinical Trials March 2021 Issue (PDF)

A Bevy of COVID-Tested Successes

The Road to True Data Harmonization

Turning Challenges into Opportunities: Evaluation of Digital Health Products

COVID-19 Vaccine Makers Overcome Challenges to Ramp Up Production

Europe's Leaders Still Discussing How to Respond to the COVID Pandemic

Navigating a Decentralized Trial Model and the Changing Clinical Landscape During COVID-19

HTA—Easier to Reach Global Consensus Than an Agreement in Europe

More Collaborative Clinical Trials Sought to Combat COVID-19