CN108898316A - Settling fee method for early warning and system - Google Patents
Settling fee method for early warning and system Download PDFInfo
- Publication number
- CN108898316A CN108898316A CN201810706899.7A CN201810706899A CN108898316A CN 108898316 A CN108898316 A CN 108898316A CN 201810706899 A CN201810706899 A CN 201810706899A CN 108898316 A CN108898316 A CN 108898316A
- Authority
- CN
- China
- Prior art keywords
- client
- medical
- early warning
- group
- expense
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
This disclosure relates to which a kind of settling fee method for early warning and system, the described method comprises the following steps：Step 1, the diagnosis information that the client is extracted from the Claims Resolution request of client and cost information relevant to each project of going to a doctor；Step 2, according to the diagnosis information, determine that corresponding International Classification of Diseases is requested in the Claims Resolution of client, and further determine that group belonging to the International Classification of Diseases；Step 3 obtains statistical data corresponding with the group from medical big data, and the statistical data includes the distributed intelligence of the corresponding medical expense of the group；Step 4, according to the distributed intelligence and the cost information of the client, determine whether the medical expenditure of the client deviates from preset range；If the medical expenditure of step 5, the client deviates from preset range, cost early-warning prompt is carried out.
Description
Technical field
The present invention relates to based on Internet application insurance service technical field more particularly to a kind of pre- police of settling fee
Method and system.
Background technique
A considerable amount of medical insurance frauds are currently, there are, and the prior art lacks effective science for medical insurance fraud
Identification means have seriously affected the balance between revenue and expenditure of medical insurance fund, have encroached on the interests or even public interest of vast insured people.
Health insurance is influenced by medical diagnosis on disease, medical behavior, medication and diagnosis and treatment means, between the settling fee of different cases
There are great differences, and is difficult to establish a set of effective standard from the angle of expense to determine whether there is Claims Resolution fraud, doctor
Treat the risks such as abuse and over-treatment.
Therefore, for the efficiency for inhibiting above-mentioned bad phenomenon, saving social resources, promoting insurance business, it is high to there is exploitation
Imitate the needs of accurately settling fee Early-warning Model.
Summary of the invention
In view of the above problem of the prior art, inventor is made that the present invention, pathology principle is based primarily upon, to disease
Disease is sorted out and is grouped, and all Claims Resolution cases are marked, and calculates the expense distribution of Claims Resolution case in each grouping, it
Afterwards, early warning value is set according to practical business situation, system automatically prompts the case for being more than early warning value, to submit backstage
Marine claim department does further investigation.
According to an embodiment of the invention, providing a kind of settling fee method for early warning, it is characterised in that including following
Step：
Step 1, from client Claims Resolution request in extract the client diagnosis information and to it is each go to a doctor project it is related
Cost information；
Step 2, according to the diagnosis information, determine that corresponding International Classification of Diseases is requested in the Claims Resolution of client, and further
Determine group belonging to the International Classification of Diseases；
Step 3 obtains statistical data corresponding with the group from medical big data, and the statistical data includes described
The distributed intelligence of the corresponding medical expense of group；
Step 4, according to the distributed intelligence and the cost information of the client, determine the medical expenditure of the client
Whether normal range (NR) is deviated from；
If the medical expenditure of step 5, the client deviates from normal range (NR), cost early-warning prompt is carried out.
According to an embodiment of the invention, the corresponding relationship of the group and International Classification of Diseases is grouped by medical events
What tool determined after analyzing history case
According to an embodiment of the invention, the step 1 includes：
Step 1-1, determine whether the medical expense in the cost information of the client is lower than predetermined threshold, if being lower than institute
Predetermined threshold is stated, then terminates this method.
According to an embodiment of the invention, the medical treatment big data includes the demography of the retrievable each client of insurance institution
Data, geodata, time data, medical data, cost data,
Wherein, above-mentioned data can be obtained from interior business database, public information channel, and/or third party's information channel.
According to an embodiment of the invention, the step 4 includes：
Step 4-1, outlier Max=(IQR Upper-IQR Lower) * 2+IQR Upper is calculated, wherein IQR
Upper indicates the third quartile in the expense section of described group of other statistical data, and described group of IQR Lower expression is other to take
With the first quartile in section；
Step 4-2, determine whether the medical expense of the client has been more than n*Max, wherein n is the coefficient between 1~3；
If the medical expense of step 4-3, the described client has been more than n*Max, determine that the medical expenditure of the client is inclined
From normal range (NR).
According to an embodiment of the invention, determining above-mentioned coefficient n by following steps：
Step a, coefficient n is enabled to be respectively equal to n1, n2, n3, n4；
Step b, based on the medical big data, accounting is requested in the Claims Resolution of statistics n*Max or more, obtain with n1, n2, n3,
Corresponding four ratios of n4；
Step c, two coefficients in n1, n2, n3, n4 that selection and estimated rate are closer to；
Step d, the Claims Resolution request for the n*Max or more that described two coefficients each relate to is calculated more than predetermined threshold
Relative ratios shared by case；
Step e, the coefficient for taking the relative ratios in described two coefficients, as final coefficient n.
According to an embodiment of the invention, the coefficient n is set according to the medical expenditure of the client.
According to an embodiment of the invention, Claims Resolution request below for predetermined dollar value sets lesser coefficient n, for predetermined
The Claims Resolution request more than amount of money sets lesser coefficient n.
According to an embodiment of the invention, a kind of settling fee early warning system used to perform the method is provided, it is special
Sign is to include data acquisition module, Claims Resolution grouping module, classified statistic module, bias determining module, early warning module.
Wherein, the data acquisition module be used for from client Claims Resolution request in extract the client diagnosis information, with
And cost information relevant to each project of going to a doctor；
Wherein, the Claims Resolution grouping module determines the corresponding international disease of the Claims Resolution request of client according to the diagnosis information
Disease classification, and further determine that group belonging to the International Classification of Diseases；
Wherein, the classified statistic module is used to obtain statistical data corresponding with the group from medical big data,
The statistical data includes the distributed intelligence of the corresponding medical expense of the group；
Wherein, the bias determining module is used for the cost information according to the distributed intelligence and the client, determines
Whether the medical expenditure of the client deviates from normal range (NR)；
Wherein, it if medical expenditure of the early warning module for the client deviates from normal range (NR), carries out
Cost early-warning prompt.
According to an embodiment of the invention, a kind of computer readable storage medium is additionally provided, the computer-readable storage
The program for the above method is stored on medium, when described program is executed by processor, the step of execution according to the method.
Beneficial effects of the present invention essentially consist in that：1, the risks such as the fraud of discovery Claims Resolution in time, medical treatment abuse and over-treatment,
Promote Claims Resolution control efficiency；2, case investigation is targetedly carried out, Claims Resolution control cost is reduced.
Detailed description of the invention
Fig. 1 is the conceptual schematic view for showing settling fee Early-warning Model according to the present invention；
Fig. 2 to 4 is the part flow diagram according to the settling fee method for early warning of the embodiment of the present invention；
Fig. 5 is the functional block diagram according to the settling fee early warning system of the embodiment of the present invention；
Fig. 6 is the schematic diagram according to the running environment of the system for being mounted with application program of the embodiment of the present invention.
Specific embodiment
In the following, being described in further detail in conjunction with attached drawing to the implementation of technical solution.
It will be appreciated by those of skill in the art that although the following description is related to many of embodiment for the present invention
Technical detail, but be only for not meaning that any restrictions for illustrating the example of the principle of the present invention.The present invention can be applicable in
In the occasion being different from except technical detail exemplified below, without departing from the principle and spirit of the invention.
It, may be to can be in description in the present specification in addition, tedious in order to avoid being limited to the description of this specification
The portion of techniques details obtained in prior art data has carried out the processing such as omission, simplification, accommodation, this technology for this field
It will be understood by for personnel, and this will not influence the open adequacy of this specification.
Hereinafter, description is used to carry out the embodiment of the present invention.Note that by description is provided with following order：1, it sends out
The summary (Fig. 1) of bright design；2, settling fee method for early warning (Fig. 2 to 4)；3, settling fee early warning system (Fig. 5)；4, according to this
The system (Fig. 6) for being mounted with application program of the embodiment of invention.
1, the summary of inventive concept
The present invention uses intelligent algorithm, by the quantile in statistical data, determine the corresponding basis each ICD from
Group's value, and the medical expenditure of client is combined to be distributed, determine the value coefficient that peels off, in this way, after model generates, it can be by simply comparing
It more directly finds potential expense abnormal conditions, and then can find the wind such as Claims Resolution fraud, medical treatment abuse and over-treatment in time
Danger promotes Claims Resolution control efficiency.
Fig. 1 is the application conceptual schematic view for showing settling fee Early-warning Model according to the present invention.
Design major embodiment of the invention is in the following areas：
1) it is based on pathology principle, disease is sorted out and is grouped；
2) all Claims Resolution cases are marked, calculate the expense distribution of Claims Resolution case in each grouping；
Above-mentioned label refers generally to handmarking (or mark), for the case as training set, marks its attribute, with
Continue training after an action of the bowels, this is well known in the field machine learning (AI).
3) early warning value is set according to practical business situation, system automatically prompts the case for being more than early warning value, submits
Claims personnel further investigates.
4) cost model is based on medical insurance data and medical big data is established, it is intended to which point diagnosis carries out high cost Claims Resolution pre-
It is alert.
In the following, in conjunction with the embodiments come illustrate foregoing invention design realization.
2, settling fee method for early warning
Fig. 2 is the overall procedure schematic diagram according to the settling fee method for early warning of the embodiment of the present invention.
The embodiment provides a kind of settling fee method for early warning, include the following steps：
Step S100, from client Claims Resolution request in extract the client diagnosis information and with it is each go to a doctor project
Relevant cost information；
Step S200, according to the diagnosis information, determine that corresponding ICD (International Classification of Diseases) is requested in the Claims Resolution of client,
And further determine that group belonging to ICD, wherein the corresponding relationship of the group and ICD are to be grouped tool by medical events
It is determined after analyzing history case；
Wherein, the medical events grouping tool can be DEG (Diagnosis Event Grouping) tool,
Step S300, statistical data corresponding with the group is obtained from medical big data, including the group corresponds to
Medical expense distributed intelligence；
Step S400, according to the distributed intelligence and the cost information of the client, determine the client with regard to consultation fee
With whether deviating from normal range (NR)；
If the medical expenditure of step S500, the described client deviates from normal range (NR), cost early-warning prompt is carried out, specifically
, prompt need to carry out backstage intervention (manual examination and verification).
Wherein, step S100 may include：
Step S110, determines whether the medical expense of the client is lower than predetermined threshold (such as 500 yuan), if being lower than institute
Predetermined threshold is stated, then terminates this method.
Wherein, the medical big data include the demographic characteristics of the retrievable each client of insurance institution, geographical feature,
Temporal characteristics (such as consultation time, medical interphase, insured time), medical characteristics are (as diagnosis, accurate visit, Medical Consumption are clear
List, medical institutions' scale, medical institutions' grade, medical institutions' the past criminal record label, doctor academic title etc.), fee properties (as every time
It is medical to spend), etc..
For example, above-mentioned data can be obtained from interior business data, public information channel, and/or third party's information channel.
The medical treatment big data may also include the secondary data by carrying out analytical calculation acquisition to statistical data, such as medical
The distribution at any time of the medical frequency (in 1 year) of person, medical expenditure, period always spend, expenditure pattern ratio, etc..
As an example, the diagnosis information includes the medical settlement data of client, as shown in Table 1 below：
Table 1
As shown in Figure 3, wherein in step S400, determine whether the medical expenditure of the client deviates from normally as follows
Range：
Step S410, outlier Max=(IQR Upper-IQR Lower) * 2+IQR Upper is calculated, wherein IQR
Upper indicates the third quartile in the expense section of described group of other statistical data, and described group of IQR Lower expression is other to take
With the first quartile in section；
Step S420, determine whether the medical expense of the client has been more than n*Max, wherein n is the coefficient between 1~3；
If the medical expense of step S430, the described client has been more than n*Max, determine that the medical expenditure of the client is inclined
From normal range (NR)；
Wherein, optionally, step S410 can be carried out before the progress of entire method, that is, it is other that calculated in advance goes out all groups
Outlier.
Wherein, history case being divided into multiple groups by medical events grouping tool can be indicated with representative, such as MIS
020, CAR140 etc.., wherein each group covers multiple ICD, and (ICD that client goes to a doctor every time must belong to and be pertaining only to 1
A group)；
Wherein, expense statistics other for each group, can set up Max and Min value, MAX=(IQR Upper-IQR
Lower) * 2+IQR Upper, Min are the minimum value for counting section；
As an example, third quartile (IQR Upper) and the first quartile (IQR in IQR quartile point
Lower) range between number, the expense that can indicate determine normal range；
As an example, the range between IQR Upper and Max (or n*Max), indicates that potential abnormal range (can be remembered
Record/early warning is flexibly handled)；
As an example, the range on Max (or n*Max), indicate to determine the range for needing manual examination and verification (issue early warning,
Manual examination and verification).
As shown in Figure 4, wherein can determine above-mentioned coefficient n as follows：
Step a, coefficient n is enabled to be respectively equal to n1 (1.5), n2 (2), n3 (2.5), n4 (3)；
Step b, based on the medical big data, the case accounting of n*Max or more is counted, is obtained and 1.5,2,2.5,3 points
Not corresponding four ratios；
Step c, the coefficient that selection and estimated rate are closer to, such as n1 and n2；
Step d, the case that the case (case of n*Max or more) that n1 and n2 is eachd relate to is in predetermined threshold or more is calculated
Shared relative ratios, that is, be directed to n1 and n2, calculate separately the accounting of wholesale case；
Step e, the relatively large coefficient of wholesale case accounting (such as n2) is taken, as final coefficient n.
Optionally, also different coefficient n can be set according to the medical expenditure of the client, to meet preset risk
Rate is sifted out, (the risk rate of sifting out of small amount case can be inclined for example, setting coefficient n=2.5 or 3.0 for 1000 yuan of cases below
It is small), the case for being 1000 yuan or more sets coefficient n=2 (the risk rate of sifting out of wholesale case can be bigger than normal).
The above process can be as shown in the table.
| Early warning value | 1.5*MAX | 2*MAX | 2.5*MAX | 3*MAX |
| Case number of packages | 1134 | 611 | 379 | 245 |
| Case accounting | 8.45% | 4.55% | 2.82% | 1.83% |
Table 1
| Expense | 500 or less | 500-1000 | 1000 or more |
| 1.5MAX | 97 | 487 | 550 |
| 2MAX | 14 | 196 | 401 |
| Accounting | 14.43% | 40.25% | 72.91% |
Table 2
As seen from the above table, it is analyzed for 13420 history Claims Resolution data, case accounting of 1.5 times higher than outlier
8.45%, being higher than 2 times is 4.55% (table 1)
Later, emphasis analyzes 1.5-2MAX case, and the single-visit amount of money is more than 2MAX case accounting after 1000 yuan
It is obviously improved (table 2).
Analysis shows, in 1.5MAX case, there are a large amount of small amount of money cases, Risk Screening has little significance above.
To sum up analysis can obtain, early warning value=2 times outlier, and modelling effect is preferable, it is contemplated that risk sifts out rate 5% or so.
Two comparative examples are provided below to illustrate the above process.
Example one：
Client's membranous nephropathy outpatient dispensing, 6 kinds of single-prescription drug, expense nearly 1.5 ten thousand.Wherein Ta Kemosi capsule is (immune
Inhibitor) match 15 boxes, more than 10,000 yuan of expense.
Traditional manual examination and verification are such as used, do not find claims rejected item, normal compensate of settling a claim.
Cost early-warning model of the invention, 2050 yuan of model nephrosis early warning value are such as used, the far super early warning value of expense exists indiscriminate
With therefore, early warning or claims rejected can be prompted.
Example two：
Client's infection of the upper respiratory tract is medical, 4475 yuan of expenses for medicine, does not provide inventory.
Traditional manual examination and verification are such as used, do not find claims rejected item, normal compensate of settling a claim.
Such as use cost early-warning model of the invention, 1278 yuan of infection of the upper respiratory tract early warning value, hence it is evident that be higher than early warning value, deposit
It is abusing, therefore, early warning or claims rejected can prompted.
3, settling fee early warning system
According to an embodiment of the invention, a kind of settling fee early warning system is additionally provided, for executing implementation of the invention
Each step of example the method.
Fig. 5 is the functional block diagram according to the settling fee early warning system of the embodiment of the present invention.As shown in figure 5,
The settling fee early warning system mainly includes data acquisition module, Claims Resolution grouping module, classified statistic module, bias determining mould
Block, early warning module.
Wherein, the data acquisition module be used for from client Claims Resolution request in extract the client diagnosis information, with
And cost information relevant to each project of going to a doctor；
Wherein, the Claims Resolution grouping module is used to determine that corresponding ICD is requested in the Claims Resolution of client according to the diagnosis information
(International Classification of Diseases), and further determine that group belonging to ICD, wherein the corresponding relationship of the group and ICD are to pass through doctor
What treatment event packets tool determined after analyzing history case；
Wherein, the classified statistic module is used to obtain statistical data corresponding with the group from medical big data,
Distributed intelligence including the corresponding medical expense of the group；
Wherein, the bias determining module is used for the cost information according to the distributed intelligence and the client, determines
Whether the medical expenditure of the client deviates from normal range (NR)；
Wherein, it if medical expenditure of the early warning module for the client deviates from normal range (NR), prompts
It need to carry out backstage intervention (manual examination and verification).
In addition, different embodiments of the invention by software module or can also be stored in one or more computer-readable
The mode of computer-readable instruction on medium is realized, wherein the computer-readable instruction is when by processor or equipment group
When part executes, different embodiment of the present invention is executed.Similarly, software module, computer-readable medium and Hardware Subdivision
Any combination of part is all expected from the present invention.The software module can be stored in any type of computer-readable storage
On medium, such as RAM, EPROM, EEPROM, flash memory, register, hard disk, CD-ROM, DVD etc..
4, the system for being mounted with application program of embodiment according to the present invention
Referring to Fig. 6, it illustrates the running environment of the system according to an embodiment of the present invention for being mounted with application program.
In the present embodiment, the system of the installation application program is installed and is run in electronic device.The electronics
Device can be desktop PC, notebook, palm PC and server etc. and calculate equipment.The electronic device may include but not
It is limited to memory, processor and display.This Figure only shows the electronic devices with said modules, it should be understood that
It is not required for implementing all components shown, the implementation that can be substituted is more or less component.
The memory can be the internal storage unit of the electronic device, such as electronics dress in some embodiments
The hard disk or memory set.The memory is also possible to the External memory equipment of the electronic device in further embodiments,
Such as the plug-in type hard disk being equipped on the electronic device, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card) etc..Further, the memory can also both include institute
The internal storage unit for stating electronic device also includes External memory equipment.The memory is installed on the electronics dress for storing
The application software and Various types of data set, such as the program code etc. of the system for installing application program.The memory may be used also
For temporarily storing the data that has exported or will export.
The processor can be in some embodiments central processing unit (Central Processing Unit,
CPU), microprocessor or other data processing chips, for running the program code stored in the memory or processing data,
Such as execute the system etc. of the installation application program.
The display can be in some embodiments light-emitting diode display, liquid crystal display, touch-control liquid crystal display with
And OLED (Organic Light-Emitting Diode, Organic Light Emitting Diode) touches device etc..The display is for showing
Show the information handled in the electronic device and for showing visual customer interface, such as application menu interface, answers
With icon interface etc..The component of the electronic device is in communication with each other by system bus.
Through the above description of the embodiments, those skilled in the art is it will be clearly understood that above embodiment
In method can realize by means of software and necessary general hardware platform, naturally it is also possible to realized by hardware,
But the former is more preferably embodiment in many cases.Based on this understanding, the technical solution of the application of the present invention is substantially
The part that contributes to existing technology can be embodied in the form of Software Commodities in other words, which deposits
Storage in a storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (can be with
It is mobile phone, computer, server, air conditioner or the network equipment etc.) execute side described in each embodiment of the application of the present invention
Method.
That is, according to an embodiment of the invention, additionally provide a kind of computer readable storage medium, the computer
The program of the method for executing embodiment according to the present invention is stored on readable storage medium storing program for executing, described program is processed
When device executes, each step of the method is executed.
By upper, it will be appreciated that for illustrative purposes, specific embodiments of the present invention are described herein, still, can make
Each modification, without departing from the scope of the present invention.It will be apparent to one skilled in the art that drawn in flow chart step or this
In the operation that describes and routine can be varied in many ways.More specifically, the order of step can be rearranged, step can be executed parallel
Suddenly, step can be omitted, it may include other steps can make the various combinations or omission of routine.Thus, the present invention is only by appended power
Benefit requires limitation.
Claims (10)
1. a kind of settling fee method for early warning, it is characterised in that include the following steps：
Step 1, the diagnosis information that the client is extracted from the Claims Resolution request of client and expense relevant to each project of going to a doctor
Use information；
Step 2, according to the diagnosis information, determine that corresponding International Classification of Diseases is requested in the Claims Resolution of client, and further determine that
Group belonging to the International Classification of Diseases；
Step 3 obtains statistical data corresponding with the group from medical big data, and the statistical data includes the group
The distributed intelligence of corresponding medical expense；
Step 4, according to the distributed intelligence and the cost information of the client, determine the client medical expenditure whether
Deviate from normal range (NR)；
If the medical expenditure of step 5, the client deviates from normal range (NR), cost early-warning prompt is carried out.
2. settling fee method for early warning according to claim 1, which is characterized in that the group and International Classification of Diseases
Corresponding relationship is to be grouped after tool analyzes history case to determine by medical events.
3. settling fee method for early warning according to claim 1, which is characterized in that the step 1 includes：
Step 1-1, determine whether the medical expense in the cost information of the client is lower than predetermined threshold, if lower than described pre-
Determine threshold value, then terminates this method.
4. settling fee method for early warning according to claim 1, which is characterized in that the medical treatment big data includes safety
Demography data, geodata, the time data, medical data, cost data of the retrievable each client of structure,
Wherein, above-mentioned data can be obtained from interior business database, public information channel, and/or third party's information channel.
5. settling fee method for early warning according to claim 1, which is characterized in that the step 4 includes：
Step 4-1, outlier Max=(IQR Upper-IQR Lower) * 2+IQR Upper is calculated, wherein IQR Upper table
Show the third quartile in the expense section of described group of other statistical data, IQR Lower indicates described group of other expense section
First quartile；
Step 4-2, determine whether the medical expense of the client has been more than n*Max, wherein n is the coefficient between 1~3；
If the medical expense of step 4-3, the described client has been more than n*Max, determine that the medical expenditure of the client deviates from
Normal range (NR).
6. settling fee method for early warning according to claim 5, which is characterized in that determine above-mentioned coefficient by following steps
n：
Step a, coefficient n is enabled to be respectively equal to n1, n2, n3, n4；
Step b, based on the medical big data, accounting is requested in the Claims Resolution of statistics n*Max or more, is obtained and n1, n2, n3, n4 points
Not corresponding four ratios；
Step c, two coefficients in n1, n2, n3, n4 that selection and estimated rate are closer to；
Step d, case of the Claims Resolution request for the n*Max or more that described two coefficients each relate to more than predetermined threshold is calculated
Shared relative ratios；
Step e, the coefficient for taking the relative ratios in described two coefficients, as final coefficient n.
7. settling fee method for early warning according to claim 5, which is characterized in that the coefficient n is according to the client
Medical expenditure and set.
8. settling fee method for early warning according to claim 7, which is characterized in that Claims Resolution below for predetermined dollar value is asked
The lesser coefficient n of setting is sought, lesser coefficient n is set for Claims Resolution request more than predetermined dollar value.
9. it is a kind of for executing the settling fee early warning system to method described in any of 8 according to claim 1, it is special
Sign be include data acquisition module, settle a claim grouping module, classified statistic module, bias determining module, early warning module,
Wherein, the data acquisition module is used to extract diagnosis information, the Yi Jiyu of the client from the Claims Resolution request of client
The medical relevant cost information of project every time；
Wherein, the Claims Resolution grouping module determines the corresponding international disease point of the Claims Resolution request of client according to the diagnosis information
Class, and further determine that group belonging to the International Classification of Diseases；
Wherein, the classified statistic module is used to obtain statistical data corresponding with the group from medical big data, described
Statistical data includes the distributed intelligence of the corresponding medical expense of the group；
Wherein, the bias determining module is used for according to the cost information of the distributed intelligence and the client, determine described in
Whether the medical expenditure of client deviates from normal range (NR)；
Wherein, if medical expenditure of the early warning module for the client deviates from normal range (NR), expense is carried out
Early warning.
10. a kind of computer readable storage medium, which is characterized in that be stored on the computer readable storage medium for holding
Row is according to claim 1 to the program of method described in any of 8, when described program is executed by processor, described in execution
The step of method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810706899.7A CN108898316A (en) | 2018-07-02 | 2018-07-02 | Settling fee method for early warning and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810706899.7A CN108898316A (en) | 2018-07-02 | 2018-07-02 | Settling fee method for early warning and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108898316A true CN108898316A (en) | 2018-11-27 |
Family
ID=64347315
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810706899.7A Pending CN108898316A (en) | 2018-07-02 | 2018-07-02 | Settling fee method for early warning and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108898316A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109616216A (en) * | 2018-11-30 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Medical expense prediction technique, device, equipment and computer readable storage medium |
| WO2020119120A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Abnormal medicine purchase identification method and device, terminal and computer-readable storage medium |
| CN111597548A (en) * | 2020-07-17 | 2020-08-28 | 支付宝(杭州)信息技术有限公司 | Data processing method and device for realizing privacy protection |
| CN112132624A (en) * | 2020-09-27 | 2020-12-25 | 平安医疗健康管理股份有限公司 | Medical claims data prediction system |
| CN113139875A (en) * | 2021-03-15 | 2021-07-20 | 青岛国新健康产业科技有限公司 | Fraud case searching method and device, electronic equipment and storage medium |
| TWI809635B (en) * | 2021-12-29 | 2023-07-21 | 國泰世紀產物保險股份有限公司 | Insurance claims fraud detecting system and method for assessing the risk of insurance claims fraud using the same |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170017760A1 (en) * | 2010-03-31 | 2017-01-19 | Fortel Analytics LLC | Healthcare claims fraud, waste and abuse detection system using non-parametric statistics and probability based scores |
| CN107871285A (en) * | 2017-12-06 | 2018-04-03 | 和金在线（北京）科技有限公司 | A kind of health insurance pays for the method for detecting and system of fraud and abuse |
| CN107871284A (en) * | 2017-11-22 | 2018-04-03 | 平安科技（深圳）有限公司 | The appraisal procedure and device of risk Claims Resolution |
-
2018
- 2018-07-02 CN CN201810706899.7A patent/CN108898316A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170017760A1 (en) * | 2010-03-31 | 2017-01-19 | Fortel Analytics LLC | Healthcare claims fraud, waste and abuse detection system using non-parametric statistics and probability based scores |
| CN107871284A (en) * | 2017-11-22 | 2018-04-03 | 平安科技（深圳）有限公司 | The appraisal procedure and device of risk Claims Resolution |
| CN107871285A (en) * | 2017-12-06 | 2018-04-03 | 和金在线（北京）科技有限公司 | A kind of health insurance pays for the method for detecting and system of fraud and abuse |
Non-Patent Citations (1)
| Title |
|---|
| 阎玉霞 等: "住院病人内外科治疗的病例组合研究" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109616216A (en) * | 2018-11-30 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Medical expense prediction technique, device, equipment and computer readable storage medium |
| WO2020119120A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Abnormal medicine purchase identification method and device, terminal and computer-readable storage medium |
| CN111597548A (en) * | 2020-07-17 | 2020-08-28 | 支付宝(杭州)信息技术有限公司 | Data processing method and device for realizing privacy protection |
| CN112132624A (en) * | 2020-09-27 | 2020-12-25 | 平安医疗健康管理股份有限公司 | Medical claims data prediction system |
| CN113139875A (en) * | 2021-03-15 | 2021-07-20 | 青岛国新健康产业科技有限公司 | Fraud case searching method and device, electronic equipment and storage medium |
| TWI809635B (en) * | 2021-12-29 | 2023-07-21 | 國泰世紀產物保險股份有限公司 | Insurance claims fraud detecting system and method for assessing the risk of insurance claims fraud using the same |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108898316A (en) | Settling fee method for early warning and system | |
| WO2019169826A1 (en) | Risk control method for determining irregular medical insurance behavior by means of data analysis | |
| CN105159948B (en) | A kind of Medicare fraud detection method based on multiple features | |
| US20160267396A1 (en) | System and Method for Using Machine Learning to Generate a Model from Audited Data | |
| CN108921710A (en) | The method and system of medical insurance abnormality detection | |
| CN108511059A (en) | Chronic diseases management method and system | |
| US9390121B2 (en) | Analyzing large data sets to find deviation patterns | |
| EP1960952A2 (en) | Analyzing administrative healthcare claims data and other data sources | |
| US20160180264A1 (en) | Retention risk determiner | |
| CN109636085A (en) | Based on the pre-authorization of data processing from kernel method and system | |
| US9037607B2 (en) | Unsupervised analytical review | |
| CN111210321B (en) | Risk early warning method and system based on contract management | |
| CN114612194A (en) | Product recommendation method and device, electronic equipment and storage medium | |
| CN115081025A (en) | Sensitive data management method and device based on digital middlebox and electronic equipment | |
| CN111582879A (en) | Anti-fraud medical insurance identification method based on genetic algorithm | |
| CN116843481A (en) | Knowledge graph analysis method, device, equipment and storage medium | |
| WO2011150097A2 (en) | Identifying and using critical fields in quality management | |
| CN110335145A (en) | A kind of influence factor Dynamic Display method, apparatus, electronic equipment and storage medium | |
| CN114625975A (en) | Knowledge graph-based customer behavior analysis system | |
| CN114742412A (en) | Software technology service system and method | |
| CN114996386A (en) | Business role identification method, device, equipment and storage medium | |
| CN114742594A (en) | Financial promotion investment data processing and evaluation device and method | |
| Lavanya et al. | Auto capture on drug text detection in social media through NLP from the heterogeneous data | |
| CN113298530A (en) | Transaction configuration method, device, equipment and medium based on market data classification | |
| CN113435746A (en) | User workload scoring method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |