Beyond Accuracy: A Smarter Approach to Clinical Coding

Author: Denis Avetisyan

A new multi-agent system, Hybrid-Code, combines the strengths of AI with robust data validation to deliver reliable and privacy-preserving diagnostic coding.

A hybrid system architecture manages the complexities of clinical code assignment by employing a tiered approach-semantic reasoning with a language model <span class="katex-eq" data-katex-display="false">BioMistral-7B</span> initially attempts structured output, gracefully degrading to deterministic keyword matching when parsing fails, and finally subjecting all candidate codes to rigorous validation against a knowledge base of 257 ICD-10 codes and contextual evidence, ensuring a continuous pipeline that both generates and verifies outputs while meticulously logging reasons for rejections. — A hybrid system architecture manages the complexities of clinical code assignment by employing a tiered approach-semantic reasoning with a language model $BioMistral-7B$ initially attempts structured output, gracefully degrading to deterministic keyword matching when parsing fails, and finally subjecting all candidate codes to rigorous validation against a knowledge base of 257 ICD-10 codes and contextual evidence, ensuring a continuous pipeline that both generates and verifies outputs while meticulously logging reasons for rejections.

Hybrid-Code leverages a neuro-symbolic framework with redundant agents to achieve zero hallucinations and maintain performance even with imperfect language model outputs for ICD-10 coding.

Despite advances in large language models, deploying automated clinical coding systems on-premise remains challenging due to privacy concerns and reliability issues. This paper introduces ‘Hybrid-Code: A Privacy-Preserving, Redundant Multi-Agent Framework for Reliable Local Clinical Coding’, a novel multi-agent system that achieves zero hallucinated codes within its knowledge base by combining semantic reasoning with deterministic fallback and symbolic verification. Our key finding is that prioritizing redundancy and verification over raw model performance is crucial for building trustworthy AI in healthcare settings. Could this hybrid approach unlock broader adoption of on-premise AI solutions where data privacy and system stability are paramount?

The Inevitable Decay of Clinical Precision

The foundation of modern healthcare analytics and proper financial reimbursement rests upon the precise translation of patient encounters into standardized clinical codes, most notably ICD-10. This process, clinical coding, transforms narrative clinical documentation – encompassing diagnoses, procedures, and patient conditions – into alphanumeric representations that facilitate data aggregation and reporting. Accurate coding is not merely administrative; it directly impacts resource allocation, public health surveillance, and the validity of clinical research. A miscoded record can lead to incorrect billing, flawed epidemiological studies, and ultimately, compromised patient care, highlighting the critical need for robust and reliable coding practices within healthcare systems.

Healthcare relies heavily on the detailed narratives within clinical notes, yet translating these complex texts into standardized codes for billing and analysis presents a significant challenge. Traditional coding methods, often manual or rule-based, frequently struggle with the inherent ambiguity, context-dependent meanings, and sheer volume of information contained within these notes. This struggle leads to inconsistencies in coding practices, resulting in both inaccurate reimbursement claims and flawed data for crucial healthcare analytics. The subtleties of medical language – negations, qualifiers, and implied relationships – are particularly difficult for these systems to capture, increasing the risk of coding errors and creating administrative burdens for healthcare providers. Consequently, the inefficiencies associated with traditional methods not only impact financial resources but also hinder the ability to effectively track disease patterns, evaluate treatment outcomes, and improve patient care.

While large language models (LLMs) present a promising avenue for automating and improving clinical coding, their implementation demands careful consideration. The inherent complexity of medical language, coupled with the potential for LLMs to generate plausible but incorrect interpretations, introduces a risk of coding inaccuracies that could impact both data analytics and financial reimbursement. Beyond accuracy, naive deployment of these models raises significant data privacy concerns; clinical notes contain protected health information, and LLMs require robust safeguards to prevent breaches or unintended disclosures. Successful integration necessitates not only rigorous validation of model outputs against established coding guidelines, but also the implementation of privacy-preserving techniques like differential privacy or federated learning to mitigate the risks associated with sensitive patient data.

A System of Checks: Embracing Collaborative Intelligence

Hybrid-Code employs a multi-agent system architecture, consisting of a Coder Agent and an Auditor Agent, to facilitate a collaborative coding workflow. The Coder Agent is responsible for generating potential coding solutions based on input clinical text, while the Auditor Agent independently validates these proposals. This division of labor allows for a cyclical process of code generation and verification, leveraging the strengths of both agents to improve the overall accuracy and reliability of the coding process. Communication between the agents is integral to the system, enabling the refinement of coding suggestions through iterative feedback and correction.

The Coder Agent utilizes the BioMistral-7B large language model to generate potential medical codes based on input clinical text. BioMistral-7B is a decoder-only 7 billion parameter language model, enabling it to process and interpret the semantic content of clinical notes, discharge summaries, and other medical documentation. The agent’s function is to translate the natural language descriptions within these texts into standardized medical codes, such as ICD-10, CPT, and SNOMED CT. This process relies on the model’s pre-training on a substantial corpus of medical texts, allowing it to identify relevant concepts and relationships to suggest appropriate codes for billing, data analysis, and clinical decision support.

Neuro-Symbolic AI, as implemented in Hybrid-Code, integrates the statistical learning capabilities of large language models with the deterministic logic of symbolic reasoning systems. This approach addresses limitations inherent in purely statistical models, such as a propensity for generating plausible but incorrect outputs – often termed ‘hallucinations’. By grounding the language model’s output in a formal, rule-based system, Neuro-Symbolic AI enhances reliability and trustworthiness. Specifically, it allows for verifiable reasoning steps and ensures adherence to predefined constraints, moving beyond pattern recognition to achieve a level of interpretability and correctness critical for sensitive applications like clinical coding.

The Auditor Agent functions as a critical validation layer within the Hybrid-Code system, systematically assessing code proposals generated by the Coder Agent. This verification process relies on a pre-defined Knowledge Base comprising a comprehensive set of coding rules and established medical guidelines. By comparing proposed code with the rules within the Knowledge Base, the Auditor Agent identifies potential errors, inconsistencies, or deviations from accepted standards. This structured validation significantly improves the accuracy of the generated codes and mitigates the risk of ‘hallucinations’ – instances where the language model generates outputs not grounded in factual or logical reasoning – thereby enhancing the overall reliability of the system.

The Imperative of Data Sanctuary

Hybrid-Code is designed with a local deployment architecture, meaning all data processing occurs within the hospital’s existing on-site infrastructure. This configuration is fundamental to guaranteeing zero data egress; patient data remains entirely within the hospital’s control and does not transmit to external servers or cloud environments. This approach minimizes potential data breaches and supports the maintenance of strict data governance policies, as all data handling is subject to the hospital’s established security protocols and physical access controls.

The Hybrid-Code architecture is designed to facilitate compliance with stringent data privacy regulations, specifically the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. HIPAA mandates the protection of Protected Health Information (PHI), requiring secure data storage and transmission, while GDPR focuses on data protection and privacy for all EU citizens. By enabling local data processing and guaranteeing zero data egress – meaning patient data remains entirely within the hospital’s infrastructure – the system avoids the complexities of cross-border data transfers and minimizes the risk of non-compliance with these regulations, thereby supporting adherence to both US and EU data protection standards.

Confidence-Aware Rejection is a critical component of the system’s error mitigation strategy. This process evaluates each proposed code modification based on the model’s internal confidence score. Proposals falling below a predetermined threshold are automatically flagged and rejected, preventing the introduction of potentially erroneous or invalid code. This dynamic filtering mechanism operates continuously throughout the system’s operation, proactively identifying and discarding low-confidence outputs before they can impact functionality or data integrity. The rejection threshold is calibrated to balance minimizing errors against maintaining system responsiveness and avoiding unnecessary intervention.

Constrained decoding is implemented within the Hybrid-Code system to enforce the generation of syntactically correct and valid code. This technique operates by limiting the possible output tokens during the decoding process to only those that adhere to the defined grammar and structure of the target programming language. By restricting the search space to valid code elements, constrained decoding prevents the generation of incomplete, malformed, or syntactically incorrect code snippets, ensuring that all proposed code adheres to established coding standards and can be directly executed or integrated into existing systems. This process significantly reduces the need for post-generation error correction and enhances the overall reliability of the system’s output.

Demonstrating Resilience Through Real-World Application

Hybrid-Code’s performance was rigorously tested using the MIMIC-III dataset, a comprehensive and publicly available resource frequently employed within the clinical data analysis community. This benchmark comprises de-identified data from intensive care unit stays, offering a realistic and challenging environment for evaluating the system’s ability to accurately interpret medical information and generate appropriate diagnostic codes. Utilizing MIMIC-III allows for direct comparison against existing methodologies and provides a standardized measure of Hybrid-Code’s effectiveness in a real-world clinical context, ensuring the results are both reproducible and relevant to practicing healthcare professionals.

Evaluation on the MIMIC-III dataset reveals Hybrid-Code achieves a noteworthy 34.11% coverage rate across a substantial 1,000-case study, with a 95% confidence interval ranging from 31.2% to 37.0%. Critically, the system demonstrates a 0% hallucination rate for conditions successfully covered within its 257-code knowledge base, indicating a high degree of accuracy and reliability in its diagnostic suggestions. This performance suggests the model not only identifies relevant conditions but does so without generating spurious or incorrect codes, a crucial characteristic for clinical applications where precision is paramount.

Across a rigorous evaluation involving 1,000 clinical cases, the Hybrid-Code system demonstrated exceptional stability, achieving a zero percent system failure rate. This consistent performance underscores the robustness of the model and its capacity to reliably process diverse and complex medical data without interruption. The absence of failures suggests a well-engineered architecture capable of handling real-world variability and maintaining operational integrity, which is critical for deployment in sensitive healthcare settings where consistent and dependable results are paramount. This level of reliability positions the system as a trustworthy tool for assisting with clinical coding and data analysis tasks.

The implementation of an Auditor agent proved highly effective in refining the accuracy of generated medical codes. Rigorous evaluation demonstrated a substantial 21.5% reduction in invalidly formatted codes, decreasing the occurrence rate from an initial 1.35% to a refined 1.06%. This improvement underscores the agent’s capability to identify and correct errors in code structure, contributing to a more reliable and clinically sound output. The observed reduction isn’t merely statistical; it reflects a significant enhancement in data quality, bolstering the trustworthiness of the entire code generation process and minimizing potential downstream errors in analysis or billing.

The system’s efficacy hinges on substantial contributions from its underlying language model, which independently generated $85.96%$ of all diagnostic codes within the evaluation. This high rate of autonomous code generation signifies efficient utilization of the model’s learned knowledge and capacity for clinical reasoning. Such a performance level suggests the language model isn’t merely assisting the process, but is a primary driver of code assignment, reducing reliance on other system components and pointing toward a scalable and potentially cost-effective approach to automated medical coding. The demonstrated ability to independently formulate a vast majority of the codes highlights the model’s proficiency and its potential for broader application in clinical data analysis.

A Vision for Adaptable, Intelligent Healthcare

Continued development of Hybrid-Code necessitates a significantly expanded knowledge base, moving beyond initial coding parameters to encompass a wider spectrum of medical conditions, procedures, and evolving clinical guidelines. This expansion isn’t simply about accumulating data; it demands a sophisticated system for integrating nuanced, often ambiguous, medical directives and translating them into precise, auditable codes. Researchers are prioritizing the incorporation of complex guidelines – those involving conditional logic, multiple criteria, and frequent updates – to ensure the system remains current and reliable. Furthermore, the project aims to leverage machine learning to automatically extract and incorporate new information from medical literature and regulatory updates, creating a self-improving knowledge base capable of adapting to the ever-changing landscape of healthcare practices and policies.

The seamless integration of Hybrid-Code directly into existing electronic health records (EHRs) represents a significant step towards automating and improving clinical coding practices. This envisioned connectivity allows for real-time analysis of patient data as it is documented, proactively suggesting appropriate diagnostic and procedural codes. By processing information at the point of care, Hybrid-Code minimizes the delays and potential errors associated with traditional, retrospective coding workflows. Such an integration not only streamlines administrative processes for healthcare providers but also facilitates more accurate billing and claims submissions, ultimately contributing to a more efficient and financially sound healthcare ecosystem. The system is designed to operate as a supportive tool, offering suggestions that clinicians can review and validate, ensuring that human expertise remains central to the coding process.

The foundational approach of Hybrid-Code – combining the pattern recognition of neural networks with the logical rigor of symbolic reasoning, all while prioritizing data privacy – holds considerable promise beyond clinical coding. Researchers anticipate adapting these core principles to significantly enhance diagnostic accuracy and personalize treatment strategies. By leveraging neuro-symbolic AI, systems could analyze complex patient data, identify subtle patterns indicative of disease, and propose evidence-based treatment plans, all within a secure and privacy-preserving framework. This extends to areas like genomic medicine, where intricate genetic data requires both nuanced interpretation and robust security, and predictive analytics, enabling proactive healthcare interventions based on individual patient risk profiles. Ultimately, this methodology seeks to establish a new paradigm in healthcare AI, one where intelligent systems augment clinical expertise with reliable, transparent, and ethically sound decision support.

The development of Hybrid-Code envisions a transformative shift in healthcare delivery, prioritizing the augmentation of clinical expertise through trustworthy artificial intelligence. This system is not intended to replace healthcare professionals, but rather to equip them with tools that minimize administrative burdens and reduce the potential for coding errors, ultimately streamlining workflows and optimizing resource allocation. By providing reliable, secure, and readily accessible coding assistance, Hybrid-Code aims to free up valuable time for clinicians, allowing them to focus more intently on patient care and complex medical decision-making. The anticipated outcome is a healthcare ecosystem characterized by improved accuracy, increased efficiency, and, crucially, enhanced patient outcomes driven by more focused and effective clinical attention.

The pursuit of reliable systems, as demonstrated by Hybrid-Code, echoes a fundamental truth about all complex structures. This framework, designed for on-premise clinical coding with zero hallucinations, acknowledges that even sophisticated neuro-symbolic AI is subject to the constraints of its operational environment. Donald Davies observed, “I think the best way to look at networks is as a way of sharing resources.” Hybrid-Code embodies this principle by distributing the coding task across multiple agents, creating redundancy and resilience. The system doesn’t attempt to achieve perfect accuracy in isolation; rather, it builds a robust process capable of graceful degradation, recognizing that time-the timeline of patient data and evolving medical knowledge-is the medium in which its reliability is measured.

What Lies Ahead?

The pursuit of reliable automated clinical coding, as exemplified by Hybrid-Code, invariably trades one set of vulnerabilities for another. Achieving zero hallucinations within a defined scope is a noteworthy, if temporary, victory. The system’s memory – its inherent technical debt – lies in the boundaries of ‘covered conditions’. Expansion will necessitate a corresponding accrual of complexity, and with it, new failure modes. The question isn’t if errors will emerge, but rather the nature of their presentation, and the cost of their detection.

The local deployment strategy, intended to safeguard patient privacy, introduces its own limitations. Maintaining parity between centrally trained models and these isolated instances demands ongoing refinement, a constant expenditure of resources. Furthermore, the inherent variability of clinical language suggests that even robust neuro-symbolic architectures will struggle to achieve true generalizability without access to broader datasets – a paradox for privacy-preserving systems.

Ultimately, the field will likely shift towards accepting probabilistic correctness as the norm, prioritizing graceful degradation over absolute certainty. The challenge then becomes not simply detecting errors, but quantifying and mitigating the risk they pose – a move from ‘zero hallucinations’ to ‘acceptable uncertainty’. Each simplification, each abstraction, carries a future cost, and the longevity of any such system will be measured by its ability to absorb that debt.

Original article: https://arxiv.org/pdf/2512.23743.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/