Algorithmic Approaches to Medical Billing

The administrative burden of clinical billing activities for health insurance reimbursement is substantial and contributes to rising healthcare costs. Our team at #BrighamWomensHospital and #HarvardMedicalSchool recently developed a natural language processing (NLP) approach with open-source transformer models that predicts outpatient Evaluation and Management (E/M) level of service codes using clinicians' notes from outpatient medical clinics. ❓ Why might this be a good approach? AI-assisted billing might prompt a redesign of administrative billing processes to reduce resource and cost burden. 📈 The classification models trained on data across several different medical and surgical specialties and achieved an E/M level of service code classification accuracy approaching 75%. 💡 Why only 75% performance? Medical language is often characterized by complex terminology, abbreviations, and jargon that can be challenging for an NLP model to accurately classify. It is possible that some medical specialties may have more complex or specialized language. We also suspect there is variability in documentation practices such as differences in documentation styles, structure, and terminology between specialties. More to this point, clinicians within medical specialties may have idiosyncratic standards for assigning E/M LoS codes. ❗ Another unexpected finding from our study was the significant drop in model accuracy following the de-identification process, where sensitive patient information was removed. This resulted in an accuracy approaching 48%. NLP models depend on contextual information and discriminative features. De-identification processes may inadvertently remove or obscure relevant contextual information that contains predictive features. It is also possible that the model was relying on “shortcut” features (i.e., learning to associate specific clinicians, specialty designation, or departments with specific coding patterns). We intend to delve deeper into the complexities of clinical narratives as it relates to billing, investigate the impact of de-identification on model performance, and validate models across various healthcare delivery contexts and patient populations. Check out our preprint here: https://lnkd.in/eAqq4pjm #AI #NLP #EHR #Billing #Healthcare #Automation #Operations

When a doctor spends 20 minutes on a complex case and bills for a routine visit, everybody loses. The doctor gets underpaid. The data gets distorted. And the next patient waits longer because the system is running on fumes. This has been happening for years, at scale. Drawing on data from 1.2 million providers across 40+ states, the MDaudit 2025 Benchmark Report found that technology and AI-driven approaches are delivering measurable improvements: risk-based audits increased 25% and pre-bill audits increased 30%, as revenue integrity teams shift from reactive fixes to proactive capture. AI is accelerating that shift. A 2024 Texas Oncology study found that ambient AI scribes increased documented diagnoses from 3.0 to 4.1 per encounter. Riverside Health in Virginia saw an 11% rise in physician work RVUs and a 14% increase in documented diagnoses per encounter after deployment. That's not inflation. That's capture. Care that was delivered, finally reflected in the record. And yes, billing may go up. But here's what else goes up with it: Clean claims. Faster cycles. Lower cost to collect. What used to cost hours of clinical and admin time, chart review, translating notes into codes, chasing down denials, is now handled in minutes. That's clinical teams getting time back. That's fewer missed diagnoses buried in documentation backlog. That's revenue cycle teams not spending their days on rework. Is AI inflating healthcare costs? That's the wrong question. The right one: are we finally closing the gap between what care was delivered and what actually gets reimbursed? For the first time, the answer might be yes. The doctor who spent 20 minutes on a complex case? They should get paid for it. So should every one of their colleagues.

🚨 Medical coding isn’t just “plugging in an LLM.” When building SaveLife.ai, we quickly realized: • HCP, DRG, ICD-10, CPT → each layer adds massive complexity. • Large Language Models (LLMs) alone can’t capture every nuance. • We had to combine NER (Named Entity Recognition), Bayesian methods, and pure statistical modeling with LLMs. 👉 Why? Because real-world healthcare demands precision. Coding errors can impact care, billing, compliance, and patient outcomes. This is the invisible side of AI in healthcare—where rigorous data science meets clinical reality. 💡 Curious to know: do you believe hybrid AI approaches (LLMs + statistics + domain-specific models) are the only viable way forward in healthcare AI? #HealthcareAI #MedicalCoding #ArtificialIntelligence #SaveLifeAI #HealthTech #DigitalHealth #AIinHealthcare #ClinicalInformatics

Routing Medical Claims with an Intelligent Agent: Deterministic Logic Meets Structured AI Output Article : https://lnkd.in/ep8JscWt How I Built a Claims Prioritization Engine Using Agent Workflows, Tool Calls, and Pydantic-Validated Outputs TL;DR Hospital billing departments receive hundreds — sometimes thousands — of insurance claims per day. Every one of them needs to be triaged, sorted, and assigned to the right specialist before money can flow. In this experimental project, I built an agentic pipeline called ClaimsRouter-AI that handles this automatically. The agent runs a chain of deterministic, rule-based tools first (no LLM involved at the computation stage), then hands the results to a Gemini model for structured reasoning, and finally validates every output through a Pydantic v2 schema. The result is a system that routes a batch of 50 synthetic claims in under a second, assigns each to the correct revenue cycle queue, and produces a complete audit trail of every decision — including which tool computed what, and why. Introduction When I first started poking at the revenue cycle management space, the friction was immediately clear. A billing specialist in a mid-size hospital receives a claim, manually reads the denial code, checks how long the claim has been sitting in accounts receivable, cross-references the payer’s contracted rates, and then — based on years of experience — decides whether to appeal, escalate, or write it off. That process, done manually for hundreds of claims per day, is exactly the kind of thing that agent-based systems are quietly starting to replace. What caught my attention was not the AI angle specifically. It was the hybrid nature of the problem. Some parts of it — aging bands, financial impact scoring, payer-specific appeal windows — are pure deterministic computation. There is no ambiguity: a claim that is 95 days old is in the “90+ DAYS” bucket. That is not a judgment call. It is a lookup. But other parts of the problem — synthesizing the right rationale, recommending specific actions for a specialist, deciding whether to escalate a $95,000 claim to a supervisor — benefit enormously from language model reasoning. In my opinion, one of the biggest mistakes builders make with agentic workflows is letting the LLM do everything. The model calls a tool, the model computes numbers, the model decides priority, the model writes the report. That is fragile, expensive, and difficult to audit. The more interesting design — and the one I settled on for this experiment — separates the concerns cleanly: tools compute, the LLM synthesizes, and Pydantic validates.

🧠 AI-Driven Payment Integrity Architecture: Turning Healthcare Knowledge into Claim Editing Rules 🚨 Healthcare payment integrity still relies heavily on manual interpretation of policies, contracts, and clinical documentation. Yet health plans process millions of claims every day through automated adjudication systems powered by EDI 837 transactions. So the real challenge becomes: 👉 How do we convert healthcare knowledge into automated claim editing rules? Most of the logic required to validate claims does not live inside claims systems. Instead, it exists across multiple structured and unstructured knowledge sources, including: 📄 Reimbursement policies and CMS guidelines 📑 Provider contracts and fee schedules 🩺 Clinical documentation and medical records 📚 Coding manuals and regulatory guidance Payment integrity teams spend significant effort interpreting these sources and translating them into claim edits, audit rules, and payment policies. This is where Artificial Intelligence (AI) and Natural Language Processing (NLP) can help transform payment integrity programs. 👇 The architecture below illustrates how healthcare organizations can convert policy knowledge and claims data into intelligent payment integrity rules. 🔹 Key Architectural Components 1️⃣ Knowledge Sources Healthcare insights originate from: • Claims data (EDI 837 / 835 transactions) • Policy documents (CMS policies, coding guidelines) • Provider contracts (fee schedules, coverage rules) • Clinical documentation (medical notes, coding manuals) 2️⃣ Healthcare Knowledge Processing Engine This layer converts raw information into structured insights through: • EDI parsing and claims normalization • Document processing and OCR pipelines • Policy knowledge extraction • Contract clause identification 3️⃣ AI / NLP Intelligence AI analyzes extracted knowledge to identify patterns such as: • Clinical entity recognition (CPT, HCPCS, ICD) • Policy rule interpretation • Billing pattern detection across claims history • Automated generation of payment integrity rules 4️⃣ SME Validation Layer Payment integrity analysts and clinical SMEs help: • Validate AI-generated rules • Simulate rule impact on claims • Refine logic before deployment This human-in-the-loop approach ensures accuracy and compliance. 5️⃣ Payment Integrity Systems Validated rules can be deployed into: • Pre-Pay Claim Editing • Post-Pay Audits • Fraud, Waste, and Abuse Detection 6️⃣ Healthcare Data Standards The architecture relies on common standards such as: X12 EDI, CPT/HCPCS, ICD-10, SNOMED 📊 Outcome AI-powered architectures like this can help organizations move from reactive auditing → proactive payment integrity intelligence. #HealthcareAI, #PaymentIntegrity, #HealthcareArchitecture, #ClaimsProcessing, #EDI, #FHIR, #HealthcareInnovation, #DigitalHealth, #HealthTech