hese case studies serve as practical illustrations of how an analytical approach can make a meaningful impact in ensuring process adherence and enabling study quality oversight throughout the clinical trial lifecycle and across audience tiers within the organization. However, as alluded to in the introduction, analytical use cases always require collaboration and—importantly—sponsorship from other areas of the business. This section will outline a range of strategies for gaining endorsement for and removing barriers to implementation.

Reducing the Burden of Quality on the Organization

Digitization

A fundamental strategic pillar for any pharmaceutical organization is to closely align process and system/platform technology to embed quality into the workflow of study teams and reduce the burden on the organization to separately track and report on quality. Platform functionality will drive process adherence and generate the correct data to assess quality and process performance. Measurement of quality should not be an auxiliary process carried out by study teams but something that results naturally through normal study conduct. To return to the first case study: Before implementing this solution, study teams were already tracking the status of their study plans in separate spreadsheets because (1) the study oversight SOP requires them to assess that core study documents are in place and on time; and (2) simply uploading the approved plan—part of normal study conduct—was not by itself enough to generate the requisite reporting.

The solution enabled this entire process to be digitized since the information needed to generate the relevant reporting was supplied through normal study conduct (i.e., by following the relevant document approval/upload protocols). By shifting processes and tracking out of spreadsheets, organizations can manage key processes while also uplifting their capability to measure quality. This in turn allows the organization to redeploy study resources to more value-added activities like patient recruitment and site activation.

Layering Capability

Replacing manual processes through digitization is rarely frictionless. Jumping into enhanced capabilities like diagnostic reporting and data science only increases the challenge of getting key stakeholders to adopt new processes and tools. One key strategy to help alleviate this burden is to layer capability into existing processes and gradually move up the “tiers” of analytics on a case-by-case basis. This philosophy eases adoption of data science tools by embedding more predictive methodology into a process that is already well-defined and embedded into the work of study conduct. Layering new functionality into well-accepted tools makes it easier for teams to digest it.

This is the methodology highlighted in the third case study. Key to the success of this solution was that it did not simply build a flashy visualization handed off to study teams to use. Instead, the team worked gradually to replace a manual process by layering predictive analytics in phases. This won trust across all stakeholders because they first came to trust the data being automating for them. The uplift in capability delivered through data science was piloted after that.

Enabling a Risk-Based Approach

Study team resources continue to be at a premium. As portfolios grow and pharmaceutical organizations expand, organizations must carefully plan and manage these resources to optimize value. Analytics can provide additional benefit by helping them to focus on issues that matter, through a risk-based approach. This is aligned to ICH E6 R2 5.0, which states that the methods used to assure and control the quality of a clinical trial should be proportionate to the risks inherent in the trial and the importance of the information collected.

Through automating processes and data capture, organizations can leverage analytics to quickly point them to the studies, sites, and issues that matter while still being able to maintain the comfort of holistically tracking quality across their portfolio. An organization may start with “inspection readiness” reporting, tracking key metrics and data by study that can alert study teams to quality or process issues that may have arisen during study conduct. Looking at the first case study above as an example, having reporting that is focused on studies with upcoming inspections and building reporting that can track missing or incomplete study plans can allow the study team to focus on just the plans that may be missing or overdue from the eTMF, instead of conducting in-depth reviews of all study plans. This risk-based approach allows study teams to target investigations at inspection time instead of collating information and data across all SOPs and then go through the process of analyzing it all (which many organizations do in the current state). Inspection readiness is one area where organizations can gain the most traction for analytics because inspections are highly visible throughout the organization, an area of great stress for study teams, and a necessary step for drug commercialization.

Common Barriers to Implementation

The success of a quality analytics implementation is driven by approach and strategy. However, even when equipped with both, many organizations still struggle to move their quality analytics forward and face numerous barriers to effective implementation. These barriers can range from common barriers shared across industry to barriers that may be unique to each organization. This section will highlight common barriers and methods to alleviate them.

Organization Prioritization and Bandwidth

The pharmaceutical industry’s stringent regulatory environment demands a focus on quality throughout study conduct; however, prioritizing quality over operational efficiency and financial performance can be challenging. While quality is essential for safeguarding study participants and ensuring reliable results, many organizations struggle to prioritize innovation beyond meeting these requirements. Crossing this barrier involves two approaches: integrating tools and reporting into existing study team processes, and streamlining data and metric provision while automating labor-intensive tracking and reporting in the quality space. This aims to alleviate the burden on study teams and allows them to allocate their bandwidth to value-adding activities crucial for the success of clinical trials, pipeline, and portfolio. Uptake is facilitated by integrating tools into existing processes, while solutions cater to tangible capability needs. Furthermore, embedding technology and analytics into existing processes not only saves study team bandwidth but also lays the groundwork for future analytics and data science integration (demonstrated by the third case study).

Organizational Literacy and Technical Maturity

The pharmaceutical industry encompasses a wide array of organizations, each with its unique position in the analytics journey. While it may seem feasible to develop a universal framework applicable to all, reality is far more complex. Technical maturity—encompassing expertise, literacy, and an organization’s placement on the analytics spectrum—plays a pivotal role. Organizations at a lower technical maturity level may track and report numerous processes using traditional tools like spreadsheets and slideshow presentations, while those at a higher maturity level employ advanced systems and business intelligence software for seamless data capture and reporting.

Overcoming the barriers of analytics expertise and organizational literacy is a formidable challenge, often necessitating years of effort. Demonstrating the potential of analytics through tangible examples, proofs of concept, and successful deployments is crucial. Moreover, uplifting an organization’s technical maturity and literacy requires a meticulous change management strategy coupled with comprehensive education planning to enhance leaders’ literacy.

Breaking down silos within functions, processes, systems, and data domains is imperative for achieving optimal technical maturity. Assessing an organization’s current state of technical maturity is essential to identify priorities, quick wins, and long-term strategic initiatives. This approach should center on enhancing data and reporting capabilities, complemented by a robust change plan to drive usage, adoption, and engagement, thereby fostering continual advancement along the quality analytics roadmap.

Change Management to Overcome Barriers

The success of any analytics strategy hinges on the development of impactful technical data and analytics solutions. However, success goes beyond the technical aspect and relies heavily on an effective change management strategy. This involves communication, training, and embedding change within the organization. Alignment at all levels of the organization is crucial to ensure that analytics solutions meet the business goals. Managing momentum and keeping the organization engaged through demonstrations and corporate communications is vital. Training and education play a key role, catering to leaders and stakeholders for effective usage, while also focusing on raising data, analytics, and technical literacy across the organization. Promoting quality analytics within a pharmaceutical organization is challenging and requires a comprehensive change strategy to bring study teams and clinical operations along for the journey.

The value and benefits of any analytics strategy must be clearly embedded into the organization’s communication strategy to ensure engagement. Realizing the full potential of an analytics strategy takes time and requires a continual focus on uplifting literacy and capability, as well as a lifecycle approach to solutions development. Lifecycle management involves decommissioning old ways of working and supporting the adoption of new ones released through the analytics strategy.

Change strategy illustrates the types of considerations that impact transformation of this magnitude in complex organizations. Tailoring each change strategy for each organization is recommended, and should begin with a current-state assessment to determine major change barriers that need to be overcome. No matter what organization or where they are along the analytics spectrum strategy, cross-functional collaboration is essential to increase the sense of ownership among analytics users and encourage them to adapt to new paradigms of work. The benefits of analytics can only be realized if the analytics are used to derive insights and power actions that drive compliance, quality, and inspection readiness.

Conclusion

Proactive quality management hinges on effective quality measurement. By integrating modern data collection and analytical tools into study team workflows, the case studies provided demonstrate how to turn this abstract principle into practical reality at different capability tiers. These case studies also show how to leverage analytics across the study lifecycle, and to distinct audience groups, to drive proactive quality management.

More generally, these examples are designed to serve as practical illustrations of key strategic pillars which organizations can harness within their unique organizational context to overcome common barriers to successful analytics implementation.