The solution utilizes the best of the algorithms available to do the conversion of various digital formats to text and discrete data sets. The use of machine language-based techniques adds more accuracy and intelligence to the solution
Data curated from various sources will get extracted into the system as standard data thus the data storage and retrieval as well viewing the same becomes easier for later usage.
There is a basic level of intelligent data validation done to ensure that discrepancies are flagged at the onset and not at a later level there by saving on time and effort
Information gathered through the digitization process gets stored smartly and is made available for future references so that it can be used for profiling, fraud detection, claim management etc.
The system has inbuilt self-learning algorithms which keep developing intelligence and learning with the increasing data.
The system is scalable to read from multiple formats of different diagnostic center formats thus reducing the processing efforts and time
With the data available along with information highlighting if the report data point is beyond the limits, it makes underwriting and claims settlement, easier and with better Risk Assessment.
The curated output is coupled with the manual QC delivery near 100% accuracy which is very critical to Under Writing.
The operational costs of the underwriting process and claims settlement get reduced since the manual labor involved now is being handled in an automated manner.
The QUIC DATA solution will be able to fast scan the reports uploaded by customers in different digital formats like the PNG/JPEG/GIF/PDF from the portal file system. These reports are scanned to convert them into plausible text data, these text data streams are further processed using machine learning and natural language processing algorithms to identify the diagnostic data sets in terms of values and other attributes. These are then processed further with further validations and verification to filter out the exceptions. The processed data is transformed into standard formats like XML/CSV/JSON to make it accessible to the users. These data points are made available to the QC team to confirm the data conversion. Once approved by the QC team the data becomes available and mapped to the individual customer medical data.