Data identification methods, devices, computer equipment, and computer-readable storage media

By determining the event window and group based on the event time and data type during the medical claims process, and comparing the cost with the threshold range, the problem of low accuracy in medical record identification is solved, and the accuracy of identification results and the ability to detect fraud are improved.

CN116861351BActive Publication Date: 2026-07-17PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
PING AN TECH (SHENZHEN) CO LTD
Filing Date
2023-06-30
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies have poor accuracy in identifying medical records, making it difficult to detect fraud, especially in the process of serious illness claims where claims adjusters lack professional knowledge, leading to inaccurate identification results.

Method used

Medical claims data is acquired by responding to abnormal event signals. Event windows are determined based on the occurrence time of medical events, and groups are determined based on data types. Event costs are compared with threshold ranges to generate data identification results.

Benefits of technology

It improves the accuracy of medical claims data identification, simplifies the judgment process, reduces the impact of inaccurate identification of individual medical events on the outcome, and enhances the ability to detect fraudulent activities.

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Abstract

This invention relates to the fields of information security, healthcare, and data processing technology, and discloses a data identification method, apparatus, computer equipment, and computer-readable storage medium. The method includes: in response to an abnormal event signal, acquiring medical claim data of a customer corresponding to the abnormal event signal; determining at least one event window based on the occurrence time of each medical event in the medical claim data; determining a group of the medical claim data based on the types of data contained in the medical claim data; and comparing the event cost corresponding to each event window with a corresponding threshold range in the group to obtain a data identification result. Since the medical claim data contains multiple data types, it is possible to match the data types to groups that are closer to the medical claim data, thereby using a more accurate threshold range to compare the event costs corresponding to each event window, resulting in a more accurate data identification result.
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