Missing Charges
ftc.govThis product analyzes medical claims to detect potentially omitted charges. It evaluates all charges listed on a claim and estimates the likelihood that specific billing codes are missing, using insurance claim data that includes common code sets such as HCPCS, CPT, ICD-10, REV, and LOC. For each possible charge code, the model generates a score indicating how likely it is that the code should be present; higher scores suggest that a charge may need to be added to the claim.
Users can view a preview of the machine learning models by subscribing. A sample Output Data preview is available after entering suggested Input Data, but this sample output is only illustrative and does not use the provided input in its calculations. Actual Output Data produced by the machine learning models is delivered through a private offer.
LLM mention score The LLM mention score is the total number of mentions of this brand in different LLM chatbots, normalized to the scale from 0 to 100. You can get actual, non-normalized numbers via the LLM Mention API from DataForSEO.
Normalized 0–100 · last 8 weeks
Get LLM mention data of any company via DataForSEO API
Get access to the structured data on keyword, brand, and website mentions in LLMs, including metrics like AI search volume, impressions, and mentions count.
How to get LLM mention data →// Fetch Missing Charges mention score POST /v3/content_analysis/summary { "keyword": "Missing Charges", "type": "llm_mentions", "date_from": "2025-01-01" }
