Page 115 - JCTR-11-5

Journal of Clinical and
Translational Research Predicting coordinator workload with adapted OPAL

Table 2. Summary of study characteristics and results by protocol
Trial number Adapted OPAL score Trial phase Sponsor type Intervention Estimated hours a Actual hours Difference
1 7.5 3 Industry Drug 370.2 538 167.8
2 6.5 3 Industry Drug 370.2 492 121.8
3 7.0 2/3 Industry Drug 293.0 438 145.0
4 9.5 3 Industry Drug 679.2 310 −369.2
5 6.5 3 Federal Behavioral 215.8 330 114.2
6 6.5 3 Industry Drug 215.8 336 120.2
7 7.0 2 Federal Drug 293.0 162 −131.0
Note: Hours were estimated for 12 months.
a
Abbreviation: OPAL: Ontario Protocol Assessment Level.
in estimated or actual coordinator hours. However, estimates. Future multi-site, prospective time-tracking
interpretation of these comparisons is limited by the small could be used to refine the weighting system further and
number of trials and the limited behavioral studies in the reduce variability, particularly for protocols with high
dataset. operational complexity or atypical team structures. These
These findings suggest that the adapted OPAL score findings highlight the value of the tool in helping research
is a promising tool for estimating workload, particularly sites anticipate and manage staffing needs, potentially
in later-phase trials characterized by diverse and complex reducing the risk of understaffing, missed milestones,
operational requirements. No statistically significant and staff burnout. As pressure mounts for research
difference was observed between the estimated and actual operations to become more efficient, particularly in light of
hours, which may be due to the small sample size. Because proposed reductions in administrative cost allowances for
the MAE reflects a 12-month study period, even if the grants, accurate workload estimation and budgeting will
difference was robust in larger samples, it would remain become increasingly critical for maintaining operational
relatively small, representing merely 7–8% of the annual sustainability.
CRC workload. Variability in prediction accuracy across By providing workload estimates in both 7.5-h
trials suggests the tool may benefit from further refinement (academic) and 8-h (industry) workday equivalents, the
to account for study-specific factors. score enhances its practical relevance for a broad range of
stakeholders, including academic health centers, contract
4. Discussion research organizations, and community-based research
This study builds on the foundational work of Tyson et al., institutions. This flexibility ensures that operational
9
who introduced the adapted OPAL score as a tool for estimates remain meaningful regardless of institutional
estimating CRC effort. Unlike the original retrospective norms, improving cross-site comparability and planning.
analysis, the present study utilizes the tool prospectively, By quantifying protocol complexity and translating
demonstrating its real-time utility for operational planning it into time-based estimates, the adapted OPAL score
and staffing allocation. Future multi-center validation addresses a key limitation in traditional trial feasibility
studies should consider stratifying results by coordinator practices, which often rely on subjective judgment or
experience or including experience as a covariate in historical precedent. 7,18,19 The adapted OPAL score allows
predictive modeling to further enhance predictive for a more data-driven and scalable approach to workload
accuracy. forecasting and supports a more efficient staffing model
The findings indicate that the adapted OPAL score can for better resource alignment and enhanced financial
predict coordinator effort with a high degree of accuracy. sustainability. The adapted OPAL builds on a methodology
No statistically significant difference was observed between designed to include both protocol-driven and ancillary
the estimated and actual hours, and the MAE reflected a activities; however, some unstructured tasks will inevitably
manageable variance of approximately 8%, equivalent to remain unmeasured. Pairing OPAL estimates with periodic
less than one month of full-time work. Although absolute portfolio-level reviews and integration into a Clinical Trial
differences in hours were sometimes large for individual Management System (CTMS) can help identify emerging
studies, the percentage variance was modest, thereby or unaccounted workload, enabling mid-course staffing
supporting the adapted OPAL’s utility for budget and staffing adjustments.

Volume 11 Issue 5 (2025) 109 doi: 10.36922/JCTR025260032