What a 30% trial increase means for coordinator workload

Clinical trial volume has grown by more than 30% since 2020. The coordinator workforce needed to actually run those trials hasn't kept pace, and a national shortage of qualified coordinators persists, driven by escalating responsibilities and workload rather than a simple lack of interest in the role. An effort to quantify that workload more precisely offers a useful window into exactly where the burden actually sits.

The existing tool wasn't accounting for real-world variation

Site capacity planning has typically relied on tools like the Ontario Protocol Assessment Level, or OPAL, which scores a protocol's complexity to estimate the coordinator effort it will require. The researchers behind this study identified a specific limitation: OPAL, on its own, didn't account for organisational structure and budget constraints that materially affect coordinator productivity in practice. Two coordinators facing protocols with identical OPAL complexity scores could have genuinely different real workloads depending on the resources and organisational support around them, a gap the standard scoring approach wasn't capturing.

To address this, the researchers tracked actual coordinator hours worked against protocol complexity scores across seven active protocols over six months, aiming to build a refined version of the scoring model grounded in real effort data rather than complexity estimates alone.

The refined model was genuinely predictive

The result held up well statistically: the adapted OPAL score significantly predicted actual coordinator hours, with a strong correlation of R² = 0.78. That's a meaningfully strong result for a workload prediction model in an operational, real-world setting, and it validates the core premise, that protocol complexity, properly measured and calibrated against real effort data, is a genuinely useful basis for predicting coordinator workload rather than just a rough proxy.

Where the workload actually concentrated

Beyond validating the model itself, the study surfaced two specific patterns in where effort concentrated, both worth attention for anyone allocating coordinator time across a site's active protocols:

• Industry-sponsored trials required more coordinator effort than federally funded studies. That's a notable finding for site-level resourcing, since it suggests a site can't treat its portfolio of protocols as uniform in demand just because they're nominally similar in scope or subject area. The funding source itself correlated with a real difference in the work required.
• Behavioural interventions demanded more coordinator resources than drug studies. This runs somewhat against an intuition that a drug trial, with its associated safety monitoring and dosing procedures, would automatically be the more demanding protocol type. The data suggests behavioural intervention studies carry their own distinct, and apparently heavier, coordination burden, plausibly reflecting the intensity of participant contact, session scheduling, and adherence tracking these studies typically require.

Neither finding is intuitive enough to have been safely assumed without the data. Both are exactly the kind of pattern a site needs visibility into if it's distributing coordinator time across a mixed portfolio of active studies, rather than assuming workload scales predictably with the more obvious markers like therapeutic area or perceived protocol complexity.

Why this connects directly to the coordinator shortage

The workforce shortage the researchers describe isn't just a headcount problem to be solved by hiring more coordinators, though that would help. It's also a distribution problem: if workload is being allocated based on assumptions that don't match where the actual effort sits, some coordinators end up carrying meaningfully more real burden than their nominal assignment suggests, while the assignment process has no visibility into that gap until burnout or turnover makes it visible the hard way.

A capacity planning approach grounded in actual effort data, like the refined OPAL model this study developed, addresses that directly. It gives a site the ability to distribute new protocols based on evidence about what they'll actually demand, rather than intuition or a generic complexity score that, on this study's own finding, wasn't previously accounting for real organisational and budget context.

What this means for how sites plan capacity

A few practical implications follow for site management and workforce planning:

• Treat workload prediction as a genuine measurement problem, not an assumption. The strong correlation (R² = 0.78) this study achieved came from grounding a complexity score in actual tracked hours, not from complexity scoring alone.
• Don't assume funding source or study type predict workload the way therapeutic complexity might. Industry-sponsored studies and behavioural interventions both required more coordinator effort than comparable alternatives, in ways that wouldn't necessarily show up in a standard protocol complexity assessment.
• Objective project distribution is a retention tool, not just an efficiency one. The researchers explicitly connect better capacity assessment to reduced burnout and turnover, treating workload visibility as a lever against the coordinator shortage itself, not merely a scheduling convenience.
• Systems that track actual time and effort against protocols, continuously, are what make this kind of model possible at all. A refined workload model is only as good as the effort data feeding it, and that data has to come from somewhere more reliable than end-of-study recollection.

With trial volume up more than 30% since 2020 and the coordinator shortage showing no sign of resolving on its own, treating workload distribution as a measurable, manageable problem, rather than an unavoidable consequence of an under-resourced field, is one of the more directly actionable findings to come out of site operations research.