AI can help health systems cut unnecessary costs—fast. In Part 2 of our 3-part series, we explore how AI in healthcare combines activity-based costing and clinician engagement metrics to reveal case-level variation and support targeted, evidence-based clinical, operational, and financial improvement.
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Editor’s note: AI is no longer a distant promise in healthcare—it’s reshaping how systems deliver care, understand data, and run operations. But with so much hype, it’s hard to see what truly works.
This 3-part series highlights real use cases and lessons from AI in healthcare, with one goal: to help leaders find traction today and move toward better patient care, sustainable systems, and stronger margins.
Healthcare AI automates what hospitals have long tried to do manually with ease but couldn’t: pinpoint waste without compromising care.
The key to sustainable savings and improving bottom lines often lies in identifying unwarranted clinical variation—reconciling differences in supply use, improving operating room (OR) time, or enhancing post-acute care to mitigate poor outcomes. But without case-level visibility, those patterns are hard to detect and even harder to act on.
In our webinar, Clinician-Guided Strategies: Cut Waste, Not Care, we introduced a new approach using AI. By combining activity-based costing, clinician input, and AI to uncover unwarranted cost variation, health systems can align practices with evidence-based care and optimize operational, financial, and clinical performance across sites.
AI-enabled clinical cost control offers a structured, clinician-led improvement process that often yields measurable ROI in the first year.
Why is this solution important from an economic standpoint?
Today’s financial pressures—elevated labor and supply costs, increasing pharmaceutical spend, and reduced reimbursement—have led many organizations to suspect that provider-level variation contributes significantly to excess costs. Yet, legacy systems typically don’t offer the details needed to verify it.
Our approach addresses this gap by applying activity-based costing (ABC) at the case level.
Unlike traditional approaches that rely on relative value units (RVUs) or departmental averages, ABC assigns actual costs to each activity and resource involved in a patient’s care—from OR time and staff labor to supplies and medications spend.
The result is a case-specific view of care delivery that allows for direct comparison across providers, resources, and sites.
This process starts by fostering genuine clinician engagement and building their trust in the data.
Often, clinicians disengage from cost-reduction efforts because they conflict with their core values. Plus, financial teams typically control the tools used to identify care variation issues. This creates a disconnect between teams driving financial outcomes and those providing patient care.
Without clinician buy-in, initiatives aimed at reducing care variation may lack the necessary support for successful implementation, leading to missed opportunities. Trust continues to erode.
With AI-driven reports presenting data at the individual case level, clinicians can clearly see how their practice patterns differ from those of their peers.
They can also see which supplies were opened but not used, for instance, or how certain post-operative practices may be extending a patient’s length of stay. This level of detail supports focused, constructive discussions around clinical cost variation and standardization, while still respecting clinical judgment.
“Physicians want to know that the numbers reflect what actually happened. They need to see their actual cases, not generalized reports.”—Kathleen Merkley, Senior Vice President, Professional Services, Health Catalyst
AI in Healthcare utilizes a large language model to process thousands of clinical and financial records and automatically highlight patterns of variation. This eliminates the need for manual data reviews or custom-built reports.
The AI-generated insights are paired with context and explanation to ensure they’re defensible. Clinicians and service line leaders can use these outputs to prioritize improvement work and track results over time.
“AI helps us move faster and focus on what matters. It finds meaningful variation that teams can act on.”—Kathleen Merkley, Senior Vice President, Professional Services, Health Catalyst
This approach doesn’t just happen in theory; it delivers real results.
A recent example concerns hip and knee joint replacements. Our approach revealed variation across providers in supply usage, length of stay, and implant choices. In some cases, our solution found that clinicians may have opened supplies but didn’t use them during operations. Also, variation in recovery protocols led to longer inpatient stays for similar procedures.
These differences were not visible in traditional reports but became clear through case-level costing. With this insight, orthopedic teams were able to identify areas for standardization and cost reduction without compromising care quality. According to a case study, the health system achieved $815,103 in cost savings within two years.
"Generating data alone isn’t enough. Tools must support action."—Kathleen Merkley, Senior Vice President, Professional Services, Health Catalyst
Our AI-powered clinical cost optimization tool is designed to align with how service line teams operate. Rather than relying on abstract dashboards or financial summaries, our approach presents insights linked to individual cases, specific procedures, and frontline decisions. It also supports prioritization, helping teams focus on variation that is both clinically addressable and financially significant.
Because of its structured design and built-in analytics, many organizations using Health Catalyst’s cost management solution see measurable results in their first year, without the heavy setup typically required by traditional cost accounting systems.
Before moving beyond traditional costing methods, health systems should assess their readiness. Carefully consider the following key questions to ensure a smooth and successful transition:
Clinical cost management with AI represents a shift from reactive cost-cutting to targeted, evidence-based improvement. By combining activity-based costing, clinician engagement, and AI-powered analytics, the approach provides the granularity needed to identify waste and the tools to act on it.
Cutting costs doesn’t have to mean cutting care. With the right level of detail and a system built for action, organizations can make sustainable improvements that benefit both their financial performance and patient outcomes.
“One of the most effective interventions we recommend, one we’ve seen our hospital and health system clients achieve meaningful results with time and time again, is the use of machine learning and AI, alongside data and analytics to guide clinical practice and drive real measurable improvement. By pairing standardized, evidence-based pathways with near real-time actionable insights, clinicians gain a single source of truth that highlights unwarranted variation and tracks compliance with proven bundles, such as those for sepsis, heart failure, and infection prevention.” —Daniel Samarov, Chief AI Officer, Health Catalyst
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