Medical information departments at pharmaceutical companies handle a volume of inbound requests that most people outside the function do not fully appreciate. A mid-size pharma company supporting four to eight marketed products may receive thousands of medical information requests per year, each one requiring the same basic sequence: identify what the caller or sender is asking, locate the relevant source documents, confirm the data is from a current version, draft a response that addresses the question within label, and get that response reviewed before it goes out.
The workflow is structured, auditable, and time-sensitive. Physicians and other healthcare professionals asking product questions often need answers on a timeframe relevant to a clinical decision they are making. The medical information team is the bridge between the company's clinical data package and the HCP who needs specific information right now.
Where the Time Goes in a Medical Information Request
The bottleneck in medical information request handling is almost always the document work, not the clinical judgment. Medical information specialists are trained professionals who understand the clinical context of the questions they receive. What takes time is the search: navigating to the right document, finding the right section, confirming that the document version is current, and assembling the source citations for the response.
A typical on-label request for dosing information in a specific patient population might require opening the prescribing information, navigating to the dosing section, finding the subsection on the relevant patient characteristic, cross-referencing the pharmacokinetic parameters section for supporting data, and checking whether any recent label updates have modified that section. That is a fifteen to thirty minute task, depending on how well-organized the document systems are.
Off-label requests are more complex. A healthcare professional asking about use in a patient population not covered by the approved indication requires the medical information specialist to identify whether there is relevant published literature, locate it, assess whether it meets the threshold for a formal response (some companies have specific policies about what published evidence is sufficient to trigger a formal response versus a referral to the evidence summary), and draft a response that meets the company's legal and regulatory requirements for off-label communication.
How AI-Assisted Retrieval Fits Into This Workflow
AI-assisted retrieval is useful in medical information request workflows at a specific, bounded stage: the document search step. Given a question, the system can identify the most relevant passages across the indexed document corpus, return those passages with source citations, and surface the current version of each relevant document.
This acceleration is meaningful. A medical information specialist who can run a query and have the relevant passages from the prescribing information, recent publications, and standard response library surfaced in under two minutes is working more efficiently than one who is navigating each system manually. The specialist still reads the passages, still applies clinical judgment about whether the retrieved content answers the question, and still drafts the response. The AI handles the search, not the analysis.
We want to be precise about what AI-assisted retrieval does not do in this context. It does not determine whether a question is on or off-label. It does not assess whether retrieved literature meets the threshold for a formal off-label response. It does not draft the response. It does not make the compliance determination about what can be communicated. All of those are human professional judgments that belong with the medical information specialist, before any content reaches the HCP.
The Citation Requirement in Medical Information Responses
Medical information responses are required to be citable. When a company sends a formal written response to a healthcare professional's medical information request, that response must identify the source documents supporting each claim. This is not a best practice. It is a regulatory expectation, and it is audited.
This citation requirement is why the retrieval architecture matters specifically for medical information, beyond general efficiency. An AI system that returns answers without source citations does not reduce the work of compiling citations for a formal response. The specialist still has to go find the source pages after receiving the answer. A source-grounded retrieval system that returns the passage and the citation together reduces the two steps to one.
More importantly, a citation-first system keeps the specialist in the habit of verifying rather than trusting. Medical information is a function where verifying the source is not optional. A system design that makes the source easy to read immediately after receiving an answer is better aligned with that workflow than one where the citation is a separate step.
High-Volume Periods and Queue Management
Medical information departments experience uneven request volumes. A major congress presentation of new clinical data, a label update that generates physician questions, a safety communication, or a widely-covered publication can generate a spike in request volume that strains the team's capacity to respond within standard turnaround times.
In high-volume periods, the document search time that might be acceptable at baseline becomes the critical constraint. A team that can reduce per-request document search time from 25 minutes to 8 minutes has effectively expanded its capacity during peak periods without hiring. The time savings compound at scale: at 50 requests per day during a high-volume period, 17 minutes saved per request is over 14 hours of analyst time per day redirected from search to clinical judgment.
We are not suggesting AI retrieval eliminates the need for adequate staffing in medical information. High-volume periods require human professional capacity regardless of how efficient the tools are. But the bottleneck in a well-staffed team during a demand spike is often the document work, not the clinical analysis, and that is where retrieval assistance has the largest impact.
Where AI Should Not Reach in Medical Information Workflows
The boundaries are worth stating clearly, because the medical information function is one where overreach by an AI system creates compliance problems that are genuinely serious.
AI-assisted retrieval should not be used to automatically classify requests as on-label or off-label without human review. That classification has compliance implications that require professional judgment about the specific inquiry context. AI-assisted retrieval should not draft responses that are sent to HCPs without a qualified medical information specialist reviewing the content. FDA's regulations on promotional and non-promotional labeling apply, and the accountability for compliant communication sits with a qualified person, not the software tool.
AI-assisted retrieval should not generate new content for medical information letters from scratch. The appropriate role is to surface source passages that a specialist then uses to draft or refine a response. Generative AI that creates plausible-sounding response text without grounding every claim in the indexed source corpus is not appropriate for this workflow, for the same reasons discussed in our article on source grounding and hallucination risk.
The State of AI Adoption in Medical Information
As of 2025, a meaningful number of pharmaceutical medical information departments are piloting or actively using AI retrieval tools, primarily for document search acceleration. The more cautious adoption pattern, which is representative of most of the teams we have spoken with, is phased: AI handles the search step first, while the specialist maintains full authorship of the response. Broader AI involvement in response drafting is being evaluated more slowly, with careful attention to the audit trail and human review requirements.
This phased pattern makes sense. The efficiency gains from accelerating the search step are real and verifiable. The compliance implications of AI involvement in the clinical content of medical information responses require more deliberate validation before deployment at scale. A team that has demonstrated reliable retrieval assistance in the search step is better positioned to evaluate expanded AI involvement with a clear evidence base, rather than adopting broader AI capabilities without a baseline to compare against.