Assessing Clinical Trial–Associated Workload in Community-Based Research Programs Using the ASCO Clinical Trial Workload Assessment Tool

Abstract

QUESTION ASKED:Recognizing that it is a challenge for clinical research managers to determine appropriate workload for research staff, we asked if the ASCO Clinical Trial Workload Assessment Tool is helpful at gauging protocol-specific complexity and workload effort for clinical trials across multiple practice settings. SUMMARY ANSWER:The majority of 51 participating research programs (96%) were able to provide at least 5 months of data. This response rate, along with feedback received from the participants, demonstrates that the Tool is simple and easy to use and supports its long-term feasibility and utility for community-based research programs. METHODS:Two interrelated tools were used to conduct the project. First, the "ASCO Protocol Acuity Scoring Worksheet" which incorporated a 4-point protocol complexity rating scale where a score of 1.0 reflected a lower complexity and workload and a score of 4.0 reflected a complex trial and greater workload. Second, a web-based platform entitled the "ASCO Clinical Trial Workload Assessment Tool" was created to facilitate the collection of clinical trial-associated workload data. Data were collected monthly over 6 months beginning May 2013. RESULTS:In total, 51 research programs entered clinical trial associated workload data for 323 staff members representing 963 unique protocols and 165 unique sponsors. The results from this project support the idea that work associated with some trials exceeds that associated with others. Treatment trial acuity scores were consistently higher compared with cancer control trials, and industry trials had higher acuity scores than NIH/NCI funded trials. Evidence of trial acuity (complexity) being a better measure of workload was also evident when comparing groups. BIAS, CONFOUNDING FACTOR(S), DRAWBACKS:An initial objective of the project was to establish an average, single benchmark acuity score for various types of trials and trial sponsors that could be used as a reference for community-based research programs to anticipate workload and staffing needs. Due to high variability in the participating programs, this objective proved to be unrealistic. In an effort to counter this dilemma, programs were grouped into similar categories based on type and size. This approach would consequently allow similar research programs to be compared. Participation in this project was limited to community-based research programs. However, notwithstanding infrastructure and organizational differences, the data obtained as a result of this project could also be referred to and used by non-community-based programs, such as academicbased centers. REAL-LIFE IMPLICATIONS:No matter the tool(s) used or type of program, every research program should be regularly assessing its staff workload and staffing metrics, and 457Original Contribution CARE DELIVERY Original Contribution CARE DELIVERY preferably should consider the level of complexity of the work being conducted. Such information captured and assessed at regular intervals over time may provide a means for r...

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