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- Assessing Protocol Complexity and its Impact on Trial Outcomes
Complexity is the new normal in clinical research — reshaping how trials are designed and executed. Industry-wide, protocols continue to increase in complexity, challenging sponsors and sites to adapt and innovate. Since 2005, there has been a 139% increase in procedure quantity, a 214% rise in endpoints and a 600% surge in data points collected.1
A significant rise in personalized medicine, increasing number of endpoints and procedures, expanded geographies, regulatory requirements and the need for more sophisticated data collection methods are all contributing to this greater complexity.
As clinical trials evolve, the growing complexity of protocols brings significant implications for both patients and sponsors. Tufts CSDD shows that as protocol complexity increases — via more procedures, endpoints, and eligibility criteria — clinical trial burden rises in parallel, elevating site execution workload and patient participation demands, which in turn lengthens study cycle times and erodes enrollment and retention. Protocols with relatively high participation burden had a higher number of protocol deviations and amendments, on average, compared to protocols having lower burden.2 Higher patient and site burdens are correlated with longer start-up times, higher screen failure rates and more amendments — all of which inflate costs and delay access to therapies.
Leveraging AI and analytics can now help solve protocol complexity challenges. This emerging technology allows for digitalized protocols to be automatically assessed with patient burden, site burden and protocol complexity analytics. These advanced methodologies deliver insights that better inform trial design decisions by identifying specific factors driving patient and site burden and pinpointing elements that add unnecessary complexity. These insights empower sponsors to streamline protocol design, proactively address challenges and improve trial outcomes.
How does protocol complexity increase burden?
As protocols increase in complexity, the impact is multi-faceted, resulting in a need for extra training, specialized equipment and additional amendments. These factors not only extend timelines but also heighten the risk of protocol deviations, compounding the overall burden on sites and patients.
Site burden rises sharply with protocol intricacy, as staff must manage more procedures, eligibility criteria and overlapping studies. Complexity also translates into greater patient burden, including more frequent visits, invasive assessments and longer trial durations, making participation harder and increasing dropout rates.
The combined effect of patient and site burden can significantly disrupt trial operations. When patients disengage or decline participation and sites struggle to comply with overly complex protocols, dropout rates increase and protocol deviations become more prevalent. Every added procedure, data point or eligibility criterion can introduce challenges that ripple across patients, sites and sponsors. Enlisting sites to run the trial, recruiting patients to enroll and stay in the trial and overcoming amendments become bigger challenges. Understanding the impact of design choices on sites and patients is essential to designing studies that minimize protocol complexity.
What is site burden?
Site burden is the administrative, logistical and regulatory workload that participation in a trial places on a clinical research site. This type of burden arises from multiple factors, including protocol complexity, administrative demands, resource limitations and technology overload. Complex protocols with numerous endpoints and frequent amendments increase operational risk and strain the site team. Administrative inefficiencies and fragmented technology systems further exacerbate workloads, while inadequate funding and staffing shortages limit capacity. These pressures not only reduce site willingness to participate but also lead to missed visits, protocol deviations and delayed timelines. High site burden correlates with elevated dropout rates of 22%, versus 19% for protocols with lower burden.2
Site burden is determined by many factors including:
- Number and characteristics of patients enrolled
- Complexity and length of the protocol
- Need for specialized equipment and training, especially tech-enabled tools
- Data entry and regulatory requirements
Site burden must be considered as a key component to optimize operational outcomes. A trial can only be as successful as the sites conducting it. Understanding and leveraging site burden data through design analytics gives decision makers insight into their sites. By digitalizing and automating protocol assessments, sponsors can rapidly identify site burden factors and make better-informed design decisions.
Applying analytics to protocol design with a focus on sites allows for earlier risk identification and optimization of mitigation strategies. Operational risks that increase site burden, such as excessive endpoints and non-core procedures that add unnecessary complexity can be assessed. Insights generated can show where procedures can be streamlined, visit schedules optimized and overall protocol complexity decreased. By using real-world patient data and AI-powered solutions, study design teams can make smarter design decisions like removing non-essential endpoints or assessments to reduce complexity, refining eligibility criteria and creating protocols that minimize amendments, reduce operational challenges and support overall success by significantly reducing site burden. They can then remove or mitigate those specific drivers of site burden in their trial, balancing operational success with scientific and regulatory must-have’s.
What is patient burden?
Patient burden is the physical, psychological and emotional toll that participating in a trial can take on a participant. It is largely determined by the design elements in the protocol.
These burdens or challenges can include:
- Number and frequency of visits
- Time commitment for patients and caregivers, including travel times and transportation
- Pain/discomfort of procedures
- Potential for side effects from the treatment regimen
- Concerns about how the treatment regimen is administered, especially if it is performed by patients themselves
It is important to accurately understand the impact of the design elements on the patient to uncover where burden can potentially be mitigated. Real-world patient data and AI can be leveraged to lessen patient burden and improve retention. Digitalized protocols can now be automatically assessed to determine the elements that contribute to patient burden using advanced analytics.
These insights reveal how design elements, such as visit frequency, procedure intensity and treatment requirements, may impact patient willingness to participate. Using this type of evaluation, sponsors can consider implementing strategies that maintain the integrity of the trial design but that improve overall patient experience. During the design phase, teams can model multiple scenarios to understand perceived burden and proactively adjust protocol components, reducing barriers to enrollment and mitigating risk.
The benefits of applying analytics and real-world insights to identify complexity and reduce burden
Every choice made during trial planning is interconnected and can significantly affect the success of the study. By benchmarking protocol complexity scores against a broad, anonymized dataset of digitalized protocol designs and comparing them to trial outcomes, sponsors can identify which design elements have the greatest impact on enrollment rates, site performance and overall operational efficiency.
When revisions are made prior to protocol finalization, risk of avoidable amendments can be reduced — potentially saving the sponsor up to $535k USD and three months of delay per amendment, according to statistics by Tufts CSDD Impact Reports.3
Protocol digitalization allows protocols to be rapidly assessed, providing sponsors with the ability to quickly generate various scenarios and understand the impact of different design elements — leading to more optimized designs. These insights inform strategies such as streamlining non-core procedures and offering flexible visit options or decentralized components to ease logistical pressure. With this detailed feedback, sponsors have the ability create less-complex protocols — ultimately improving site satisfaction, accelerating timelines and enhancing trial success.
Conclusion
As the healthcare landscape continues to evolve, embracing data-driven insights and AI-powered approaches is essential. Emerging technologies are transforming protocol design by automating complexity scoring and enabling real-time scenario modeling. With advanced analytics and benchmarking capabilities, sponsors can identify opportunities to streamline protocols, reducing unnecessary procedures and inefficiencies. These improvements not only minimize risk but also alleviate the burden on sites and patients, ultimately driving faster timelines, better engagement and higher-quality outcomes. Leveraging next-generation AI, IQVIA is redefining the future of clinical trials, helping deliver smarter designs, operational efficiency and faster access to life-changing therapies.
Sources:
1 Protocol Design Variables Highly Correlated with, and Predictive of, Clinical Trial Performance. Ther Innov Regul Sci 56, 333-345 (2022)
2 Tufts CSDD Impact Report, September/October 2024, Vol. 26(5)
3 Tufts CSDD Impact Report 2023;25(2)