Medical quality evaluation oriented big data mining method

Abstract

The invention discloses a medical quality evaluation oriented big data mining method, relates to the field of big data mining for medical quality evaluation and aims at solving the technical problems that in the prior art, data sampling values are selected excessively based on experience, there is too much redundant data in a data processing structure and the association degree between a medical quality evaluation model and case data is too low. The method provided by the invention mainly comprises a preprocessing step of enabling processed data to satisfy demands of follow-up steps; a model clustering and classifying step of applying a clustering thought in data mining to scoring of various factors in the medical quality evaluation; a model case quality calculation step of carrying out score calculation based on a case quality model on each case; and a model result output step of calculating the final medical quality score of a medical institution according to the quality score of each case, thereby obtaining a final result. The method is used for providing the medical quality evaluation.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps analyze healthcare records efficiently while reducing human error or effort required. It uses cluster algorithms instead of manually analyzed data, resulting in better accuracy compared to previous methods that were used beforehand. Additionally, it proposes an improved way to evaluate patient outcomes through machine learning techniques, making them easier than previously possible. Overall, this innovation improves efficiency and effectiveness in collecting high-quality datasets related to medicine care management issues.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving the accuracy and reliability of evaluating large amounts of data from various sources like clinics or electronic devices (such as smartphones). Traditional methods for analyzing patient records often result in subjective assessment that can be influenced negatively with real world impact factors such as time pressure or lack thereof. Therefore, new ways must emerge to improve these qualities without relying solely upon trial results or sampling techniques.

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