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

A fast-changing labor market

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.¹ 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 2017.²

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:

Complex reasoning and adaptive thinking. 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.

Agility and self-directed learning. 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.

Communication and persuasion: 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:

Artificial intelligence (AI) sustainers. These individuals will ensure that AI systems operate as designed and intended.³ 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.⁴ Sustainers will also oversee the application of critical thinking to AI performance and design of interfaces for an AI-enabled workforce.⁵

Artificial intelligence trainers. 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”.⁶ 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.⁷

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

Data translators/explainers. 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 managers⁸—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.⁹

Patient navigators. 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.¹⁰ They will also play an important role in breaking through literacy barriers, building trust, reducing fear, and supporting the improvement of patient-provider communication;¹¹ thereby, supporting the drug development process.

Technology navigators. 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.

Anticipated challenges

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.

Increasing cost of data management

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:

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.”¹³

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.”¹⁴

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.”¹⁵ Teams and individuals must be afforded the space to take calculated risks and be inventive thinkers. 

Motivating with purpose

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:

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.

Conclusion

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.

Maria I. Florez is a Research Consultant; and Kenneth Getz, MBA is a Director and Professor, both with Tufts Center for the Study of Drug Development, Tufts University School of Medicine.

References

1. https://www.biospace.com/article/the-6-most-in-demand-biotech-jobs-right-now/
2. These figures are based on our analysis of major completion data at a major US university.
3. Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416. PG 15-16 http://mitsmr.com/2odREFJ
4. Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416. PG 15-16 http://mitsmr.com/2odREFJ
5. Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416. PG 15-16 http://mitsmr.com/2odREFJ
6. https://x.ai/the-role-of-ai-trainers-in-teaching-a-machine-to-understand-us/
7. https://x.ai/the-role-of-ai-trainers-in-teaching-a-machine-to-understand-us/
8. Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416, PG 15 http://mitsmr.com/2odREFJ
9. Wilson, JH; Daugherty, PR; Morini-Bianzino, N; MIT Sloan Management Review, Vol. 58, No. 4 Reprint #58416, PG 15 http://mitsmr.com/2odREFJ
10. https://www.healthcareitnews.com/news/screeners-navigators-and-nudgers-future-conversational-ai-healthcare
11. https://www.healthcareitnews.com/news/screeners-navigators-and-nudgers-future-conversational-ai-healthcare
12. 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
13. Macdonald, GJ, Genetic Engineering & Biotechnology News Vol. 40, No. S3, August 1, 2020 Copyright © GEN Publishing pp. S7–S10 DOI: 10.1089/gen.40.S3.03
14. 10.1377/hlthaff.2014.0053 HEALTH AFFAIRS 33, NO. 7 (2014): 1163–1170 ©2014 Project HOPE— The People-to People Health Foundation, Inc. PG 1163
15. Kramer, RM; Cook, KS; Trust and Distrust in Organizations – Dilemmas and Approaches (2004); PG 241.