CN107038669A - Abnormal settlement data warning system and method - Google Patents
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Abstract
A kind of abnormal settlement data early warning and reminding method, including：Receive all medical settlement datas of medical institutions at different levels；The sample drawn data set from all medical settlement datas of above-mentioned reception；Data analysis step, analyzes the distribution situation of the data set；Early warning susceptibility is set according to medical settlement data situation；And the early warning result of display medical settlement datas at different levels.The present invention also provides a kind of abnormal settlement data warning system.The present invention can be effectively monitored and analyzed to Medical Benefits Fund.
Description
Technical field
The present invention relates to data regulation technique field, particularly a kind of abnormal settlement data warning system and
Method.
Background technology
Medical Benefits Fund refers to that country, to ensure the basic medical of worker, state is pressed by medical insurance handling institution
Family's pertinent regulations, the special fund for worker's basic medical insurance is raised to entity and individual.
To ensure the even running of Medical Benefits Fund, it is necessary to be carried out from many levels to the expense of medical insurance fund
Monitoring and analysis, carry out early warning, strengthen the management of the cost of medical insurance.
The content of the invention
In view of the foregoing, it is necessary to provide a kind of abnormal settlement data warning system, it can be to doctor
Insurance fund is treated to be effectively monitored and analyzed.
In view of the foregoing, it there is a need to a kind of abnormal settlement data early warning and reminding method of offer, it can be right
Medical Benefits Fund is effectively monitored and analyzed.
A kind of abnormal settlement data warning system, including：Interface module, for receiving therapeutic machines at different levels
All medical settlement datas of structure；Data extraction module, for all medical settlement datas from above-mentioned reception
Middle sample drawn data set；Data analysis module, the distribution situation for analyzing the data set；Early warning mould
Block, for setting early warning susceptibility according to medical settlement data situation；And display module, it is at different levels for showing
The early warning result of medical settlement data.
A kind of abnormal settlement data early warning and reminding method, including：Interface step, receives medical institutions at different levels
All medical settlement datas；Data pick-up step, sample is extracted from all medical settlement datas of above-mentioned reception
Notebook data collection；Data analysis step, analyzes the distribution situation of the data set；Warning step, according to medical treatment
Settlement data situation sets early warning susceptibility；And step display, show the early warning knot of medical settlement datas at different levels
Really.
Formula early warning analysis mould is entered with layer using abnormal settlement data warning system of the present invention and method
Formula ensure that the even running of Medical Benefits Fund, the expense of medical insurance fund is monitored from many levels and
Analysis, carries out early warning, strengthens the management of the cost of medical insurance；The performance indicators of hospital is evaluated,
Offer quantitative basis is distributed rationally for medical insurance resource.Analyzed from the total amount of hospital's aspect, with step by step
The mode of decomposition, statistics expense and person-time unit composition situation, by the decomposition of different levels,
Different condition of the searching expense in each point.
Brief description of the drawings
Fig. 1 is the business framework figure of abnormal settlement data warning system preferred embodiment of the invention.
Fig. 2 is the functional block diagram of abnormal settlement data warning system preferred embodiment of the invention.
Fig. 3 is the implementing procedure figure of abnormal settlement data early warning and reminding method preferred embodiment of the invention.
Fig. 4 is each rank probable range schematic diagram under present pre-ferred embodiments Plays normal distribution curve.
Fig. 5 to Fig. 7 be respectively high threshold value of warning scope under normal distribution curve, middle threshold value of warning scope,
The schematic diagram of low threshold value of warning scope.
Embodiment
As shown in fig.1, being the business frame of abnormal settlement data warning system preferred embodiment of the invention
Frame figure.In the present embodiment, the abnormal settlement data warning system basic service flow is：Insured people
The diagnosis information of member 3 generates medical settlement data by the settlement system of medical institutions 2.The medical treatment clearing
Data are regular or are real-time transmitted to Medical Insurance Organizations 1.Medical Insurance Organizations 1 can settle accounts the medical treatment
Data storage detects medical treatment clearing number in database 3, and using abnormal settlement data warning system 10
The abnormity point changed according to middle every medical expense for segmenting dimension, help is effectively managed to medical expenses at different levels
Control.
The Medical Insurance Organizations 1 are led to by network, such as LAN or internet with the medical institutions 2
News connection.
The Medical Insurance Organizations 1 can be various regions social security office, and the medical institutions 2 can be each of various regions
The hospital of individual rank.
The abnormal settlement data warning system 10 of the Medical Insurance Organizations 1 can be a server system
System.The server system is as a hardware system, with higher computing capability.The server system
Main hardware is constituted comprising following several major parts：Central processing unit, internal memory, chipset, I/O buses,
I/O equipment, power supply, cabinet and related software.
In other embodiments of the invention, the abnormal settlement data early warning of the Medical Insurance Organizations 1
System 10 can also be the software systems being made up of program code, and it can install and run on any
Server with higher computational power or any personal electric product in, in the server or electricity
Under the execution of the processor of sub- product, such as central processing unit (CPU, Central Processing Unit),
Certain default function is realized, as the medical expense of every subdivision dimension in the medical settlement data of detection changes
Abnormity point, helps to carry out effective management and control to medical expenses at different levels.
Refering to the function mould for described in Fig. 2, being abnormal settlement data warning system preferred embodiment of the invention
Block figure.Abnormal settlement data warning system 10 of the present invention is bottom-up to be divided into interface layer, computing
Layer and boundary layer.
The interface layer externally provides various interface mode, and it includes interface module 100.The interface module
100 include data-interface, service interface and/or other access API etc..The data-interface can be USB
Interface, serial ports, infrared interface and blue tooth interface etc., are the interfaces carried out data transmission, e.g., from medical treatment
Mechanism 2 receives medical settlement data.
The operation layer is the core of whole medical supervision system 10, and it includes data extraction module 101, number
According to analysis module 102 and warning module 103.Each module of the operation layer is used to connect from interface module 100
Sample drawn data set, the distribution situation of analyze data collection in the medical settlement data of receipts, according to data cases
Set the operation such as early warning susceptibility.
The boundary layer is responsible for showing and man-machine interaction for interface, and it includes display module 104, each for showing
Early warning result of level medical expense etc..
Hereinafter, with reference to Fig. 3, the function of above-mentioned each module is described in detail.
As shown in fig.3, being the implementation stream of abnormal settlement data early warning and reminding method preferred embodiment of the invention
Cheng Tu.
Abnormal settlement data early warning and reminding method is not limited to step shown in flow chart described in the present embodiment, this
Shown in outer flow chart in step, some steps can be omitted, the order between step can change.
Step S10, interface module 100 receives all medical settlement datas of medical institutions 2 at different levels.It is described to connect
Debit's formula can update for automatic race batch, full dose.The medical settlement data of above-mentioned reception can be stored in and cure
Treat in the database 3 that insurance institution 1 connects.
Step S11, the sample drawn number from all medical settlement datas of above-mentioned reception of data extraction module 101
According to collection.The step includes：
1) each level subdirectory row variable of medical settlement data is decomposed step by step according to index item.
Enter the design framework of formula early warning analysis according to layer, in this case each hierarchical data crawl scope can be unified fixed
For the year-on-year ring ratio of the index all same level mechanisms (assuming that mechanism number is N) in past 12 months,
Then the data set observation number is 12 × N.
2) row record in each level subdirectory of medical settlement data is decomposed step by step according to subdivision dimension.
Enter the design framework of formula early warning analysis according to layer, in this case each hierarchical data crawl scope can be unified fixed
For index subdivision dimension corresponding in past 12 months (assuming that each dimension value ordered series of numbers for (N1, N2,
N3 ... Nn)) year-on-year ring ratio, the data set observation number be 12 × N1 × N2 × N3 × ... × Nn.
Step S12, data analysis module 102 analyzes the distribution situation of the data set.
Based on law of great number, it is believed that the approximate Normal Distribution of data set, i.e. Xn~N (μ, σ2), density letter
Number is：
And obtain estimating that parameter (μ, σ) calculates following statistic based on data set：
Average：
Variance：
And then determine normal distribution situation of the year-on-year ring of the index than data set, i.e.,：
Xn~N (μ, σ2)。
It is shown in Figure 4, it is each rank probable range under standardized normal distribution curve.
Step S13, warning module 103 sets early warning susceptibility according to medical settlement data situation.
The analysis and decision-making of follow-up business are, it is necessary to construct the threshold range under different early warning susceptibilitys for convenience.
Under normal distribution curve, standard deviation has certain quantitative relation with probability (area), can be according to normal state
Three important area ratios (68.26%, 95.44%, 99.72%, see Fig. 1) of distribution define corresponding
Early warning susceptibility：
The high early warning of §：Between the positive negative one standard deviation of average (± 1 σ), the gross area (non-exception is included
Probability) 68.26%, see Fig. 5；
Early warning in §：Between positive and negative two standard deviations (± 2 σ) of average, the gross area (non-exception is included
Probability) 95.44%, see Fig. 6；
The low early warning of §：Between positive and negative three standard deviations (± 3 σ) of average, the gross area (non-exception is included
Probability) 99.72%, see Fig. 7；
With following equation and substitute into estimation parameter (average, standard deviation) calculate each rank early warning respectively
Threshold range：
The high threshold value of warning of § is interval：[μ -1 σ, μ+1 σ]；
Threshold value of warning is interval in §：[μ -2 σ, μ+2 σ]；And
The low threshold value of warning of § is interval：[μ -3 σ, μ+3 σ].
The determination of final threshold value scope still needs to combine operational angle.Acquiescence sets threshold interval by middle early warning.
There will be chain to attach to index allocation menu per page table, sample data set scope need to be shown, i.e., all/
Common medical institutions of * * families of mechanism at the same level/similar, past 12/24/36/60 month, sample size N=medical institutions
The number x time cycles, as N≤35, user is pointed out " because sample size is less, it is impossible to use normal distribution meter
Threshold value is calculated, please suitably expands contrast range or sampling range ", as N >=36, show the threshold calculated
It is worth reference interval.The sample data set of each index acquiescence refers to requirement documents text, concurrently sets and manually adjusts
Function, can be selected to change by user out of all/peer/similar mechanism and past 12/24/36/60 month,
User can manually adjust threshold interval simultaneously, and choose whether generate early warning event by user.
There is no year-on-year ring than the index of numerical value for index column, mouse is moved to a certain achievement data, then has outstanding
Floating frame shows the year-on-year ring of the mechanism index when abnormal conditions.
Indices can be added by user, deleted, and achievement data can ascending order or descending arrangement.
Achievement data for needing manual entry, will have corresponding interface prompt to carry out logging data to user.
During into next stage catalogue, the screening conditions of each gauge outfit are all continued to use, including Warning years (timing statisticses),
Medical institutions and pharmacy, public basic unit, medical insurance catalogue；Such as user clicks through mechanism first, then into next stage mesh
During record, each achievement data of indication mechanism first is given tacit consent to；Such as user clicks through a certain classification mechanism, then into next
During level catalogue, acquiescence shows the overall each achievement data of category mechanism.
After when retracting upper level catalogue, acquiescence shows the page as former state.
Step S14, display module 104 shows the early warning result of medical settlement datas at different levels.
Each level medical expense different condition of detection of the present invention is from overall overview, from totality to structure
Into being decomposed step by step, the situation of each composition point of medical expense is analyzed, so that the root that tracing problem occurs
And foundation.User can be directed to amplification or costly mechanism in total overview and be double-clicked, into mechanism hair
Ticket project/disease situation；The mechanism abnormal by double-clicking invoice terms again, into detailed programs situation table, with
This analogizes.
Abnormal settlement data warning system of the present invention requires that top navigation bar shows level path, can
Advance, can retreat.Root, first class catalogue, second-level directory, the signal of three-level catalogue below figure：
Wherein, remaining expense of second-level directory includes：Nurse fees, bed take, Diagnostic Fee, radiation expense, it is defeated
Blood expense, oxygen therapy expense, the expense of delivering a child, observation bed etc..
The analysis method that layer enters formula early warning analysis has：Analysis (contrast of the similar or same period), ring are than analysis on year-on-year basis
(similar or last contrast), trend analysis (the similar trend during certain), mean analysis (equal line
Comparative analysis).By setting rational threshold range, the index beyond threshold range is labeled,
User is pointed out to carry out the analysis of next level, so as to find out the abnormity point of parameter changes.
In addition to root, since first class catalogue, each single item catalogue can show summary table, subordinate list 1, subordinate list
2nd, subordinate list 2-1, subordinate list 3, subordinate list 4, schematically as follows scheme：
This section document is illustrated by taking " total overview " and the analysis and early warning table of " invoice terms " as an example.
Top navigation bar shows the level path of " root-first class catalogue-second-level directory ", can advance,
It can retreat, and the hierarchical location that highlighted user is currently located.
Bottom navigation shows " return summary table ", " enters：Subordinate list 1- historical datas, subordinate list 2- section office situation,
Subordinate list 3- expenses ranking, subordinate list 4-FWA are related ", which table can be specifically entered, to refer to the tool of each table
Body desired content.
Indices its year-on-year ring ratio in hind computation, is marked red with prompting if year-on-year or ring ratio exceedes threshold value
Early warning.
It should be noted last that, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted,
Although the present invention is described in detail with reference to preferred embodiment, one of ordinary skill in the art should manage
Solution, can modify or equivalent substitution to technical scheme, without departing from technical solution of the present invention
Spirit and scope.
Claims (8)
1. a kind of abnormal settlement data warning system, it is characterised in that the abnormal settlement data early warning is carried
Show that system includes：
Interface module, all medical settlement datas for receiving medical institutions at different levels；
Data extraction module, for the sample drawn data set from all medical settlement datas of above-mentioned reception；
Data analysis module, the distribution situation for analyzing the data set；
Warning module, for setting early warning susceptibility according to medical settlement data situation；And
Display module, the early warning result for showing medical settlement datas at different levels.
2. exception settlement data warning system as claimed in claim 1, it is characterised in that the number
Include according to extraction：
Each level subdirectory row variable of medical settlement data is decomposed step by step according to index item；And
Row record in each level subdirectory of medical settlement data is decomposed step by step according to subdivision dimension.
3. exception settlement data warning system as claimed in claim 1, it is characterised in that described point
Analysing the distribution situation of the data set includes：
Based on law of great number, it is believed that the approximate Normal Distribution of data set, i.e. Xn~N (μ, σ2), density letter
Number is：
And obtain estimating that parameter (μ, σ) calculates following statistic based on data set：
Average：
Variance：
And then determine normal distribution situation of the year-on-year ring of the index than data set, i.e.,：
Xn~N (μ, σ2)。
4. exception settlement data warning system as claimed in claim 1, it is characterised in that described pre-
Alert susceptibility includes：
High early warning：Between the positive negative one standard deviation of average (± 1 σ), comprising the gross area, (non-exception is general
Rate) 68.26%；
Middle early warning：Between positive and negative two standard deviations (± 2 σ) of average, comprising the gross area, (non-exception is general
Rate) 95.44%；And
Low early warning：Between positive and negative three standard deviations (± 3 σ) of average, comprising the gross area, (non-exception is general
Rate) 99.72%.
5. a kind of abnormal settlement data early warning and reminding method, it is characterised in that this method includes：
Interface step, receives all medical settlement datas of medical institutions at different levels；
Data pick-up step, the sample drawn data set from all medical settlement datas of above-mentioned reception；
Data analysis step, analyzes the distribution situation of the data set；
Warning step, early warning susceptibility is set according to medical settlement data situation；And
Step display, shows the early warning result of medical settlement datas at different levels.
6. exception settlement data early warning and reminding method as claimed in claim 5, it is characterised in that the number
Include according to extraction：
Each level subdirectory row variable of medical settlement data is decomposed step by step according to index item；And
Row record in each level subdirectory of medical settlement data is decomposed step by step according to subdivision dimension.
7. exception settlement data early warning and reminding method as claimed in claim 5, it is characterised in that described point
Analysing the distribution situation of the data set includes：
Based on law of great number, it is believed that the approximate Normal Distribution of data set, i.e. Xn~N (μ, σ2), density letter
Number is：
And obtain estimating that parameter (μ, σ) calculates following statistic based on data set：
Average：
Variance：
And then determine normal distribution situation of the year-on-year ring of the index than data set, i.e.,：
Xn~N (μ, σ2)。
8. exception settlement data early warning and reminding method as claimed in claim 5, it is characterised in that described pre-
Alert susceptibility includes：
High early warning：Between the positive negative one standard deviation of average (± 1 σ), comprising the gross area, (non-exception is general
Rate) 68.26%；
Middle early warning：Between positive and negative two standard deviations (± 2 σ) of average, comprising the gross area, (non-exception is general
Rate) 95.44%；And
Low early warning：Between positive and negative three standard deviations (± 3 σ) of average, comprising the gross area, (non-exception is general
Rate) 99.72%.
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