Publications

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

Author(s): Good MJ,  Hurley P,  Woo KM,  Szczepanek C,  Stewart T,  Robert N,  Lyss A,  Gönen M,  Lilenbaum R

Journal: J Oncol Pract

Date: 2016 May

Major Program(s) or Research Group(s): COPTRG

PubMed ID: 27006354

PMC ID: PMC5702801

Abstract: PURPOSE: Clinical research program managers are regularly faced with the quandary of determining how much of a workload research staff members can manage while they balance clinical practice and still achieve clinical trial accrual goals, maintain data quality and protocol compliance, and stay within budget. A tool was developed to measure clinical trial-associated workload, to apply objective metrics toward documentation of work, and to provide clearer insight to better meet clinical research program challenges and aid in balancing staff workloads. A project was conducted to assess the feasibility and utility of using this tool in diverse research settings. METHODS: Community-based research programs were recruited to collect and enter clinical trial-associated monthly workload data into a web-based tool for 6 consecutive months. Descriptive statistics were computed for self-reported program characteristics and workload data, including staff acuity scores and number of patient encounters. RESULTS: Fifty-one research programs that represented 30 states participated. Median staff acuity scores were highest for staff with patients enrolled in studies and receiving treatment, relative to staff with patients in follow-up status. Treatment trials typically resulted in higher median staff acuity, relative to cancer control, observational/registry, and prevention trials. Industry trials exhibited higher median staff acuity scores than trials sponsored by the National Institutes of Health/National Cancer Institute, academic institutions, or others. CONCLUSION: The results from this project demonstrate that trial-specific acuity measurement is a better measure of workload than simply counting the number of patients. The tool was shown to be feasible and useable in diverse community-based research settings.