- Zyter|TruCare
Insights from a local, verifiable AI coding study in regulated healthcare environments
In published research examining regulated healthcare workflows, Dr. Yunguo Yu, Vice President, AI Innovations & Prototyping at Zyter|TruCare, investigates a core operational challenge facing healthcare organizations: how artificial intelligence can be applied to clinical coding while preserving accuracy, regulatory compliance, and patient data protection. The study analyzes a locally deployed system designed to translate clinical documentation into standardized billing codes under real-world healthcare constraints.
The research centers on how system architecture determines whether AI can be trusted in production healthcare environments. By keeping all processing local and enforcing verification directly within the coding workflow, the study provides empirical evidence of how AI can support high-volume administrative processes without introducing privacy risk or compliance exposure.
Research Context and System Design
Clinical coding sits at the intersection of documentation quality, reimbursement accuracy, and regulatory scrutiny. Errors or unsupported automation in this domain carry direct financial and compliance consequences, making it a meaningful test case for evaluating whether AI systems can be trusted in production environments.
Clinical coding under operational constraints
For providers to get reimbursed, clinical documentation must be translated into standardized billing codes, such as ICD-10 for diagnoses. This process is traditionally manual, contributing to reimbursement delays or lost revenue. While AI-based approaches have shown promise in accelerating coding, many rely on cloud-based models that require PHI to leave the organization. For healthcare entities operating under strict privacy, audit, and data governance requirements, that architectural choice introduces risk and complexity that can limit real-world adoption.
System architecture evaluated in the study
The research introduces a new model, Hybrid-Code, that uses AI to generate billing codes from medical records, while operating entirely within a healthcare organization’s local environment. The breakthrough is that Hybrid-Code uses local language models, so that patient data is never transmitted externally.
The system integrates two coordinated components:
- A language model that proposes candidate medical codes based on clinical documentation
- A symbolic auditing layer that verifies each proposed code against official coding standards and documented clinical evidence
When the language model encounters uncertainty or ambiguity, a rule-based fallback mechanism preserves continuity without sacrificing control.
Design intent
These architectural decisions are central to the research question. By enforcing locality, verification, and redundancy at the system level, the study examines whether AI-driven coding can achieve operational usefulness without compromising documentation integrity, auditability, or data protection.
This context establishes why clinical coding serves as an effective lens for examining how trust must be designed into healthcare AI systems, rather than assumed after deployment.
From Clinical Confidence to System Reliability
Our previous research on trust in healthcare AI focused on clinician interaction with AI-generated diagnoses. Transparency, confidence signaling, and alignment with clinical reasoning were central to building confidence at the point of care. That work established an important foundation and continues to influence how AI is introduced into clinical environments.
As AI expands into administrative and operational workflows, trust takes on a broader and more structural meaning. It now includes how systems handle sensitive data, enforce compliance requirements, and behave consistently under real operating conditions. In these settings, trust is demonstrated through system behavior rather than user perception alone.
Healthcare organizations are increasingly applying AI to workflows that directly affect reimbursement, audit readiness, and data governance. Clinical coding, utilization management, documentation, and related processes operate under constraints where accuracy, traceability, and continuity are essential.
Automation delivers operational value only when organizations can rely on how systems behave under regulatory and operational pressure. For many healthcare organizations, cloud-based AI introduces uncertainty around data control, third-party exposure, and audit defensibility. These factors shape whether AI can move beyond pilots and into sustained production use.
Clinical Coding as a Lens into System Trust
Clinical coding provides a clear lens into how system-level trust is established.
Coding accuracy directly affects reimbursement integrity and audit outcomes. Automation supports these workflows only when outputs are verifiable and grounded in documented evidence.
In the study, the locally deployed system, Hybrid-Code, processed 1,000 real discharge summaries and proposed nearly 7,000 potential ICD-10 codes. More than 75 percent of these proposed codes were rejected because the AI agent determined that clinical documentation did not provide sufficient supporting evidence. This restraint reflects a compliance-aligned approach that prioritizes documentation integrity over aggressive automation. That distinction is critical in regulated healthcare environments where accuracy and auditability outweigh volume.
Within the system’s verified knowledge base, no unsupported or hallucinated codes were accepted. Every accepted output was validated against official references and supported by clinical text, achieving a zero percent hallucination rate. This outcome is particularly significant in production healthcare systems, where coding errors can carry material financial and compliance consequences.
Reliability and Data Protection by Design
Beyond accuracy, the study demonstrated consistent operational performance.
The system completed 100 percent of workflows without interruption, including scenarios where language model outputs required fallback handling. Redundancy and verification ensured continuity while maintaining predictable behavior.
Patient data protection was enforced through local deployment. All processing occurred within the organization’s environment, ensuring protected health information remained contained throughout execution. This architectural choice simplifies alignment with healthcare data governance requirements and reduces exposure associated with data movement.
Despite layered safeguards, performance remained suitable for operational use. The system processed cases in approximately half a second on standard hardware, supporting day-to-day workflows without specialized infrastructure.
For healthcare organizations operating at scale, this combination of accuracy, reliability, and data protection represents a meaningful advancement in how AI can be applied to core administrative processes.
Trust as a Design Discipline
As AI becomes embedded in operational workflows, healthcare organizations increasingly evaluate systems based on how they behave under regulatory, security, and operational constraints.
Systems that earn trust in these environments consistently demonstrate:
- Data protection enforced through architecture rather than policy alone
- Outputs that are verifiable and defensible under audit
- Evidence-driven automation aligned with documentation standards
- Reliable operation that supports continuity when individual components encounter limitations
These characteristics reflect how health plans and providers assess risk when deploying technology across reimbursement, compliance, and patient data workflows.
Trust, in this context, is reinforced through design decisions that prioritize accountability, predictability, and control.
Applying These Principles in Practice
These principles inform how healthcare technology platforms approach AI-enabled workflows today.
At Zyter|TruCare, system-level trust is treated as a foundational requirement rather than an add-on. AI-enabled capabilities are designed to operate within existing healthcare environments, with an emphasis on local control of sensitive data, verification of AI outputs against formal standards, and predictable system behavior aligned with real operational conditions.
As healthcare organizations continue to introduce AI across regulated workflows, approaches grounded in containment, verification, and reliability help support responsible adoption while maintaining confidence in data, processes, and outcomes.
Continuing the Conversation
For readers interested in the technical details behind this evaluation, the full Hybrid-Code study is available to explore the architecture, methodology, and results in depth.
Read the full study here: Hybrid-Code: A Privacy-Preserving, Redundant Multi-Agent Framework for Reliable Local Clinical Coding
For more perspective on how trust is built at the clinical level, you can also read our earlier discussion on clinician trust in AI: Building Clinician Trust in AI: Dr. Yunguo Yu’s Breakthrough in AI Diagnostics – Zyter|TruCare
If you are evaluating how AI can be applied within regulated healthcare workflows and want to discuss approaches that prioritize data security, verification, and operational reliability, contact Zyter|TruCare to continue the conversation.
