Introduction

The clinical research industry is undergoing a quiet but significant transformation. For decades, the clinical trial protocol has existed as a static, monolithic document—written once, revised repeatedly, and copied manually into every system that depends on it. But recent initiatives by the FDA, TransCelerate BioPharma, and CDISC are pushing the industry towards a more structured, interoperable, and machine-readable future.

Central to this transformation are two complementary efforts: the ICH M11 Clinical Electronic Structured Harmonised Protocol (CeSHarP) and the Unified Study Definitions Model (USDM). Together, they aim to standardise not only what goes into a clinical protocol, but how that information can be reused across tools, systems, and regulatory platforms.

At the same time, platforms like Momentum have emerged with a practical, automation-first approach to protocol management. While distinct from USDM in philosophy and structure, Momentum is already solving some of the hardest day-to-day challenges in clinical documentation—rapid iteration, downstream consistency, and document synchronisation for trial teams.

This article explores the strengths of both worlds: the foundational rigour of ICH M11 and USDM, and the operational acceleration delivered by Momentum.

What is Momentum?

Momentum was created to address a fundamental challenge in clinical research: the disproportionate amount of time and resource spent generating, updating, and managing trial documentation. The platform was designed to reimagine how trials are initiated, amended, and documented—without being shackled by the inefficiencies of legacy systems.

Its core goal is simple: to accelerate the start-up and amendment process by centralising structured trial information, automating the generation of downstream documents, and providing real-time review and revision tools. While many platforms serve sponsors or data management teams, Momentum was intentionally built with CROs, CTUs, medical writers, and lone investigators in mind—users who need immediate value, intuitive interfaces, and full control over every line of trial content.

The result is a platform focused not only on regulatory compliance but on operational fluidity—where changes can be made, reviewed, and deployed across all trial materials in a matter of minutes.

What Is ICH M11?

The ICH M11 initiative, led by the International Council for Harmonisation (ICH), is an effort to define a harmonised, structured format for clinical trial protocols. It provides both a standardised template and a technical implementation guide that allows the protocol to be expressed in machine-readable formats like XML.

The goals of M11 are:

• Consistency across sponsors, regulatory bodies, and geographies
• Structured authoring, replacing free-text narratives with reusable blocks
• Regulatory readiness, allowing protocol information to be submitted digitally

M11 defines what information should be included in a protocol, how it should be ordered, and what fields are required. But it does not deeply address how these fields relate to each other in a dynamic, traceable, or system-integrated way.

What Is USDM?

The Unified Study Definitions Model (USDM) fills that gap. Developed by TransCelerate in collaboration with CDISC and the FDA, USDM provides a relational data model for representing the contents of a protocol. Instead of a flat list of fields, it treats protocol components like objects that relate to one another.

For example:

• An Objective is linked to an Endpoint
• An Endpoint is measured by an Assessment
• An Assessment occurs at specific Visits in the Schedule of Activities
• An Arm is assigned an Intervention and follows a specific schedule

This relational framework enables traceability, automation, and integration with systems like EDCs, CTMSs, and regulatory databases. It is foundational for future-ready protocol ecosystems.

USDM also supports machine-readable output in JSON or FHIR-based formats, enabling interoperability between modern clinical systems.

The Rationale for Relationships in USDM

At first glance, modelling relationships might seem like overkill. Why not just define the protocol once and export the documents?

Here’s the rationale:

1. Traceability: If a primary objective changes, which endpoints, assessments, or visits are affected? A relational model lets you trace those dependencies.
2. Consistency Checking: Are all objectives covered by endpoints? Do endpoints map to assessments? Gaps are easier to detect.
3. Automation: Tools can generate downstream artifacts like CRFs, analysis plans, and regulatory submissions from a single source.
4. System Integration: Protocol data can be injected into clinical systems without human translation or duplication.

This structure is critical for real-time collaboration, faster amendments, and quality assurance across sponsors, CROs, and regulators.

Delivering Impact Today: How Momentum Complements the Ecosystem

Momentum takes a deliberately holistic approach to clinical trial setup. Rather than inferring or deriving fields from a core driver like the Schedule of Assessments (as some systems do), Momentum requires users to input all core trial data directly and explicitly. This means nothing is left to assumption—users see and own every component of their protocol from the outset – after all, regulators expect trial teams to have an in-depth knowledge of every detail of their trial.

This approach ensures greater clarity and control. For example, rather than generating eligibility criteria based on population metadata, or populating endpoint sections based on objective text, Momentum guides users through each field as a standalone element. While this might seem less dynamic on the surface, it has a significant benefit: trial teams are forced to confront—and fully understand—what’s in their documentation. This mitigates the risk of misalignment between intent and implementation, especially when multiple stakeholders are involved.

Importantly, this approach does not exclude structured reuse. Users can pre-load templates, import historical content, or use suggested entries—but always with the final say resting with the trial team. This balance between structured guidance and user-driven control is what allows Momentum to support both rigour and flexibility.

Template-Driven Simplicity with Broad Utility

Momentum is designed around smart templates. Users enter structured data once, and the platform populates all downstream documents, such as protocols, informed consent forms (ICFs), GP letters, participant information sheets/booklets, internal documents, and other regulatory documents. It includes site-facing and patient-facing documentation—areas often overlooked by standards like M11 and USDM.

It also tracks over hundreds data points across more than 15 domains. These go beyond regulatory concerns to include pharmacy logistics, trial website content, and real-world operational data, enabling a broader end-to-end solution.

Amendment Engine

Momentum’s amendment engine allows users to create and manage multiple versions of a trial environment. This means edits can be made in parallel without overwriting the live study, preserving data integrity while enabling rapid experimentation or sponsor review. It also allows for document regeneration across the protocol suite, instantly reflecting any changes.

AI-Powered Document Review in Context

Embedded AI tooling supports real-time data review. Instead of back-loading quality control to the document stage, Momentum provides inline prompts, validations, and language checks as the user builds their trial definition. This supports consistency and accelerates sign-off.

Practical Now, Compatible Later

Momentum’s model is structured around immediate usability and document automation, with strong alignment to M11 fields and terminology. While it does not yet employ a relational schema like USDM, its architecture allows for future export into USDM-compatible formats such as JSON or XML. This ensures compatibility with evolving regulatory expectations, while maintaining a user-friendly interface that delivers rapid value to teams managing protocol documentation today. Over time, enhanced relational features can be layered in to support integration with broader digital data flow initiatives.

In this way, Momentum acts as a practical enabler of digital protocol goals—supporting the direction of M11/USDM while offering tangible operational advantages in the present.

Final Thoughts

The USDM and ICH M11 initiatives represent a vital evolution in clinical research. They bring much-needed structure, interoperability, and automation potential to protocol development.

But real-world tools like Momentum demonstrate that there’s another path forward—one grounded in simplicity, breadth, and real-time operational impact.

Momentum may not yet model complex relationships between data points, but it already delivers value where it matters most: accelerating the documentation burden, supporting amendments, and ensuring every trial document is accurate, current, and consistent.

The challenge ahead is to build bridges: between structured data and human workflows, between regulatory precision and practical delivery. Platforms like Momentum are well-positioned to lead that future.