Rob DiCicco, vice president of portfolio management, and Bill Illis, workstream leader, digital data flow at TransCelerate BioPharma, highlight the advantages of digital protocols for clinical trials.
Clinical research has no shortage of technology. What it lacks is the ability to scale in ways that allow information to flow automatically across systems and support digitally enabled workflows for critical stakeholders.
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While protocols are the blueprint for clinical studies, the information still moves through organisations in documents: re-entered, reformatted, reconciled, and corrected across systems that were never designed to talk to one another. That reality has negatively impacted timelines, efficiency, and quality for decades. In the absence of committing to standardisation, the industry has been using existing digital formats as “paper on glass”.
Digital protocols are increasingly being evaluated and used as a way to break that cycle. And the questions around them have shifted, too. It is no longer “Should we digitise protocols?” but rather “Can we do it in a way that allows protocol information to move between users and systems without manual effort?”
For those involved in advancing structured protocol standards through initiatives like Digital Data Flow (DDF), that shift is becoming visible.
At the most recent US SCOPE Summit, for instance, digital protocols were not confined to a single session track. Across three and a half days, more than 30 agenda references touched on digital protocols: organic mentions and presentations from sponsors, vendors, and other stakeholders sharing adoption stories, lessons learned, and real implementation data. The industry isn’t debating the concept anymore. The focus is now on implementation.
The pieces for implementation are falling into place
Embedding protocol data standards is expanding across several fronts at once. Regulators, including the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA), are exploring how digital protocol assets may enable implementation of ICH M11, which was approved by the ICH Assembly at the end of 2025. Their engagement places structured protocols firmly within the regulatory dialogue around modern study design and execution. Shared opportunities of interest include smarter study design, automating aspects of review, and automated registry posting.
Sponsors are progressing along different paths. Smaller organisations with fewer legacy constraints have implemented structured protocols more broadly across the study lifecycle. Larger sponsors are concentrating on defined operational bottlenecks like study startup, contracting, and sample management, and building from there.
This divergence reflects infrastructure realities more than strategic differences. Most large organisations are not attempting wholesale transformation. They are identifying where the friction is highest and starting there.
At the same time, vendor capabilities have expanded significantly. Tools now support digital protocol authoring – which then enables study design optimisation through more automated risk assessment, budget automation, patient burden estimation, and more. With both standards and applications maturing together, the ecosystem is increasingly capable of supporting real implementation.
The value conversation
Where does that implementation start to matter operationally?
In many organisations, the answer lies in the Schedule of Activities (SoA), one of the most frequently replicated components of the protocol. The same information is typically re-entered across budgeting tools, contracting systems, data acquisition systems, and data transfer specifications. Each repetition adds time and potential variation. Structured, machine-readable protocol data streamlines how those workflows can operate.
Demonstrations at SCOPE showed automated budget builds and sample management reduced time spent on that process by roughly 70 to 85%, and study build activities were completed dramatically faster through automated generation of SoA-driven components. Contracting and reporting steps that previously took weeks were cut down through automation. These results vary by organisation and context, but the consistent theme is that reducing manual transcription reduces variability.
Long-term, the effect extends beyond individual workflows. As more organisations adopt industry-standard structured protocols, the ability to easily connect to systems that consume protocol information improves. Tooling expands and implementation becomes more widespread. What begins as targeted optimisation gradually becomes shared infrastructure.
Where the friction lives
Somewhere between protocol finalisation and study startup, the same information gets manually entered into many systems, such as EDC systems, CTMS, budget tools, and contracting templates. Each handoff is a chance for something to go slightly wrong. And often, those discrepancies surface late as protocol deviations, when they are more disruptive and more costly to correct via protocol amendments.
Structured protocol data reduces the number of manual touchpoints required to operationalise a study. When information flows directly into downstream systems rather than being re-keyed at each step, avoidable errors – the ones that exist simply because humans are repeatedly handling the same data – can be meaningfully reduced. Fewer re-entries mean fewer transcription errors. Fewer inconsistencies mean less reconciliation. Less reconciliation means more predictable timelines.
The magnitude of this matters. According to data from Tufts CSDD, approximately 75% of clinical studies involve at least one substantial protocol amendment. Roughly one-third of clinical trial participants experience a protocol deviation. Better design upfront, supported by structured protocol analytics, has the potential to reduce both, and recent demonstrations have shown how digital protocols can support automated consistency checks against established guidance, surfacing gaps earlier, before they become costly downstream problems.
Better data, better AI
AI is often introduced into this discussion with the assumption that advanced models can compensate for inconsistent inputs.
A corollary to “garbage in, garbage out”: Better data produces better AI. It’s possible to feed unstructured PDF protocols into an AI engine and generate outputs that look impressive. The problem is that plausible is not the same as reliable. In a regulated environment, where the goal is not only to automate but to do so in a way that is explainable, defensible, and repeatable, the margin for plausible-but-wrong is extremely thin.
Structured protocol data strengthens those conditions. Standardised elements, defined relationships, and controlled terminology create more stable inputs for automation and analytics. That stability improves confidence in AI-enabled workflows and reduces ambiguity in model interpretation.
Applications such as digital twins, external control arms, and integration of real-world data depend on that consistency.
What moves adoption forward
None of this happens without executive buy-in – real, coordinated sponsorship across clinical development, clinical operations, data management, statistics, programming, and regulatory. In a highly matrixed organisation, enthusiasm at the working level only goes so far. When leadership is aligned, decisions get made. When it isn’t, momentum lags. The other requirement is less discussed but equally important: patience. There is a real latency between building out the infrastructure and seeing value downstream. Processes need to be repeated before they become reliable.
The wave of innovators and early adopters currently driving implementation has a tolerance for experimentation that the next wave of more pragmatic adopters may not share. The experiences and learnings gained by early adopters will likely create value far beyond their own walls
It’s the evidence base that the rest of the industry is waiting for.
The signals we’re watching for
There won’t be a single moment when digital protocols become standard practice. There will just be a point when the question changes. When organisations no longer debate whether to adopt and instead ask when and how – supported by available tooling, established implementation approaches, and repeatable value signals – the case will feel self-evident. In the same way, no one debates whether to use email.
Getting there requires keeping the near-term conversation ruthlessly practical: Where is protocol information being handled manually today, where does that create friction, and where does structured data create the clearest path to automation? Every use case demonstrated, every efficiency replicated, every implementation that moves from pilot to production shortens the distance between where the industry is now and where it’s clearly heading.
