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Three things your data needs before you trust AI with it
AI tools like Claude and ChatGPT are changing how marketers analyze data and build reports. Tasks that once took hours, and sometimes days, can now be completed in minutes.
But there’s an important catch: AI is only as reliable as the data it’s working with. If that data is incomplete, inaccurate, or disconnected, even the most sophisticated tools can produce misleading results. And because those results are delivered so quickly and convincingly, it’s not always obvious when something is wrong.
At Cardinal Digital’s Scaling Up 2026, Liine co-founder and CRO Charlie Winn walked through how Geode Health approached this challenge during his session, Critical Data Requirements to Power AI Marketing Analytics.
Great results just lead to more questions
Geode Health is a multi-location behavioral health group, and they were Cardinal Digital and Liine’s first joint client. Over the past few years, they’ve cut cost per booking from paid ads by 68%, grown total bookings by 256%, and cut customer acquisition costs in half.
Results like that don’t quiet leadership down, though. They lead to more questions. What does this look like by week? By state? By location? Where should more marketing budget go?
AI can answer those fast, but only if the data underneath is right.
Junk in, junk out, now at AI speed
The concept of “junk in, junk out” isn’t new to marketers. Anyone who’s worked with a CRM or data platform knows that inconsistent data entry, missing information, or poorly defined processes can make reporting unreliable.
AI doesn’t eliminate those problems, and it can actually make them harder to spot.
Instead of spending hours manually building a report, marketers can now generate detailed analyses in minutes. That’s a major efficiency gain, but it also means inaccurate or incomplete data can turn into seemingly polished and credible insights just as quickly.
So, what does reliable data actually look like when it comes to healthcare marketing analytics?
There are three foundational requirements worth addressing before trusting AI with your reporting.
1. Capture and deduplicate every patient interaction
Patients rarely follow a perfect path from their first interaction with a practice to booking an appointment. If those interactions aren’t connected, your reporting can tell a very different story from what actually happened.
Consider this example: a prospective patient clicks a Google Ad and calls the practice, but they don’t book. Later that evening, after the practice has closed, they search for the practice again and schedule an appointment online.
Without deduplication, your reporting might show two separate leads, a 50% conversion rate, and a booking attributed to organic search.
In reality, there was one prospective patient who ultimately booked an appointment after first discovering the practice through Google Ads. That’s a significant difference, especially when you’re using that data to evaluate campaign performance and make budgeting decisions.
This is exactly why Liine emphasizes tracking on a leads basis, connecting individual interactions to the same prospective patient so marketers can understand the full journey from first click to appointment.
Geode’s patient acquisition process highlights why this is so important. They receive inbound calls, web forms from multiple sources, follow-up calls, online bookings through their own scheduling system, and appointments through Zocdoc. To accurately understand marketing performance, all of those interactions need to be captured, connected, and accounted for in the same place.
2. Match to your EHR or PMS with more than a phone number
Knowing which leads booked an appointment is valuable, but for healthcare marketers, that’s only part of the picture. You’ll also want to understand which patients actually showed up, which ones were new patients, and how much revenue those appointments generated.
This requires connecting marketing data with patient records in your electronic health record (EHR) or practice management system (PMS).
Many platforms rely primarily on phone numbers to make those connections. While that approach is relatively straightforward, it typically matches only about 65% of interactions to the right patient records. Here’s one example where it can fall short:
A parent calls a dental practice and schedules appointments for three children, all of whom are new patients.
If the practice relies solely on phone numbers to match marketing interactions to patient records, it may only associate one child’s appointment with the original call. The other two could be incorrectly classified as existing patients or duplicate records. As a result, the marketing campaign that generated those appointments may receive credit for only a third of the revenue it actually drove.
A more reliable approach uses multiple identifiers, including legal name, date of birth, and other available information, to connect marketing interactions with patient records.
Liine captures approximately 15 data points from each interaction, which helps practices achieve average match rates above 97%. For Geode Health, that match rate reaches >99% through Liine’s integration with athenahealth.
The more accurately you can connect marketing activity to patient outcomes, the more confidently you can evaluate what’s working in your marketing strategy and where to invest budget.
3. Keep PHI and PII away from your AI tools
As healthcare marketers explore AI-powered reporting, data privacy needs to be part of the conversation from the beginning.
One approach teams may take is exporting reports from their existing systems, manually removing sensitive information, and uploading the remaining data into an AI tool for analysis.
The problem is that this process leaves a lot of room for human error.
A single overlooked column could expose protected health information (PHI) or personally identifiable information (PII), and as more employees begin using AI tools for reporting, the risk of human error increases.
A safer approach is to use an MCP (Model Context Protocol) connector designed specifically to keep sensitive patient information separate from the AI tool entirely. This separation allows marketers to access the data they need without exposing underlying patient details.
When evaluating an AI data connection, look for three safeguards:
- Separation from PHI and PII: Sensitive patient information should remain inaccessible to the AI tool.
- Read-only access: The AI tool should be able to retrieve and analyze permitted data without modifying the underlying records.
- De-identified outputs: Responses should provide aggregated metrics, such as counts, conversion rates, and trends, rather than individual patient information.
These protections allow healthcare marketers to take advantage of AI-powered analytics while maintaining the necessary security measures around sensitive data.
The bottom line
AI won’t fix bad data. It’ll just get you to the wrong answer faster. Get these three foundations in place first, and the next round of questions from leadership becomes a quick query instead of a week of spreadsheets.
If you’re interested in using AI to improve your marketing analytics but aren’t sure where to begin, you don’t have to figure it out alone.
Ready to get more out of your marketing data?
Liine is an AI-powered call tracking, marketing analytics and ad optimization platform built for multisite healthcare, dental and vet practices. Our platform captures and analyzes all patient leads across calls, web forms and online bookings in one intuitive dashboard. This empowers healthcare marketers to auto-optimize ad campaigns, increase lead-to-appointment conversion rates and quickly identify which strategies are driving new patient bookings.