Page 113 - JCTR-11-5

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

1. Introduction measurable confidence that its estimates reliably reflect
the construct being assessed—in this case, the Clinical
The increasing complexity of clinical trial protocols has Research Coordinator (CRC) effort. According to Streiner
significantly amplified the demands placed on research et al., validation requires demonstrating that tool-derived
10
coordinators, who serve as the operational backbone of predictions align with observed outcomes, ideally across
study implementation. These professionals are responsible multiple contexts and study designs. For tools predicting
for a wide range of critical tasks, including regulatory operational metrics, prospective validation enhances
compliance, patient engagement, data collection, adverse external validity by assessing real-time workflows rather
event reporting, and visit schedule adherence, all of than relying solely on retrospective analyses. 11,12
which have become more time-consuming and resource-
intensive. As protocols become more intricate, workload In this study, prospective validation methods are
imbalances among coordinators are becoming increasingly applied to evaluate the adapted OPAL score by comparing
common, contributing to elevated stress, burnout, and its predicted coordinator workload against actual hours
staff turnover. These challenges are further exacerbated logged across seven interventional trials at a single site. This
1-5
by staffing shortages and funding limitations at many approach aligns with best practices in validating prediction
academic and community-based research sites. models and workload estimation frameworks. 13-15
Accurately estimating research coordinator effort is 2. Materials and methods
essential for informed decision-making around staffing,
resource allocation, and study feasibility. Effective This prospective observational study was conducted at an
planning depends on the ability to forecast the operational academic clinical research site to evaluate the accuracy of
and administrative complexity of a study before it the adapted OPAL score in predicting research coordinator
begins. Without reliable workload prediction models, workload. Between January 01 and December 31, 2024,
6,7
seven CRCs tracked the hours they spent managing seven
sites risk under- or over-allocating personnel, potentially actively enrolling interventional trials. The selected studies
compromising compliance and performance.
varied in sponsor type (industry-sponsored vs. federally
The Ontario Protocol Assessment Level (OPAL) funded) and intervention type (drug vs. behavioral).
score was previously developed to quantify protocol The adapted OPAL score was calculated for each trial
complexity by assigning numerical values to objective based on predefined criteria, including procedural volume,
trial characteristics such as intervention type, number of visit intensity, monitoring requirements, and biospecimen
8
study procedures, and frequency of patient visits. While complexity. Estimated coordinator workload hours were
the OPAL score has gained broad adoption as a baseline then derived using a previously published reference table
tool, it has limitations when used in isolation. Specifically, developed by Tyson et al., which was constructed from
9
it does not account for site-level operational variables that retrospective coordinator time-tracking data collected
meaningfully influence workload, such as brief recruitment across a range of experience levels. This approach was
windows, complex specimen handling requirements, intended to produce weights representative of overall
language barriers, or high-intensity data queries and coordinator effort rather than a single experience stratum.
monitoring activities. To address these limitations, it is The reference table maps OPAL score tiers to predicted
recommended that the score be adapted by reweighing effort (Table 1).
existing elements and incorporating additional workload
drivers. 8,9 Each CRC prospectively logged actual hours worked per
protocol using a standardized digital time-tracking system.
Tyson et al. developed an adapted OPAL score that Data were reconciled weekly to ensure completeness and
9
integrates supplemental complexity indicators and links accuracy. Estimated workload hours were also converted
the score to observed coordinator effort using retrospective into workday equivalents using both a 7.5-h academic
data from a diverse portfolio of clinical trials. Their analysis standard and an 8-h industry standard. This conversion was
revealed a strong linear relationship between the adapted performed solely to facilitate cross-sector benchmarking.
OPAL score and actual hours worked by coordinators Such conversions are common in resource planning
(β = 77.22; p=0.01; R = 0.78), resulting in a practical models, allowing institutions to interpret workload
2
reference table for estimating staff effort during trial estimates in the context of their operational norms.
planning. However, this tool has not yet been validated.
Validation of workload estimation tools is critical in 2.1. Statistical analysis
confirming their utility, accuracy, and generalizability To assess the agreement between the estimated and actual
across research settings. A validated tool provides workload, descriptive statistics–including mean absolute

Volume 11 Issue 5 (2025) 107 doi: 10.36922/JCTR025260032