The lack clean claims is proving to be a catalyst in threatening provider’s livelihood. It is one of the primary reasons leading to increasing claims denial problem. It is also contributing to the extension of A/R days. What is most important to understand is that it cannot be fixed by humans. How do we know? We have been trying for over 20 years. But, it can be fixed by an AI based auto coder.
Auto Coder
Solving the costly and manual process of healthcare coding.
The problem
Hospital administrative staff has grown a whopping 3,200% between 1975 and 2010 in order to keep pace with the drastic changes in healthcare delivery, particularly change driven by technology and ever-more-complex regulations. These armies of administrators are doing little to relieve the documentation burden on clinicians, while creating layers of high-salaried bureaucratic bloat in the providers.
Of an estimated $3 trillion in claims submitted by hospitals in 2016, an estimated 9% of charges ($262 billion) were initially denied. For the typical health system, as much as 3.3% of Net Patient Revenue, an average of $4.9 million per hospital, was put at risk due to these denials.
Why the problem exists
Medical coding is strictly a manual process performed by professional medical coders that read through patient records in order to code diagnosis, procedures, charge capture, and facility codes - this process is costly and riddled with errors.
The coding is cumbersome and complicated which has created a combinatorial data problem. ICD1- 10-CM has over 70,000 unique identifiers alone which is fueling claims denials.
AI solution.
The AI auto coder is an intelligent system of engagement that provides a solution to the combinatorial math problem – capable of evaluating a claim based on a seven to ten factor analysis, resulting in over 6.1 billion combinations of codes and modifiers, and then applying the right ones for ICD-10, CPT, and HCPCS.
These codes are then systemically applied to the correct claim form (e.g. 1500, UB04) that can be routed internally for other approvals or coding (e.g. charge master) or dropped directly to the clearing house for immediate processing to the payers.
The system can also read and evaluate an 834 form in order to assess and apply codes based on a patient’s policy coverage. The auto coder was initially trained with 50 million adjudicated claims and one of the largest data dictionaries in the industry. MVP-1 was delivered with 89% accuracy of coding.
As the product works through its MVP stages, the system will become smarter as a result of being trained with 4 to 6 billion records and is expected to reach 98% coding accuracy.
Results
70% reduction in claims denials
3% to 5% increase in net income
