Why “Black Box” AI isn’t enough for providers

Paul Burke, CPO, Reveleer

While artificial intelligence is revolutionizing healthcare, the effective application of AI is use case specific. What works for retrospective risk adjustment may not work for prospective risk adjustment needs. In clinical applications, black box AI, systems that deliver results without transparency, simply isn’t enough. While black box models provide confidence scores that are proven to work well in retrospective applications to review past medical care, providers want to understand how and why those recommendations are made.

Explainable AI provides clear reasoning and evidence for every recommendation, allowing clinicians to understand not just what the AI suggests, but why. This transparency is essential for regulatory compliance, clinical decision-making, and provider trust. When clinicians can see the logic and evidence behind AI-driven suspect lists or diagnoses expressed in familiar clinical language, they are empowered to make informed decisions, reduce documentation errors, and improve patient outcomes. Ultimately, the driver behind the push for explainable AI is to ensure that technology serves as a reliable partner in care delivery, rather than an opaque tool that adds uncertainty or friction to clinical workflows.

Some open-source large language models (LLM) also offer a great level of explainability and can tell you how they arrived at an answer. The challenge with those singular generative AI (Gen AI) solutions however, is that you have to train the models with a large body of training data to follow the reasoning you want. And once trained, a level of unpredictable variation is still always present and expected. There starts an infinite and expensive repeating cycle of fine tuning and training to get the model to consistently follow the reasoning.

In regulated, high-stakes environments, results must be measurable, provable, clinically consistent, and seamlessly integrated into workflows. Below we describe why and how Reveleer’s new cognitive architecture solution sets a new standard for clinical AI that can avoid the expensive and infinite loop of retraining while delivering on the promise of this new LLM technology.

The problem with Black Box AI for providers
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Black Box AI refers to models that generate outputs without revealing the underlying logic or evidence. In healthcare, legacy natural language processing (NLP)engines and some LLM-based solutions analyze vast amounts of structured and unstructured data. They surface recommendations with little insight into how those results were reached. Providers and payers are left with probability scores (“87% confidence this patient has diabetes”), but no clear evidence or reasoning to back up the metric.

Why this isn’t enough for clinicians

• ‍Regulatory scrutiny: Healthcare decisions must be defensible and auditable. If black box models can’t provide the lineage or provenance of their recommendations, they become challenging to defend.‍
• Provider trust: Clinicians need to understand the “why” behind a recommendation, not just a confidence score.‍
• Workflow disruption: If AI outputs can’t be traced, explained, or adjusted, they create friction and provider abrasion and administrative burden as providers must back trace through mountains of medical data to justify a result, wasting time and undermining adoption.‍
• Evolution paralysis: Teams spend months building synonym dictionaries and format parsers, only to rebuild when guidelines change. Statistical models offer confidence when clinicians need certainty and clarity.

The structured data paradox
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Healthcare data is notoriously complex. With 71,000 LOINC codes and 70,000 ICD codes, even structured data drowns providers in unusable complexity. From a user perspective, physicians think in clinical concepts, not codes. Black box AI often fails to bridge this gap, leaving clinicians with outputs that are hard to interpret and act upon.

Sidebar: Patients are a combination of data points compounding over time.

Medical data is an interconnected network of health concepts that create a unique graph of an individual. Clinicians must make sense of all these interconnects and efficiently diagnose and treat patients in an administratively overloaded delivery model. They need solutions that think like a physician to truly find flow in their work.

The Reveleer approach: measurable, explainable, and integrated

Measurable results

Our new clinical evidence AI technology layer is built for transparency and benchmarking. Performance isn’t just about match rates. It’s about reducing noise, improving provider experience, and ensuring every result can be measured against real-world outcomes. Our Gen AI-based engine shows competitive match rates than its black box predecessor while dramatically reducing noise (26% vs.46%), resulting in providers spending less time sifting through irrelevant suggestions and more time on care delivery. Our embedded benchmarking tools allow for rapid formula refinement and automated quality assurance to replace manual overread processes. This enables same-day adjustments based on customer feedback.

Explainable results—provably consistent in their clinical reasoning‍

Our hybrid architecture separates AI pattern recognition from clinical decision logic. The platform uses a proprietary evidence extraction agent to pull facts from data, then applies deterministic, provider-readable formulas (e.g., “ifA1C > 6.5 AND fasting_glucose > 200 then suspect_diabetes”). The solution includes:

• ‍Transparent formulas: Instead of opaque probability bands, our technology uses formulas clinicians can read, validate, and adjust in minutes.‍
• Clinical language corpus: Results are expressed in language clinicians use and understand, not just codes or statistical abstractions.‍
• Auditability: Every recommendation can be traced back to the exact evidence and logic that produced it.‍
• Persistent evidence layer: Clinical indicators are saved permanently and build up over time for each patient across every chart. This persistent record doesn’t just support the current suspecting, it’s also creates the foundation for future uses that include utilization management, population health mining, and customer-specific analytics.

The clinical decisions and deterministic results within our technology layer are expert-driven—not LLM driven—ensuring the clinical logic remains the gold standard. Reveleer’s team of MDs and nurse practitioners have condensed four decades of combined experience into 3,300+ clinical formulas to represent the industry’s most comprehensive prospective suspecting clinical logic. The team continuously evolves these formulas and clinical language through customer feedback loops and self-correcting AI agents.

We skip the retraining and potential for hallucinations in LLMs by skipping the reasoning step entirely. Our hybrid solution allows the LLM to explain the objective truth pulled from unstructured data while the proprietary clinical suspecting formulas in our proprietary evidence translation layer ensures that the results are explainable and provably consistent in their clinical reasoning. This guarantees reasoning consistency for providers, allows us to adjust our clinical reasoning in real time based on feedback, and re-run different reasoning logic based on already extracted evidences.

We’ve built this technology for providers to improve and increase their confidence and adoption, and for us to quickly evolve the technology to meet their needs. This is our mindset at Reveleer. Use the best of bleeding edge technology for the value it actually provides to customers in the real world. We use AI where it is most valuable, not for the sake of it being AI.

Workflow integrated results‍

AI must also fit into existing clinical workflows, not disrupt them. This new hybrid technology is available and in use for new and existing prospective risk suspecting customers. While we have initially focused on prospective suspecting, our new agentic evidence extraction engine extracts evidence once and is capable of applying the results across multiple use cases like risk adjustment, quality gap closure, care management, and more.

Clinical indicators are extracted once, persisted forever, and reused infinitely. When anew chart is ingested, only indicators from that document are extracted and the existing evidence graph is updated automatically. Multiple formula models can execute simultaneously, and updates happen instantly when formulas change. As a result, providers experience consistent, explainable results wherever they interact with the system.

Today,3,300 formulas power prospective suspecting within this technology layer. Tomorrow, the same framework will be able to power quality gap closure, prior authorization, care coordination, and more.

Sidebar Head-to-Head: agentic pipeline based NLP vs. Black Box NLP

Ina benchmark comparison, it is easy to look for one statistic as an indicator of performance. But we must remember that AI performance lives in the context of human work. The goal of AI is to enable a person, in this case a provider, to work more efficiently in the service of delivering better care.

For the provider in the context of preparing for and delivering the right care, itis about getting a high quantity of suspects with care gaps with a manageable amount of noise. Having a competitive match rate with significantly less noise (26% vs.46%) enables the provider to help more patients get the right care, faster. Having more matches with more noise means more results to sift through. In addition, providers benefit from evidence-backed suspect lists, improved documentation workflows, and enhanced coding accuracy.

Why This matters
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Explainable ,auditable AI is essential for meeting healthcare regulations and contractual obligations. Clinicians are more likely to trust and use AI that is transparent and integrated with their workflow. Measurable, explainable AI reduces provider abrasion, improves documentation, and drives better patient outcomes.

Blackbox AI may offer impressive performance in the lab, but in healthcare, trust, transparency, and integration are non-negotiable. By making results measurable, explainable, and workflow-integrated, Reveleer’s clinical evidence AI technology combines cognitive AI architecture with deterministic clinical logic to deliver transparent, explainable, and scalable suspecting across risk, quality, and utilization domains.

This new hybrid architecture powers suspecting, quality gap closure, prior auth, and care coordination from a single evidence layer and sets a new standard for clinical AI. It empowers providers, satisfies regulators, and delivers real value to patients and health plans. This solution redefines clinical suspecting by separating AI pattern recognition from clinical decision logic, enabling real-time, explainable insights that clinicians trust and regulators accept.