CN111275086A

CN111275086A - Method and device for detecting medical insurance group fraud abnormal behaviors and electronic equipment

Info

Publication number: CN111275086A
Application number: CN202010045791.5A
Authority: CN
Inventors: 赵蒙海
Original assignee: Shanghai Jinshida Weining Software Technology Co ltd
Current assignee: Shanghai Jinshida Weining Software Technology Co ltd
Priority date: 2020-01-16
Filing date: 2020-01-16
Publication date: 2020-06-12
Anticipated expiration: 2040-01-16
Also published as: CN111275086B

Abstract

The invention discloses a method, a device and electronic equipment for detecting medical insurance group fraud abnormal behaviors, wherein the method comprises the following steps: classifying the patient information according to the hospital and the disease type; analyzing at least two pieces of patient information of a target disease category of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, the hospital admission time difference between the at least two target patients is smaller than or equal to a first target time threshold, and the hospital discharge time difference between the at least two target patients is smaller than or equal to a second target time threshold; counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as an abnormal patient group, wherein the at least two abnormal patients are simultaneously present in more than or equal to N target patient groups, and N is more than or equal to 3. The method of the embodiment can determine abnormal patient groups with regular visiting behaviors, further determine the medical insurance group fraud abnormal behaviors, and improve the accuracy rate of positioning patients with medical insurance fraud.

Description

Method and device for detecting medical insurance group fraud abnormal behaviors and electronic equipment

Technical Field

The invention relates to the technical field of medical insurance, in particular to a method and a device for detecting fraud abnormal behaviors of medical insurance groups and electronic equipment.

Background

Medical insurance fund supervision is a long-standing and difficult task, is a key measure for guaranteeing the benefits of the vast ginseng and insurance people in China, and always occupies an important position in the construction of medical guarantee supervision systems. Particularly, in 2018, 9 and 11 months, a notice about carrying out behavior for fighting fraud and cheating on medical insurance fund special is jointly issued by the national medical insurance agency, the national health commission, the public security department and the national drug administration, and the work is changed into a hotspot sensitivity problem concerned by the whole society after the behavior for fighting fraud and cheating on the medical insurance fund special is carried out nationwide.

The 'cheat insurance' is a universal and multi-occurrence situation, and for the current cheat insurance behaviors, experts are described in 'a few aspects and a wide chain of long and secret behaviors for supervision' so that the cheat insurance is many in cheat insurance and cannot be defended. The deliberate fraud is to cheat the insurer to obtain the benefit from the medical insurance fund, and the medical insurance fund is collected by means of counterfeit cases, bed hanging hospitalization and the like, and the property is relatively bad.

At present, the conventional method adopts statistical analysis and a single threshold rule supervision method to supervise patients suffering from various diseases, but the accuracy rate of positioning patients with medical insurance fraud is low.

Disclosure of Invention

The embodiment of the invention provides a method, a device and electronic equipment for detecting medical insurance group fraud abnormal behaviors, and aims to solve the problems that patients suffering from various diseases are supervised in the prior art, but the accuracy rate of positioning medical insurance fraud patients is low.

In order to solve the technical problem, the invention is realized as follows:

in a first aspect, a method for detecting fraud and anomaly behavior of medical insurance groups is provided, the method comprising:

classifying the patient information according to the hospital and the disease type;

analyzing at least two pieces of patient information of a target disease category of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, the hospital admission time difference between the at least two target patients is smaller than or equal to a first target time threshold, and the hospital discharge time difference between the at least two target patients is smaller than or equal to a second target time threshold;

counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as an abnormal patient group, wherein the at least two abnormal patients are simultaneously present in more than or equal to N target patient groups, and N is more than or equal to 3.

In a second aspect, a device for detecting fraud and anomaly behavior of medical insurance group is provided, the device comprising:

the first classification module is used for classifying the patient information according to the hospital and the disease type;

the analysis module is used for analyzing at least two pieces of patient information of a target disease category of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, the hospital admission time difference between the at least two target patients is smaller than or equal to a first target time threshold, and the hospital discharge time difference between the at least two target patients is smaller than or equal to a second target time threshold;

the determining module is used for counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as abnormal patient groups, wherein the at least two abnormal patients are simultaneously present in the target patient groups which are more than or equal to N, and N is more than or equal to 3.

In a third aspect, an electronic device is provided, comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to the first aspect.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method according to the first aspect.

In the embodiment of the invention, the patient information is classified according to the hospital and the disease type of the visit, and a plurality of disease categories of a plurality of hospitals are obtained; analyzing at least two pieces of patient information of target disease categories of a target hospital to obtain a target patient group consisting of at least two target patients, wherein the admission time and the discharge time between the at least two target patients are both smaller than corresponding time thresholds; and counting target patient groups of a plurality of disease categories of a plurality of hospitals, and determining at least two target patients in the target patient groups with the occurrence of N or more at the same time as abnormal patients and using the abnormal patients as an abnormal patient group. At least two patients who are admitted and discharged in the same hospital in the same time period are determined as a target patient group, the target patient groups of multiple disease categories of multiple hospitals are counted, the frequency of occurrence of the target patient group in the same group exceeds N times and is determined as an abnormal patient group, N is larger than or equal to 3, the abnormal patient group can be determined as a group with regular diagnosis behaviors, further, the abnormal behaviors of medical insurance group fraud can be determined, and the accuracy of positioning patients with medical insurance fraud is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:

FIG. 1 is a flow chart of a method for detecting fraud and anomaly behavior of medical insurance groups according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a method for detecting fraud and anomaly in medical insurance group according to another embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a device for detecting fraud and anomaly in medical insurance groups according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to another embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a schematic flow chart of a method for detecting medical insurance group fraud abnormal behaviors according to an embodiment of the present invention, where the method shown in fig. 1 can be executed by a device for detecting medical insurance group fraud abnormal behaviors, as shown in fig. 1, the method includes:

step S102, classifying the patient information according to the hospital and the disease type.

It is understood that the classification of the patient information according to the disease type may be the classification of the patient information according to the disease name, the disease treatment duration, and other manners, and is not limited in any way. For example, if classified by disease name, appendicitis is classified as one, diabetes as one, and cerebral infarction as one. If the disease is classified according to the treatment duration, diabetes and cerebral infarction with almost the same treatment duration are classified into one category, and appendicitis is classified into another category.

In step S102, patient information is acquired, the patient information including the patient, the hospital the patient is visiting, and the disease the patient gets. The patient information is classified according to the hospital and the disease type of the patient, a plurality of disease categories of a plurality of hospitals are obtained, and the patient information of the target disease category of the target hospital can be conveniently and specifically processed in the follow-up process. For example, Zhang three, Li four, Wang five, Liu six and Xiao Qin, wherein Zhang three, Li four and Wang five are treated in the same hospital, Liu six is treated in another hospital, Zhang three, Li four and Wang five are classified into one category according to the hospital for treatment, and Liu six is classified into another category; and classifying Zhang III, Li IV and Wang Wu according to the disease types to obtain at least one disease category.

Optionally, in some embodiments, the classification of the disease type is correlated to the length of the visit for the disease.

It can be understood that the difference of the treatment time of the disease is less than the preset value, and is classified into a disease category, thereby facilitating the statistics and determining the time threshold corresponding to the disease category. The time threshold may be a numerical value, or an interval.

For example, Zhang III, Li IV and Wang Wu respectively suffer from diabetes, acute appendicitis and acute cerebral infarction, the diagnosis time of the diabetes is 22 days, the diagnosis time of the acute appendicitis is 5 days, the diagnosis time of the acute cerebral infarction is 20 days, the preset value is 3 days, the diabetes and the acute cerebral infarction with the diagnosis time difference smaller than 3 are classified into one disease category, the time threshold corresponding to the disease category is determined to be [20, 21 and 22], and the acute appendicitis is classified into another disease category.

Or the diagnosis time of the diseases 1 to 6 is 9 days, 10 days, 11 days, 12 days, 13 days and 14 days, the preset value is 3 days, the diseases 1 to 3 with the diagnosis time difference smaller than 3 are classified into a disease class, and the time threshold value corresponding to the disease class is [9, 10, 11], namely the disease diagnosis time of the disease class is [9, 10, 11 ]; classifying the diseases 4 to 6 with the diagnosis time difference less than 3 into another disease category, wherein the time threshold value corresponding to the disease category is [12, 13, 14], namely the disease diagnosis time of the disease category is [12, 13, 14 ].

Optionally, before the patient information is acquired, the initial information is acquired, the code matching processing of the abnormal value, the null value and the disease name is performed on the initial information, the error and redundant information is removed, the patient information is obtained, and the quality of the patient information is guaranteed.

Step S104, analyzing at least two pieces of patient information of target disease categories of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, the hospital admission time difference between the at least two target patients is smaller than or equal to a first target time threshold, and the hospital discharge time difference between the at least two target patients is smaller than or equal to a second target time threshold.

It should be understood that the first target time threshold and the second target time threshold are set according to actual requirements.

Taking the disease category A of the hospital A as an example of a target disease category, the disease diagnosis time of the target disease category is [12, 13, 14], and a plurality of target patient groups which are discharged and admitted in the same time period are obtained by combining the floating admission date and discharge date.

Specifically, if the disease visit duration of the target disease category is [12, 13, 14], the first target time threshold may be 1 day, the second target time threshold may be 1 day, 100 patient information of the target disease category is analyzed, and 15 target patient groups are obtained, and each target patient group includes at least two target patients, that is, the number of the target patient groups may be 2, 3, 4, 5, and so on, or even more.

For example, the first group includes two target patients, the first target patient is admitted at 1 month and 1 day, the second target patient is admitted at 1 month and 1 day, the discharge time is 1 month and 14 days, the difference between the admission time and the discharge time between the two target patients is less than 1 day, and the difference between the discharge time and the discharge time between the two target patients is equal to 1 day.

The second group includes six target patients, the first target patient having an admission time of 2 months and 1 day and an discharge time of 2 months and 13 days, the second and third target patients having an admission time of 2 months and 1 day and a discharge time of 2 months and 14 days, the fourth target patient having an admission time of 2 months and 2 days and a discharge time of 2 months and 13 days, and the fifth and sixth target patients having an admission time of 2 months and 14 days. The difference in time between admission between the six target patients is less than or equal to 1 day, and the difference in time between discharge between the six target patients is less than or equal to 1 day.

And so on until a fifteenth target patient group is determined. By analyzing at least two patients with the same disease category, a target patient group is obtained, and the analysis of at least two patients with different disease categories can be avoided, so that the analysis range is reduced, and the analysis efficiency is improved.

Step S106, counting target patient groups of a plurality of disease categories in a plurality of hospitals, determining at least two abnormal patients as abnormal patient groups, wherein the at least two abnormal patients are simultaneously present in the target patient groups which are more than or equal to N, and N is more than or equal to 3.

For example, there are 5 hospitals, each hospital having 10 disease categories, each disease category having 15 target patient groups, and then there are 750 target patient groups for the 5 hospitals 10 disease categories. In the case that N is 5, if at least two target patients are simultaneously present in 8 target patient groups, it indicates that the frequency of hospital discharge of the at least two target patients in the same time period is 8, the at least two target patients are determined as abnormal patients, the abnormal patient groups can be determined as medical insurance fraud patient groups with regular clinic activities, and the medical insurance group fraud abnormity behaviors are determined. Based on the fact that the target patient who is discharged and admitted in the same time period for more than or equal to N times is determined as the medical insurance fraud patient, the contingency among the patients is eliminated, and the accuracy of positioning the medical insurance fraud patient can be improved.

Optionally, in some embodiments, the method shown in fig. 1 further includes:

and deleting the target abnormal patient when the expense reimbursement proportion of the target abnormal patient meets a first threshold.

It should be understood that the first threshold and the second threshold may be a single value, and the first threshold and the second threshold may be a single interval.

For example, if the first threshold is 80%, if the expense reimbursement rate of the target abnormal patient is greater than 80%, which indicates that the target abnormal patient has a low self-expense rate and a high reimbursement rate, the target abnormal patient is determined as a medical insurance fraud patient, and the target abnormal patient is kept.

Or if the first threshold is 60%, if the expense reimbursement proportion of the target abnormal patient is less than 60%, which indicates that the self-expense proportion of the target abnormal patient is high and the reimbursement proportion is low, determining the target abnormal patient as a non-medical insurance fraudulent patient, and deleting the target abnormal patient. By excluding non-medical insurance fraud patients with high self-fee proportion and low reimbursement proportion, the accuracy rate of locating medical insurance fraud patients can be improved.

Optionally, in other embodiments, the method shown in fig. 1 further includes:

and deleting the target abnormal patient when the empty bed rate of the hospital for the target abnormal patient meets a second threshold.

It is understood that if a hospital has 100 bed numbers per se, but only 50 patients, the empty bed rate is 50%.

For example, if the second threshold is 40%, if the empty bed rate of the hospital for the target abnormal patient is lower than 20%, the hospital is determined to be a saturated hospital for the patient to see, there is no possibility of bed-hanging hospitalization in the saturated hospital, that is, the target abnormal patient for the patient to see is a non-medical-insurance fraudulent patient, the target abnormal patient is deleted, and the false positive rate of the detection result is reduced by excluding the non-medical-insurance fraudulent patient for the patient to see in the saturated hospital.

It should be understood that a positive result refers to illegal medical visits, false medical visits, etc. The false positive rate indicates the percentage of false positive results in the screening results.

Optionally, in still other embodiments, the method shown in fig. 1 further comprises:

the diseases of the target abnormal patients are deleted from a disease list, and the diseases in the disease list are diseases which need frequent regular medical visits.

For example, a target abnormal patient with uremia goes to a hospital for dialysis at intervals, and the target abnormal patient is a non-medical insurance fraudulent patient and is deleted based on the fact that uremia per se is a disease requiring frequent regular visits.

Or, the target abnormal patient suffering from diabetes needs to be checked for blood sugar at least once every year, and based on the fact that the diabetes is a disease needing frequent regular visits, the target abnormal patient is indicated to be a non-medical insurance fraudulent patient, the target abnormal patient is deleted, and the accuracy of locating the medical insurance fraudulent patient can be improved by eliminating the non-medical insurance fraudulent patient suffering from the disease needing frequent regular visits.

Optionally, in some embodiments, the behavioral characteristics of the target abnormal patient group are classified in combination with the business analysis characteristics (for example, discharge and admission within a short time, the number of hospitalization days of the related diseases exceeds the corresponding normal hospitalization threshold), so as to obtain a continuous discharge and admission group and a regular discharge and admission group, which group the target abnormal patient group belongs to can be accurately determined, the practical value is embodied, and the method has the characteristics of accuracy and landing.

It is understood that a continuous discharge and admission group refers to the condition that at least two patients are discharged and admitted on multiple days or are in long-term admission, long-term admission refers to the condition that the number of days of admission exceeds the normal admission threshold of the relevant diseases, which indicates that the disassembly admission problem exists, and a regular discharge and admission group refers to the condition that at least two patients are discharged and admitted synchronously or regularly for multiple times, which indicates that the hanging bed admission problem exists.

Optionally, in some embodiments, the method shown in fig. 1 further includes:

classifying the target abnormal patient group according to the hospital for the target abnormal patient in the target abnormal patient group and the frequency of the treatment;

and displaying the target abnormal patient group according to the category of the target abnormal patient group.

In some embodiments, the target abnormal patient group is classified according to the hospital for the target abnormal patient and the frequency of the treatment, the category of the target abnormal patient group is obtained, and the target abnormal patient group is displayed according to the category, so that the staff can quickly search the required patient information. For example, patients who visit the same hospital between 4-6 visits per month are classified into one category.

Fig. 2 is a schematic flow chart of another method for detecting fraud and anomaly behavior of medical insurance group of the invention, as shown in fig. 2, the method includes:

step S202, obtaining patient information, wherein the patient information comprises a patient, a hospital for treatment and diseases obtained by the patient, and classifying the patient information according to the hospital for treatment and the disease types to obtain different disease categories of different hospitals.

Step S204, analyzing at least two pieces of patient information of target disease categories of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, and the target patients meet the conditions of similar admission time and similar discharge time in the same hospital.

Step S206, counting target patient groups of different disease categories of different hospitals, if a plurality of target patient groups all appear in the target patient groups more than or equal to N, N is more than or equal to 3, which indicates that the frequency of the target patient groups all appear more than or equal to N, then taking the target patient groups as abnormal patient groups, and connecting the abnormal patient groups to form a social network, wherein the social network is also called as an abnormal patient group.

And S208, analyzing abnormal patient groups according to the characteristics of cost, hospitals, disease rules and the like, eliminating some abnormal patients corresponding to the interference diseases to obtain target abnormal patient groups, classifying the target abnormal patient groups to obtain continuous hospital access groups and regular hospital access groups, listing the continuous hospital access groups and the regular hospital access groups in corresponding abnormal patient lists and displaying the abnormal patient lists, further reducing the false positive rate, accurately positioning patients with medical insurance fraud, and providing multi-dimensional support for medical insurance supervision.

Fig. 3 is a schematic structural diagram of a device for detecting fraud and anomaly behavior of medical insurance group according to an embodiment of the present invention, as shown in fig. 3, the device 30 includes:

a first classification module 31 for classifying the patient information according to the hospital and the disease type of the patient;

the analysis module 32 is configured to analyze at least two pieces of patient information of a target disease category of a target hospital to obtain a target patient group, where the target patient group includes at least two target patients, an admission time difference between the at least two target patients is less than or equal to a first target time threshold, and a discharge time difference between the at least two target patients is less than or equal to a second target time threshold;

and the determining module 33 is used for counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as abnormal patient groups, wherein the at least two abnormal patients simultaneously appear in the target patient groups which are more than or equal to N.

Optionally, as an embodiment, the classification of the disease type is related to the length of the visit for the disease.

Optionally, as an embodiment, the apparatus 30 further includes:

the first deleting module is used for deleting the target abnormal patient under the condition that the expense reimbursement proportion of the target abnormal patient meets a first threshold.

Optionally, as an embodiment, the apparatus 30 further includes:

and the second deleting module is used for deleting the target abnormal patient under the condition that the empty bed rate of the hospital for the target abnormal patient to see a doctor meets a second threshold value.

Optionally, as an embodiment, the apparatus 30 further includes:

and the third deleting module is used for deleting the target abnormal patient from the disease list of the target abnormal patient, wherein the disease in the disease list is a disease which needs to be frequently and regularly treated.

Optionally, as an embodiment, the apparatus 30 further includes:

the second classification module is used for classifying the target abnormal patient group according to the hospital for the target abnormal patient in the target abnormal patient group and the frequency of the treatment;

and the display module is used for displaying the target abnormal patient group according to the category of the target abnormal patient group.

The mobile terminal provided in the embodiment of the present invention can implement each process implemented in the method embodiment of fig. 1, and is not described here again to avoid repetition.

An electronic device according to an embodiment of the present application will be described in detail below with reference to fig. 4. Referring to fig. 4, at a hardware level, the electronic device includes a processor, optionally an internal bus, a network interface, and a memory. The memory may include a memory, such as a Random-access memory (RAM), and may further include a non-volatile memory, such as at least 1 disk memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be interconnected by an internal bus, which may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended EISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code including computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form a detection device for medical insurance group fraud abnormal behaviors on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

the method comprises the steps of counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as abnormal patient groups, wherein the at least two abnormal patients simultaneously appear in the target patient groups which are larger than or equal to N.

The method executed by the device for detecting the medical insurance group fraud abnormal behavior disclosed by the embodiment shown in fig. 1 of the application can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.

The embodiment of the invention provides a computer-readable storage medium, which classifies the information of patients according to the hospital and the disease type; analyzing at least two pieces of patient information of a target disease category of a target hospital to obtain a target patient group, wherein the target patient group comprises at least two target patients, the hospital admission time difference between the at least two target patients is smaller than or equal to a first target time threshold, and the hospital discharge time difference between the at least two target patients is smaller than or equal to a second target time threshold; the method comprises the steps of counting target patient groups of a plurality of disease categories in a plurality of hospitals, and determining at least two abnormal patients as abnormal patient groups, wherein the at least two abnormal patients simultaneously appear in the target patient groups which are larger than or equal to N.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transient media) such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

The above are merely examples of the present invention, and are not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for detecting medical insurance group fraud abnormal behaviors, which is characterized by comprising the following steps: :

2. The method of claim 1, wherein the classification of the disease type is associated with a length of visit for the disease.

3. The method of claim 1 or 2, wherein the method further comprises:

4. The method of claim 3, wherein the method further comprises:

and deleting the target abnormal patient when the empty bed rate of the hospital for the visit of the target abnormal patient meets a second threshold value.

5. The method of claim 4, wherein the method further comprises:

and deleting the target abnormal patient from a disease list of the target abnormal patient, wherein the diseases in the disease list are diseases requiring frequent regular medical visits.

6. The method of claim 5, wherein the method further comprises:

and displaying the target abnormal patient population according to the category of the target abnormal patient population.

7. A device for detecting fraud and anomaly in medical insurance groups, the device comprising: :

8. The apparatus of claim 7, wherein the classification of the disease type is related to a length of time a disease is attended.

9. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.