How explainable AI delivers measurable healthcare outcomes

Over the past few years, AI in healthcare has moved from strategy to reality, but the key question has not changed: is it improving outcomes for patients, providers, and plans in the real world? At Reveleer, we’ve focused on answering “yes” and building AI that is transparent, measurable, and tightly integrated into the workflows where care is delivered; an idea reinforced recently in a few blogs from our Chief Product Officer, Paul Burke, and our broader product and engineering team.

Why “good enough” AI is no longer enough

In his blog “Why ‘Black Box’ AI Isn’t Enough for Providers,” Paul lays out the limitations of AI systems that surface scores without exposing their reasoning. Those approaches may be acceptable for some narrow retrospective use cases, but they fall short at the point of care, where clinicians must defend every decision and cannot rely on opaque probability outputs alone.

Healthcare’s complexity amplifies this problem: tens of thousands of codes, variable chart quality, and fragmented data sources mean that even “structured” data can feel unusable without clinical context. As Paul highlights, physicians think in clinical concepts, not codes, and expecting them to reverse engineer an algorithm undermines trust and adoption from the start.

What explainable clinical AI must deliver

Paul’s explainable AI blog also outlines the core design choices behind our platform’s clinical evidence AI layer: a proprietary evidence extraction agent that pulls facts from structured and unstructured data, and then applies a deterministic, clinician-authored rules layer that delivers easily interpreted insights to the physician at the point of care. That separation empowers customers auditable reasoning with formulas they can read, challenge, and tune in minutes, rather than relying on a monolithic model’s hidden logic.

Across these technologies, our product leaders emphasize that more than 3,300 expert-built clinical formulas now sit on top of a persistent evidence graph that accumulates indicators for each member over time. The result is not just a suspect list, but an evidence chain, from chart page to clinical indicator to rule logic, designed to satisfy regulators, earn provider confidence, and reduce documentation friction.

Measuring AI by the outcomes that matter

In “The Six Factors: How Reveleer Measures to Establish a Shared Framework to Drive Outcomes with Retrospective Customers,” Paul Burke and Dave Meyer remind us that AI performance is shaped as much by operating decisions as by algorithms. Choices around who completes the work, coding guidelines, coder proficiency, chart quality, software version, and data completeness all influence throughput, accuracy, and the value customers ultimately see. ​

Our AI framework focuses on the metrics that matter most to health plans and providers:

• Throughput and processing speed
• RAF accuracy and quality uplift
• Cost and workforce efficiency  
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Our newest hybrid AI also delivers competitive match rates while cutting noise. For example, driving noise down from the mid40% range to the mid20%s directly translates into fewer irrelevant suspects for more actionable work, and better ROI on both human and technology investments.

How agentic AI transforms prospective suspecting

In “Transforming Prospective Risk Adjustment with Agentic AI,” Paul Burke and Julien Brinas describe the next step in this journey as moving from retrospective programs to proactive, prospective risk adjustment and quality improvement workflows powered by an agentic, LLM-enabled data pipeline. Their work details how charts are ingested once, evidence is extracted and validated, and clinical indicators persist in a reusable evidence graph that can power suspecting, quality gap closure, care management, and more.

On top of that evidence layer, our prospective suspecting engine applies thousands of clinical rules across multiple HCC models, enabling customers to identify chronic conditions and care gaps before the visit, with proven noise reductions of 50% versus legacy NLP-based suspecting. This means clinicians see fewer false positives and can focus more time on treating patients while experiencing consistent, traceable logic within their native workflows within the EHR or admin systems.

Building a platform for the next decade

Taken together, these blogs from Paul, Julien, Dave, and our technology leaders express a shared belief:  

“ AI should serve clinicians, not the other way around. ”‍

By separating pattern recognition from clinical reasoning, persisting evidence once and reusing it everywhere, and aligning our measurement framework with customer outcomes, Reveleer is building a healthcare platform that can grow and scale for value-based care.  

The technology that will succeed in these next few years will not be those with the flashiest GenAI demo, but those that deliver repeatable, explainable, workflow-integrated results at scale. That is the bar our product, AI, and development teams have set in our product vision. It is the standard we are holding ourselves to at Reveleer as we partner with our customers to support and drive their business growth in value-based care.