{"id":7053,"date":"2025-07-22T06:54:00","date_gmt":"2025-07-22T06:54:00","guid":{"rendered":"https:\/\/medical-article.com\/?p=7053"},"modified":"2025-07-22T06:54:00","modified_gmt":"2025-07-22T06:54:00","slug":"healthcare-ai-whats-in-your-chatbot","status":"publish","type":"post","link":"https:\/\/medical-article.com\/?p=7053","title":{"rendered":"Healthcare AI: What\u2019s in your chatbot?"},"content":{"rendered":"
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By OWEN TRIPP<\/p>\n

So much of the early energy around generative AI in healthcare has been geared toward speed and efficiency: freeing doctors from admin tasks, automating patient intake, streamlining paperwork-heavy pain points. This is all necessary and helpful, but much of it boils down to established players optimizing the existing system to suit their own needs. As consumers flock to AI for healthcare, their questions and needs highlight the limits of off-the-shelf bots \u2014 and the pent-up demand for no judgment, all-in-one, personalized help.<\/p>\n

Transforming healthcare so that it actually works for patients and consumers \u2014 ahem, people<\/em> \u2014 requires more than incumbent-led efficiency. Generative AI will be game-changing, no doubt, but only when it\u2019s embedded and embraced as a trusted guide that steers people toward high-quality care and empowers them to make better decisions.<\/p>\n

Upgrading Dr. Google<\/strong><\/h2>\n

From my vantage point, virtual agents and assistants are the most important frontier in healthcare AI right now \u2014 and in people-centered healthcare, period. Tens of millions of people (especially younger generations<\/a>) are already leaning into AI for help with health and wellness, testing the waters of off-the-shelf apps and tools like ChatGPT.<\/p>\n

You see, people realize that AI isn\u2019t just for polishing emails and vacation itineraries. One-fifth of adults consult AI chatbots with health questions<\/a> at least once a month (and given AI\u2019s unprecedented adoption curve<\/a>, we can assume that number is rising by the day). For most, AI serves as a souped-up, user-friendly alternative to search engines. It offers people a more engaging<\/a> way to research symptoms, explore potential treatments, and determine if they actually need to see a doctor or head to urgent care.<\/p>\n

But people are going a lot<\/em> deeper with chatbots than they ever did with Dr. Google or WebMD. Beyond the usual self-triage, the numbers tell us that up to 40% of ChatGPT<\/a> users have consulted AI after<\/em> a doctor\u2019s appointment. They were looking to verify and validate what they\u2019d heard. Even more surprising, after conferring with ChatGPT, a similar percentage then re-engaged with their doctor \u2014 to request referrals or tests, changes to medications, or schedule a follow-up.<\/p>\n

These trends highlight AI\u2019s enormous potential as an engagement tool, and they also suggest that people are defaulting to AI because the healthcare system is (still) too difficult and frustrating to navigate. Why are people asking ChatGPT how to manage symptoms? Because accessing primary and preventive care is a challenge. Why are they second-guessing advice and prescriptions? Sadly, they don\u2019t fully trust their doctor, are embarrassed to speak up, or don\u2019t have enough time to talk through their questions and concerns during appointments.<\/p>\n

Chatbots have all the time in the world, and they\u2019re responsive, supportive, knowledgeable, and nonjudgmental<\/a>. This is the essence of the healthcare experience people want, need, and deserve, but that experience can\u2019t be built with chatbots alone. AI has a critical role to play, to be sure, but to fulfill its potential it has to evolve well beyond off-the-shelf chatbot competence.<\/p>\n

Chatbots 2.0<\/strong><\/h2>\n

When it comes to their healthcare, the people currently flocking to mass-market apps like ChatGPT will inevitably realize diminishing returns. Though the current experience feels<\/em> personal, the advice and information is ultimately very generic, built on the same foundation of publicly available data, medical journals, websites, and countless other sources. Even the purpose-built healthcare chatbots in the market today are overwhelmingly relying on public data and outsourced AI models.<\/p>\n

Generic responses and transactional experiences have inherent shortcomings. As we\u2019ve seen with other health-tech advances, including 1.0 telehealth<\/a> and navigation platforms<\/a>, impersonal, one-off services driven primarily by in-the-moment-need, efficiency, or convenience don\u2019t equate to long-term value.<\/p>\n

For chatbots to avoid the 1.0 trap, they need to do more than put the world\u2019s medical knowledge at our fingertips. <\/p>\n

<\/span><\/p>\n

They need to be connected to the full range of healthcare settings and interactions, including providing access to human experts and relevant next steps that individuals can take while in the flow of getting answers. Creating that experience requires two big things:<\/p>\n

The first is personalization<\/strong>. In healthcare, that involves more than just a personified user experience. The most promising use cases for AI \u2014 including automated nudges, appointment summaries, automated scheduling and care coordination, and fast answers to benefits and billing questions \u2014 depend on having built-in access to individuals\u2019 health benefits and medical records. Without those (private and secure) data connections, the guidance AI provides will never be truly personalized, no matter how engaging the interface. Knowledge alone isn\u2019t enough; with bots as with doctors, feeling seen and heard \u2014 and understood and remembered \u2014\u00a0is critical to building trust.<\/p>\n

The second is humans<\/strong>, standing by. As time goes on, AI will be able to handle a greater range of questions and tasks, but human expertise \u2014 and clinical expertise, in particular \u2014 is an indispensable backstop. Even if chatbots are someday able to autonomously prescribe drugs and tests (as some envision<\/a>), many essential healthcare interactions will still require the involvement of a human care team. The fusion of artificial and human intelligence \u2014 what I call AI+EQ<\/a> \u2014 is exponentially more powerful than either one alone.<\/p>\n

Joining forces<\/strong><\/h2>\n

Who, exactly, is going to deliver this experience? No one player in healthcare today has all of the necessary capabilities.\u00a0<\/p>\n

OpenAI, Google, and the other companies leading the AI revolution certainly have the technology, but they lack the healthcare connectedness and expertise (including the doctors) required to bring together the clinical, financial, and administrative aspects of healthcare in a single experience. Not to mention, but many tech giants over the years have dipped their toe into healthcare, only to reconsider.<\/a><\/p>\n

Health systems and health insurance companies certainly have the healthcare expertise, and they\u2019re hard at work incorporating AI into their businesses, but many have lost people\u2019s trust<\/a>. With AI-powered navigation tools<\/a> and prior authorizations, insurers already have a track record of disguising cost-control initiatives as \u201cmember-centric\u201d services. By the same token, it\u2019s not hard to envision AI tools created by hospitals and health systems that \u2014 intentionally or otherwise \u2014 would be biased toward high-cost specialty care regardless of appropriateness.<\/p>\n

The entity that can deliver the healthcare AI experience people deserve likely doesn\u2019t exist quite yet. It\u2019s probably a partnership \u2014 not a single company \u2014 that brings purpose-built AI models, clinical expertise, leading healthcare connectedness, system-wide access, and person-specific data under one roof.<\/p>\n

People want AI they can trust that actually makes healthcare work for them<\/em>. They\u2019re open to it, but they can\u2019t build it by themselves.<\/p>\n

Owen Tripp is the co-founder and CEO of <\/em>Included Health<\/em><\/a>, a personalized all-in-one healthcare company.<\/em><\/p>","protected":false},"excerpt":{"rendered":"

By OWEN TRIPP So much of the early energy around generative AI in healthcare has been geared toward speed and efficiency: freeing doctors from admin tasks, automating patient intake, streamlining paperwork-heavy pain points. This is all necessary and helpful, but much of it boils down to established players optimizing the existing system to suit their…<\/p>\n","protected":false},"author":0,"featured_media":7052,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-7053","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles"],"_links":{"self":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/7053"}],"collection":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7053"}],"version-history":[{"count":0,"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/7053\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/media\/7052"}],"wp:attachment":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7053"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7053"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7053"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}