From static documents to strategic capability: The case for protocol ingestion

Total research and development (R&D) expenditure from top pharmaceutical companies increased from $163 billion in 2023 to $190 billion in 2024 – a $27 billion surge in one year. All of that spend traces back to a single document: the clinical trial protocol, which serves as the blueprint of clinical trials.

Yet, protocols are still typically a PDF or Word document that’s manually translated into operational plans. These documents are written in dense, lawyer-like language that requires individual interpretation from each function; they’re also growing longer and more complex each year to accommodate adaptive designs, sub-studies, and decentralised trial elements, which add even more nuance that can take months to translate downstream. This outdated approach creates too much room for redundancies, risk, and delays.

While protocol documentation is currently a data challenge that determines how quickly and accurately an organisation can move from design to execution, the future is more strategic. Protocol documentation will become protocol ingestion – an approach that’s automated, consistent, and intelligent, creating fewer surprises downstream, less rework, and more confident decisions across the trial lifecycle. This is more than traditional document digitisation that makes PDFs searchable; it’s semantic structuring that transforms protocols into data assets.

The current state of protocol documentation

Clinical trial protocols are currently static, sometimes paper-based documents that contain rich, complex information such as visit schedules, procedures, patient populations, inclusion criteria, endpoints, and sub-studies, which systems cannot directly interpret. This is where errors come into play – not from experts making mistakes, but from analog protocols holding too much power.

Consider a protocol with a schedule of activities where footnotes, section references, and conditional language connect to different focal points. For example, a protocol might say “patients who achieve partial response at week 10 will do an additional scan at week 15, per investigator discretion.” One team may interpret “per investigator discretion” as optional and exclude it from the budget. Another team may treat it as mandatory and account for it in site activation timelines. Each function exercises professional judgement, but unique interpretations inevitably introduce small differences. While not wrong in isolation, once those differences flow into the budget or contracts, mismatches emerge that are difficult and time-consuming to resolve.

Amendments amplify this challenge. When missing elements need to be added or updated, amendments alter the protocol structure. Each team must manually extract, re-interpret, and re-enter the information. The ripple effects of amendments and manual re-interpretation have a significant financial impact. A Tufts Center study found that 76% of clinical trial protocols require amendments, ranging from $141,000-$535,000 in direct costs per amendment.

Manual, error-prone documentation challenges have a solution. With modern automation technology, protocol documentation can shift from an expensive bottleneck into a strategic digital capability that turns an ocean of data into consumable, structured insights for every clinical trial stakeholder.

A game changer: Protocol ingestion

It’s human nature to struggle to apply the same interpretation across hundreds of decisions and multiple functions every time. Automation technology reduces inconsistency, making protocol interpretations visible and traceable.

It surfaces where logic is conditional, where discrepancies exist, and where assumptions are made. And it eliminates mechanical, manual work, such as locating information buried in 100-page documents. When experts can rely on structured, traceable information from automation structures, they can more easily navigate protocol nuances and make informed, smarter decisions across the clinical trial lifecycle.

When a protocol amendment changes conditional visit logic – for example, adding an optional follow-up visit for patients who meet specific criteria – AI can immediately flag the change and trace its impact across the budget. The team can then see how the amendment affects cost projections for enrolled patients and adjust forecasts accordingly. It also allows them to update sponsor budgets and site contracts quickly (hours instead of weeks) to help avoid delays.

Importantly, this partnership between human experts and automation technology relies on trust and feedback. For instance, teams may worry about losing control when using AI to translate content, but that's why explainability, traceability, and a continuous feedback loop are critical. Experts still hold the reins and make final decisions, while leaning on AI as an assistant or partner to ensure consistency and expedite the documentation process.

A low risk, high reward solution

The strategic case for protocol ingestion isn’t complicated: inconsistent manual interpretation creates errors and risk, while structured, automated digital protocols enable consistency and strategic judgement. It’s the most straightforward workflow that allows doers to become reviewers.

For clinical trial stakeholders feeling pressure to move faster and operate more efficiently to bring new therapies to market, protocol ingestion is a pressure lever. It offers a rare combination of low risk–high reward, as it comes into play well before patients enter the clinical trial process, while also influencing nearly every downstream system and function.

The question pharmaceutical organisations are asking is where to deploy modern automation technologies to drive enterprise transformation and gain a competitve advantage. The answer is simple: elevate protocols to intelligent, structured data assets, rather than workflow improvements.

About the authors

Nawal Baili is director of artificial intelligence and emerging technologies at IQVIA, leading teams that build AI-driven capabilities to automate complex clinical and operational workflows. With 10+ years of experience in life sciences analytics, she specialises in digital transformation, intelligent document extraction, and explainable AI designed for regulated environments. She holds a PhD in Computer Engineering and Computer Science. Her work focuses on turning unstructured content, such as clinical trial protocols, clinical trial agreements, and invoices, into structured, traceable data that improves consistency, reduces rework, and accelerates execution across the trial lifecycle.

Aaron Squires is a project manager for the IQVIA GrantPlan application, bringing more than 20 years of experience in clinical trial budgeting and forecasting. He has a strong track record of leading enterprise technology initiatives and cross functional teams, with deep expertise in aligning business and clinical requirements to scalable Fair Market Value (FMV) solutions. Squires focuses on enabling faster, more efficient study startup by reducing complexity in budgeting, forecasting, and FMV decision making, partnering closely with clinical, operational, and technology stakeholders to deliver measurable business impact across global trial programmes.