Healthcare is a complex industry with numerous internal medical and administrative processes. But AI technology is transforming the sector by solving some of the most difficult problems.
One of the risk adjustment programs’ biggest challenges is timely access to clinical charts. This challenge often results in compressed submission timelines and reduced productivity.
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AI-powered RA platform
Using AI to improve the RA process is one of many technologies that will bring data-driven healthcare to life. It will enable providers to gain visibility and insight into patients in ways humans cannot. This will lead to faster diagnosis and targeted solutions to help them manage their conditions. It will also help reduce costs by reducing unnecessary treatment and interventions. In this age of value-based care and risk-based contracting, it is more important than ever for physician organizations to ensure that HCC coding is accurate. This is especially true for those in accountable care organizations or bundled payments. Proper coding is crucial to characterize patient-specific risk, enhance shared savings and provide quality-driven, patient-centered care. As a result, healthcare providers must have a robust, automated system that can assist them with this complex, time-consuming task. However, implementing AI into existing systems can be a challenge. The best solution is to partner with an AI solutions provider with a track record of success.
The platform uses Natural Language Processing (NLP) to read unstructured and structured medical records, allowing it to find ICD-10 & HCC codes and MEAT evidence and gaps. It also automates and streamlines HCC chart review workflows to enable physicians to capture more opportunities to improve RAF scores. It is the only RA platform that combines AI with an integrated clinical and administrative suite for superior results.
AI-powered HCC auditing platform
The volume of patient data collected in the healthcare industry is exponentially growing. However, resources are limited to analyze this structured and unstructured data. This leads to an overreliance on large coding and analytics teams that need more time to perform their work. As a result, they often need to take advantage of opportunities to identify HCCs. These missed opportunities can have significant financial implications for healthcare payers and providers. To avoid these pitfalls, healthcare payers should look for an AI-powered HCC auditing platform to automate medical chart review and audit workflows. These platforms use natural language processing and machine learning to structure data. They also integrate with existing EHRs. This way, the healthcare provider can make real-time clinical decisions and improve patient outcomes.
With the frequency of Medicare Advantage audits increasing and other government agencies weighing in on MA payments, every submitted HCC code must be supported by appropriate documentation. An AI-powered hierarchical condition category coding platform can help health plans and providers perform internal audits in significantly less time than traditional methods.
AI-powered HCC coding platform
With new healthcare models such as accountable care organizations (ACOs), bundled payments, and value-based purchasing increasingly shifting financial risk to providers, accurate HCC coding is critical. This enables physicians to maximize reimbursements. But manual or immature technology-enabled chart review processes are ill-suited to the task.
AI-driven coding solutions can help address these challenges, improving the efficiency of chart reviews and reducing RAF scores by up to 45%. These technologies utilize natural language processing to scan member data and identify ICD-10 codes and HCCs. This eliminates the need for certified coders to comb through charts without prioritization manually and could result in more precise reimbursements.
This new approach to coding uses an artificial intelligence engine that combines natural language processing, optical character recognition, and machine learning models to automate chart verification and validation and identify net-new encounters, diagnoses, and hierarchical condition categories (HCCs). The AI-assisted coding process leveraging broad match technology and confidence scoring reduces false positives and delivers first-pass ideal HCC discovery accuracy. AAI-powered coding solutions can also improve the productivity of medical coders and healthcare payers by enabling them to focus on the highest-risk members first. This allows them to benefit from their investment in a risk adjustment coding solution and ensures that RAF scores are submitted accurately. This enables MA organizations and healthcare payers to balance quality, productivity, and cost.
AI-powered HCC QA platform
AI has the potential to transform healthcare by automating coding and audit processes and improving accuracy and efficiency. Moreover, it can identify patterns in medical images and other data, reducing costs by enabling better decision-making in the diagnosis and treatment of patients. This is especially important in the changing value-based care model, where patient outcomes will be tied to provider reimbursements.
With the growing volume of structured and unstructured data in healthcare, human teams can only review some of the information and extract actionable insights. The risk adjustment coding and QA process is complex, and it can take time to capture the right information promptly. This is why it is necessary to implement an AI-powered QA platform.
AI-based coding and QA solutions utilize natural language processing and deep learning on unstructured and structured clinical data to optimize HCC code extraction. They also enable real-time coder-to-provider query communication. These tools help reduce physician burnout and improve patient care quality, operational efficiency, and financial performance.
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