AI RCM

AI Clinical Documentation Integrity (CDI) products leverage artificial intelligence to review and evaluate clinical documentation in support of appropriate patient care, billing, and compliance with regulatory requirements.  These solutions can operate at the point of care, ensuring the documentation reflects the patient-clinician conversation and available clinical data, and supports the assigned medical codes. They can also operate post-encounter, between the medical coding and billing steps, to review the documentation against what has been coded and surface suggestions for revenue and quality opportunities. Many of these solutions include built-in functionality to suggest potential diagnoses that may have been missed based on information in the patient record. While similar in concept to risk adjustment solutions, AI CDI products are predominantly used in a fee-for-service context to ensure more accurate and complete billing and improve care quality.

AI Denials Management involves the identification, analysis, and resolution of denied claims to ensure that providers receive reimbursement for services rendered. Products in this category perform functions including: - Recognizing claims that have been denied by the payer. This can be due to various reasons such as coding errors, incomplete information, or issues with patient eligibility. - Investigating or understanding the reasons for denial. This often involves using AI to review claim details, patient records, and communications with the payer. - Making necessary corrections to the denied claims and resubmitting them for approval. This might involve updating coding, providing additional documentation, or correcting patient information. Given the need to interpret claims responses and automate them in many cases, there are strong connections between this category and products in Revenue Cycle Automation. Additionally, given that the goal of denial management is ultimately to reduce denials, many products in End-to-End Revenue Cycle Management also offer denials management workflows.

These are products that use AI to automate the patient billing process for healthcare providers, increasing their ability to responsibly collect patient payments. Some of the applications for these products include: - AI-Generated Patient Bills for minimizing errors and better explanations to patients - Billing Inquiry Chatbots available for handling routine billing questions - Optimized Payments for improved scheduling of payment reminders and timetables, as well as finding the right channels and messaging for patients - Ability to integrate relevant data and predict ability to pay in order to provide discounts, payment schedules, and other financial support for patients

These are prior authorization platforms for health plans and other entities that do utilization management. They use AI to automate UM decisions and improve efficiency of UM nurses / clinicians. Functions usually include: - Ingestion of clinical policies and translation into decision trees - Automatic population of prior auth decisions for approval - Automatic approval of auths that meet certain criteria - Documentation of decision reasons, i.e. traceable decisions Prior authorization vendors vary meaningfully in their service coverage, with some specialized for pharmacy and others focused on certain types of medical services.

These are tools that use AI to streamline the provider end of the prior authorization process. Functions usually include: - Identification of prior authorization requirements and processes - Automatic population of prior authorization forms - Submission of prior authorization requests - Reporting and checks on prior authorization status - Support with appealing prior auth denials Prior authorization vendors vary meaningfully in their service coverage, with some specialized for pharmacy and others focused on certain types of medical services.

Autonomous Coding refers to advanced AI-enabled medical coding systems that operate independently to translate clinical documentation into accurate billing codes with minimal human involvement. These systems represent a significant leap from traditional Computer-Assisted Coding (CAC) by automating the end-to-end coding process, providing scalability, accuracy, and faster claims processing. Unlike CAC, where human coders must review and validate the software's suggestions, autonomous coding handles the majority of clinical encounters without requiring manual oversight. Human coders are only involved in edge cases where confidence scores fall below a threshold or documentation complexity demands intervention. Key Characteristics of Autonomous Coding 1. Machine Learning with Continuous Improvement Autonomous coding systems leverage machine learning models that improve over time by learning from corrections made during audits and updates to coding guidelines. These models adapt to specific provider workflows, ensuring more accurate predictions with repeated use. 2. Minimal Human Intervention Once trained and fine-tuned, autonomous coding systems can manage coding for most encounters without human validation, freeing up coders to focus on complex cases or other tasks. 3. Confidence Scoring for Quality Control Autonomous systems assign confidence scores to coding outputs, determining whether the codes are finalized automatically or escalated to human reviewers. This mechanism balances automation with quality assurance. 4. Human-in-the-Loop for Complex Cases Although the goal is to automate as much as possible, human coders are still involved when the model encounters ambiguous documentation or when confidence scores fall below a threshold. Technology Underpinning Autonomous Coding Autonomous coding relies on breakthroughs in natural language processing (NLP), deep learning, and large language models (LLMs), which allow it to interpret unstructured clinical notes accurately. Key technological components include: - Contextualized Embeddings Systems differentiate words based on context; for example, distinguishing "block" as a nerve block versus an arterial blockage. - Self-Attention Mechanisms Models analyze relationships across words in clinical documentation, capturing nuanced meanings. - Pre-training and Fine-tuning Models are initially trained on large datasets and then fine-tuned with domain-specific clinical documentation, ensuring relevance to medical coding tasks. Architectural Approaches to Autonomous Coding 1. End-to-End Deep Learning Models These models transform raw clinical documentation directly into structured billing codes. While powerful, they require significant computational resources and large, labeled datasets. 2. NLP and Rules-Based Hybrid Systems Combines machine learning with predefined rules. NLP handles text extraction, while rules ensure compliance with medical coding standards. This approach offers transparency but may struggle with nuanced cases. 3. Hybrid Architecture A blend of deep learning and rules-based systems where AI predicts codes, but complex edge cases are managed by rule-based checks. Benefits of Autonomous Coding 1. Speed and Efficiency Autonomous coding dramatically reduces the time required for coding encounters, enabling claims submission within hours instead of days. This faster processing accelerates the revenue cycle and minimizes delays in reimbursement. 2. Scalability These systems can easily handle fluctuations in documentation volume without requiring additional staff, ensuring smooth operations even during high-demand periods. 3. Consistency and Accuracy Autonomous coding applies uniform logic across all encounters, reducing errors caused by human variability. This consistency improves compliance, minimizes rework, and lowers the risk of denied claims. 4. Continuous Learning Systems continuously improve by incorporating audit feedback and new coding guidelines, ensuring they remain up-to-date with evolving medical and regulatory standards. 5. Cost Savings By reducing the need for a large team of coders, autonomous coding lowers labor costs and eliminates expenses associated with training, recruitment, and overtime. Use Cases and Specialty Coverage Autonomous coding solutions have seen early adoption in areas with straightforward coding rules, such as radiology, pathology, and lab coding. Over time, as models become more advanced and datasets grow, these solutions are expanding into more complex specialties, such as: - Emergency Department (ED) Visits and Urgent Care - Inpatient Professional Coding - Office-Based Specialties (e.g., dermatology, cardiology) However, autonomous coding for inpatient facility coding—a critical revenue driver—remains in development, requiring further advancements before achieving full automation. Challenges in Implementing Autonomous Coding 1. Integration and Implementation Successful adoption requires seamless integration with Electronic Health Records (EHR) and claims management systems. Vendors often need to provide implementation support to ensure smooth onboarding and data flow. 2. Automation Rates and Model Maturity Automation rates vary across vendors and specialties. Simple specialties achieve higher rates, while more complex areas still rely on human oversight. Organizations must balance expectations with vendor capabilities. 3. Regulatory and Compliance Considerations Automated coding systems must adhere to strict compliance standards. The ability to quickly adapt to changes in guidelines is critical to avoid compliance risks. 4. Transparency and Trust End users need confidence in the system's coding decisions. Hybrid models offer greater transparency, but reliance on deep learning can introduce challenges around interpretability. Evaluating Autonomous Coding Solutions When assessing autonomous coding vendors, provider organizations should consider: - Specialty Coverage Ensure the solution meets your organization's specialty-specific needs. - Technical Architecture Understand whether the system relies on deep learning, rules-based methods, or a hybrid approach. - Processing Time and Throughput Measure how quickly the system codes encounters and whether it can handle peak volumes. - Vendor Support and Service Level Agreements (SLAs) Evaluate the vendor's commitment to service levels, including accuracy and turnaround times. - Reporting and Auditing Tools Assess the availability of tools to monitor and audit coding outputs over time.

Revenue Cycle Automation products use AI and elements of RPA to automate key workflows in revenue cycle management, including claims processing, prior authorization, patient payment collection, eligibility verification, and denials management. These platforms generally integrate with EHRs, practice management systems, and other RCM tools, and provide agents that can accomplish key workflows. Core capabilities include: - Integration with existing systems through APIs or advanced web browsing capabilities - Ability to define key workflows for automation, with well-defined inputs and outputs - Tracking and analytics to understand effectiveness of workflow automations and identify key issues While there are many RPA platforms, our focus with this category is on vendors who specialize in healthcare and RCM, or who have the scale to have deep expertise on healthcare workflows.