CN110993117A - Abnormal medical insurance identification method and device based on medical big data - Google Patents
Abnormal medical insurance identification method and device based on medical big data Download PDFInfo
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- CN110993117A CN110993117A CN201911370580.2A CN201911370580A CN110993117A CN 110993117 A CN110993117 A CN 110993117A CN 201911370580 A CN201911370580 A CN 201911370580A CN 110993117 A CN110993117 A CN 110993117A
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Abstract
The present disclosure provides an abnormal medical insurance identification method based on medical big data, which includes: step one, according to a first field to be detected, a server acquires group original medical data from an authorized medical database; step two, according to a second field to be detected, the server screens out target medical data from the group original medical data; and step three, calculating a ratio between the target medical data and the group original medical data, and if the ratio is less than a first threshold, indicating that the target medical data is the screened target object. The method can screen most of characteristic people meeting the rules, then manually and mainly inspects, can greatly reduce the labor intensity of workers, saves manpower, and has the dual advantages of accuracy and speed.
Description
Technical Field
The disclosure relates to the technical field of medical information, in particular to an abnormal medical insurance identification method and device based on medical big data.
Background
With the continuous enhancement of the national basic medical insurance system, more and more people join the medical insurance ranks, which greatly facilitates the hospitalization of the insured people, but the current medical insurance system is still not perfect enough, so that the situation of false reimbursement occurs in the reimbursement process, and abnormal medical insurance behaviors (cheating insurance) and the behaviors of collecting medical insurance fund and opening medicines occur. At present, the screening and judgment of the target people mainly adopt a manual mode, because the manual inspection efficiency is extremely low, the progress of discovering abnormal medical insurance people can not be kept up with under the condition that massive diagnosis information is generated every year, and a large number of professional doctors are employed for auditing, so that the method is unlikely to be used in the modern with shortage of medical resources.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides an abnormal medical insurance identification method based on medical big data.
According to one aspect of the disclosure, the abnormal medical insurance identification method based on medical big data comprises the following steps:
step one, according to a first field to be detected, a server acquires group original medical data from an authorized medical database, wherein the group original medical data is taken from a natural year, namely medical data from 1 month and 1 day to 12 months and 31 days of each year;
step two, according to a second field to be detected, the server screens out target medical data from the group original medical data;
step three, calculating a ratio between the target medical data and the group original medical data, and if the ratio is less than a first threshold value, indicating that the target medical data is the screened target object; and if the ratio is larger than or equal to the first threshold, the target medical data is over-large, the step two is returned, the second field to be detected is adjusted, and the number of the screened target medical data is reduced until the ratio between the target medical data and the group original medical data is smaller than the first threshold.
According to at least one embodiment of the present disclosure, in the step one, the first field to be detected includes an age, a medical insurance reimbursement attribute, diagnosis information, a 12-month consumption amount, a full-year consumption amount, and a medical insurance balance amount.
According to at least one embodiment of the present disclosure, in the second step, the second pending detection field includes: a) the age field is more than 50 years old; b) the diagnosis field takes any one of diabetes, coronary heart disease, hypertension, cerebrovascular accident sequelae, chronic nephropathy, chronic bronchial pneumonia, emphysema and pulmonary heart disease; c) month 12 charges/year round charges > a second threshold; d) and the medical insurance balance is less than 5 on 31 days of 12 months.
According to at least one embodiment of the present disclosure, if the ratio between the target medical data and the group original medical data is greater than or equal to the first threshold, the second threshold is adjusted, and the number of the target medical data is reduced until the ratio between the target medical data and the group original medical data is less than the first threshold.
According to at least one embodiment of the present disclosure, the second threshold is set to 50%.
According to at least one embodiment of the present disclosure, in the third step, the first threshold is set to 1 ‰.
The present disclosure also provides an abnormal medical insurance recognition apparatus based on medical big data, including:
the acquisition module is used for acquiring group original medical data of a natural year from a plurality of medical databases;
the screening module is used for screening out target medical data from the group original medical data;
and the detection module is used for calculating the ratio between the target medical data and the group original medical data, judging whether the ratio result meets the requirement or not, and if not, further screening by the screening module until the ratio result meets the requirement.
The present disclosure also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the abnormal medical insurance identification method based on medical big data according to the above.
The present disclosure also provides an electronic terminal, including:
a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to operate via the executable instructions according to the above-mentioned medical big data-based abnormal medical insurance identification method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
Fig. 1 is a logic diagram of an abnormal medical insurance recognition device based on medical big data according to the present disclosure.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The disclosure discloses an abnormal medical insurance identification method based on medical big data, which comprises the following steps:
step one, according to a first field to be detected, a server acquires group original medical data from an authorized medical database, wherein the group original medical data is taken from a natural year, namely medical data from 1 month and 1 day to 12 months and 31 days of each year; the first field to be detected comprises age, medical insurance reimbursement attribute, diagnosis information, 12-month consumption amount, annual consumption amount and medical insurance balance amount. For example, the medical database may be a HIS system of a hospital, a database of medical insurance reimbursements, and the like. The medical insurance reimbursement attributes are, for example, insurance of urban residents, five groups of people, difficult assistance, five households, new agriculture and the like. The diagnosis information is diagnosis content of doctors, such as hypertension, diabetes and the like.
Step two, according to a second field to be detected, the server screens out target medical data from the group original medical data; the second field to be examined includes: a) the age field is more than 50 years old; b) the diagnosis field takes any one of diabetes, coronary heart disease, hypertension, cerebrovascular accident sequelae, chronic nephropathy, chronic bronchial pneumonia, emphysema and pulmonary heart disease; c) 12 months of the drug charge/year round of the drug charge > a second threshold, and the second threshold is set to be 50%; d) and the medical insurance balance is less than 5 on 31 days of 12 months.
Calculating the ratio of the target medical data to the group original medical data, and setting the first threshold to be 1 per thousand; if the ratio is less than 1 per thousand, the target medical data is the screened target object; and if the ratio is larger than or equal to 1 per thousand, the target medical data are too large, the step two is returned, the second field to be detected is adjusted, and the number of the screened target medical data is reduced until the ratio between the target medical data and the group original medical data is smaller than 1 per thousand.
According to at least one embodiment of the present disclosure, the ratio between the target medical data and the group original medical data is greater than or equal to 1%, and if the suspected cases are still too many and the difficulty of manual review is too large, the second threshold is adjusted, and the number of the target medical data is reduced until the ratio between the target medical data and the group original medical data is less than 1%. This ratio is close to the limit of the ability for manual review.
As shown in fig. 1, the present disclosure also provides an abnormal medical insurance identifying apparatus 10 based on medical big data, including:
the acquisition module 101 is used for acquiring group original medical data of a natural year from a plurality of medical databases;
a screening module 102, configured to screen out target medical data from the group of original medical data;
the detection module 103 is configured to calculate a ratio between the target medical data and the group original medical data, determine whether a ratio result meets a requirement, and if not, further screen the ratio result by the screening module until the ratio result meets the requirement.
The present disclosure also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the abnormal medical insurance identification method based on medical big data according to the above.
The present disclosure also provides an electronic terminal, including:
a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to operate via the executable instructions according to the above-mentioned medical big data-based abnormal medical insurance identification method.
The method comprises the steps of extracting common personal diagnosis characteristic information of characteristic people who register more drugs with medical insurance funds as analysis fields, obtaining data corresponding to the fields in a defined time period from a medical database, then defining the medical behavior characteristics of the characteristic people, making a computer executable formula, and adjusting the result value to be less than 1 per mill of the total number of the people according to the formula result, so that the high-risk people needing important monitoring are obtained.
The practical cases summarized by clinical experience are adopted to refine the summarized behavior characteristics, most of characteristic people meeting the rules can be screened out, then manual key investigation can be carried out, the labor intensity of workers can be greatly reduced, manpower is saved, and the dual advantages of accuracy and speed are achieved.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of illustration of the disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.
Claims (9)
1. An abnormal medical insurance identification method based on medical big data is characterized by comprising the following steps:
step one, according to a first field to be detected, a server acquires group original medical data from an authorized medical database, wherein the group original medical data is taken from a natural year, namely medical data from 1 month and 1 day to 12 months and 31 days of each year;
step two, according to a second field to be detected, the server screens out target medical data from the group original medical data;
step three, calculating a ratio between the target medical data and the group original medical data, and if the ratio is less than a first threshold value, indicating that the target medical data is the screened target object; and if the ratio is larger than or equal to the first threshold, the target medical data is over-large, the step two is returned, the second field to be detected is adjusted, and the number of the screened target medical data is reduced until the ratio between the target medical data and the group original medical data is smaller than the first threshold.
2. The method according to claim 1, wherein in the first step, the first field to be checked includes age, medical insurance reimbursement attribute, diagnosis information, 12-month consumption amount, annual consumption amount, and medical insurance balance amount.
3. The abnormal medical insurance identification method according to claim 1, wherein in the second step, the second field to be checked includes: a) the age field is more than 50 years old; b) the diagnosis field takes any one of diabetes, coronary heart disease, hypertension, cerebrovascular accident sequelae, chronic nephropathy, chronic bronchial pneumonia, emphysema and pulmonary heart disease; c) month 12 charges/year round charges > a second threshold; d) and the medical insurance balance is less than 5 on 31 days of 12 months.
4. The abnormal medical insurance identification method of claim 3, wherein if the ratio between the target medical data and the group original medical data is greater than or equal to a first threshold, the second threshold is adjusted to reduce the number of the target medical data until the ratio between the target medical data and the group original medical data is less than the first threshold.
5. The abnormal medical insurance identification method of claim 3, wherein the second threshold is set to 50%.
6. The method for identifying an abnormal medical insurance of claim 1, wherein in the third step, the first threshold is set to 1 ‰.
7. An abnormal medical insurance recognition apparatus based on medical big data, comprising:
the acquisition module is used for acquiring group original medical data of a natural year from a plurality of medical databases;
the screening module is used for screening out target medical data from the group original medical data;
and the detection module is used for calculating the ratio between the target medical data and the group original medical data, judging whether the ratio result meets the requirement or not, and if not, further screening by the screening module until the ratio result meets the requirement.
8. A storage medium, having stored thereon a computer program which, when executed by a processor, carries out the method according to any one of claims 1-6.
9. An electronic terminal, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to operate in accordance with the method of any one of claims 1-6 via the executable instructions.
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| CN201911370580.2A CN110993117A (en) | 2019-12-26 | 2019-12-26 | Abnormal medical insurance identification method and device based on medical big data |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105279382A (en) * | 2015-11-10 | 2016-01-27 | 成都数联易康科技有限公司 | Medical insurance abnormal data on-line intelligent detection method |
| CN107391724A (en) * | 2017-08-01 | 2017-11-24 | 佛山市深研信息技术有限公司 | A kind of screening technique of big data |
| CN107785057A (en) * | 2017-06-19 | 2018-03-09 | 平安医疗健康管理股份有限公司 | Medical data processing method, device, storage medium and computer equipment |
| CN109065175A (en) * | 2018-08-14 | 2018-12-21 | 平安医疗健康管理股份有限公司 | Medical characteristics screening technique, device, computer equipment and storage medium |
| US20190311377A1 (en) * | 2017-02-20 | 2019-10-10 | Ping An Technology (Shenzhen) Co., Ltd. | Social security fraud behaviors identification method, device, apparatus and computer-readable storage medium |
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2019
- 2019-12-26 CN CN201911370580.2A patent/CN110993117A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105279382A (en) * | 2015-11-10 | 2016-01-27 | 成都数联易康科技有限公司 | Medical insurance abnormal data on-line intelligent detection method |
| US20190311377A1 (en) * | 2017-02-20 | 2019-10-10 | Ping An Technology (Shenzhen) Co., Ltd. | Social security fraud behaviors identification method, device, apparatus and computer-readable storage medium |
| CN107785057A (en) * | 2017-06-19 | 2018-03-09 | 平安医疗健康管理股份有限公司 | Medical data processing method, device, storage medium and computer equipment |
| CN107391724A (en) * | 2017-08-01 | 2017-11-24 | 佛山市深研信息技术有限公司 | A kind of screening technique of big data |
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Application publication date: 20200410 |