Healthcare organizations are under tremendous financial pressure to accurately identify risk-adjusted diseases to ensure maximum yet allowable Medicare reimbursements. Identify more than are properly documented and you risk severe penalties. Identify less than what you are due, and you leave earned income on the table.
Today’s market offers a range of sophisticated Natural Language Processing (NLP) solutions that can help provider organizations detect and uncover previously ignored or miscoded diagnosis codes to ensure proper reimbursements. The problem is every NLP vendor does their best to convince you their solution is the best.
How can you evaluate competing solutions side by side for how they perform within your documentation and coding system and case mix? Which NLP engines can truly help you properly balance the fear of penalties against the greed of maximum collections?
NLP Engine Selection Considerations
- How to avoid solutions that create more problems than they solve
- Avoiding solutions that do not perform as advertised for your case mix
- Fear of sharing patient data with an outside vendor
- Preventing drawn-out implementation and training process
- Overcoming challenges to adopting high-tech solutions in-house
- Changing from a known risk adjustment process
NLP Engine Vendor Witness Test
- Solutions that have been proven in the market
- Measurably completing chart reviews in less time
- Demonstrated accuracy of diagnosis code reporting
- Reducing risk adjustment coder administrative burdens
- Maximizing ROI without opening the door to potential penalties
There has been an ongoing debate among decision-makers when it comes to choosing fear over greed or greed over fear. The adoption of natural language processing in healthcare is rising because of its recognized potential to search, analyze and interpret mammoth amounts of patient datasets. Using advanced medical algorithms and machine learning in healthcare, NLP technology has the potential to harness relevant insights and concepts from clinical notes that were previously considered by the healthcare industry as buried in text data form. NLP in healthcare can accurately give voice to the unstructured data of the healthcare universe, giving incredible insight into understanding quality, improving methods, and better results for patients.
RAAPID: Risk Adjustment APIs Delivered
- RAAPID’s NLP SaaS Platform is an AI-based technology that processes the ingested raw patient data – both unstructured and structured formats from multiple sources and transforms them into a usable form
- Tried and tested on 10’s millions of real clinical patient data (charts)
- Successfully applied for CAC and CDI now demonstrably customized for Risk Adjustment (RA)
- Uses industry-leading Knowledge Graph infused clinical NLP technology to bring you better outcomes. Get auto-code suggestions, review charts, validate codes, and get actionable clinical insights right at your fingertips
- Performs automated QA Review using AI/NLP technology on the medical charts & HCC coding
- Supports health plans, medical coders, and providers with Retrospective and Prospective chart review & audit solutions
What Sets Us Apart?
- No Workflow Risk: Benefit from RAAPIDs NLP without making any workflow changes & costly integrations
- No Timeline Risk: As your existing workflow remains intact, you can still do project delivery without our results
- No financial risk: RAAPIDs HCC Coding QA Review Solution is free for the first 1,000 charts
- A partnership based on trust and collaboration
- Secure, scalable, and compliant
- With more consistent accuracy than the ‘human only’ or ‘tech only’ approach. We combine coders with technology to ensure accurate Risk Adjustment codes
Proof of Concept Offer:
See for yourself with RAAPID’s free, no obligation, HCC code review of 1000 charts.
Compare our results against your current process or the results from any other vendor.
RAAPID’s HCC chart review Proof of Concept offer can be customized to match your existing risk adjustment workflow process.
Contact RAAPID today for details on how to get started.