Denise Calaprice, PhD

The acceleration of drug discovery and development has taken on even greater importance as we face a third year of the COVID-19 pandemic. The industry has responded by incorporating remote monitoring techniques and fast-tracking the use of hybrid and decentralized clinical trial (DCT) models, at a pace never seen before. To sustain the momentum on these and other innovations that support study execution, we have also seen the rise of the #NoGoingBack initiative which aims to ensure that the progress made doesn’t fade when the pandemic finally fades.*

Despite these short term achievements, it is still taking more than 20 years to reach industry-wide adoption of various technologies and innovations that could prove beneficial to clinical research operations. Interactive Voice Response Systems (IVRS) and Electronic Data Capture (EDC) solutions were introduced in the late 1980s and early 1990s but these are only now reaching wide adoption

 by sponsors and CROs. Electronic Patient Reported Outcomes (ePRO), Electronic Clinical Outcomes Assessment (eCOA), and eSource technologies, while introduced in the mid to late 1990s, have yet to achieve wide adoption.

 This pattern holds true for risk-based monitoring, digital health outcomes, and other innovative approaches and solutions. While these innovations offer great promise, their potential can’t be realized if it takes two decades or more for them to be fully embedded into the drug development process.

In a first attempt to understand the factors impacting technology adoption, a 2020 Tufts CSDD study revealed that financial and regulatory concerns topped the list.

 Lack of trust in the technology was also reported as an important factor in that study. Additionally, it also revealed that organizational culture and senior leadership’s approach presented significant challenges to the process of technology adoption.

To gain further insights into the barriers to and opportunities for accelerating innovation adoption at both the industry and company levels, the Tufts Center for the Study of Drug Development (Tufts CSDD) undertook additional research in 2021. The purpose of this research is to characterize the long term adoption experience of technologies and capabilities supporting clinical trial execution. This research takes a long term view, and since we have yet to see the permanence of the changes that are taking place during the Covid-19 pandemic, these changes were not the focus of this research.

The research comprised informational interviews and an online global survey of biopharmaceutical executives and clinical research operations personnel. The research was completed in collaboration with WCG Avoca and seventeen biopharmaceutical sponsors who formed the working group (WG) participating in the project.

This article will provide some high-level findings from the research with a focus on actionable steps that biopharmaceutical executives can take to foster and support the adoption of innovations within their companies, namely:

Instituting mandates for greater operational efficiency

Greater support, involvement and engagement by senior leadership throughout all stages of the innovation adoption process

Offering incentives and recognition—and eliminating penalties—to individuals who promote innovative behaviors, approaches, and technologies within the organization; and

Implementing better change management, planning, and communication activities early on, and throughout the process.

The Primary stages of innovation adoption

Two predominant models exist that provide context for how innovations are adopted. The first model is the Gartner Hype Cycle, developed by Fenn in 1995. The second model is known as the Innovation Diffusion Model, developed by Rogers in 1963.

The initiation stage involves identification of the need to try a different technology, process, system, or approach to optimize clinical trial execution. This is where organizations gauge the interest and start the initial planning for trying or testing out an innovation. This is followed by the evaluation phase, which typically involves pilot projects or other assessments of the potential innovation. Based on results from the evaluation phase, organizations typically go through a decision-making process to either abandon or move forward with enterprise-wide adoption of the innovation. This is finally followed by a series of communications and training activities in support of broad implementation of the innovation throughout the organization.

These models informed the development of an interview guide and global survey. Twenty interviews were conducted with various stakeholders from the working group’s member companies. The interviews provided insights into the stages and decision-making processes that sponsor organizations follow to identify and evaluate potential innovations in the clinical development process. The findings from the interviews then shaped the creation of a global survey. The survey was organized into four key stages of the innovation adoption process, namely, initiation, evaluation, adoption decision and full implementation (Figure 1).

Figure 1. The Biopharma Innovation Adoption Cycle

Survey methodology and data characteristics

The survey was primarly structured according to this four-step process with questions aimed at further understanding the process of adoption for new processes, systems and/or technologies that are transformative in terms of how the industry designs, plans, executes and manages data supporting clinical trials. Specifically, respondents were asked to consider innovations that are still emerging and haven’t yet reached industry-wide adoption. Illustrative examples were provided for reference and included eSource, eConsent, Risk Based Monitoring (RBM), Digital Health Endpoints and AI capabilities.

The survey asked respondents to characterize the timeframes for going through the various stages, the functional groups involved in the process, the types of quantitative and qualitative information used to advance from one stage to the next along with key challenges and barriers associated with each stage. Broader questions related to perceptions about how companies fared in their innovation adoption approach compared to others, and about how respondents viewed their company across the innovation diffusion model—innovator to late adopter or laggard—were included. The survey also captured perceptions of how the pharmaceutical industry compared to other industries when it comes to speed of innovation. Finally, the survey focused on opportunities for accelerating the innovation adoption process.

A total of 552 responses were received from a global audience primarily representing pharmaceutical and biotechnology companies. Approximately six percent of the respondents were from CROs or other organizations; however, responses from these individuals were not included in the primary analyses. Just over half of the respondents (53.9%) worked across large sponsors with 28% working at mid-size companies and 18.1% at small biopharma.

Nearly half of responses (49.3%) were received from individuals whose company headquarters were in Europe. 38.8% and 12% of responses were received from respondents with company headquarters based in North America and Rest of World (ROW), respectively.

A variety of roles were also represented, including professionals from Clinical Operations (46%); Clinical Development (9%); Innovation Management (17%); Data Science & Data Management (10%); Information Technology (3%) and 15% from other roles. About half (51%) of respondents reported that they had some influence on the adoption decision making process, with 30% indicating they had little to no impact on the decisions. 19% of respondents indicated that they were key decision-makers when it comes to the process of pursuing, evaluating, or implementing innovations in their organizations. Finally, 14.5% of respondents were part of a centralized or dedicated innovation group whereas the majority (85.5%) worked outside of such an innovation group.

Over two-thirds (69%) of respondents indicated that the adoption process within their organization generally followed the four-step approach as outlined above (Figure 1). Where the process differed, respondents indicated that there was no formal process, or that certain steps were less defined or skipped altogether. Interestingly, some respondents from smaller companies reported having staff with experience at large companies where things were “overprocessed”, and have therefore made the decision to eliminate the process altogether. These companies intentionally decide to move straight into implementation, preferring to sort out issues along the way rather than formalizing the method and/or metrics by which they evaluate and implement an innovation.

Thus, responses reflect a broad crosssection of views and perspectives from which to learn more about how the biopharmaceutical industry approaches the innovation adoption process, what gets in the way and how organizations, and the industry more broadly, can accelerate new technologies and innovations.

The survey results revealed that within individual biopharmaceutical companies the average innovation adoption cycle for a given clinical operations innovation lasts about 6 years. The longest reported cycle lasted 10 years with the shortest lasting 1.5 years (Figure 2).

Figure 2. Average Duration of Stages in the Biopharma Company’s Innovation Adoption Cycle

Cycle times varied by company size. Small biopharma reported the fastest cycle times and mid-size companies reported the longest (Figure 3).In general, the time to complete each stage increased across the four stages with the initiation step being the shortest and the full implementation stage taking the longest. The high coefficient of variation (CV) suggests wide variation in the length of each stage of the innovation adoption cycle.

Figure 3. The Biopharmaceutical Innovation Adoption Cycle by Company Size

When it comes to the impetus to pursue or explore an innovation, the results suggest that the primary factors are (1) a new or updated regulatory guidance, (2) an operating objective to improve the speed of clinical development, or (3) a mandate from senior management to pursue an innovation (Figure 4). The support and advocacy of senior leadership along with regulatory compliance and approval considerations were also big drivers of the decision to progress to the next stages. Financial considerations were among the top three drivers during the evaluation, adoption decision, and full implementation stages. The presence of change management strategies played a key role in the decision to move forward with full implementation.

Figure 4. Key Factors Driving the Progression of Stages in the Innovation Adoption Process

When asked about barriers and challeges, study participants reported that the easiest stages to complete were the earlier stages of the process (initiation and evaluation) with the adoption decision and full implementation stages being the most challenging (Table 1).

Table 1. Perceived Difficulty of Stages in the Innovation Adoption Cycle

Some interesting themes emerged in the analysis of barriers to completing or advancing to the next stages of the innovation adoption process. Key themes include:

Specifically, employee turnover and lack of cross-functional alignment are key factors that play a role across most stages. The lack of senior management involvement and support was identified as a key barrier at the evaluation and full implementation stages.

The lack of regulatory clarity and need for legal review and approval of the given innovation were reported as barriers across all stages indicating that companies like to ensure legal and regulatory acceptability prior ot moving to the next stage.

Financial constraints were noted as barriers in the three later stages, but were not reported as an issue impacting the initial pursuit of a potential innovation. Interestingly, the misalignment of incentives was also noted as a key barrier through the three later stages as well. This comes in the form of lack of rewards or recognition for exploring and advancing new ideas and the perceived risks associated with not meeting deadlines or budgets.

During the evaluation and adoption decision stages, difficulties in comparing vendors and uncertainty about the viability of a given vendor factored more heavily into the process to advance to the next stage, compared to vendor related considerations in the initiation or full implementation stages.

The absence of change management strategies and planning were identified as important challenges at the beginning and end stages of the process.

Opportunities to accelerate adoption of innovations

Many of the challenges and barriers noted by study participants are actionable.While there is recognition that greater regulatory clarity would be helpful in accelerating the process, this is often outside the direct influence of senior leadership (but something that regulatory agencies could do to facilitate more rapid adoption of new innovations). Nonetheless, respondents identified many opportunities for improvement in areas related to personnel, finances and incentives, and operations.

Senior leadership’s presence and support throughout the process

The most striking finding is that visible senior management support and promotion of an innovation throughout all stages of the process can lead to its faster adoption. Repondents echoed the signifincance of senior leadership mandates—as well as their presence and engagement at all stages—in improving the speed of innovation adoption. These perceptions align well with the correlations observed between companies with shorter timelines and the presence of senior leadership at each stage of the adoption cycle. Those companies that had senior leadership involvement at all stages also showed shorter timelines compared to those without. The mandates and support of senior leadership, coupled with the establishment of operational objectives to improve the speed of clinical development, can lead to shorter adoption cycles.

It is important to note that active, visible involvement and encouragement by senior leadership is currently a key factor in the speed of innovation adoption because the decision-making power on matters that affect innovation adoption tends to reside exclusively with this select group. An alternative approach that has been looked at, particularly within larger organizations, and which warrants further investigation is one where this decision-making power—and the accountability that follows it—is adequately distributed among teams and individuals throughout the organization.iv This approach could offer companies the ability to adapt easily, and to quickly make decisions that directly impact the speed of innovation adoption.

In a similar vein, company leadership are encouraged to continue to develop a culture of innovation within their organizations and across industry. Our research showed that nearly half of the respondents (42%) indicated either high or moderate risk to their performance review when pursuing adoption of an innovation. Thus, leaders are encouraged to build incentives for participating in the innovation adoption process into individual performance reviews.Offering incentives and recognition for the pursuit of a new approach instead of only holding individuals accountable for adherence to study timelines and budgets tends to be correlated with higher satisfaction with the innovation adoption process within biopharmaceutical companies. Providing adequate funding, resources and financial incentives to these individuals so that they can explore, pilot, and drive the adoption process can lead to quicker innovation adoption timelines. Additionally, leaders should be mindful of inadvertently penalizing individuals who develop, champion, and/or try new ideas, approaches and technologies.

Another important element for the development of a culture of innovation is the presence of a change management strategy. Study participants reported that change management strategies and communication planning play significant roles in fostering faster adoption. Waiting for the full implementation stage to think through how the organization will use, integrate, and manage a given innovation may hamper its rapid adoption.

An important outcome of this research is that substantive differences in cycle times were not identified between companies that have a dedicated innovation group compared to those that don’t. Intuitively, the presence of a dedicated innovation group would lead to faster cycle times or fewer challenges in the later stages of the process; this hypothesis was not validated here, however. Interestingly, respondents from smaller biopharma companies suggested in high proportion that innovation groups could help accelerate the adoption process. These were also the same companies that reported the presence of innovation groups in the lowest proportions compared to mid-sized and large pharma, while reporting the fastest timelines. The faster timelines observed at smaller biopharma might be explained by their greater reliance on CRO partners, the smaller scales in which they work, and their high degree of specialization in one or few therapeutic areas, among other factors. This topic warrants further investigation and is an area that we hope to explore further in future research.Based on our extensive review of examples in the literature, the stages of the innovation adoption process, and the durations and challenges, are consistent with those encountered by similar, highly regulated, research intensive industries.

The industry may be missing out on key opportunities to accelerate drug development through a lengthy and inefficient process for evaluating and adopting innovations. Cross-company collaborations, partnerships with CRO and vendor partners, and regulatory guidance and clarity can support broader industry-wide adoption of innovations.We recognize that the rapid adoption of DCT solutions during the pandemic was facilitated in part by unprecedented proactive and accommodating regulatory agency involvement

At the company level, this research found that full implementation of a new technology or process in clinical operations can be achieved in under two years. Through mandates, senior leadership involvement, cross-functional engagement, financial support, better alignment of incentives, and improved change management and planning, biopharmaceutical companies can be more proactive and intentional about the way they explore and pursue innovations.

We would like to acknowledge the study’s Working Group for their contributions to this research.

*In this research we are defining “wide-adoption’ as approximately 70% of industry having decided or being in the process of fully adoping the technology or capability.

https://www.outsourcing-pharma.com/Article/2020/10/29/NoGoingBack-mission-brings-trial-pros-together

Tufts CSDD Analysis, presented at Veeva R&D Summit, June 10 2019, Barcelona, Spain

Tufts CSDD White Paper – Adoption of Innovative Technologies within Pharmaceutical R&D, May 2020

Articles

Characterizing the long-term adoption experience of clinical technologies and capabilities—and what senior leaders can do to drive adoption within their walls.

Accelerating Innovation Adoption to Support Drug Development Operations

	The Avoca Quality Consortium (AQC) commissioned a patient survey as part of its ongoing commitment to transform clinical trials. The overarching objective of the survey was to identify actions that pharmaceutical companies, CROs, and study teams might take to positively influence clinical trial participation and impressions of the clinical trial experience, and to increase the relevance of clinical trial results to patients. 

	This was a standalone survey, but incorporated parallels with questions that had been asked in other AQC and Avoca surveys of sponsors and clinical survey providers, to allow for comparisons where applicable. After the research plan and survey were reviewed and granted exemption status by Western IRB, potential participants were contacted by email using email addresses from the PMG Research, PLM (PatientsLikeMe), CISCRP (Center for Information and Study on Clinical Research Participation), and Avoca Quality Consortium databases, all of which are “opt in” databases comprised of individuals who have elected to receive email contacts from the relevant organizations.

	On average, survey participants did not feel that their healthcare providers had a very good understanding of what it was like to be a patient with their condition. Mean “perceived understanding” ratings ranged from only 2.9 to 3.4 on a scale of 1 (no perceived understanding at all) to 5 (perceived to understand “very well”), and for each question, between 26% and 39% of patients gave a rating of 1 or 2. 

	There was pronounced heterogeneity across patients in the extent to which they felt understood. Multivariate analyses that accounted for multiple disease and demographic characteristics at once were performed to better understand this heterogeneity. In these models, the strongest drivers of differences across patients were age group (patients aged 76+ generally felt the most understood by their providers, and those aged 31-45 the least), marital status (single and married patients generally felt the most understood, and divorced and widowed patients the least), and role (or not) in providing financial support or care for family members (providers generally felt less understood than non-providers). Disease characteristics and history of clinical trial participation had relatively little impact on these results.

	With respect to confidence in the information provided to prospective clinical trial participants, most of those surveyed had at least a fair level of confidence that trial participants are fully and honestly informed. Participants were most likely to be confident in information provided about the right to withdraw, procedures, and possible benefits, and were most skeptical about information relating to treatment alternatives, risks and side effects, and doctors’ motivations. 

	With respect to confidence in the quality of the clinical trial process overall, most participants again had at least a fair level of confidence in the compliance, caring, and ethics of the site study teams and patients involved in clinical trials; however, many were skeptical about the honesty of pharmaceutical companies and about the extent to which they cared about study patients.

	In both cases, the distributions reflected considerable response heterogeneity, particularly for areas that were rated, on average, most poorly. Interestingly, in multivariate models, the most frequent drivers of differences across patients with respect to confidence in the information provided appeared to be a history of clinical trial participation (positive impact on confidence), disease impact (highest confidence in those with mild disease), and marital status (divorced people demonstrate less confidence); all other disease and demographic characteristics had relatively little impact on the results once the effects of these three were accounted for. 

	The strongest drivers of differences across patients with respect to confidence in clinical trial compliance and ethics appeared to be a history of clinical trial participation (positive impact on confidence), educational level (highest confidence in those with least education), and marital status (divorce associated with less confidence); all other disease and demographic characteristics were again of relatively low impact.

	Common themes in patient comments to this section included: the positive impacts of including patients in the design of both protocols and operational approaches to study execution; demonstrating respect and caring of patients in all aspects of execution, including compensation; and exercising transparency at every level. Business concerns both positively and negatively impacted confidence levels: some patients were skeptical of the ethics of pharmaceutical companies, given the large amounts of money involved in researching and selling therapeutics, while others saw the high stakes as providing assurance that maximum rigor would be applied. Regulatory requirements and a belief in human nature and professionalism further bolstered confidence, while media stories regarding litigation and drug recalls eroded it.

	Overall, the results from this study lead to the clear conclusion that as an industry, we can do more, today, to impact both patients’ perceptions of clinical trial participation, and the relevance of our trial designs to patients’ personal experiences of their diseases. From basic training in mindsets for key stakeholders to incorporating the patient voice in the protocol design process, there is work to be done to move the needle on patient engagement.

- Denise Calaprice, PhD, is Senior Consultant, The Avoca Group; Jennifer Byrne is Chief Executive Officer, PMG Research; and Jeremy Gilbert, is Vice President, Product and Strategy, PatientsLikeMe

Survey examines issues related to clinical trials quality and participation from the patients' view.

Bridging the Gap for Better Patient Engagement

http://www.appliedclinicaltrialsonline.com/lean-outsourcing-models-clinical-trials-factors-achieving-successreference

), we discussed the recent trend toward “leaner,” more efficient models for the outsourcing of clinical trials, and summarized the findings of our recent research in this space. As described, The Avoca Group, working on behalf of Purdue Pharma, interviewed representatives from companies that execute lean models effectively, in order to gather information regarding strategies, decision models, tactics, and experiences, for the benefit of those exploring their use. As might be expected, efficient and effective oversight under such models typically requires the use of a well-designed set of metrics and associated thresholds, reports, and dashboards to measure performance and manage risk. A key success factor in lean models is therefore the possession of technology that can provide these in a well-organized and user-friendly manner, optimizing staff focus and productivity. For this reason, we conducted an investigation specifically to identify and describe the basic requirements for such technology solutions, as well as the factors considered by successful lean outsourcers in selecting and implementing the right solutions for their companies. Here we share the findings of that segment of our research.

	According to our investigation, suitable technology solutions address five key requirements:

1) They provide the means to identify important and actionable trends in operational data by:

		collating, presenting, and monitoring an appropriate, well-organized, and configurable selection of Key Performance Indicators (KPIs) and/or Key Risk Indicators (KRIs).

		highlighting KPIs and KRIs that are failing or at risk of failing through configurable (and potentially dynamic) thresholds. Color-coded stoplight indicators (red, yellow, green) are the common illustration of such status for individual KRIs and KPIs, with separate views for trending over time.

		providing configurable, proactive (“push”) alerts when KPI or KRI thresholds are met.

2) They provide support for detailed evaluation and root cause analysis of performance issues by:

		offering trend, pattern, and outlier identification in an easy to visualize and interpret manner (ideally offering the ability to select from a menu of pre-configured graphic visualizations and analyses).

		offering filtering and drill-down capability, including from within a graphic visualization. 

3) They provide compliant audit trails and workflows for tracking reviews, observations, issue escalation, and issue resolution across all relevant roles and levels.

		Ideally, they also support “push” notification of actions that are due or otherwise at risk of noncompliance. 

4) They provide a user experience suited to the type of resource accountable for operating the technology solution, with specific focus on a user-friendly interface for the clinical operations user-base:

		user-friendly screens to create, modify and respond to metric thresholds and other associated parameters.

		reports, exports and alerts in customizable formats, aligned to stakeholder roles.

		appropriate support and training, including self-service tools within the software.

5) They allow for external/third party access, and integrate easily with both third party systems and other industry-leading tools and practices. 

	Given these requirements, careful consideration of a number of questions drives the selection of specific solutions suitable for specific companies. These include:

A. Could the technology solution be used for data analysis and visualization beyond support of clinical operations? For example, could it also be used for risk-based monitoring (RBM), or other functions completely outside of clinical operations? 

	Given the broad potential utility of a user-friendly data aggregation, analysis, and visualization tool, a sponsor company may wish to leverage such a solution to support a broad range of functions in a cost-effective manner, including functions such RBM or safety monitoring that utilize clinical as well as operational data. If this is the case, the sponsor may want to selectively explore and choose solutions with broad aggregation, analysis, and visualization capabilities.

What works well within existing oversight processes and practices that must be supported by the system, and conversely, what key gaps and opportunities characterize current processes and practices that may be addressed by the system?

	If changing from a more traditional to a lean outsourcing model is presenting a challenge for the sponsor, tools that allow for permission-based, configurable dashboards aligned to role-specific accountabilities may help to focus the staff on only the oversight areas for which they hold accountability, and only those metrics that are deemed important for the desired level of oversight. In this manner, “detail-level” operational metrics that are to be monitored and managed by staff at the CRO partner may be excluded from the first-line dashboard presented to sponsor managers. Similarly, if staff skills relating to trend or root-cause analysis are limited, a system that allows a central user to pre-program a library of logical reports (e.g. drill-down, filtering, time-series) that are triggered in certain data-defined circumstances may be desirable, whereas if staff skills in this area are sophisticated and advanced, it may be important to select a technology solution that allows any user to create custom trial- or function-specific algorithms and advanced visualizations including box plots, funnel plots, etc.

	As another example, technology solutions vary in the extent to which they directly integrate or support replication of existing risk planning tools, such as the TransCelerate RACT tool. If such tools are being used to positive effect as a current practice or corporate requirement, solutions that fully support and even enhance their use would be good choices. Conversely, adoption of a solution that does not support such integration may require a process or even an additional system, to reconcile data and actions tracked in different places. As a final example, many technology solutions maintain an electronic audit trail that identifies users and tracks navigation within the system as evidence that the data are being reviewed. However, not all solutions capture the processes that feed data and other information (thresholds) into the system, and/or capture the observations, actions and status arising from data review as evidence of oversight. Consideration must be given to whether the functionality offered by a specific solution aligns with existing procedures for documenting evidence of review and action as well as whether there is the need for additional integration with other systems (e.g. CTMS) necessary to avoid duplicate data and data reconciliation.

C. What is the existing technical infrastructure for housing source data for key performance and risk indicators? With how many solutions will integration be required? 

	Significant consideration must be given to the ability of a solution to integrate with existing sponsor and CRO partner systems that collect and store source data for key performance and risk indicators. This integration must ultimately be seamless enough to support or enhance existing processes while preventing gaps and overlaps. The need for resources to design, test, and manage interfaces can be a key driver in the time and effort required to implement and support a technology solution. As an example, technologies that support RBM all rely on receiving data from other systems, and thus will require some level of technical integration to either receive feeds directly or through a data transfer/import process. The more systems (at multiple partners) that currently house such data, the more integration interfaces will be required. Many technology solutions offer off-the-shelf integration with established eClinical technologies, have open APIs that facilitate integration with other tools, and/or utilize ETL (extract, transform and load) processes to pull data from source systems, but some do not.

D. How mature is the oversight model to be supported, and what foreseeable changes may need to be accommodated by the system in the future? 

	The considerations above involve assessment of model details, current processes and gaps, staff skills, and technical infrastructure, but in many cases these may be in various stages of development. If the oversight model, corporate or partnership structure, volume of study data, or technical infrastructure is likely to be in flux over the expected timeframe for use of the technology solution, it will be critical to select a tool that is scalable and flexible enough to accommodate–and potentially to take advantage of–such changes. For example, technology solutions may vary in their ability to add additional value with larger volumes of data, and/or with greater levels of sophistication in predictive modeling. Some offer not only simple user-defined thresholds, but also statistically derived thresholds and/or the ability to set multiple, varying thresholds based on factors such as sample size or site-specific enrollment. Likewise, some technology solutions incorporate “unsupervised,” exploratory methods, whereby statistical engines run across all available data rather than just the data outlined by the plan, to surface potential risks and/or performance outliers. Fewer solutions offer such features rather than track pre-identified KPIs and KRIs against pre-defined thresholds; thus although the enhanced functionality may be useful, the anticipated maturity of the oversight, risk management, and RBM strategies over the course of the anticipated period for use of the tool will determine whether such features would likely add enough value to compensate for limiting the field of solutions from which to choose. 

	A successful assessment of technology solutions to support lean oversight models involves careful consideration of each of the above questions, followed by collection and documentation of the formal business requirements that emerge. These business requirements may then be translated into an RFI questionnaire for assessment of suitable technology solution providers. Technology solution providers that meet critical business requirements may then be invited to demonstrate their solutions, and a selected solution moved forward to pilot on a limited basis. 

	Data analysis and visualization technologies that support clinical trial oversight and risk-based planning and monitoring are rapidly evolving as industry experience grows and the requirements of individual companies mature. When selected carefully, such technology configurations can be powerful tools to align actual practice with strategy, to prevent duplication of effort (both internally and externally) while ensuring that key oversight requirements are met, and even to elevate the ability of the companies’ staff to provide oversight in a more sophisticated, comprehensive, and efficient manner. 

, is Senior Consultant with The Avoca Group.

, is Head of Clinical Research & Drug Safety Operations at Purdue Pharma.

A key success factor for lean models is the possession of technology that can measure performance and manage risk in a well-organized and user-friendly manner. This article describes the results of an investigation to identify and describe the basic requirements for such technology solutions.

Lean Outsourcing Models for Clinical Trials, Part 2: Optimizing Technology

	A number of recent trends within the industry, including reduced headcounts and an increased focus on “nimble” product development strategies, have prompted many companies to pursue “lean” models for the outsourcing of clinical development. Potential advantages of such models include resource and geographic adaptability, reduced fixed resource consumption, and use of “best in class” resources for each task, whereas perceived challenges include a lack of “tight control” over outsourcing partners, a tendency for sponsor staff to revert to traditional behaviors despite resource reductions, and difficulty finding personnel experienced in such models.

	On behalf of Purdue Pharma, The Avoca Group gathered experiences from companies that have evaluated and employed lean models, for the benefit of those that are exploring them. In-depth interviews were conducted with management representatives from 14 sponsor companies and three CROs that are currently conducting clinical development using “lean” models. For the purpose of this project, “lean outsourcing models” included any designed by sponsor companies to reduce or minimize the level of resources used for CRO oversight compared to traditional, “standard” models (Figure 1). During interviews, respondents were asked to describe their models in detail, including the responsibilities held and value added by each party; the decision criteria that led to choice of the models; and their histories with the models, including both facets that have worked well, and challenges. Specific tools, technologies, and best practices used for oversight, and specific traits of sponsor personnel that work most effectively under the model, were also discussed.

	For some of the smaller sponsor companies interviewed, development of a full-capability internal clinical research staff was never part of their corporate strategy and would not have been practical; thus they had always employed “lean” outsourcing models. Other companies had originally performed clinical trials using in-house staff and/or more “traditional” outsourcing models, but had moved at least some portion of their trials to leaner models because of headcount reductions and/or other budget constraints, a strategic mandate to refocus internal staff into areas of higher value add, movement toward increased (or increasingly variable) volumes of clinical development activity, and/or dissatisfaction with the quality or efficiency obtained using traditional outsourcing models. Positive results were reported to include not only greater efficiency in use of both sponsor and CRO resources (due in part to greater operational control by CROs and hence greater freedom to innovate and willingness to contain pricing), but also improved/faster problem-solving with fewer escalations (by getting some cooks out of the kitchen); improved chemistry across sponsor-CRO teams; and improved ability to utilize lessons learned due to increased commitment to the relationship.

	Through the interviews, it became clear that successful lean outsourcers had made carefully considered decisions in each of seven key areas, as follows.

	Sponsor executives reported that their companies had made carefully-considered decisions about the functional competencies that would be retained in-house. For regulatory reasons, all maintained at least project management/oversight in-house; the nature of any other competencies retained in-house was driven by corporate strategy regarding areas of internal expertise thought to provide competitive advantage and/or by historic factors, i.e., past difficulty outsourcing successfully in certain operational areas, and/or strong desire to retain current functions for which highly skilled and experienced staff were already in place.

	Some examples of competencies to be retained in-house included: 

 All individuals interviewed expressed the importance of employing very experienced individuals, preferably functionally cross-trained and competent in advanced aspects of project management (risk assessment, proactive issue recognition, use of metrics, etc.), to perform project management tasks under a lean model.

 Often sponsor companies felt themselves to be in a superior position of therapeutic area expertise compared to their providers, as well as being more intimately familiar with their business strategy for a product candidate. Thus most frequently, protocol concept documents were developed by sponsors in-house, even under lean models. However, there were exceptions where sponsors were branching into new therapeutic areas, or employing a business strategy that involved regular in-licensing of product candidates without any particular therapeutic area pattern. It should be noted, however, that it was quite popular to have CROs build the full protocols out from the sponsor-generated outlines. CROs were thought to have the operational expertise to do this well, as well as to feel greater ownership in execution when they had a significant role in protocol development.

 was felt by some to be second in importance only to project management. Given the reduced level of hands-on oversight under a lean model, a well-conceived and efficiently executed strategy for quality oversight becomes increasingly important. Sponsors that did not possess this competency internally often outsourced it to a specialty vendor, independent of their CROs.

 were occasionally maintained in-house for relationship-building reasons, and to expedite the study start-up process.

 activities were sometimes maintained in-house, in some cases because analysis and interpretation of clinical trial data was thought to be a “core” strategic activity, and in other cases because of historical difficulty with outsourcing such activities satisfactorily. In contrast, routine data management and safety case management activities were nearly always outsourced fully.

	Some sponsor companies were satisfied with their CROs and found the full-service model to be simple (seamless, coordinated) and relatively resource efficient (internally), whereas others preferred to outsource functionally to what they believe to be best in class. Companies reported success with both strategies; considerations included the volume of work that could be committed to a provider (i.e., desire not to dilute volume if it’s already low), and process-related considerations (i.e., if highly specialized expertise, training, requirements, and/or processes are needed across programs, it can be easiest to outsource functions to the same provider across programs).

	The sponsors interviewed did not typically move into full-service lean outsourcing without having had considerable prior positive experiences with a given CRO. Those that did not have this opportunity sometimes built a “qualification” stage for lean sourcing into the initial work with the provider. This could involve using lean oversight models for only a subset of trials. This stage would assess performance, or performance-based qualification criteria within a trial. If the CRO met that criteria, it would trigger a reduction in the level of direct oversight.

	Sponsors that preferred to outsource functionally recognized that this approach was more resource-intensive for internal project management staff, but felt that the ability to engage the best available provider for each functional area was worth the additional effort, particularly given that the highly-qualified internal project management staff had the level of experience and competence to handle this additional burden.

#3: Number and types/sizes of CROs to engage

	Sponsors took a variety of approaches to determine the numbers and types/sizes of CROs to work with under their lean models. Some, particularly those with narrow therapeutic foci and those transitioning from intensive oversight models, chose to work with only one. The rationale being that that lean outsourcing models require a level of trust and understanding of sponsor expectations that are difficult to achieve with more than one CRO at a time, and that lessons learned from a pilot could be applied to others in the future. An additional rationale, especially for smaller companies, was the need to concentrate business as much as possible with one CRO in order to command the volume discounts, dedicated resources, A-team members, process integration investment, and team stability required for success under a lean model.

	Other sponsors, particularly larger ones, chose to utilize multiple CROs out of reluctance to place “too many eggs in one basket,” particularly given the reduction in intensity of oversight. Anticipated development pipelines with therapeutic area or geographic diversity too great to be appropriately serviced by one provider, and a desire for healthy competition among providers from both a pricing and a performance perspective, also factored into the decision.

#4: Use of independent contractors and/or specialty shops

	The effective use of independent contractors was a nearly ubiquitous theme among successful lean outsourcers. As a means of augmenting sponsor capabilities when necessary, independent contractors tend to be stable and reliable resources, have a relatively high average experience level per dollar, and be role-flexible (i.e., contract CRAs can also be used, in some cases, for auditing tasks). These traits can help to offset turnover, and the use of mixed teams can be a very effective means of raising the bar for all staff, especially when accompanied by a program of performance management that looks at individual-level performance.

#5: Types of trials to outsource under the lean model

	Although some sponsors outsourced exclusively using lean models, others used lean models only for a subset of their clinical trials. Decisions about which trials to outsource using lean models were generally made based on considerations surrounding clinical trial phase, therapeutic area, and region, as well as internal resource availability. Some companies used a cost-based approach, finding it more cost effective to manage different types of trials under different models. Others used a risk-based approach, for example using lean models for regionally limited vs. global trials; less complex protocols, indications, or populations; lower priority portfolio assets or phases; trials requiring great flexibility in resource requirements (e.g., adaptive); and/or only when they had considerable positive past experience with provider(s).

	Sponsor companies generally reported positive experiences with carefully conceived risk-sharing models, and those that had not incorporated such contractual provisions generally expressed regret. Risk-sharing contractual provisions can work best with lean oversight models since these reduce the probability that CRO performance will depend upon sponsor involvement at a large number of touch points.

	Options reported to work well included both fixed-price models, and risk-sharing models whereby CROs had access to bonuses in exchange for meeting performance targets and/or suffered penalties for not doing so. In this context, sponsors also reported positive experiences with contractually binding Quality Agreements, in order to ensure that quality requirements were clear and not compromised in attempt to meet time and budget targets. The utility of risk-sharing approaches was said to be maximized with early engagement of, and substantial decision-making power by, the CROs. For example, heavy involvement of CROs in the composition of protocols and timelines helps to ensure full commitment to milestone goals.

	The effective use of tools and best practices was found by most of the participants in this research to be critical when operating under a lean model. Such tools/practices included:

		A playbook or manual that describes, in detail, the roles of CRO and sponsor team members (in particular placing restrictions on activities of sponsor functional area staff); standards and/or expectations for specific functional tasks, deliverables, and staff qualifications; communication and escalation plans; risk assessment and management activities; etc. Such documents are often long and detailed, and development from scratch can require significant levels of investment on the parts of both sponsor and CRO.

		A concise set of key performance, risk, and quality metrics, together with associated targets, thresholds for action, reports, and review plans. In addition to their obvious role in performance, risk, and quality management, negotiations surrounding metrics, thresholds, targets, and review plans are also often powerful aids in clarifying performance and role expectations, and in focusing oversight at the correct level.

		A fit-for-purpose governance model with well-articulated mandates, primarily including performance and relationship management, procedures for aligning on higher level issues with significant financial implications, and handling of escalated issues, along with a commitment to meeting at logical frequency.

		Ample face-to-face meeting time, to promote and maintain trust, openness, honestly, and transparency.

	Although technology solutions that permit status transparency were mentioned by several of those interviewed as of potential benefit, the use of dedicated clinical data integration and dashboarding technologies for clinical operations oversight was not widespread. Reasons for not investing in such solutions included the financial investment, lack of resources or reticence to invest required resources to support system implementation, a feeling that available technologies offer much more than is required, and, for smaller companies, a low volume of active trials. Instead, the surveyed companies generally relied on a blend of reports from existing clinical systems owned by both the sponsor and the partner CRO(s), despite the common challenges of insufficient report frequency and insufficient accompanying information beyond the reported metrics.

	Those companies that had transitioned from more traditional to leaner outsourcing models shared a number of lessons learned regarding the process. The importance of strong top-down support was found to be critical; senior management must not only articulate the model to others, but must hold staff accountable for living by it, for example by strictly upholding limitations on internal resource use. As part of demonstrating this support, the development of a joint (sponsor-CRO) transition plan was recommended, involving, for example, high visibility joint communications from leadership of both companies. If senior management support is not sufficiently strong and visible, divisiveness can remain, with not everyone on board with lean models, thus leading to a failure to gain the expected efficiency due to undermining of the model in certain areas.

	In general, at least six months of considerable effort was said to be necessary to revise SOPs, develop joint metrics, develop charters and oversight and governance plans, compose operational manuals, refine early engagement and risk-sharing models, etc. Some companies found it useful for operating manuals and/or other documents to depict the transition in oversight over time, e.g., performance criteria for qualifying for lower level of oversight, as well as what each oversight level will look like.

	In the shifting landscape of product development, the pharma and biotech arenas increasingly emphasize the need to use less internal R&D resources, increase efficiency, and reduce costs. One product development strategy is the utilization of lean outsourcing models for clinical studies. Our research shows that companies who successfully use lean models for clinical development have carefully evaluated several key areas in their decision-making, including what core competencies to retain in-house, the types of trials to be outsourced under the lean model, the number and type of CROs to engage, contractual provisions such as risk-sharing, and extensive use of supporting tools and best practices. These are among the key areas of critical importance in finding success with lean outsourcing models. 

	Denise Calaprice, PhD, Senior Consultant, The Avoca Group

	Mitchell Katz, PhD, Head of Clinical Research & Drug Safety Operations, Purdue Pharma

In the shifting landscape of product development, the pharma and biotech arenas increasingly emphasize the need to use less internal R&D resources, increase efficiency, and reduce costs. One product development strategy is the utilization of lean outsourcing models for clinical studies.