Your Highest ROI Before AI? Fixing Your Provider Data

According to rater8's 2026 Patient Choice Report, 47% of patients now use AI tools to research healthcare providers and 55% of those patients trust AI summaries without verifying them.

It's clear that every health plan and health system has an AI roadmap in place. With patient expectations and internal investment moving in the same direction, the urgency to modernize an organization's AI infrastructure has never been greater. However, the bar that healthcare organizations have to clear to gain patient trust is measurably higher versus other businesses.

That same rater8 report found that 66% of patients who used AI to research providers encountered incorrect provider information including addresses, specialties, affiliations, and network participation details. With industry-wide accuracy rates estimated at only 50–55%, the data feeding these AI tools is already broken before any query is run.

As we look at healthcare AI initiatives, a critical issue persists: most of these initiatives are being built upon a flawed foundation. Automation cannot resolve this fundamental problem; it can only accelerate the propagation of existing inaccuracies. The message for organizations is clear: before the next modernization project goes live, fix the data everything depends on.

AI Is Only as Good as the Data Beneath It

This isn't a new idea, but it's one that gets ignored under the pressure to ship something impressive. An AI care navigation tool that recommends a provider at a closed location doesn't look intelligent. It looks broken. An automated referral engine that routes a member to a doctor who left the network six months ago doesn't save time. It creates a denied claim, a frustrated member, and a compliance exposure.

The pattern repeats across every use case. Automation amplifies whatever you feed it. Feed it fragmented, stale, self-reported data, and you scale the very problems you were trying to solve. The reasons provider data keeps breaking are structural, not accidental. No model, however sophisticated, fixes a structural problem at the input layer.

That's why the overlooked bottleneck of provider data accuracy can determine the ceiling on every AI investment you make. You can buy the best technology on the market, but if the data is unreliable, the output will be too.

A Real-World Example: Getting the Order Right

Sana Benefits is a case study in fixing the foundation before building on top of it.

Sana's concierge care navigation model depends entirely on accurate provider data. As they scaled, fragmented and unreliable information spread across 13 different platforms began to undermine the whole operation.

The cost showed up everywhere. Navigators manually verified every referral across multiple sources, pushing processing time to around two hours. Members using the self-service directory hit the same broken data: wrong specialties, outdated locations, dead ends.

Instead of layering automation on top of that mess, Sana fixed the foundation first by replacing all 13 disconnected sources with Candor Health's platform to continuously validate and enrich provider data in real time.

The results speak for themselves:

• Referral processing time cut by 50%, dropping from roughly two hours to under one

• Care navigation volume up more than 50% with no additional headcount

• High-value provider identification climbing from about 45% to over 75%

• Recommendations to directly contracted providers jumping from 17% to 53%

These numbers aren't the result of a flashy AI feature. They're the result of clean, continuously validated data that let navigators stop chasing information and start serving members. Network operations felt it too: provider rosters that once took hours to standardize were processed in under five minutes.

Sana's leadership was blunt about the sequencing: "AI is only as good as the underlying data. Solving provider data accuracy was the essential first step." Their advanced, automated care navigation ambitions only became credible once the foundation was solid.

Sequence Beats Speed

It's tempting to treat data cleanup as a parallel workstream that your organization can handle while the AI project moves forward. But that instinct is backward. Data accuracy isn't a side quest; it's the prerequisite. If you build automation on unreliable inputs, you'll get rework, member harm, and compliance risk that scales alongside your AI initiatives.

The organizations that win the next phase of healthcare modernization won't be the ones who deployed AI first. They'll be the ones who fixed their provider data first, then deployed AI on a foundation they could actually trust.

Before the next modernization project, ask one question: is the data everything depends on accurate enough to build on? If the honest answer is no, that's where the real work starts.