Modern clinical trials are often spread across multiple centers, countries, and regions, making preservation of data integrity a growing challenge, particularly for endpoints that rely on subjective or semi subjective results. Outcomes can be influenced by differences in assessment, inconsistent interpretation across sites, and unclear handling of intercurrent events. For laboratory and clinical teams alike, these variabilities can make it difficult to draw reliable conclusions from trial data.
Sources of Inconsistency in clinical data
Some clinical endpoints inevitably rely on a degree of human judgment, including clinician reported outcomes, patient reported symptoms, and visually assessed disease severity. Even with structured scoring systems, interpretation can vary between investigators and sites, and over time.
Dermatology illustrates this well, as visual assessments of erythema, lesion thickness, and the proportion of body surface area affected all feed into composite scores used to determine treatment response.
Tools such as the Psoriasis Area and Severity Index and the Eczema Area and Severity Index provide a structured framework, but they still rely on clinical interpretation, meaning small differences, such as body surface area estimation, can alter composite scores and introduce subtle variability.
Building consistency across teams
Rater training helps to ensure that investigators apply scoring systems consistently, while ongoing monitoring can flag rater drift over time. However, improving consistency extends beyond investigators to operational teams such as project managers, clinical research associates, and clinical trial managers, who also play a key role in maintaining data quality.
When these teams understand how endpoints are derived and how variability can arise, they are better positioned to proactively identify potential issues and deliver timely intervention before overall study outcomes are affected. Early focus on quality is essential; aligning endpoint assessment and interpretation during site selection, feasibility, and protocol development ensures that expectations are clearly defined before recruitment begins.
Structuring with estimand planning and clear communication
The Estimand Framework described in ICH E9(R1) provides a structured approach to defining the treatment effect of interest and ensures that all stakeholders understand what the trial is designed to evaluate from the outset. This includes the treatment condition, the population, the endpoint, and the population level summary, as well as a clear definition of intercurrent events, which occur after treatment initiation, and can affect the interpretation or availability of outcome data. Specifying these elements early allows teams to align on how data will be collected, analyzed, and interpreted, reducing ambiguity and enabling cross functional collaboration and more robust study design.
This framework also depends on articulate and effective communication between contributors with different priorities, working environments, and information needs. Balancing communication is essential, as too much information creates complexity while too little leads to gaps and delayed responses, underlining the importance of selecting appropriate channels and tailoring messages so that information remains relevant and actionable.
When communication strategies are aligned with stakeholder needs, teams are better equipped to identify issues early, adhere to protocol, and maintain consistency across the study.
Looking ahead
Technological advances may also enhance consistency in endpoint assessment; digital imaging, wearable sensors and artificial intelligence (AI)-based tools are all being explored for their potential to give more subjective interpretation of clinical data, particularly in fields such as dermatology.
For example, early research suggests that AI may enhance pattern recognition and improve the visual evaluation of skin lesions, although challenges remain in areas such as data quality, algorithm transparency, and regulatory acceptance.1,2
While some of these technologies show promise, their impact depends on how effectively they are implemented within the broader trial framework. The continued evolution of clinical trials depends on their integration with comprehensive training, structured statistical planning, and effective communication. Together, these elements will ensure that trial data provides a reliable foundation for clinical and regulatory decision making.
References:
- Martínez-Vargas E, Mora-Jiménez J, Arguedas-Chacón S, et al. The emerging role of artificial intelligence in dermatology: A systematic review of its clinical applications. Dermato. 2025, 5(2):9
- Cugliari G, Bodini M, Zbrzezny AM, Krzywicki T. Artificial intelligence in dermatology: A review of methods, clinical applications, and perspectives. Appl. Sci. 2025, 15(14):7856
