HealthcareCross-System Patient Acquisition Signal Architecture

Each system in NextCare's patient acquisition environment was doing its job correctly.
Building the HIPAA-safe signal architecture that connected all five into one patient acquisition view.

Client
NextCare
Vertical
Healthcare
Core problem
No governed signal path from ad click to confirmed booking; personally identifiable information flowing unredacted into the analytics layer
Signal environment
Web · iOS/Android app · Solv booking platform · Salesforce CRM · Google Ads
The architectural gap
No governed signal path from ad click to confirmed booking; personally identifiable information flowing unredacted into the analytics layer
What changed
HIPAA-safe server-side collection; offline conversion integration; cross-system identity; BigQuery truth layer

NextCare operates more than 170 urgent care clinics across 12 states.

Patient acquisition is digital-first: prospective patients search for urgent care, arrive on the website or mobile app, and book an appointment through Solv, the third-party booking platform NextCare uses across its network. The confirmed booking lands in Salesforce, where clinical and operational teams manage the care relationship from there.

Each system in this environment was chosen deliberately and worked correctly for its own purpose. The website drove visits. The app supported patient engagement. Solv handled booking reliably. Salesforce managed patient records. Google Ads ran campaigns at scale. The limit the architecture had reached was connection: no governed signal path ran from the original ad click through the booking confirmation and into the patient outcome record.

Campaigns were optimizing on signals that approximated the outcome rather than measuring it.

Google Ads was receiving conversion signals from the website: form submissions, page engagements, session completions. These were proxy conversions: structurally correlated with confirmed patient acquisition, but not identical to it. The Solv booking confirmation (the point in the journey closest to actual patient acquisition) was not being routed back to the ad platform. Campaigns were optimizing on signals that approximated the outcome rather than measuring it.

The mobile app journey sat outside the web attribution architecture. A patient who researched on the website and then booked through the app existed as two separate sessions in two separate systems, with no identity connection.

Salesforce held downstream patient data (repeat visits, patient value, care outcomes) with no clean line back to the originating acquisition channel. The question "which campaigns produce patients who return" had no answer, because the acquisition and outcome sides of the data had never been joined.

The Health Insurance Portability and Accountability Act (HIPAA) dimension compounded the structural problem. The Google Analytics 4 (GA4) implementation was passing URL path components that contained appointment details: fragments of patient health information flowing into the analytics layer and on to advertising platforms without a redaction layer at the collection point.

Building the HIPAA-safe signal architecture

HIPAA-safe server-side collection layerServer-side Google Tag Manager (sGTM) deployed as the server-side routing layer between the digital environment and the analytics and advertising platforms. Personally identifiable information (PII) redaction logic implemented at the server: patient name, email, phone number, date of birth, and health condition parameters stripped or hashed before any data reaches GA4, Google Ads, or any third-party endpoint. The collection architecture is now HIPAA-compatible.
Offline conversion integration from SolvConfirmed booking events forwarded from the Solv booking platform through the server-side layer to Google Ads as offline conversions. The original Google click ID carried through the booking session to join the conversion back to the originating campaign. Google Ads now optimizes on confirmed patient bookings, not web proxy events.
Cross-system identity architectureA consistent patient identifier set at the first web session and carried through the app install attribution layer. A patient who engaged on the website and then booked through the app is resolved to a single acquisition record in the measurement system, not two unconnected sessions.
Salesforce integrationEach patient record created in Salesforce connected back to the originating acquisition channel via the Google click ID and Solv booking reference chain. Downstream patient data (repeat visits, patient lifetime value) joinable to the channel and campaign that drove the original acquisition.
BigQuery warehouse truth layerGA4 event stream connected to BigQuery. Salesforce patient data imported on a governed schedule. The warehouse layer that joins acquisition signal to downstream patient outcomes, and that marketing, operations, and finance all read from.
Web and mobile app measurement alignmentWeb measurement restructured in Google Tag Manager (GTM) for GA4. App measurement implemented with the same event taxonomy and identity model as the web layer. The two surfaces produce a consistent, joinable dataset.

Google Ads began bidding on confirmed Solv bookings.

"What we appreciate most about Analytico is their commitment to delivering results. They don't just provide data and analytics; they work with us to develop actionable strategies that have helped improve our business's performance and bottom line."
— Richard Keech, Senior Digital Marketing Manager, NextCare

The optimization signal shifted from proxy web conversions to the actual booking outcome. The collection architecture now meets HIPAA requirements. Patient health information no longer reaches the analytics or advertising layer.

Web and app patient journeys became one record. The acquisition story for a patient who started on the website and booked through the app is traceable from originating ad click through confirmed booking to downstream patient outcome.

The BigQuery truth layer made the acquisition-to-outcome question answerable. Which channels produce patients who return for a second visit, and what those patients are worth over time, is now a reportable metric.

Internally, less time was spent reconciling which system's numbers were correct. The conversation shifted to what to do with a measurement system the team could trust.

If patient acquisition runs across multiple systems and the signal chain doesn't follow it, the campaign optimization is working with an incomplete picture.

The Assessment maps the gap.

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