The Ambulatory Intelligence Gap

The Ambulatory Intelligence Gap | Databricks Blog

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The gap: Ambulatory growth stalls when access, referral, capacity, and financial data sit in disconnected systems.

The solution: Health Catalyst's Ambulatory Intelligence combines AI with nearly two decades of healthcare improvement expertise to explain what's driving the numbers and where to act.

The outcome: Prebuilt metrics deliver same-week visibility and real results.

Ambulatory care, the outpatient clinics and physician practices where most patients interact with a health system, is where growth is won or lost. Yet many health systems struggle with the operational constraints that limit access to care: provider capacity, referral retention, and downstream financial performance.

Access is moving in the wrong direction. A new patient trying to book a routine appointment waits an average of 31 days to be seen, a number that has climbed 19 percent since 2022, according to AMN Healthcare's 2025 survey of physician access.

Referrals are another source of pressure. U.S. health systems lose an estimated $150 billion annually to referral leakage, according to ReferralMD, while MGMA's 2025 Referral Coordination Benchmarking Study found that 38 percent of referrals never close the loop.

For ambulatory leaders, these aren't isolated problems. Access, provider capacity, referral retention, panel management, and financial performance are deeply interconnected. The challenge isn't recognizing the problems as much as it is understanding where constraints in the network exist, how decisions in one area affect outcomes in another, and where to focus first.

Robbie Hughes, Chief Product Officer at Health Catalyst, sees the same pattern across the health systems his teams work with:

"Every day, patients wait weeks for an appointment only to find their doctor isn't taking new patients. Referrals get lost. Providers are busy, but they still can't make the economics work," Hughes said. "It isn't because they don't care. The information they need to improve is scattered across five or six disconnected systems, and by the time it's pulled together, it's already out of date."

The Gap Between Data and Action

"The time from understanding you have a problem to actually doing something about it can be many months," Hughes said. "That isn't a lack of data. As often as not, it's a lack of alignment around what the data means. Everyone has dashboards. Everyone has an opinion.”

The usual toolkit doesn't close that gap: EHR reports, a Tableau view sitting on a stale extract, a consultant engagement that produces a snapshot and then expires. Each one answers a question once at the time of extract. None of them produces a single, current picture that an operations leader and a finance leader can look at together, trust, and agree on.

Even when organizations have confidence in the numbers they are seeing, they often struggle to understand the operational drivers behind them. A long wait time may indicate insufficient provider capacity but may also be the result of  referral bottlenecks, misaligned scheduling practices, or uneven panel distribution. Lower provider productivity may signal excess capacity in one clinic and access constraints in another. The same metric can point to a variety of very different underlying problems.

That complexity is what creates the ambulatory intelligence gap. Access, provider productivity, referrals, panel management, and financial performance do not operate independently. They influence one another. Improving one area can shift constraints to another. Understanding what is happening is important. Understanding why it is happening, what is driving it, and where intervention will have the greatest impact is what enables improvement.

Turning Expertise Into Software

Recent advances in AI have expanded what's possible. Today’s AI models can now analyze complex operational relationships, surface patterns across large volumes of data, and rationalize over information in ways that were previously difficult or impractical.

But AI alone doesn't solve the problem.

AI can identify patterns in data, but it doesn't inherently understand ambulatory operations and the complex interrelations between components in the network. It doesn't understand which interventions have historically improved access, increased provider utilization, or reduced referral leakage. Those answers require operational context and improvement expertise, something that’s long been the domain of long-tenured experts in the field.

This is where Health Catalyst's approach differs. Health Catalyst’s Ambulatory Intelligence solution combines modern AI capabilities with nearly two decades of healthcare improvement expertise, embedding healthcare-specific knowledge, operational best practices, and proven improvement methodologies directly into the solution.

But connecting an organization’s sensitive patient data to external resources has always been problematic for healthcare providers. For this reason, Health Catalyst deploys its AI solution directly into the customer's own Databricks workspace. Health Catalyst brings its expertise to the data, ensuring it never leaves the health system's environment.

That choice follows a shift Hughes has watched accelerate. Customers increasingly want their data hosted on their own lakehouse, he says, and in the age of AI, they are focused on governance and control. They don't want it, as he puts it, "used as a training ground for someone else to derive value they never consented to."

Brian Eliason, who leads strategic vendor partnerships at Health Catalyst, says the market made the direction obvious: "Customers have shifted and want to control their own data. But they've also told us they want to continue the relationship with Health Catalyst, with them owning it. That's why we moved forward with the shipped model."

Built for the Customer's Environment

Getting there took real engineering. The medallion architecture, Hughes says, let the company turn its services expertise into a common semantic layer, doing "in weeks what would previously have taken months or years."

Building inside the customer's environment required balancing governance, performance, and usability. Health Catalyst's solution relies on several Databricks components, each serving a specific purpose:

Governance starts with Unity Catalog. Because the solution installs into the customer's workspace, Unity Catalog governs the entire data footprint from one place. Access controls, lineage, and audit trails stay with the customer. That settles the first question any CIO asks about a new healthcare deployment, which is where the data goes and who can see it. Nothing leaves the tenant.

Performance comes from Lakebase. The application serves operational data that leaders pull up daily, so query lag isn't an option. Performance was the key driver behind choosing Lakebase as the serving layer, Hughes says, for its low-latency response and the room to add transactional capability over time. The Lakebase tables are a subset of the gold models, while the complete set stays available through Unity Catalog for anyone reaching the data through traditional analytics, BI, ad hoc queries, or Genie.

Genie answers the next question. Dashboards and natural-language querying aren't an either-or choice. "The opportunity isn't to look at either in isolation. It's to combine them," Hughes says. A dashboard shows a leader where they stand. Genie handles the follow-up, the why behind a shift in the numbers, in the moment rather than in a ticket queue.

The practical effect is a different kind of meeting. Instead of an analyst and a principal trading questions over days, the answer comes back while everyone is still in the room.

Bringing Ambulatory Performance Into Focus

Because the product arrives with Health Catalyst's domain models already built in, an operations leader has visibility the same week it goes in, not after a six-month analytics project.

The solution ships with dozens of prebuilt metrics organized into four domains: Access Optimization, Revenue Intelligence, Panel Management, and Referral Insights. Each gives operational leaders a complete picture of a distinct dimension of ambulatory performance.

Cross-domain executive and provider scorecards connect those insights across the ambulatory enterprise, helping leaders understand how operational decisions in one area influence outcomes in another, prioritize the highest-value opportunities, and focus improvement efforts where they are most likely to create measurable impact.

And because it runs in the customer's own environment, the health system keeps the ability to adapt it. Hughes confirmed that customers can adjust terminology and tailor workflows to the specific markets they serve, rather than bending to a vendor's fixed definitions.

The impact is reflected in the results organizations have achieved through Health Catalyst's ambulatory improvement work:

At Thibodaux Regional, Health Catalyst supported a $1.2 million increase in annual revenue, a 15.3 percent increase in encounters, and a 6.75 percent increase in work RVUs per encounter.

At INTEGRIS Health, Health Catalyst helped the organization achieve a $2.2 million increase in annual revenue, close 55,000 care gaps, and double depression screening rates.

At WakeMed, Health Catalyst supported a $25.4 million increase in annual revenue, a 15.8 percent increase in outpatient visits, and a 7.1 percent reduction in cancellations without reschedules.

Scaling What Works

Ambulatory Intelligence is the first of a portfolio, not a standalone tool. Costing and clinical quality solutions follow on the same data foundation, and Hughes expects them to compound as they connect, the underlying data assets reinforcing one another and feeding Ignite Intelligence, which mines prior improvement patterns to surface relationships a customer may never have seen.

The impact is reflected in the results organizations have achieved through Health Catalyst's ambulatory improvement work.

Future capabilities are designed to help teams move more quickly from insight to action by drawing on Health Catalyst's growing evidence base.

The longer arc is what Hughes is most animated about. Health Catalyst has 18 years of proprietary improvement data, and the plan is to turn that history into models that don't just describe a problem:

"The single most exciting thing we're bringing is those thousands of real outcomes, turned into AI-enabled models baked into our solutions," Hughes said. "They'll help organizations understand which interventions have worked in similar situations and guide teams toward the approaches most likely to create measurable impact. Personalized playbooks, available fast."

That direction points toward agentic capability. Built on frameworks like Databricks Agent Bricks, future versions of the solution will extend beyond identifying opportunities to supporting the full path all the way through to outcome delivery. That work is still ahead, but the data foundation it depends on is already in production.

Closing the gap

The information health system leaders need to grow has always existed somewhere in their data. The gap has never been access to it. It has been the ability to identify the opportunities and constraints that matter most, intervene where action will have the greatest impact, and improve outcomes with confidence.

Health Catalyst built Ambulatory Intelligence to help health systems remove the operational barriers limiting ambulatory growth by improving access, strengthening referral retention, optimizing provider capacity, and driving financial performance.

See for yourself how Ambulatory Intelligence works, and what it can surface in your own environment.

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