Assessment of Nursing Workload and Complexity Associated with Oncology Clinical Trials: A Scoping Review

Objectives

Clinical trials (CTs) play a crucial role in advancing medical knowledge and patient care but are increasingly complex and resource-intensive. This scoping review aims to explore the current approaches for evaluating workload (WL) in oncology CTs and identify tools for measuring clinical research nurses’ WL.

Methods

The search was conducted through MEDLINE, Scopus, CINAHL, and COCHRANE databases and carried out through the framework developed by Arksey and O'Malley and revised by the Joanna Briggs Institute. Data extraction and synthesis were performed to analyze instruments used for WL assessment and their dimensions.

Results

Of the 1,005 records identified, 12 meet the inclusion criteria. The complexity and WL associated with CTs can be attributed to five main domains: (1) protocol, (2) single case, (3) data management, (4) regulatory, and (5) worker-related. These instruments varied in their approaches, scoring systems, and domains assessed. Notably, the protocol-related domain was prevalent across most instruments, highlighting its importance in WL evaluation. Furthermore, findings revealed a wide range of WL scores across different studies, emphasizing the complexity and variability in WL management within CTs.

Conclusions

This scoping review underscores the importance of evaluating WL in CTs and provides insights into existing tools and approaches. Nurses, as integral members of clinical research teams, bear significant responsibilities in trial management, necessitating a balanced approach to WL allocation. Future research should focus on validating and standardizing assessment tools to optimize resource allocation and enhance research efficiency in CT centers.

Implications for Nursing Practice

Understanding WL dynamics in CTs is essential for nurses involved in research delivery. By utilizing validated WL assessment tools, nurses can advocate for appropriate staffing levels and promote efficient trial management, ultimately improving patient outcomes and research quality in CT settings.