{"id":21229,"date":"2026-11-05T01:44:00","date_gmt":"2026-11-05T06:44:00","guid":{"rendered":"https:\/\/advertisingweek.com\/aw360\/?p=21229"},"modified":"2026-11-02T11:49:32","modified_gmt":"2026-11-02T16:49:32","slug":"pharma-marketers-need-to-optimize-for-the-question","status":"publish","type":"post","link":"https:\/\/advertisingweek.com\/aw360\/news\/pharma-marketers-need-to-optimize-for-the-question\/21229\/","title":{"rendered":"Pharma Marketers Need to Optimize for the Question"},"content":{"rendered":"\n

By Lauren Boyer,<\/a> CEO, Underscore<\/em><\/p>\n\n\n\n

For years, healthcare marketers have built digital strategies around search behavior. We study the terms healthcare professionals use, understand the information they are seeking and develop content that helps brands appear at the right point in that journey. Generative AI is changing that behavior in a meaningful way. Physicians are increasingly asking complete, highly specific clinical questions and receiving synthesized answers without necessarily visiting the websites that supplied the underlying information.<\/p>\n\n\n\n

That shift is already happening quickly. According to Doximity\u2019s 2026 State of AI in Medicine report<\/a>, 63% of physicians now use AI in practice, up from 47% nine months earlier, while 37% report using it daily. Literature search is the most commonly cited use case, accounting for 35% of physician AI usage.<\/p>\n\n\n\n

For pharma marketers, the interesting part is what happens inside those interactions. A physician rarely approaches a clinical AI tool with a broad keyword. They may ask which therapies are indicated for a particular patient population, how two treatments compare, what current guidelines recommend or what options exist after a patient has progressed on first-line therapy. The wording carries context about the patient, the physician\u2019s information needs and where that physician may be in a treatment decision.<\/p>\n\n\n\n

That specificity can dramatically affect which evidence appears in the answer. Consider a newly launched second-line therapy in a rare disease category where an established treatment dominates first line. A broad question about the standard first-line treatment may never surface the newer therapy. A physician asking about options for a patient who has progressed on first-line treatment, however, is asking a very different clinical question, one where evidence supporting the newer therapy may be directly relevant.<\/p>\n\n\n\n

This creates a new consideration for pharma content strategy. Brand websites and evidence assets have traditionally been developed with both human audiences and conventional search engines in mind. Increasingly, that same information may become source material for an AI-generated answer. Content therefore needs to reflect the questions physicians actually ask, use language appropriate to the indication and line of therapy, and make current evidence easy for an engine to identify and cite.<\/p>\n\n\n\n

Currency matters as well. New approvals, indications and clinical evidence can take time to work their way through the information ecosystem, creating the possibility that an AI answer is incomplete or outdated. Representation can also vary depending on the question, the sources available to the model and how recently those sources have been updated. These issues become especially important in oncology, rare disease and other specialty categories where the prescribing universe may consist of only a few hundred physicians.<\/p>\n\n\n\n

For marketers, this means understanding AI visibility should become an ongoing source of intelligence. Looking at the questions clinicians ask, how those questions differ across specialties, which sources inform the answers and where evidence gaps exist can help shape content and media decisions over time. The goal is to understand how clinical information discovery itself is evolving and ensure that accurate, current evidence is available wherever physicians increasingly turn for answers.<\/p>\n\n\n\n

As AI becomes a more routine part of clinical information seeking, pharma marketers have an opportunity to rethink what discoverability means. The keyword still matters, but the question may tell us far more about what a physician actually needs to know.<\/p>\n","protected":false},"excerpt":{"rendered":"

For years, healthcare marketers have built digital strategies around search behavior. We study the terms healthcare professionals use, understand the information they are seeking and develop content that helps brands appear at the right point in that journey. Generative AI is changing that behavior in a meaningful way. Physicians are increasingly asking complete, highly specific clinical questions and receiving synthesized answers without necessarily visiting the websites that supplied the underlying information.<\/p>\n","protected":false},"author":26,"featured_media":21230,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","editor_notices":[],"footnotes":""},"categories":[1],"tags":[19],"topics":[2],"class_list":["post-21229","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-health","topics-editorial"],"acf":[],"yoast_head":"\nPharma Marketers Need to Optimize for the Question | AW360<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/advertisingweek.com\/aw360\/news\/pharma-marketers-need-to-optimize-for-the-question\/21229\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pharma Marketers Need to Optimize for the Question\" \/>\n<meta property=\"og:description\" content=\"For years, healthcare marketers have built digital strategies around search behavior. 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