A Clinical Trial-Related Workload Tool for Research Managers


INTRODUCTION: Identifying whether research staff is overburdened and quantifying it in a clinical trial setting is a challenge for research managers. Quantifying workload could help managers to equitably distribute burden and to accurately staff for better functioning of clinical trials. In this study we developed a workload tool for quantifying the workload of Clinical Research Coordinators in a cancer clinical trial setting.

METHODS: We adapted the ASCO Clinical Trial Workload Assessment Tool for application in a cancer clinical trial setting. First we assigned all active clinical trials to one of four categories with the assignment of a complexity score. Then we categorized the workload of Clinical Research Coordinators into static and dynamic works. Static works are those independent of the number of patients seen by Clinical Research Coordinators and dynamic works are those dependent on the number of patients (active patients versus follow-up patients). Thereafter we developed an equation to quantify the workload of each Clinical Research Coordinator with a score of 100 indicating ideal burden; those with a score of >100 were considered overburdened. Based on this we could calculate the number of additional Clinical Research Coordinators needed for better functioning of clinical trials.

RESULTS: We measured the workload of Clinical Research Coordinators from June 2018 to March 2019. During this period, there were a median number of 12 (range: 11-15) Clinical Re-search Coordinators working for median of 37 (range: 29-39) clinical trials. The median active patients were 170 (range: 100-196) and follow-up patients were 13 (range: 2-19). During the study period, a median number of 9 (range: 6-14) Clinical Research Coordinators were working above their ideal workload. After applying the equation, a median of 3.2 (range: 2-4) extra Clinical Re-search Coordinators were needed to complete their jobs without additional burden.

CONCLUSION: The workload tool could be used to accurately identify the number of prospective employees needed based on the number and complexity of active clinical trials. The workload tool showed consistent results indicating good precision. However, statistical validation is required to confirm the accuracy and reliability of the tool. We have further adapted our workload tool to assess burden in other research roles including Research Assistants, Research Finance Specialists, Budget and Contract Analysts, Research Registered Nurses, Infusion Registered Nurses, Quality Assurance Specialists, and Regulatory Coordinators. Future applications of the workload tool include use for budget justification of current and future research staff and use in performance improvement initiatives.

Publication Date


Presented At:

14th Annual BHSF Research Conference

Content Type

Poster Presentation

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