Structured content and the emerging tooling
Regulatory documents are moving from monolithic files to structured content: components created once in a central system, tagged with metadata and reused within and across a product's documents under rules that lock some and leave others editable. Tools that draft, tools that predict and generative AI configured on the components are reaching the same documents, each aimed at a different part of the work, and together they change how the content is bought, governed and written and what the writer's skill set has to include.
Structured and unstructured content
A product's lifecycle produces the protocol, a series of informed consent forms, the clinical study report and periodic safety reports, and writers quite often have to copy information from one of these documents into the next by hand. Many writers, asked by a show of hands, would like that information pulled across automatically, and structured content authoring looks at how content is reused across all of those deliverables at once. The protocol's study outline, for example, can be reused in the report and in a development safety update report or another periodic report.
The distinction underneath is between two kinds of content. Unstructured content exists as a single long piece of information in the form of a document, the monolithic file writers are used to, managed in a document management system such as Veeva Vault. Structured content is broken into individual components, for a protocol the summary statement, the schedule of assessments and the study outline, which can be taken out, rearranged and reused in another document. The authoring model separates content from layout: components are generated and edited in a structured content management system, independently of what the final document will look like, so the writer develops text without having to worry about formatting first.
Content components are individual pieces, such as the study design, participant characteristics and study interventions, defined, created, managed and archived in a central repository with minimal formatting so that they can be reused across clinical and regulatory documents; content standards are the rules governing how sentences make paragraphs, paragraphs make sections or components, and components make a deliverable; structured content authoring is the process and rules by which an author creates content in defined structures that can be easily reused, repurposed and automated; and a structured content management tool is a central platform for creating, managing and reusing digital content. The principle is create once, use often, with components tagged by metadata so that they can be retrieved for a purpose, much as metadata fields on ClinicalTrials.gov support a targeted search. The FAIR data principles of findability, accessibility, interoperability and reusability can be applied to structured content as well as to data.
Document authoring, structured authoring and component authoring are three separate terms. Document authoring is the writing of text-heavy documents. Structured authoring is a rules-driven writing technique that delivers simplified, focused content, increasing readability and the ability to find information, and it enables component authoring, which creates content in chunks with downstream reuse in mind, using parent-child relationships and structured templates so that documents are easy to change and consistent with one another. Component authoring is necessary for content reuse platforms, software applications that integrate component content, and it enables automated document creation; together the two let machine learning, artificial intelligence and automation help develop documents, and in 2020 regulatory writing was the current application, with component-based authoring tools in use there to handle the volume and variety of documents that contain much the same information. One 2020 account held that the idea is not new, but that standardisation and technical advances were finally making it workable and that it would continue to grow more robust. In the same account, the push comes from efficiency at a time when writers are asked to do more with less: automation can accelerate content creation, increase consistency and reduce resourcing effort, and it lets the writer concentrate on what matters, the narrative that accurately describes the data and conveys the key points.
Reuse and its governance
Reuse then runs at two levels. Intra-document reuse repeats content within one document, as when the number of participants in the protocol's study outline reappears in the protocol body. Inter-document reuse carries it between documents, and the protocol-to-report case is downstream reuse: content from a main source document pulled into the documents that follow it. Once the content is mapped, each component marked with the documents it is to be reused in, a document pulling in those components is generated with the click of a button. The components can also be governed individually: locked where they must not change across the whole lifecycle's documentation, as with the study title and the study drug name, or editable where the downstream document has to adapt them, as with the participant information whose tense changes in the report.
The benefits divide into general ones and ones for the individual writer. Efficiency arrives as a reduced need for consistency checks between repeated content, and for the writer as a shorter turnaround time and greater confidence that repeated components agree. Governance is a fairly new element in medical writing: a set of rules is essential once content is reused downstream, and strict governance suits writers because it reduces the copying and pasting from one deliverable to another. Reusability is the ability to control individual components, which gives the writer up-to-date content and faster template updates, because editing a parent component, with the options set up correctly in the system, updates it automatically in every downstream document across the product line.
Unstructured documents carry many inconsistencies. A protocol goes through quality-control checks and review; the report then copies its information, and accidental mistakes are introduced, unintended, as when only the tense is being changed. Working on components rather than on documents reduces that risk of inconsistency across documents. The system also makes the information traceable: a search shows which deliverables use the study outline component, or where the efficacy or the safety results sit, and can go further down to components specific to a study drug or an indication.
By structuring and standardising clinical and regulatory content, the pharmaceutical industry can shift from a document-based to a content-based approach, a transition that requires adopting structured content management tools and common structures and standardising content. Conventionally a document's content is unstructured: apart from the high-level section headers in the table of contents, the writer arranges free-form text, figures and tables as they see fit, provided the content requirements in the authoring guidance are met. Documents developed independently, such as a briefing document and a protocol written by two different writers, create and manage similar information separately, so where the writers have no tool or process to keep the two consistent an extra step is needed before finalisation to align them. Because the information is not linked, independent revisions can change its meaning so that it drifts apart over time; resolving that information drift takes repeated, deliberate consistency checks that weigh especially on tight timelines, and revising one item means checking and updating the whole document, which, unless the sections not being aligned are locked, risks stakeholders reconsidering content that needed no revision.
How an organisation defines its content components depends on how it intends to use the content. For example, a study outline upstream of the protocol can have its subsections, such as design, overall rationale, study interventions and statistical methods, defined as components. Once the team finalises the outline, the writer generates a partly completed draft protocol from it under a prespecified reuse plan and rules, with no manual copying, and once the study team has approved the outline its components can be locked for later reviews, making review faster and more targeted. A report shell is populated the same way from the protocol's background, study design, participant characteristics and interventions.
By structuring content and adopting content standards, the industry can reduce document development time through the reuse of defined standard content. Repeated information in documents is prone to inconsistency, while repeated components are not, because they share content; information cannot be tracked across documents, while reuse of components is tracked; repeated information is hard to standardise, while repeated content can be set as editable or restricted to use as is; specific information is hard to search for and retrieve, while components can be retrieved from repositories by their metadata; and information is copied and pasted by hand within and between documents, while content mapping reduces the need for manual intervention.
A structured protocol template
One sponsor, Merck, built structure into its protocol template with the Merck Structured Authoring Tool, MStAT. The TransCelerate common protocol template was originally designed to standardise the flow of protocol development, and Merck chose its technology-enabled version to create structure from unstructured content, with the aim of eliminating copying and pasting. The company first made its core content uniform, then made the template progressively more complex by adding specific content libraries and operational activities. The tool offers an outline format and table of contents, instructional text and content libraries, inclusion and exclusion criteria, and extraction; the TransCelerate template and Merck's content outlines were merged, with the common language updated monthly. Version 5 went live on 11 December 2017. Behind it, a request form records the type of trial, and specific content, including a specified content library, is prepopulated into a Word template to produce a customised protocol. Its elements carry content controls, bound prespecified content that can be pulled out later, destination controls for library content the user inserts, and both mutable, editable content and immutable, untouchable content, which shading that can later be removed marks for review. The content libraries are kept in a central location rather than on individual laptops, so that they are easier to update.
The benefits Merck reported for the tool are shortened writing time, more efficient use of reused text and more attention to libraries and content. Reuse within a document improves consistency and reduces errors, and understanding reuse is essential, because vetted phrasing often cannot be changed. The opportunities for reuse, which have to be explored, run beyond the protocol, to text reused in informed consent or lay-oriented documents and to translation, and an "XML bubble" is a section of text that may later be extracted and inserted into other kinds of document. The tool can help authors, but they must still use their own judgement; its development drew on scientists, study teams and physicians; and smaller companies may adopt the common protocol template with less customisation than larger ones.
A content model is the structured content together with how it is created, described, managed and used in outputs such as a protocol. Developing one means identifying a piece of variable content, how it can be reused, the information policies governing that reuse, and who should know about it. Standardising and harmonising starts with finding the inconsistencies, such as whether "randomization" and "treatment assignment" are the same thing in different places within or between documents; it goes on to whether it makes sense to build a structure that carries that content together, and to checking the output document to see that the content really flows through as anticipated, with the related instructional text in mind. The tool itself had limitations and could be inefficient in areas such as visibility and traceability.
Intelligent content and automation
The documents produced in development, such as protocols, investigator's brochures, clinical and nonclinical study reports, summary documents and labelling, are reused by medical affairs and commercial operations after approval, and constantly generating and reusing such volumes of content often duplicates effort and lengthens the time to submission, with information residing in silos. Template-based authoring is universally adopted but time- and labour-intensive, with content changes hard to manage and inconsistent tagging or indexing at document level delaying the right information reaching the right person; structured content authoring improves on it by classifying information, extracting components or topics into reusable libraries and assembling them into documents according to the outline and data structure; and intelligent content applies artificial intelligence contextualised to the life sciences to create, update and reuse content automatically.
As described in 2019, an intelligent content system extracts content from structured and unstructured sources, classifies its substance and context, stores it in a semantic model, adapts it when it is reused for a different stakeholder, keeps a catalogue searchable by natural-language query, and runs impact analysis when content is added or changed. Machine learning and natural language processing identify the verbatim and non-verbatim content shared between source and derivative documents, so that reuse patterns and lineages can be quantified, and an integrated workflow system manages discovery, authoring, approval, administration and publishing. Content mapping is the step underneath: it identifies the components in documents created during development, maps the relationships and interdependencies between upstream and downstream documents, and determines what can be generated automatically, what reused, where quality can improve and where duplication can go, so that updating a source document updates every document linked to it.
Intelligent content can be used across the medical writing function; it applies especially where documents rely on other documents as sources of content, and the best potential targets are those on the critical path of a regulatory dossier submission. In labelling, country labels can be drafted automatically from the summary of product characteristics or the company core data sheet, an update to the reference label triggers updates to every downstream label, and local labels feed patient information leaflets. Among regulatory documents, the first draft of the clinical overview and summary documents can be drafted automatically from the clinical study reports' verbatim content, and quality overall summaries from product information. Among clinical documents, protocol design can draw on prior data, the literature and regulatory guidance, and the protocol feeds the informed consent forms, the data management plan and the clinical study report, which can be drafted automatically up to section 9, with the efficacy and safety sections 11 and 12 summarised from datasets; patient narratives can be generated from datasets and lay summaries for public disclosure from the reports. Among safety documents, periodic benefit-risk evaluation reports and development safety update reports can be drafted from each other, with risk and adverse-event information kept consistent across them. In medical information, local standard response letters can be generated from global ones, beside promotional materials and claims management with assisted medico-legal review.
One 2020 forecast held that medical communications would provide disclosure-ready tables and figures usable in component authoring across internal and even external medical communication functional groups.
Generative AI on structured content
One contract research organisation reported in 2025 that traditional unstructured authoring limits the opportunity to use generative AI efficiently, that the industry was pivoting towards structured, reusable content that supports digital data flow and generative AI, and that generative AI augmentation remained in its infancy. Structured content authoring was by then fully adopted in its medical writing services and in use on a wide variety of safety and regulatory documents. For a contract research organisation's regulatory writers, protocols, informed consent forms, clinical study reports and aggregate safety reports such as development safety update reports and periodic adverse drug experience reports are high-volume deliverables, traditionally authored as unstructured text that is inefficient and error-prone, and whose lack of structure makes it difficult to set rules for a large language model to follow.
In the organisation's observation, generating a child document such as a report shell from a parent such as the protocol, through metadata-driven incorporation of standardised content, reduced the effort needed to populate the child document's sections accurately by 15%, comparing the hours budgeted for flat authoring with those for structured authoring. The platform also releases critical template updates in real time under version control, reducing the potential for missing information and keeping templates aligned with the latest regulatory and guidance documents. The approach also frees the writer to focus on more critical aspects of document development. Structured content authoring improves intra- and inter-document harmonisation, consistency and first-time quality, enhances collaborative authoring and quality control, gives easy access to content libraries for particular therapeutic areas or trial designs, and aligns with digital data flow and best practices.
A writer on protocols names two distinct benefits over flat authoring: metadata-driven customisation of template content, which selects approved library content by its metadata and gives the writer a more relevant starting point, and inter-document reuse, which cuts the time spent on consistency checks because information is reused in several places rather than copied and cross-checked after each edit. A writer on study reports names greatly increased consistency, faster development to the final report and improved regulatory compliance, with automatic population of in-text tables and text summaries generated from source data, and downstream reuse of the safety results sections in related lifecycle documents such as CTD sections and publications, or in new protocols, reports and investigator's brochures. A writer on periodic safety reports names improved consistency, efficiency and quality through standardised formatting and content, fewer errors, a streamlined process, and better version control and collaborative editing, and, from reusing related content across documents for the same investigational product, time saved through less duplicated effort, consistent information, fewer errors from manual rewriting, easier adherence to regulatory requirements and better audit trails and document linkage, leaving teams to focus on critical analysis rather than repetitive writing.
Eleven safety medical writers who had authored periodic safety reports on the platform rated it in an eighteen-question survey across six attributes, on a scale from 1, poor, to 5, excellent. Compatibility and integration scored 4.3, user interface and design 4.1, performance and efficiency 4.0, support and documentation 4.0, functionality and features 3.9 and overall satisfaction 3.9, for an overall average of 4.0. The organisation read the ratings as suggesting a platform that is well integrated but needs more refinement in functionality and features.
The organisation holds that structured content authoring is an ideal foundation for generative AI, because the AI can be tailored to specific components and that targeted use improves the precision of its outputs, and that the two complement each other's capabilities in augmenting the authoring process and data integrity. A traditional template is marked up and split into components, the components and their content libraries are created in the platform's content database, and prompts are configured for each, so that writers use pre-programmed, context-driven prompts configured for the individual components of each document template, and the augmented components appear in an organised, structured way when a document is created. Template-level prompts lower the barrier to adoption by minimising the need for writers to create prompts by hand, while the option to refine a prompt at component level keeps control targeted and the use of generated content intentional. A generative AI assistant built into the platform also lets writers query sections of a document and process source documents. The efficiencies the assistant brings lighten the writer's workload, leaving more time for critical thinking, creative problem-solving and in-depth analysis of novel content. One of the organisation's associate directors sees generative AI augmenting authoring by applying pre-configured prompts to specific components: a section that briefly summarises its source, for example, needs a different prompt and task from one that uses the source content as is.
The organisation's prompt strategy, as one of its senior managers describes it, rests on domain specificity, user-centric iteration and regulatory alignment, with modular prompts for each document type refined on writers' and reviewers' feedback, and the team upskilled as adoption grows. A principal writer there reports efficiency gains from automating tense changes, initial summaries for study reports and lay text for informed consent forms, measured by comparing assisted drafting times against manual ones.
The organisation observes that hallucinations are a common downside of generative AI and, left unchecked, may produce false content, references or data that can propagate into downstream documents; that without domain-specific training a model lacks contextual understanding, which can lead it to misinterpret complex medical data and jeopardise patient safety, so writers should always verify generated content against the source data; that output depends on the quality of the input data and the specificity of the prompt, so optimising the two can help performance; and that output can be subjective, leading to bias. Users of the platform can refresh the session, iterate the prompt and ask follow-up questions, and a grading system, automated scoring, human grading or both, can help assess and monitor output quality over time. The organisation reports that integrating structured content authoring with generative AI enhances the efficiency, consistency and quality of content generation and reuse, reducing initial drafting time and project timelines. The organisation planned to extend AI integration across the components of more document types while training writers and assessing the efficiency gained.
Tools for protocol development
In one speaker's view in October 2025, technology investment ran across the whole trial lifecycle, with medical writing for protocol development among its use cases, but the tools were still in their infancy: they appeared now and again rather than routinely, larger organisations were the more likely to have something in regular use sooner, and in the long term the tools would probably improve data-informed decisions, which were then challenging to make.
Tools for protocols fall into three categories, or technically four. Writing tools aim to reduce the time to a first draft, to make that draft as complete as possible as quickly as possible, to raise first-time quality and potentially to reduce unnecessary protocol amendments, although how that last effect would be achieved was still a point of discussion. In the speaker's view, real-world data is where the most advanced tools are: they identify the endpoints and eligibility criteria that give the most predictable enrolment rates or the greatest likelihood of a successful submission to a regulator. Design tools assess burden, such as patients' time spent in clinics, site burden or cost, and can model and do a variety of other things, essentially trying to predict the future.
Writing tools are the most prominent kind for medical writers, and in the speaker's view they can support the writer but are unlikely to replace one in the foreseeable future. A draft can be produced faster, but as teams grow larger the writer still has to work with the team, understand and focus its priorities and walk it through to a complete protocol, and that is where the speaker sees the writer adding most value.
In March 2023, one speaker saw real interest in a more dynamic relationship between the protocol, during development and afterwards, and the study sites, with a variety of tools coming up and nothing yet that could be called the de facto way of doing it.
Owning a platform or buying the output
In 2024, in one panellist's account, two shifts were under way in how the tooling is bought. Almost every large pharmaceutical company, asking why it runs 3,000 applications to do research and development work, was trying to move to a platform, into which any additional tool then has to fit seamlessly. Many platforms bring quarterly updates that have to be planned for, sometimes with functional testing their users are expected to take part in.
The other shift removes the software from the customer. Rather than owning a tool and living with maintaining it, a company buys the finished output from a provider, a model that the same panellist saw coming up because many companies were asking why they should spend time and money maintaining additional software, and called easier. Another panellist called it a very difficult model when dealing with multiple sponsors, or with a contract research organisation acting for several, since the software vendor then takes the work and produces the output; that panellist's organisation had explored the model, which had not gained much traction, but held it to be an option and the choice horses for courses.
The writer's skills and way of working
In 2023 the writer's skill set was expected to change as companies deploy structured content management tools. Writers will need training in content management principles, structured content models, content standards and reuse strategies, so that their effort goes into the unique de novo content, and into interpreting clinical data and communicating with stakeholders, rather than into finding and transferring content, and automated content reuse will reduce errors and the need for consistency checks. The way of working changes as well, because content now serves a whole product's development: the writer has to keep to the content standards and the agreed terminology and acronyms, resist stakeholders' requests to rephrase content in the way they prefer, and write content free of positional phrases such as "see below" or "as mentioned above" and of cross-references to sections that will not apply where the content is reused.
Shared standards across organisations
In 2023 data standards were widespread in clinical development, but structuring and standardising content was in its infancy and only some organisations had begun. As structured content authoring matured, more coordination was expected to emerge between sponsors, contract research organisations and regulators; TransCelerate's Clinical Content & Reuse Solutions, whose content libraries provide select content standards usable in a protocol and reused downstream, and the ICH M11 guideline for a clinical electronic structured harmonised protocol (CeSHarP), then in draft, were seen as important steps in that direction, and implementing such agreed standards was expected to level the playing field and make harmonised documents between stakeholders more likely. Organisations would also need changes to authoring processes and content governance, and time and resources to train users new to structured tools and content-based working; as each piece of content is individually created, reviewed, approved, revised, archived and, if needed, retired, the need for users to do these things by hand would diminish, and the tools would help the writer find, reuse and repurpose content so that organisations create content faster than by developing unstructured content in individual documents. For large submissions, a journal's guest editors hoped in 2023 that background text on the regulatory landscape, the product's development history and the indication could be authored and agreed once by the responsible subject-matter experts, imported into every document from the approved source, and revised once with the change carried across the set in real time, sparing reviewers the need, or the temptation, to re-review the same sections each time they meet them, keeping the documents consistent and reducing the time and effort heavy submission documents take.
In 2022, writers at sponsors and contract research organisations imagined how global teams would work if budget and technology were no limit. One writer imagined standard document templates and language across companies and regions, endorsed by health authorities worldwide and needing only minor customisation; AI creating the data-driven sections once a trial's database was locked and cleaned, and managing tables of contents, abbreviation lists, reference lists and formatting; and background product and regulatory text written once, reviewed, stored in a master location and pulled automatically into every document that needs it, changing everywhere when the master changes, with regional variations tagged, leaving writers to draft the summary and conclusion text with the subject-matter experts. Another expected intelligent access to content libraries for authoring protocols and semiautomated generation of clinical study reports and submission documents. Two others, noting that structured content was already being adopted across the industry, suggested a shared platform of common text across the industry for some topics, such as disease descriptions updated automatically from new publications. Others proposed modernising the ICH M4 guidance on the Common Technical Document, with cloud-based templates and tools, as an opportunity for the field to innovate together, and deeper collaboration between organisations on document standards, content standards and content management. In the same year one practitioner hoped that TransCelerate's freely available templates would encourage harmonised documents across the industry, and noted that offerings in place for several years were being extended with new technologies to automate initial drafts. In a separate 2022 panel, a former regulatory writing director expected workflows to be made efficient and accurate by an authoring system that reuses, and is connected with, all data elements, linked for easy searching, correcting and replication, and an industry analyst expected dynamic document templates that embed changing global regulation in real time to reshape medical writing.