Data query optimization method and device based on DIP grouping

Abstract

The invention provides a data query optimization method and device based on DIP grouping, and the method and device are applied to a DIP platform server, and the method comprises the steps: determining an in-hospital patient at a current moment and a newly-added out-of-hospital patient in a first time period before the current moment based on the in-hospital state of a patient seeing a doctor in a hospital; determining an in-hospital patient set and a discharged patient set corresponding to the current moment based on the in-hospital patients at the current moment and newly-added discharged patients within a first time period before the current moment; and performing DIP grouped data query based on the in-hospital patient set and the out-hospital patient set. The patient data query efficiency can be improved, and the user experience is improved.

Description

technical field [0001] The present disclosure relates to the field of medical big data, and more specifically, relates to a data query optimization method, device, electronic device and computer-readable medium based on DIP grouping. Background technique [0002] DIP (Big Data Diagnosis-Intervention Packet, disease score payment based on big data) is a complete management system established by taking advantage of big data. It has technical advantages such as a complete discovery mechanism for high-level groups, precise supervision and easy implementation. Compared with paying by project, DIP also has the advantage of measuring diagnosis and treatment performance by disease type. However, in the current patient management based on DIP grouping, as time goes on, the patient data becomes larger and larger. Too large patient data makes it time-consuming for medical workers to query patient information when querying DIP grouping data. long time, and the calculation of estimated ...

AI Technical Summary

Problems solved by technology

However, in the current patient management based on DIP grouping, as time goes by, the patient data becomes larger and larger. Too large patient data makes it time-consuming for medical workers to query patient information when querying DIP grouping data. Long time, too slow calculation of estimated reimbursement expenses, etc.

Images