Why AI-ready data are becoming health care’s new digital front door

Patients have more ways than ever to find care, and the entry point is shifting. Many searches now resolve on the results page or inside an artificial intelligence (AI) assistant, well before a provider’s website comes into play. AI-enabled search helps answer questions about where to go, what is covered under insurance and what the care will cost. The organizations that get cited in those answers are the ones whose underlying data are optimized for and accessible to AI agents.

How is the ‘digital front door’ changing as more patients turn to AI to find care?

Patients used to start with a health system or health plan website, a referral or a word-of-mouth recommendation. Now, they are increasingly asking an AI assistant such as ChatGPT or Google's AI Mode, talking to a call center's voice agent or searching on a third-party app to find care. For providers and payers, being cited by an AI model is the beginning of getting a patient to the right physician or other clinician. However, appearing in a patient’s query depends on whether an organization's data are visible and accurate everywhere an AI model might search.

Why does bringing payer and provider data together improve patient outcomes, not just operational efficiency?

Bad data create friction between patients, providers and payers. They can also provide incorrect information to the AI agent, which could send patients to the wrong next step. An AI agent needs a firm answer on things like network status, clinical fit and whether a provider is even taking new patients. When it doesn't have one, it either gives no recommendation or offers a guess based on what it can mine from the open web. Both options leave the patient worse off than if the data had been right in the first place.

Picture a patient seeking help for recurring migraines. A provider's specialty may be listed as general neurology, even though they have a focus on headache and migraine treatment. A health system typically has that granular detail on hand, but a health plan might not. If payers can’t make that distinction, it becomes a gap in the underlying data that AI models can’t read. Left uncorrected, that gap leads the AI agent to misdirect members. They may get recommendations for providers who look right on paper, but aren't necessarily the right clinical match. Network status carries the same risk. Knowing a provider generally takes a payer's plans isn't the same as knowing they're in-network for a member's specific plan, and that answer shifts constantly across thousands of provider-plan pairings. When these issues are multiplied across an entire directory, a payer’s data problem can become a much larger issue that, ultimately, impacts trust. Bridging payer and provider data helps patients get matched to care that fits their needs, informed by real data rather than a best guess.

What does the shift to agentic AI mean for how health systems and health plans stay visible to consumers?

It's a two-step solution. First, existing data need to be machine-readable with structured clinical specialties, accurate biographies, network status and location data that an AI model can read. Second, and this is the bigger venture, organizations need to build their own AI-ready data asset. This includes something like a Model Context Protocol (MCP) server, so they become the source that an AI agent cites and routes patients toward. An MCP uses health systems’ or health plans’ verified provider data, so AI solutions answer based on real, current records rather than guessing from what it finds on the open web. The goal is to help patients reach the appropriate clinical resource with transparency into cost, quality and availability, no matter where their search for care starts.

Owning that asset comes with additional responsibilities, such as answering high-volume questions and ensuring the quality of every recommendation. It also creates an opportunity to own the business rules. For example, a model connected through an MCP could prioritize an employed provider over an outside one or promote a higher-value care program over a specific specialist who is hard to reach, which an outside AI model would never do on its own.

What does the patient journey look like end-to-end when this actually works?

Ideally, it’s one continuous experience: finding the right provider, confirming insurance and network status, scheduling the appointment and knowing the costs up front, all without the patient having to piece together touchpoints across separate systems. Right now, those steps are frequently disconnected. A patient might get a good recommendation, but then hit a wall with scheduling or getting an estimate because those data live elsewhere. When patients don't understand what they'll owe until after the visit, they're more likely to delay or avoid payment altogether. That leaves providers chasing bills, absorbing the cost and sometimes sending patients to collections. Closing that gap is as much about the payment and revenue cycle as it is about the initial search. Done right, it benefits both sides: Patients know what to expect, and providers get paid faster and more predictably.

What about primary care and smaller physician practices? How does this apply to organizations without the scale of a large health system or health plan?

Whether it's a large health system or a smaller practice, the first step is the same. Organizations need to take stock of how data are stored, when they were last updated, and then optimize them for AI. An AI model can't act on data it can't read. A smaller organization with accurate, well-structured provider data is more likely to be surfaced and represented correctly in AI responses than one with fragmented, outdated records.

From there, independent practices have specific resources to lean on, starting with insurance plans. They should ask what mechanisms are available to improve the data in their directories, and to share more details about clinical expertise, availability and scheduling integration. Many plans are building this out now, with platforms designed to simplify that integration.

Practices can do the same with listings and reputation management vendors. They can inquire about what capabilities they have to ensure visibility everywhere people search for care, including AI assistants, and whether they're keeping up. They can also ask who maintains the website, and whether that team has real expertise in web-based MCP and AI-ready architecture.

Smaller organizations will need to ensure their data are in a platform with the economy of scale to handle that integration and bargaining on their behalf.

What's the risk of leaning too heavily on the technology itself, without fixing the data underneath it first?

AI uses the data that are available, and the agents can’t tell the difference between high-quality data and a data set that has gaps. When there are gaps in the data, patients see recommendations that are misinformed or entirely fabricated. The responsibility lies with providers and payers to ensure that their data are organized so that patients can obtain accurate information through their AI-assisted searches. An organization can roll out a conversational agent or a new interface, but if the information behind it is outdated or incomplete, all that technology does is hand the patient the wrong answer more quickly and more confidently than a human would. These mistakes show up as patients receiving a mismatched specialist or an incorrect cost estimate, and they won't know these are incorrect until they're much further into their journey, causing confusion and frustration and eroding trust. Before implementing AI solutions, organizations must ensure that the underlying data are AI-ready and that there’s a plan in place to keep them continuously updated.

Morgan Beschle is the vice president of product management for Kyruus at RevSpring. She has nearly two decades of experience across product strategy and management, UX/UI design and customer management. She has a demonstrated track record of building innovative, scalable health care technology to improve quality while reducing costs.