ElectrifAi Solutions for the Public Sector

Pre-trained and reusable models for representation of CPT, HCPCS, ICD10 codes in a lower-dimensional space to capture medical codes relationships. They can be used in a variety of cases, especially when a large dataset is not available.

Identifies procedures that have not been performed but have been charged for by analyzing the patient's procedure and diagnosis history, as well as claim physician details.

Identify missing charges in a provider's claim.

Identify providers charging for services (a particular pharmaceutical code) more often than the average provider in the same specialty.

Reflect healthcare provider's actual activity and define clusters of providers corresponding to their specialty, subspecialty, patient demographics, etc.

To help identify skin mutations to support clinical decisions and diagnosis. Non-melanoma skin cancers, such as Basal Cell Carcinomas (BCC) and Squamous Cell Carcinomas (SCC), are the most common human skin cancers. By inputting an image (.jpg, .png, or .bmp), the model outputs a probability value between 0 - 1 indicating a likelihood of BCC or SCC skin cancer. The model does not do disease detection or symptom extraction.

Identify claims where a procedure/service doesn't match provider's specialty that may result from coding errors or intentional actions.

Identify unnecessary charges in a claim to prevent denial by an insurance company.

Identify physicians who charge more high value codes compared to the average physician in the same specialty and patient demographics.