Kenneth A. Getz

Throughout the pandemic, clinical research stakeholders have been both amazed and immensely proud at the collective effort to adapt and address a monumental global health crisis in record time.

A great deal has been written and said about the factors that have contributed to the delivery of COVID-19 vaccines more than five years faster than typical development timelines and to the more than 1,000 COVID-19 treatments and vaccines now in the pipeline. The top contributing factors include:

The proactive and accommodating way that regulatory agencies and ethical review committees have supported development planning and activity.

The rapid mobilization of internal and external scientific and operating teams with a singular and common focus.

The fast deployment of remote and virtual technology solutions supported by more open data, information sharing, and advanced analytics.

The execution of parallel clinical trials and the compressed time between clinical trial phases.

The de-risking of development activity through the availability of global funding and resources.

The unusual frequency and extent of global media coverage contributing to unusually high public visibility.

The clinical research enterprise has much to celebrate, especially in light of the fact that most adaptations and changes represent a major departure from normal operating conditions. Many of the approaches and solutions deployed—such as remote monitoring, e-consent, home nursing visits, and integrated data management platforms—faced significant difficulty replacing legacy approaches before the pandemic. Many were stuck in planning or piloting stages for decades.

There is now substantial speculation about how the clinical research enterprise will function when the pandemic is fully behind us. How will the enterprise apply and embrace the experiences and lessons learned during this unprecedented period? Beginning in late 2020, articles, podcasts, webinars, and conference presentations began surfacing, most with both wishful thinking and pledges not to return to pre-pandemic operating conditions.

In recent months, we have learned more about underlying conditions that have supported pandemic response and a clearer picture is emerging of issues and challenges that will contribute to some retrenchment and more conservative application of adaptive and innovative practices as we move into a post-pandemic world. Two recent Tufts Center for the Study of Drug Development (Tufts CSDD) studies help paint that picture: the first study is an in-depth collection of interviews with principal investigators and senior study coordinators from the global community of investigative sites and site networks. The second study is a global survey—conducted in collaboration with the Bill & Melinda Gates Medical Research Institute (Gates MRI).

Tufts CSDD conducted a total of 26 in-depth interviews during March and April 2021, approximately one full year after the international community began locking down and clinical trial activity abruptly shifted to remote monitoring and decentralized support. Investigators and study coordinators noted several positive outcomes. The majority pointed to sponsor company and regulatory agency responsiveness and encouragement of adaptive practices as critical success factors. Site professionals felt that the most positive outcome in the transition to virtual and remote clinical trials has been the increased choice and accessibility offered to patients considering participation and the substantial improvement in study volunteer convenience.

But with few exceptions, the global community of investigators and study coordinators indicated that operating remotely and virtually has greatly increased workload for site personnel. Many noted the inordinate amount of time they have dedicated to learning new technology solutions; providing support to remote monitors and health professionals; training vendors unfamiliar with the clinical research process; and coordinating and integrating disparate and fragmented activities. Most interviewees pointed to significant delays that they have encountered starting up studies, receiving clinical trial supplies and lab kits.

Many conveyed concerns about the delays they experienced in receiving payments to help defray the substantial upfront risk sites assumed in supporting modified and adapted execution models.

In nearly every in-depth interview, investigative site personnel discussed the wide variability in approaches and solutions deployed by sponsors and contract research organizations (CROs) that contributed to high levels of uncertainty and the increased potential for errors and mistakes.

Between mid-January and April 2021, Tufts CSDD and Gates MRI gathered 344 global responses primarily from pharmaceutical, biotechnology companies, and CROs. The purpose of this online survey was to assess the experiences and effectiveness of development teams operating remotely. Two-thirds of respondents are employed by major and mid-sized companies and a similar percentage reports having team management responsibility within clinical, data management, and clinical operations functions.

Overall, respondents reported very high levels of productivity while operating remotely. Nearly all (97%) indicated that they can perform their role as a remote employee “very effectively” or “somewhat effectively.” But more than half of respondents (55%) report feeling burned out and 42% report that connections with their colleagues have deteriorated.

Factors contributing to burnout are intuitive and generally familiar to us all. The top three included the frequency of virtual meetings (by 67%); increased workload and additional responsibilities negatively effecting life balance (65%); and the emotional impact of dealing with uncertainty and isolation (54%). Unaddressed, it is likely that the feelings of burnout and loss of connection will continue to rise.

In open-ended responses, several indicated that the inability to travel and meet face-to-face with colleagues detracted from their collaborative effectiveness. Several also noted the challenges of not having a clear sense of the return on investment (ROI) for various solutions deployed and of the higher relative start-up cost and time investment required to support decentralized clinical trial activity.

Despite these experiences and concerns, the vast majority of respondents want to preserve their ability to operate remotely: 86% want their employer to continue offering “mostly remote” work when the pandemic is over; 6% indicate that they are uncertain about what operating model they want in place; and 8% said that they do not want their organizations remote operating model to continue.

We plan to publish the full results of the Tufts CSDD–Gates MRI study in 

Pandemic experience and response has exposed drug development organizations to numerous possibilities that would not have otherwise been widely adopted. Many observers would argue that this unusual adoption experience is not a reflection of a new standard operating environment but rather a one-time occurrence—a grand pilot program—providing a unique opportunity to consider, integrate, and apply “suitable” new options and choices.

Once the pandemic is behind us, these options and choices will likely be offered simultaneously with other more traditional approaches to balance and best satisfy study volunteer preferences, convenience, and scientific and operating requirements. Ultimately, these more expansive hybrid approaches will increase clinical trial complexity and customization, resulting in well-known outcomes: inefficiency, poorer quality, slower timelines, and higher costs.

Given cultures that have historically been risk-averse and resistant to change, many sponsor companies and their CRO partners will step-up efforts to scrutinize capital, personnel, and resource investment in, and double down on challenging, newer innovative practices. For the majority of organizations, it is likely that the pendulum will swing back toward slower, more measured response and more
limited acceptance.

A minority of organizations—those willing to take more risk—are already applying the implications of pandemic experience. Many are evaluating ways to better support teams operating remotely with more variable, agile, and flexible staffing models, infrastructure, and standard operating procedures. Several have shared the intent to consolidate their fixed, physical operating footprint, to continue to reduce study participant burden, and to entertain more open collaboration models.

Clinical research data volume has been rising dramatically for decades as protocol design objectives and scope have increased. The diversity of sources for clinical research data—including specialty labs, mobile and wearable devices, and patient health records—has also intensified. Post-pandemic, the juggling of highly customized clinical trial executional models will drive the collection of more data informing operating performance, efficiency, and quality. This rapid expected growth in rich data volume cannot be managed by traditional means alone and will rely more heavily on sophisticated and augmented analytics, including machine learning, risk-based quality management, and risk-mitigation techniques.

As the pandemic recedes and we gather more robust data on its full impact, we can anticipate wider variation in sponsor, CRO, and investigative site operating strategies and practices.

We will look back at our collective response with not only amazement and pride but also the knowledge that the pandemic did not solve our perennial drug development operating conditions. Rather, it necessitated experiences that affirmed, informed, and elevated enterprise-wide commitment to patient engagement and digital transformation.

Articles

Affirming and advancing patient engagement and digital transformation.

Contemplating the Full Measure of Pandemic Response

The drug development enterprise is undergoing a transformation of great magnitude driven by the cross-industry digitization of product development and the rise of Big Data. In 2020, we witnessed the acceleration of this evolution due to challenges created by the COVID-19 global health crisis. Out of necessity, the drug development sector is now embracing technologies and digital methods that were previously not as widely used. The sector is being forced to adapt to a new normal characterized by the maturation of data management solutions, an expanded number of data sources, and the unprecedented increase in data volume.

The sources of information and data management solutions that support drug development have evolved considerably over the past 40 years. During the 1980s and 1990s, clinical development focused on the gathering of small scientific data sets that yielded limited insights. In comparison, during the first two decades of this century, the enterprise gathered not only scientific data, but also collected operational data to improve the product development process. The sector’s use of data continues to evolve into what is now a nascent patient-centered data driven system that requires flexible models supported by augmented analytics and machine learning. These methods are expected to support an increasingly decentralized, predictive, and personalized drug development process.

The continued contribution of the drug development community toward improving the quality of lives of patients, researchers, and the public at large, is and will continue to be highly dependent upon the careful execution of strategies to make vast amounts of data meaningful and usable. This is achievable by pairing data with powerful analytics and then using those insights to develop safe and effective processes and products.

Although the drug development enterprise is undergoing major transformation, literature about what the sector should do to support and prepare its workforce for these changes is scant.

What follows is a discussion of original research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD) to address workforce development in the era of digitization. The research is primarily based on an in-depth discussion with thought leaders and senior executives. Tufts CSDD identified recurring themes for discussion in articles in academic journals and the trade press between 2015 and 2019. Discussion topics included: (1) challenges and opportunities caused by the sector’s digital transformation, (2) skills and competencies of future drug development professionals, (3) new roles that are expected to emerge within drug development, (4) changes in talent recruitment and retention practices, and (5) the reshaping of corporate mindsets and cultures to become digitally proficient organizations.

In December of 2019, Tufts CSDD moderated an in-person discussion in New York City among 60 executives from pharmaceutical and biotechnology companies, technology solutions companies, professional trade organizations, and academia. Their functional expertise included clinical research, clinical operations, data management, regulatory affairs, human resources, digital technology, and R&D strategy.

Participants first discussed how digital transformation and patient engagement have changed the nature of clinical and operational data. Industry now prioritizes the collection of patient-centered data in addition to scientific and operational data. This new emphasis on patient-engaged science is facilitating an open, customized, and predictive framework for drug discovery and development. But patient-engaged science also leads to even larger data volume; this requires wide-spread knowledge of advanced analytics, cross-platform accessibility, continuous and flexible learning, and effective leadership within and between organizations and regulatory bodies.

Trends shaping the digital transformation of the drug development enterprise

The global labor market has been experiencing widespread changes over the last decade, and the drug development enterprise has been undergoing a dramatic transformation of its own. The speed of digital innovation is leading to accelerated creation and destruction of jobs within organizations. One expert roundtable participant noted:

“Lines are blurred. New positions are developing. Existing positions need to learn how to work with and interact with the new roles.”

As an example, in 2016, GEN News reported that the most in-demand biotechnology occupations were scientific in nature and included medical and clinical laboratory technicians, medical scientists, biomedical engineers, biological technicians, biochemists/biophysicists, and chemical technicians. By 2019, however, drug development’s most in-demand jobs were of a different nature and included statisticians, mathematicians, genetic counselors, operations research analysts, phlebotomists, medical and health service managers, and computer scientists.

 In only three years, the needs of pharmaceutical and biotechnology companies evolved from primarily “basic science” knowledge to “data science” skills. Because STEM skills are in high demand and have high employment multiplier effects, wages for STEM occupations are expected to increase pointedly over the next few decades.

The high demand for technical skills has also had important consequences for the supply side of the job market. The changing distribution of undergraduate areas of concentration highlights the impact. At major US universities, for example, computer science and statistics majors have been the fastest growing concentrations. At one major east coast university, the number of completed statistics and computer science concentrations increased 235% and 220%, respectively, between 2015 and 2017.

Clinical research and clinical care convergence

At the organizational level, decision-making is shifting from volume-based to value-based models. “Approximately 80% of hospitals in the United States are now facing outcomes driven metrics,” explained one roundtable participant. This newfound focus on value for patients requires the collection of accurate patient data. To collect these data, drug developers must resort to the existing infrastructure within clinical care processes, as well as partnerships with health systems, health data aggregators, online communities, and technology companies. The high cost of healthcare, coupled with the ubiquitous presence of mobile devices and digital healthcare applications, is driving people to become more engaged with their health and wellbeing. This change in behavior is generating vast amounts of actionable patient-generated data. As a result, more than thirty percent of data gathered is healthcare-related data.

The size and complexity of these data necessitates the use of artificial intelligence and machine learning to produce insights and accelerate problem solving. But these activities can only take place if the drug development workforce is nimble when working within existing clinical infrastructures and adept at integrating and analyzing data.

Higher demand for blended backgrounds and expanded skill sets

Despite an increase in the supply of STEM professionals, a gap persists: the drug development enterprise values blended experiences and expertise, strong communication and management skills, ability to work with and across teams, and human capabilities rooted in traditional skills. As an example, science and engineering skills are most highly valued when complemented by a background in business management. Similarly, computer and data science capabilities are most attractive when paired with hands-on engineering experience. Regardless of the combination, strong communication, adaptability, strategic thinking, and ability to collaborate and work in teams are now indispensable characteristics of the drug development professional. However, many STEM professionals in drug development do not also have blended backgrounds or strong communication skills. And many of those who have strong communication skills do not also have STEM experience. An expert roundtable participant explained:

“Talents are being formed on the job. Though universities are trying to keep up, that is not enough. Companies need to take on a bigger role in identifying the skills needed to succeed in the short, medium and long term.”

To close the gap, new human-resource planning, strategy and execution are required to foster the right environment for talent to flourish. Biopharmaceutical organizations are expected to develop new kinds of training programs in which colleagues support each other throughout their continued education in both technical and human skills. The competencies that will be valued by drug development organizations include technical and human capabilities and will likely be categorized into three areas:

. The industry’s nascent patient-centric approach requires professionals who are able to adapt to rapidly changing conditions and new technologies, while foreseeing changes. Specifically, organizations require individuals who understand big data—an ability to acquire second-hand knowledge, manipulate interfaces, use IT infrastructure, and develop algorithms to generate insights. Organizations also require people who can understand concepts across multiple disciplines and who are able to work through the various workflows that make up the bench to bedside process.

. Effective patient-centric drug development depends upon having staff willing to question assumptions and learn new software packages and methodologies through self-training and group-training. It is also reliant on staff having genuine interest in mining, cleaning, and analyzing data, and the skill to discern patterns while improving individual and team workflow efficiencies.

: A data driven approach to drug development also requires exceptional communication skills, both verbally and in writing.

Emergence of new roles for the enterprise

The rapid rate of technological change can lead to abrupt changes in the labor needs of drug development organizations. For this reason, companies must retain their ability to reconfigure their workforce as needed. The senior leaders who participated in this research identified the emergence of new roles as an important trend for the next few years. The roles that are expected to emerge within the enterprise in the coming years include:

. These individuals will ensure that AI systems operate as designed and intended.

 They will work to ensure that AI systems are functioning properly as tools that exist only to help people in their work. Sustainers will engage in overriding AI decisions based on profitability, legal or ethical compliance.

Sustainers will also oversee the application of critical thinking to AI performance and design of interfaces for an AI-enabled workforce.

. AI systems are learning to adapt to us. To do so, these systems need extensive training by humans. Trainers will be involved in activities such as data cleaning and discovery and will be responsible for “making sure that data is properly annotated within strict annotation guidelines”.

They will validate and verify the annotations in large datasets, and if a machine is not picking up correctly, they will re-annotate the data.

 They are responsible for mining, cleaning, and analyzing vast datasets. They identify patterns and generate insights by using advanced analytics and programming tools.

. They will be responsible for bridging the gap in expertise between technical teams made up of data scientists, data engineers and software developers, and business managers

—that is, they will translate the findings of technical teams into actionable items for business teams. The responsibilities of a translator may include classifying the reasons a particular AI algorithm acts as a black box, or making judgment calls about which AI technologies might best be deployed for specific applications.

. These individuals will work with patients to ensure that they are given an active role in the drug development process. Navigators will use remote patient monitoring tools to keep virtual tabs on patients for potential medication complications.

 They will also play an important role in breaking through literacy barriers, building trust, reducing fear, and supporting the improvement of patient-provider communication;

 thereby, supporting the drug development process.

. They will provide technological support to clinical research associates, clinical trial coordinators, and medical and other staff who are critical to the execution of clinical trials at investigative sites. Navigators can save time for providers without compromising quality in documentation or patient experience.

Absence of biopharmaceutical R&D data strategy principles and governance frameworks

The volume of available scientific, clinical, and operational information requires that organizations execute strategies to support research and product development decisions. The drug development enterprise is also seeking guidance from regulatory bodies to determine what constitutes meaningful and useful evidence to support agency decisions and to ensure the compliance of data sources with regulatory requirements. Guidelines for data sourcing, ownership, integration, privacy and security, and use of real-world evidence remain unclear, and all stakeholders in the development process must work together to produce a shared understanding of the industry’s purpose and direction, and a set of digital thinking principles. “We need to go back to the basics of defining what is needed,” remarked an expert roundtable discussant.

Research from Veritas Technologies (March 2019) found that companies are losing at least $2 million (US) per year due to data management challenges that have severe impact on employee efficiency. This research also showed that, on average, employees lose two hours per day searching for data. This translates into a 16% reduction in workforce efficiency.

Though there are proven strategies for cost optimization, a study by Gartner (June 2020) found that many organizations lack a structured plan for executing those initiatives. Gartner research noted:

“Many organizations struggle to capitalize on potentially transformative technological innovation. In fact, innovation efforts are often physically and organizationally isolated from the operational aspects of an organization that they are suited to help. The challenges include (1) lack of ownership, (2) outdated methodologies, (3) outdated skills, (4) lack of focus, and (5) concerns about upfront costs and long-term sustainability.”

The costs of management include five disciplines: data integration, data quality, master data management (MDM), enterprise metadata (EMM) and database management. The Gartner study suggests that data leaders who are responsible for modernizing information infrastructure within their organizations should employ cost optimization techniques in the areas of people management (i.e., new and existing roles, skills and training), practices (i.e., team structures, modern architectures, team organization and collaboration), and technology deployment (i.e., tool consolidation, new deployment options, open-source solutions, and adaptive pricing strategies). “The Industry 4.0 revolution could bring enormous benefits to bioprocessing, but like any revolution, it must attract committed participants before it has a chance of success.”

The growing cost of data management is driving the adoption of unified data platforms and new workforce competencies supported by retraining to ensure high proficiency in leveraging these platforms and tools. Technology retraining will increase workforce development costs. The roundtable agreed, however, that organizations that make investments into the continued retraining of employees will also find themselves at a competitive advantage due to reductions in product development cycles.

Roundtable participants recommend that ‘people management’ and ‘practices’ are two areas requiring substantially more attention and investment. “Incorporating big data and next-generation analytics into clinical and population health research practice will require not only new data sources but also new thinking, training, and tools.”

Growing competition with the technology sector for talent

The drug development enterprise must tap into the technology sector to recruit data scientists and others with expertise in project management, systems engineering, and computer science. As competition for talent intensifies, biopharmaceutical companies must also factor the expectations of digital natives who demand flexibility, opportunity, upward mobility, and meaningful work into their people development plans.

Practical steps for the drug development enterprise: attracting and retaining talent

Digital transformation is highly dependent on the ability to attract and retain talent, but the digitization of the drug development process is exacerbating already-high employee turnover rates. To increase employee retention and satisfaction while attracting new recruits, industry leaders are advised to develop agile decision-making systems and frameworks, model behaviors from the top, develop customized training programs, collaborate with universities and organizations outside of the industry, and factor the “extended workforce” into growth and training strategies. Millennials will soon make up the majority of the workforce. The factors that matter most to these potential recruits when they are evaluating companies to join include purpose and mission-driven activity, fun, agility and opportunities for rapid self-development and professional advancement.

Develop a set of digital thinking principles for the enterprise

The drug development enterprise urgently needs to develop a set of digital thinking principles or rules of the game. These generally understood principles must focus on and guide administrative practices and workflows with emphasis on the sequence of research, development, and manufacturing.

Additionally, a collective, consensus-based definition of “drug development professional” must also be developed. To develop such a definition, executive leaders in the roundtable recommended conducting an industry-wide needs assessment.

Develop a culture of agility, flexibility, integration, and collaboration

Drug development organizations are operating in highly dynamic environments due to the rapid propagation of new software and tools. To attract and retain talent, innovating in functional processes is required. As such, biopharmaceutical executives are advised to develop agile decision-making and processes that are well-aligned with the company’s culture and ethos. Agile decision-making processes are adaptable and applicable in unforeseen scenarios. As noted by a roundtable participant, “decision-making is now based on advanced analytics, and integration is of paramount importance.” This method facilitates the shortening of timelines to retrain colleagues and onboard new recruits. This can be achieved by setting up internal retraining programs so that roles that didn’t previously exist can be filled by existing staff. Another drug development executive stated: “There is very little that is being done around the soft skills (or human skills) that accompany the investments we make in systems. We need to focus our attention on training people to work in agile ways. We need to focus on pace.”

Leaders must continue to focus on building dynamic cultures and developing feedback loops within and between teams. Community building is perceived as instrumental to the success of the digital transformation of the drug development workforce. Leaders are encouraged to communicate with staff at every level of the organization to disseminate best practices, celebrate successes, and speak candidly about the pace of implementation of strategic initiatives.

Measuring and communicating outcomes associated with the implementation of digital transformation initiatives is crucial to success. To effectively measure and communicate these outcomes, organizations must assess the state of their processes before and after rolling out initiatives, and achievements can be identified by periodically reviewing the process of collecting, cleaning and analyzing data across workflows. By using metrics such as ‘cycle time reductions’ organizations can show the impact of the rollout of customized data-science training programs on revenue. Initiatives that focus on implementing incremental changes tend to be most effective because the allow for the recognition and reward of small wins.

Agility develops when persons feel enabled to learn, take action, and communicate their impact. People are motivated by their ability to communicate about a problem they have solved and their impact on the bottom line.

Modeling agile behaviors from the top to promote psychological safety

The development of agile and flexible processes is highly dependent on the ability of staff to take calculated risks and operate outside of comfort zones. Companies that create an environment in which agile behavior and practice can flourish find that their teams can innovate faster. High turnover rates within agile companies are expected to remain high. To succeed, recruitment processes must be tightly integrated with organizational culture, decision-making and workforce management to drive faster candidate identification, recruitment and onboarding. It is important that staff are able to take calculated risks and operate outside comfort zones. Staff needs the assurance that risk-taking is acceptable within corporate culture, and this must be communicated by leaders by modeling those behaviors. People will take calculated risks if they experience high levels of psychological safety, defined by Kramer and Cook (2004), as “individuals’ perceptions about the consequences of interpersonal risks in their work environment. It consists of beliefs about how others will respond when one puts oneself on the line, such as by asking a question, seeking feedback, reporting a mistake, or proposing a new idea.”

 Teams and individuals must be afforded the space to take calculated risks and be inventive thinkers. 

Higher-purpose objectives motivate today’s workforce. It is therefore of great importance for companies and the industry as whole to adopt the practice of effectively communicating its mission and purpose, while emphasizing its role in extending human life spans improving quality of life. The sample of participants included in this research unanimously agreed that Industry outsiders will likely be encouraged to join the drug development enterprise if they find that staff members are motivated by a purpose that is personally meaningful. Motivating with a higher purpose will not only attract top talent to the biopharmaceutical industry, but it will also incentivize drug development professionals to develop new skills—technical and human—and adapt to fast-changing conditions.

Customizing training programs and creating incentives for self-development

Creating environments where self-learning is rewarded is of vital importance. The first step in creating such an environment is to offer customized mentorship and training programs geared toward addressing individual needs. Compared to past approaches, individualized programs are delivering better outcomes and have proven to be effective in the implementation of new technologies and software packages. The customized approach has also shown its value in training professionals in new methodologies and applications, and as stated by a biotechnology executive:

“Customization is the name of today’s game”

Some examples of customized training include in-house experiential training programs, simulation programs, hackathons, coding camps, and off-campus (i.e., ex-company) training programs.

Customization to individual needs does not impede individuals from interacting with other members of the organization when pursuing customized training. In fact, customized training programs can promote community building through in-team and cross-team challenges, hackathons and crowdsourcing. This sense of community often leads to greater innovation and to solutions to the problems in which people are most personally invested in.

Training must also be accompanied by incentives for continued learning and self-development. For this reason, the second step in the training process is to reward staff for their participation in training programs. A learning environment in which small wins and gradual progress are identified and communicated throughout teams can lead to greater employee engagement. This approach feeds a virtuous cycle that maintains high levels of motivation and rates of upskilling among team members. An important aspect of this approach is storytelling: team members, managers, and leaders must become adept at telling stories of success, whether big or small.

Recognition of wins and successes is an important element of this training cycle. To that end, organizations must look to develop key performance indicators at the individual and organizational levels. At the level of the individual, key performance indicators should be personalized; these should result from sincere conversations between managers and their direct reports.

At the organizational level, examples of key performance indicators include: the number of new data-science training programs deployed, staff participation in data-science training programs, staff completion of data-science programs, outcomes for staff who completed data-science training programs, number of data-science fellows and/or interns recruited, number of training programs aimed at improving quantitative and computational skills, trainee-reported proficiency in quantitative and computational skills, and disciplinary diversity of staff, among many others.

Embracing the “extended workforce” into talent development strategies

To factor the extended workforce into their growth and development strategies, organizations are encouraged to follow these practices: understand the functions and skills of the individual representing the Contract Research Organizations (CROs); understand and address the needs of clinical researchers, coordinators and other staff involved in the protocol implementation process which might include access to training in AI-powered clinical applications; define clearly the role of Clinical Research Associates (CRAs) in relationship to both the protocols that are being implemented and the larger mission of the drug development organization sponsoring a study; and delineate the roles of staff directly involved and crucial to the digital transformation process (i.e., AI sustainers, AI trainers, and data translators/explainers).

Becoming aware of talent development practices in other industries

Drug development leaders are encouraged to look beyond their industry for solutions to address the digital transformation of the biopharmaceutical sector’s workforce. Leaders must engage with and travel to companies in other sectors such as telecommunications, energy, financial services, and computing to understand their approaches to talent development and gain insights that may be applicable to the drug development enterprise.

It is also of great importance for biopharmaceutical leaders to gain insight into how companies in other sectors relate to their consumers. Learning about how companies relate to their consumers might also inform how consumers may relate to biopharmaceutical companies as well. They way that companies relate to their consumers informs what staff within companies need to learn and be aware about.

Developing partnerships with educational institutions

The drug development enterprise must invest in developing relationships with educational institutions, including elementary schools, high-schools, and universities.

One approach to partnership development with educational institutions includes the co-development of data science programs. Another approach is the development of rotational internship programs and fellowships. These programs create opportunities for biopharmaceutical companies to provide information and introduce their missions to a younger population and to soon-to-be professionals. Students who participate in any of these programs are important entryways to their friends and colleagues; when these students return to their respective educational institutions, they become educators and spokespersons for the organization and the drug development enterprise as whole.

This first effort to better understand and anticipate the impact of digital transformation on the drug development workforce has yielded some valuable and strategic insights. To remain viable, biopharmaceutical companies, along with their service providers, must develop cultures, capabilities and infrastructure that is data-enabled, flexible, agile and collaborative. And senior leadership must play an essential role in this transformation through the development and deployment of organization-wide data strategies, workforce development and management strategies, operating governance mechanisms, open engagement models, training programs, policies, procedures and new management metrics.

Through this research effort, two near-term actions have been identified as crucial in importance to facilitating the drug development enterprise’s ability to navigate the digital transformation successfully:

First, a more expansive consensus definition of ‘drug development professional’ must be developed. To develop such a definition, an industry-wide assessment of needs must be conducted.

Second, a set of digital thinking principles must be outlined by enterprise leaders to guide the use of rich data and augmented analytics in the continuum of research, development, and manufacturing of biopharmaceutical products.

This study is intended to animate discussion and coax future research about talent development and digitization of the drug development process.

is a Director and Professor, both with Tufts Center for the Study of Drug Development, Tufts University School of Medicine.

https://www.biospace.com/article/the-6-most-in-demand-biotech-jobs-right-now/

These figures are based on our analysis of major completion data at a major US university.

Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416. PG 15-16 http://mitsmr.com/2odREFJ

https://x.ai/the-role-of-ai-trainers-in-teaching-a-machine-to-understand-us/

Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416, PG 15 http://mitsmr.com/2odREFJ

https://www.healthcareitnews.com/news/screeners-navigators-and-nudgers-future-conversational-ai-healthcare

Perakslis, Eric, Takeda R&D Data Science Institute, Takeda Pharmaceuticals International, 35 Landsdowne Street, Cambridge, Massachusetts 02139, USA. doi:10.1038/nrd.2016.265, Published online 13 Jan 2016

Kramer, RM; Cook, KS; Trust and Distrust in Organizations – Dilemmas and Approaches (2004); PG 241.

The drug development sector is embracing technologies and digital methods that were previously not as widely used due to the COVID-19 global health crisis. 

Anticipating Digital Transformation of the Drug Development Workforce

The results of several studies published in 

 and elsewhere have demonstrated the impact of growing clinical trial data volume and the increasing diversity of clinical data sources on study timelines and inefficiencies.

New benchmarks show that the increase in data volume and diversity is accelerating. The typical late-stage protocol, for example, now collects on average 3.6 million data points, three times the number collected 10 years ago.

In collaboration with and funding from IBM Watson Health, Tufts CSDD has now completed a study assessing how the clinical data management function—internal teams in collaboration with their electronic data capture (EDC) partners—are managing, collecting, organizing, storing and analyzing their clinical data. This article summarizes the results of this collaborative study and provides more granular cycle time measures, experience with EDC solutions, insights into challenges and opportunities to optimize clinical data management practice.

A global survey was conducted between September and October 2020.In total, 194 verified unique individual responses were received and analyzed. Respondents had an average of 10.8 years of experience working in a variety of roles including clinical operations, data management, executive leadership, clinical development and others. The mean number of clinical trials performed per year was 54. In the discussion that follows, we’ve classified organizations into two primary subgroups: those conducting 50+ trials/year are designated as “Larger” and those conducting fewer than 50 trials/year are designated as “Smaller.”

Clinical data management and EDC solution pain points

More than twelve different EDC solutions were identified by respondents. Most organizations (73.9%) report using two or more and the remaining 26.1% of respondents report using only one EDC solution.

Table 1 below shows the wide range of clinical data management pain points reported. The typical respondent noted 3.3 different pain points associated with their primary EDC solution. Managing mid-study updates and their associated delays topped the list followed by flexibility and customization challenges.

 Pain Points Experienced with Primary EDC Solution

Nearly one third of respondents reported challenges with database go live delays and the lack of integrated patient engagement and electronic clinical outcome assessment (eCOA) applications. Slightly more than 30% reported challenges with the cost of their primary EDC solution. Pain points that were reported the least frequently included data incompatibility between platforms, customer support problems and solution complexity (slow user learning curve).

Although a relatively high percentage of respondents cited database go live delays as a key challenge, it is interesting to note that the cycle time between final protocol approval and the database go live milestone has not changed during the past three years.

 Figure 1 below shows that the average duration from final protocol approval to the database go live milestone has increased by one day.

The results of this study also show that the average study close-out cycle time—from last patient last visit to database lock—remains unchanged between 2017 and 2020 with organizations reporting an average duration of 36.8 days. Mid-study updates represent a time intensive challenge.Managing a mid-study update requires about half of the total time needed to achieve the initial go live release of the database, as shown in Figure 2 below. This helps explain why planned and unplanned study updates are the top reported pain points.

Granular Data Management Cycle Times Between Small and Large Companies

Looking at each of these cycle times individually, and comparing smaller vs. larger organizations, there were some notable differences. The results show that smaller organizations (<50 trials/year) are faster at early-stage data management processes whereas larger organizations (≥ 50 trials/year) are faster in late-stage processes.

Despite the challenges noted, respondents report a high level of satisfaction with their EDC solutions, as shown in Table 2 below. Database design and data collection tasks received the highest satisfaction levels with over 90% satisfied. Database closeout, the overall end-to-end process, and data processing and cleaning abilities also received high levels of satisfaction. Managing mid-study updates and the overall study cycle time received the lowest relative satisfaction scores.

Satisfaction with EDC Solution Performance Across Data Management Tasks

As noted in Table 3 below, EDC solutions received high marks with 84.1% and 82.3% of respondents satisfied with the solutions’ ability to reduce the number of manual errors and avoid costs in managing mid-study updates. Fewer respondents (75.8%) were satisfied with the ability of their EDC solutions to help them reduce delays in managing mid-study updates.

Satisfaction with EDC Solution's Management of Mid-Study Update Challenges

EDC solutions also received high satisfaction ratings for electronic data capture, randomization and endpoint adjudication (100% of respondents reported being satisfied). Over 90% were satisfied with medical coding, data integration, data management and eConsent capabilities. Electronic patient-reported outcomes (ePRO), eCOA and Study-Level Reporting received the lowest satisfaction ratings. Nonetheless, over 70% report being satisfied in these areas as detailed in Table 4 below.

Satisfaction with EDC Solution Functionality

Table 5 below shows how respondents ranked aspects of their EDC solutions and solution providers that they like least. Lack of responsiveness and poor individualized attention were among the top criticisms of EDC providers.

Least Liked Aspects of EDC Solutions and Solution Providers

Functionality was by far the top factor that influences respondents’ decision when selecting an EDC solution as shown by Table 6 below. A distant second was Ease of Use with 57.4% of respondents including this factor among their top three. The ability of a solution to handle clinical trial scope and complexity was also noted as a top factor influencing selection decisions. Customer support, pricing and reputation were among the least influential factors.

 Influence of Factors When Selecting an EDC Solution

Looking ahead – will EDC solutions and providers maintain their high marks?

Organizations look for their EDC solutions to support a wide range of data management tasks and provide a variety of functionality. This study found that—although cycle times are not improving—the majority of organizations are satisfied with their providers and EDC solutions on all accounts.

Cycle times from final protocol approval to database go-live and last patient visit to database lock—now average 69.4 and 36.1 days respectively—represent opportunity areas to accelerate. Similarly, the time required to implement mid-study updates (28.5 days for planned and 29.9 days for unplanned) against a backdrop of frequent amendments and an increasing number of adaptive design studies also represent a major area of opportunity requiring attention and improvement.

One issue highlighted by the contradictory relationship raised between challenges and satisfaction rates is the close dependency that data management has on other study tasks. Evaluating EDC solutions for efficiency or time savings remains impractical due to the many external confounders—such as the protocol approval process and amendment resolution—that impact data management cycle times.

How EDC solutions and providers can add or integrate new functionality, manage mid-study updates and reduce overall cycle times remains to be seen. This study serves as a baseline from which to measure future progress. We look forward to monitoring the progress in this highly important aspect of drug development.

The authors would like to thank Dilhan Weeraratne and Van Willis at IBM for their contributions to the project and their support in developing this manuscript.

Indupuri R, Rocchio S, Getz K, Harper B, Wilkinson, M. Enabling Digital Transformation: Managing External Clinical Data Sources to Advance Drug Development. 

https://www.appliedclinicaltrialsonline.com/view/enabling-digital-transformation-managing-external-clinical-data-sources-to-advance-drug-development

.Published November 13, 2020. Accessed January 4, 2021.

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Rising protocol design complexity is driving rapid growth in clinical trial data volume.

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2020 Tufts CSDD – IBM Watson Health benchmarking study highlights need for new functionality from EDC solutions and providers.

Characterizing Clinical Data Management Challenges and Their Impact

Data has truly become the currency in our everyday lives, and the volume and speed at which it is being collected, processed and exchanged is staggering. For instance, every minute, 188 million emails are sent, 4.49 million Google searches are conducted, and 4.5 million videos are watched on YouTube.

 This proliferation of information can also be seen in the world of clinical research, where the amount of clinical data per trial has increased by 183% in the last decade.

The growing volume of clinical data illustrates one significant way in which the drug development landscape is evolving. As clinical trials are becoming more decentralized, the variety of data types and sources, including genomics, biomarkers, wearables and images, that are being used in drug development is also expanding. With the amount of diverse data that must now be collected and analyzed, the life sciences industry is faced with increasingly complex studies that present new challenges in clinical data management.

From October 4 to December 30, 2019, eClinical Solutions and the Tufts Center for the Study of Drug Development (Tufts CSDD) conducted a survey to understand the trends in clinical data management and assess how sponsors are responding to current challenges. 149 individuals in senior-level data management, data sciences, biostatistics, information technology and clinical research operations roles participated in the study. Of the respondents, 48% were one of several individuals within an organization responsible for clinical data, 33% were primarily responsible for clinical data and 19% were frequent users of clinical data systems. 

The majority of respondents came from pharmaceutical (41%) and biopharmaceutical (37%) sponsors, though CROs (10%) and independent contracts (6%) were also represented. The sizes of respondents’ organizations varied from small sponsors (30%) that had initiated an average of 2.9 trials yearly, medium sponsors (29%) that had initiated an average of 23.8 trials yearly and large sponsors (17%) that had initiated an average of 112.4 trials yearly.

Sponsors are collecting more data types than ever. Though non-CRF data is the most commonly used in clinical trials among 87% of respondents, a variety of other data types and sources are also being used. Nearly two thirds of sponsors are incorporating direct data capture, apps, devices and medical images as other data sources, while roughly one-third are using electronic health records (43.4%) and omics data (35.5%).

Data sources contributing to significant delays

The growing number of external data sources is associated with longer database lock cycle times with more than half of the study respondents citing non-CRF data as a primary contributing factor. Since 2017, this has resulted in a 40% increase in time from Last Patient Last Visit (LPLV) to database lock. Specifically, large pharmaceutical companies using four or more sources are experiencing a three-week increase in Last Patient Last Visit (LPLV) to database lock.

Last Patient Last Visit (LPLV) to Database Lock Cycle Times

Labor intensive data management activities

Although the availability of richer data sets is essential for gaining deeper insights to advance drug development, sponsors are faced with challenges in all data management activities related to organizing and reviewing the immense volume of data. Among all participants in the Tufts-eClinical Solutions study, analyzing and consuming data was rated as the least challenging of these activities, though over fifty percent of respondents still noted this activity as somewhat or extremely difficult. Of the seven key data management tasks noted in the survey, participants highlighted the process of initiating relationships with data providers as the most time-consuming and labor intensive. This highlights the opportunity for improving vendor relationships as a crucial element in alleviating difficult data management duties.

Data management activity labor intensiveness

Unsophisticated tools to centralize and standardize data

Even though the variety and volume of data continues to evolve, making data management activities more difficult and time consuming, the tools used for data integration and analytics remain relatively unchanged. 75% of companies are still using SAS as the primary tool to convert, explore and analyze data.

With the changing landscape of clinical research presenting challenges in how companies organize and review disparate data types from a multitude of sources, all participants in the Tufts-eClinical Solutions study recognized the need to find solutions. Potential areas for improvement that were highlighted included handling more external data sources, decreasing cycle times through streamlined processes, encouraging patient-centered data acquisition strategies and developing better opportunities for real-world data uses. Life sciences organizations identified several ways in which they are responding to evolving clinical data management requirements in these specific areas.

When an organization outlines a formal data strategy to determine how data will be collected, processed and analyzed, there are clear benefits to clinical research. The greatest impact is to database lock times. Database lock is accomplished 10 days faster with sponsors that have implemented a data strategy compared to those that have not. Since large companies are managing more data sources, they are also more likely to have implemented a data strategy, especially given that 53% of organizations that are using five to six data sources experience longer times to database lock.

Though there are significant benefits, only one-third of the survey respondents have implemented a defined data strategy. Almost half of the respondents were either still examining or planning a data management initiative in the next few months. This means that there is still a great opportunity for all life sciences companies to leverage the benefits of a formal data strategy in reducing trial delays.

To keep pace with the growing number of disparate data, organizations are turning to more advanced tools. The survey findings show that clinical data hubs or repositories are being used more often than other systems, including EDC and SAS-based infrastructure. However, EDC is still being used most often for specifically integrating and organizing direct data capture. Tools like clinical data platforms with robust capabilities to help data managers import, transform and review data will become powerful assets as data sources continue to increase.

Sponsors using a data hub or lake also had more confidence in their analytics capabilities in areas associated with analytics dashboards, interactive visual exploration, the ability to publish and collaborate on analytic content, and augmented data discovery.

Integrating new technology into current systems is just one element in establishing mature analytical capabilities. The survey also revealed a strong association between these capabilities and the implementation of data strategies. Over 50% of respondents who have a defined data strategy noted that their analytical capabilities are more developed.

Another benefit to having definitive data strategies can be seen in the areas of artificial intelligence (AI) and data science competencies. Organizations that have implemented a data strategy are more mature in terms of researching or implementing AI, machine learning and deep learning capabilities. These companies are also more likely to be developing new data sciences functions or specific data sciences roles. In fact, 47.2% that have implemented a data strategy have also established a data sciences function.

Developing analytical and AI capabilities is crucial to preparing for the digital transformation that is sweeping across the drug development industry. Life sciences companies must now meet the increased demand for clinical data and real-time insights from a variety of distinct sources. By automating time-consuming and labor-intensive data tasks, sponsors are able to support greater access to and deeper insights into data to drive innovation and accelerate drug submissions and discoveries.

Respondents to the Tufts-eClinical solutions survey agreed that there are tangible benefits to laying the foundation for AI and data science through formal data strategies. 85% of all respondents rated these qualities as somewhat or very beneficial:

Increased visibility of data to stakeholders for faster decision making

Decreased cycle times through more collaboration and shared analytics

Cost-savings through resource optimization and reduced rework

Faster acquisition and benefits from new real-world data sources that contribute to development


Conclusion: a new era of clinical research

In this modern era of drug development, clinical data has become one of the most valuable assets for life sciences organizations in powering digital transformation. As the Tufts-eClinical Solutions study demonstrates, delays in clinical trials, measured through database lock cycle times and through manual efforts and time-consuming processes associated with assembling data sets for review and insights will continue to increase without the implementation of new strategies. The diversity and volume of clinical data is also growing exponentially, and the tools for managing these must improve to keep pace.

Industry leaders are recognizing that evolving their approach to the research process is critical. By adopting formal data strategies, implementing modern technologies like clinical data platforms and expanding data science competencies, analytics and AI capabilities, sponsors can gain more control over the research process and drive actionable insights from new data streams to continue to push and accelerate drug development.

Anticipating the Impact of the Patient Engagement Movement on Clinical Operations

With an increasing amount of diverse data that must now be collected and analyzed, the industry is faced with increasingly complex studies that present new challenges in data management.