For years, pharmaceutical companies have approached health care professional (HCP) engagement as a matter of better channel planning, sharper segmentation and more assets in market.

That is changing quickly. HCPs no longer wait for information to be delivered through the limited industry channels. They can open a large language model (LLM), ask a question and receive an answer synthesized from thousands of sources in seconds. That experience is resetting expectations for speed and access, including for the educational material manufacturers provide.

This shift comes at a time when industry is already creating more content than its audiences can absorb. Millions of scientific publications appear annually, along with thousands of educational assets, and most of this material does not arrive at the point where the clinical decision is being made.¹ To meet HCP expectations, teams should approach content development as a loop rather than a pipeline.

A process built for volume

Placing a single piece of medical content in front of a physician takes more than simply producing a finished asset. The process typically begins with a clinical study report followed by a peer-reviewed manuscript in an academic journal. From that foundational content, teams create derived formats, such as detail aids, the approved visual summaries used in conversations with HCPs, and slide decks designed to educate and build advocacy. Each piece is then translated and adapted by market to suit its audience. Before any content reaches an HCP, it must clear mandatory medical, legal and regulatory (MLR) review, which safeguards scientific accuracy, regulatory compliance and patient safety, but it is also the stage where timelines most often stall.²

Multiply that process across tens of thousands of assets, and the economics becomes difficult to sustain. Industry research indicates that a large share of the content produced for HCPs is never used.¹ The mismatch runs deeper than volume. In one survey of roughly 500 HCPs and industry respondents, what pharma teams believed physicians valued and what physicians said they wanted were largely at odds.¹

The problem has existed for years, but it is more urgent now that physicians can bypass it entirely and use artificial intelligence (AI) to obtain the answers they need on their own schedule.

Why speed alone is not sufficient

AI can substantiate claims, locate and upload references, translate and create copy, segment audiences and analyze channel preferences in a fraction of the time these tasks once required.² These gains are real, which is why the industry is moving from pilot programs to everyday use.

The same tools can also work against the goal. A manuscript can be turned into an email campaign or a detail aid in a few hours, and once that capability exists, the temptation is to create more. When more material is originated at the start of the process, the mismatch with audience demand widens and the original problem compounds. Speed on its own produces a faster version of the same congestion.

This congestion is seen at MLR review, a checkpoint that cannot be rushed. Regulators have been clear that companies cannot rely on AI to create and approve their own content; a person needs to sign off and take responsibility.² AI can complete routine checks, such as referencing, consistency and claim substantiation, far faster than a manual pass, while interpretation, judgment and risk assessment stay with experienced reviewers. The practical question is therefore not how to move more volume through review but how to be more judicious about what is created in the first place.

From a linear process to a connected loop

The deeper change is structural. Today, most organizations build the scientific narrative, interview experts about physician needs, produce the assets and release them. Measurement, where it happens, arrives long after the fact, and the narrative may not be revisited for a year. Create, distribute, measure, repeat is not a new idea. What has changed is the resolution of the measurement and the capacity to act on it. Engagement and search behavior can now be read down to specialty, channel and question, and review can keep pace at scale rather than becoming the reason a good signal goes unused.

A loop replaces guesswork with learning. It links each stage of the existing process, from creation and MLR review through local adaptation, distribution and measurement, into a single connected circuit. Instead of concluding when content is delivered, each cycle returns results to the start, showing which assets HCPs opened, what they searched for next and where engagement fell off. That information helps teams improve messaging and formats, while core content is reused and adapted rather than recreated for each market.

Audience understanding does its most valuable work at the start of the process, not the end. What HCPs search for, which channels they use and what they need to learn can shape what is originated in the first place.² Segmentation belongs here, too, at a finer grain than traditional tiering: grouping HCPs by the questions they are asking and the formats they engage with, then modeling where content will earn attention before it is commissioned. That is the difference between adjusting content reactively after a signal returns and deciding proactively what is worth creating at all. When audience evidence shapes origination, fewer speculative assets enter the queue and reviewer attention concentrates on the judgment calls that require it.

Consider a brand team six months post-launch. Specialists open and share a short mechanism-of-action explainer repeatedly while a lengthy monograph is downloaded a handful of times, and the questions HCPs type into search point to a dosing scenario current materials barely address. In a linear process, those observations surface at the annual content review, if at all. In a loop, the team acts within the next production cycle. The explainer is extended to answer the dosing question, the monograph is retired and reviewers spend their time on fewer assets more likely to be used.

The output of a well-run loop runs counter to pipeline logic, producing less content better matched to demand. Rather than scaling production because AI makes it inexpensive, organizations scale relevance, concentrating on the formats and messages the evidence shows their audiences use. Omnichannel delivery becomes a matching exercise instead of another multiplier on volume.

Relevance as the new standard

Physicians are unlikely to stop self-serving; the convenience is considerable, and how they use these tools is instructive. A general-purpose LLM tends to be treated as an informal check, useful for a routine question and triangulated against experience. However, few clinicians would rely on one for a new treatment decision or a serious adverse event. That partial trust is where industry content can be most useful.

An answer that closes the gap would be grounded in validated healthcare data rather than the general contents of the internet, shaped by what the clinician has been searching for and reviewed by accountable experts, so it can be trusted and used immediately rather than checked again. The connected, validated loop is how an organization gets there faster and more accurately.

In a market where physicians can find whatever they want, the organizations that serve them best are likely to be those whose content has learned what is worth producing.

Phil Ford is VP and general manager, global medical affairs and HCP engagement, at IQVIA

References

1. IQVIA. Advancing scientific exchange: Trends and tactics for healthcare professional engagement. IQVIA White Paper. Oct. 8, 2025. https://www.iqvia.com/library/white-papers/advancing-scientific-exchange

2. Holah J. From automation to oversight: Building an AI-ready MLR function. IQVIA Blog. July 16, 2026. https://www.iqvia.com/blogs/2026/07/from-automation-to-oversight