To remain solvent and profitable, healthcare payers must constantly seek new ways to optimize their operations, improve reimbursement accuracies, and avoid penalties. To achieve those goals, risk adjustment coding technology is becoming part of the standard toolset.
With the increasing demand for accurate and efficient documentation and claims data, natural language processing NLP has emerged as a game-changing solution, provided you work with a partner who truly understands payer workflow and what it takes to deliver a positive return on investment ROI.
By automating the code identification process and applying NLP against large-scale patient data, payers can save time and money while also improving overall accuracy. But true ROI comes from being able to identify risk conditions that could cause a patient to become more expensive to treat and making sure they receive appropriate early screening and proactive care to mitigate risks. Such proactive assessments and positive cost and care results can be delivered with properly modeled NLP Risk Adjustment technology.
Pros and Cons of NLP Technology
NLP uses algorithms to analyze and extract information from human language. In healthcare, it can automatically review electronic health records and assign appropriate ICD-10-CM codes. But as with any technology, there are pros and cons to consider before implementing any automated solution into your organization’s auditing process.
Drawbacks of using Natural Language Processing ( NLP) for ICD 10-CM code audit
There are several potential drawbacks to using natural language processing for ICD-10-CM code audits. First, less-than-mature NLP algorithms can be biased toward certain medical conditions or terms that are more commonly used in coding descriptions. These biases can lead to inaccurate results if the data being analyzed is not properly calibrated against overall patient population standards. Second, NLP developed from lesser case foundations may not be able to properly identify all relevant codes for a given condition, especially if the clinical documentation is vague or incomplete. These engine sophistication limitations could lead to under-coding or over-coding diagnoses, impacting reimbursement levels and potential penalty exposure. Finally, some NLP-based code auditing systems can be time-consuming and expensive to set up and maintain, especially if multiple coding platforms need to be supported.
Benefits of NLP for risk adjustment coding
There are many benefits that can be achieved with properly developed and configured NLP solutions for ICD-10-CM code audits. In addition to automating the coding and auditing process, NLP improves consistency and accuracy by reducing the likelihood of human error.
NLP effectively reduces the workload for coders and auditors by identifying potential errors before claims are submitted for payment. This helps to minimize the processing time and staff resources previously spent on correcting mistakes. In addition, NLP also helps to improve coding efficiency by reducing the need for manual reviews.
How NLP Helps Payors
- NLP identifies potential errors in coding.
- NLP suggests alternative codes that may be more accurate.
- NLP automates code selection and application, making it faster and easier for coders to validate, approve, and process the correct codes.
- NLP provides real-time coder feedback so they can immediately verify/correct errors.
- NLP helps payors standardize coding practices, so all coders apply the same standards and procedures.
- NLP provides detailed reports on individual coder accuracy and performance, so payor organizations can track progress and identify additional training opportunities for improvement.
How NLP Improves Payment Accuracy
1. Reduced reliance on manual coding: One of the biggest benefits of using NLP is that it reduces the need for manual coding. Manual coding is time-consuming and often error-prone. By automating the process with NLP, payors can speed up the coding process and improve accuracy.
2. Improved data quality: Another benefit of using NLP is that it can help improve the quality of data used for risk adjustment coding. When data is manually coded, there is always the potential for errors. NLP can help reduce these errors by providing more accurate and consistent data.
3. Increased transparency: Using NLP also provides increased transparency into the risk adjustment coding process. With manual coding, it can be difficult to track
NLP Vendor Selection Best Practices
Often the biggest challenge in implementing NLP for Risk Adjustment is the total volume of data that needs to be processed, making it difficult to filter out the noise. Another challenge is that less than optimally supported NLP can become resource-intensive to launch. Both challenges can be overcome through the proper due diligence of your NLP vendor partners to ensure they have the proper experience and are up to the task for a customer of your profile. References and validated performance through Proof-of-Concept trials should be conducted prior to contract signatures for side-by-side comparisons of competing technologies.
Strategies for NLP Optimization
1. Apply NLP early in the risk adjustment workflow process: By implementing NLP early in the coding workflow, payers can quickly and effectively identify potential risks and adjust their coding accordingly.
2. Utilize NLP to identify high-risk patients: NLP technology can identify high-risk patients who are more likely to experience adverse events or require higher levels of care. This information can then be used to modify risk adjustment codes and modify recommended treatment plans.
3. Incorporate NLP into existing risk adjustment models: Many payers already have existing risk adjustment processes in place. By incorporating NLP into these workflows, payers can more accurately predict risks and adjust accordingly.
4. Educate staff on how to use NLP: It is important that payer organizations educate their staff on how to use NLP technology effectively. This will ensure that staff is able to utilize the full potential of the tool.
Conclusion
RAPPID’s Risk Adjustment NLP can revolutionize the ROI for Payor risk adjustment coding and auditing workflow. Our retrospective chart review & audit tools offer proven solutions for payer organizations looking to optimize their risk adjustment processes.
Contact us to learn more about RAAPID’s Retrospective HCC Code Audit & Review dashboard features and functionalities.
