CN114464301B - Abnormal case point settlement management system - Google Patents
Abnormal case point settlement management system Download PDFInfo
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- CN114464301B CN114464301B CN202111596239.6A CN202111596239A CN114464301B CN 114464301 B CN114464301 B CN 114464301B CN 202111596239 A CN202111596239 A CN 202111596239A CN 114464301 B CN114464301 B CN 114464301B
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- G—PHYSICS
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention relates to a settlement management system for abnormal case points, which comprises: the data acquisition and quality management module is used for acquiring basic medical data; the data 360-degree analysis and display module is used for reflecting the difference from multiple dimensions; the abnormal disease seed counting, settlement and payment management module forms a DRGs-PPS system and packages the data by taking a group as a unit to determine a medical insurance payment standard; the data reporting and detecting module is used for displaying data conditions; the medical service performance evaluation module gives different weights according to the treatment difficulty and the treatment cost, realizes the direct comparison of treatment effects of the same group of cases, and compares treatment results after different groups of cases are adjusted according to the weights; the medical expense payment module is used for counting and analyzing the comprehensive medical expense, the control expense of a single disease and the abnormal hospitalization expense of a patient; and the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market. The reasonableness is better, and the medical improvement goal of win-win among all the parties is realized.
Description
Technical Field
The invention relates to the field of medical management, in particular to an abnormal case point settlement management system.
Background
The disease classification method of the medical prepayment system is that the medical records are divided into hundreds of diagnosis related groups according to the age, sex, hospitalization days, clinical diagnosis, operation, disease severity, complications, outcome and other factors of patients, and then the hospital is given a quota prepayment. At present, medical insurance institutions have unreasonable points when settling accounts for abnormal cases, and cannot meet actual use requirements.
Disclosure of Invention
The invention aims to provide an abnormal case point settlement management system to overcome the defects in the prior art.
The technical scheme for solving the technical problems is as follows:
an abnormal case point settlement management system, comprising:
the data acquisition and quality management module is used for acquiring basic medical data, comprehensively evaluating each department, each ward and each disease group and drilling and checking details of patients;
a data 360 degree analysis and presentation module for reflecting the difference from multiple dimensions;
the abnormal disease seed counting, settlement and payment management module forms a DRGs-PPS system and packages the data by taking a group as a unit to determine a medical insurance payment standard;
the data reporting and detecting module is used for displaying the data condition;
the medical service performance evaluation module is used for giving different weights according to the treatment difficulty and the treatment cost, realizing the direct comparison of treatment effects of the cases in the same group, comparing treatment results after the cases in different groups are adjusted according to the weights, and evaluating the hospitalization service efficiency, the medical service range and the difficulty of medical institutions, clinical departments and clinicians;
the medical expense payment module is used for counting and analyzing the comprehensive medical expense, the control expense of a single disease and the abnormal hospitalization expense of a patient;
and the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the basic medical data includes: a medical record home page, expense details and a settlement list;
the patient details include: QY case analysis, RW analysis.
Further, the plurality of dimensions includes: capability, efficiency and quality safety.
Further, the data reporting and detecting module supports auditing, detecting and inquiring the DRGs-PPS reported data.
Furthermore, the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market in several directions of data quality, capacity efficiency, medical safety and development balance of the medical institutions.
The invention has the beneficial effects that: the patient's condition and medical resource consumption are unified, medical cost supervision, expense payment mode, hospital operation benefit and medical service performance evaluation are practically considered, fine management in hospitals is achieved by disease diagnosis grouping, rationality is better, and the medical improvement target of multi-party win-win is facilitated.
Drawings
FIG. 1 is a flow chart of an abnormal disease point settlement payment management module according to the present invention;
FIG. 2 is a diagram illustrating the content of the basic medical data according to the present invention;
FIG. 3 is a diagram of the content encompassed by the dimension of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, the examples of which are set forth to illustrate the invention and are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, an abnormal case point settlement management system includes:
the data acquisition and quality management module is used for acquiring basic medical data, comprehensively evaluating each department, each ward and each disease group and drilling and checking details of patients;
a data 360 degree analysis and presentation module for reflecting the difference from multiple dimensions;
the abnormal disease seed counting, settlement and payment management module forms a DRGs-PPS system, and the abnormal disease seed counting, settlement and payment management module is packaged by taking a group as a unit to determine a medical insurance payment standard, thereby being beneficial to realizing a multi-win medical improvement target;
the data reporting and detecting module is used for displaying the data condition;
the medical service performance evaluation module is used for giving different weights according to the treatment difficulty and the treatment cost, realizing the direct comparison of treatment effects of the cases in the same group, comparing treatment results after the cases in different groups are adjusted according to the weights, and evaluating the hospitalization service efficiency, the medical service range and the difficulty of medical institutions, clinical departments and clinicians;
the medical expense payment module is used for counting and analyzing the comprehensive medical expense, the control expense of a single disease and the abnormal hospitalization expense of a patient;
and the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market.
Example 2
As shown in fig. 2, this embodiment is further optimized based on embodiment 1, and it specifically includes the following steps:
the basic medical data includes: a medical record home page, expense details and a settlement list;
the patient details include: QY case analysis, RW analysis.
By monitoring the basic data in real time, various monitoring indexes are intuitively reflected.
Example 3
As shown in fig. 3, this embodiment is further optimized based on embodiment 1, and it specifically includes the following steps:
the three large dimensions of capacity, efficiency and quality safety are designated by multiple dimensions, gaps are truly reflected from the three large dimensions of capacity, efficiency and quality safety, the hospital is helped to improve pertinently, and the improvement of the diagnosis and treatment level of the hospital is promoted.
Example 4
This example is a further optimization performed on the basis of example 1, and specifically includes the following:
and the data reporting and detecting module supports the auditing, detecting and inquiring of the DRGs-PPS reported data.
Example 5
This example is a further optimization performed on the basis of example 1, and specifically includes the following steps:
the medical expense payment module is mainly used for increasing the management of medical insurance limit budget, reducing medical consumables and medicine proportion and counting and analyzing medical comprehensive expense, single-disease control expense and abnormal hospitalization expense of patients.
Example 6
This example is a further optimization performed on the basis of example 1, and specifically includes the following:
the regional platform management module is used for ranking and comprehensively analyzing medical institutions in the whole city in several directions of data quality, capacity efficiency, medical safety and development balance of the medical institutions.
Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and not to be construed as limiting the invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the invention.
Claims (5)
1. An abnormal case point settlement management system, comprising:
the data acquisition and quality management module is used for acquiring basic medical data, comprehensively evaluating each department, each ward and each disease group and drilling and checking details of patients;
the patient details include: QY case analysis and RW analysis;
a data 360 degree analysis and presentation module for reflecting the difference from multiple dimensions;
the abnormal disease seed number settlement payment management module forms a DRGs-PPS system and packs the abnormal disease seed number as a unit to determine a medical insurance payment standard;
the data reporting and detecting module is used for displaying the data condition;
the medical service performance evaluation module is used for giving different weights according to the treatment difficulty and the treatment cost, realizing the direct comparison of treatment effects of the cases in the same group, comparing treatment results after the cases in different groups are adjusted according to the weights, and evaluating the hospitalization service efficiency, the medical service range and the difficulty of medical institutions, clinical departments and clinicians;
the medical expense payment module is used for counting and analyzing the comprehensive medical expense, the single-disease control expense and the abnormal hospitalization expense of the patient;
and the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market.
2. The system for settlement management of the number of abnormal cases according to claim 1, wherein:
the basic medical data includes: the first page of the medical record, the detailed expense and the settlement list.
3. The abnormal case point settlement management system according to claim 1 or 2, characterized in that:
the plurality of dimensions includes: capability, efficiency and quality safety.
4. The abnormal case point settlement management system according to claim 1 or 2, characterized in that:
and the data reporting and detecting module supports the auditing, detecting and inquiring of the DRGs-PPS reported data.
5. The system for settlement management of abnormal case points according to claim 1, characterized in that:
and the regional platform management module is used for ranking and comprehensively analyzing the medical institutions in the whole market in several directions of data quality, capacity efficiency, medical safety and balanced development of the medical institutions.
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| CN202111596239.6A CN114464301B (en) | 2021-12-24 | 2021-12-24 | Abnormal case point settlement management system |
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| CN116052887B (en) * | 2023-03-01 | 2023-06-27 | 联仁健康医疗大数据科技股份有限公司 | Method and device for detecting excessive inspection, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110866835A (en) * | 2019-11-12 | 2020-03-06 | 常州市第一人民医院 | Intelligent expense control system for hospital |
| CN111242466A (en) * | 2020-01-08 | 2020-06-05 | 袁勇 | Hospital performance management system and method based on payment according to disease types and disease component values |
| CN112686684A (en) * | 2020-12-08 | 2021-04-20 | 望海康信(北京)科技股份公司 | Medical expense management system and corresponding equipment and storage medium |
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| US20030065534A1 (en) * | 2001-10-02 | 2003-04-03 | Mccartney Michael J. | Health care management method and system |
| US20130035963A1 (en) * | 2011-08-01 | 2013-02-07 | Infosys Limited | System and method for financial transactions between insurance service provider and medical service provider |
| CN107463770A (en) * | 2017-07-11 | 2017-12-12 | 武汉金豆医疗数据科技有限公司 | A kind of evaluation method and system based on medical diagnosis on disease associated packets |
| CN110289088A (en) * | 2019-07-01 | 2019-09-27 | 太平洋医疗健康管理有限公司 | Big data intelligent management and system based on DRGs |
| CN111210355A (en) * | 2019-12-23 | 2020-05-29 | 望海康信(北京)科技股份公司 | Medical data comparison system and method |
| CN112053040A (en) * | 2020-08-19 | 2020-12-08 | 重庆市中迪医疗信息科技股份有限公司 | Evaluation method and system based on disease diagnosis related grouping |
| CN112837821A (en) * | 2021-01-26 | 2021-05-25 | 山东众阳健康科技集团有限公司 | Method and system for identifying abnormal cases in case diagnosis grouping |
| CN113593686A (en) * | 2021-08-05 | 2021-11-02 | 南方医科大学珠江医院 | Medical insurance comprehensive management system and management method based on DRG/DIP full-flow medical quality supervision |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110866835A (en) * | 2019-11-12 | 2020-03-06 | 常州市第一人民医院 | Intelligent expense control system for hospital |
| CN111242466A (en) * | 2020-01-08 | 2020-06-05 | 袁勇 | Hospital performance management system and method based on payment according to disease types and disease component values |
| CN112686684A (en) * | 2020-12-08 | 2021-04-20 | 望海康信(北京)科技股份公司 | Medical expense management system and corresponding equipment and storage medium |
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Denomination of invention: Abnormal Case Count Settlement Management System Effective date of registration: 20231101 Granted publication date: 20230324 Pledgee: Wuhan area branch of Hubei pilot free trade zone of Bank of China Ltd. Pledgor: WUHAN KINDO MEDICAL DATA TECHNOLOGY Co.,Ltd. Registration number: Y2023980063760 |