CN105069030A - Hospitalization expense estimation and judgment method for single disease - Google Patents
Hospitalization expense estimation and judgment method for single disease Download PDFInfo
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Abstract
The invention discloses a hospitalization expense estimation and judgment method for single disease. The method comprises: cleaning abnormal data, screening out qualified samples, and establishing a sample database; neatening the samples and extracting influence factors related with hospitalization expense estimation and judgment of the single disease; performing expense interval estimation according to the number of samples, wherein the number of samples has to be greater than 10; if the number of samples is greater than 50, at first, trying to delete a maximum value or a minimum value through algorithm iteration, wherein the rest samples are reserved verification samples; then verifying a sample distribution pattern, limiting the residual rate of the samples, and normalizing the samples to estimate an expense interval; and if the number of samples is smaller than or equal to 50 and greater than 10, performing t distribution estimation and directly calculating expense estimation of a confidence interval.
Description
Technical field
The hospitalization cost that the present invention relates to a kind of Single diseases estimates decision method.
Background technology
In recent years, medical expense is high also to rise year by year, and " Expensive and hard to visit doctors " problem becomes the focus that various circles of society pay close attention to day by day.For containing the unreasonable rise of medical expense, improve medical service level and medical assurance level, State-level deepens constantly medical reform, encourages social medical service, decontrols Drug Pricing; Meanwhile medical insurance handling institution in various places tries to explore new way of paying to substitute traditional fee-for-services, wherein especially extensive and deep with the social influence of DRGs-based payment system reform generation.For exploring the way controlling the unreasonable growth of medical expense from mechanism, play the positive role of DRGs-based payment system better, single disease payment and DRGs pay and arise at the historic moment in China.
Single disease payment is the various forms of payment for medical expenses determining to pay limit to pure disease according to classification of diseases.DRGs pays and refers to according to many factors such as patient age, medical diagnosis on disease, complication complication, therapeutic modality, disorder severity and curative effects, by the inpatient close for diagnosis, treatment means is close, medical expense is close, be divided into some sick group, then pay the way of paying of medical expense with the limit determined.
Because DRGs-based payment system mode relates to the links of whole diagnosis and treatment flow process, as medical cost accounting, inner management specification, medical quality control etc., also relate to the factors such as right, interest game between medical services relevant departments simultaneously, therefore current generally carry out the management system of paying by project under, carrying out DRGs-based payment system will be a very complicated systems engineering.
Compared with the way of paying of world advanced person, the single disease payment that we carry out is the blank of DRGs-based payment system, still there is many problem and shortage, one is that " sick plant " title disunity, intension are lack of standardization; Two is that the coverage rate of Single diseases is narrow, and the masses that are benefited are limited; Three is that the measuring method of disease cost is random large, and lack scientific, rationality, the disease kind payment standards that different regions, different medical mechanism determine is uneven; Four to be that single disease payment self exists a lot of not enough, as the diversity of kinds of Diseases, the otherness of individual physique, the complicacy of medical services and local economy situation, the varying of price standard, all add the enforcement difficulty of single disease payment; Five is requirements that the management philosophy of medical institutions and management level can not adapt to single disease payment, and the operation of single disease payment is had difficulty in taking a step.
Summary of the invention
For the problems referred to above, the hospitalization cost that the invention provides a kind of Single diseases estimates decision method.
To achieve the above object of the invention, the hospitalization cost of Single diseases of the present invention estimates decision method, and described method comprises:
Abnormal data cleans, and filters out qualified sample, sets up sample database;
Described sample is arranged, extracts and estimate to judge relevant influence factor to Single diseases hospitalization cost;
Carry out the estimation in expense interval respectively according to the size of sample size, wherein sample size must be greater than 10.
If when sample size is greater than 50, first attempt rejecting maximal value or minimum value by algorithm iteration, remaining sample is reserved checking sample;
Then verify sample distribution form and limit the retention ratio of sample, by sample normalize estimated cost is interval;
Finally, carry out the verification process that Costs Sample is chosen, adopt the fiducial interval of the standard variance estimated cost of 2 times.
If Costs Sample cannot be verified by normal distribution or sample still exceptional value be greater than 80%, we can reclassify sample by greatest hope value-based algorithm, then find out the difference (cost impacts factor) between inhomogeneity sample, normalize sample distribution.For the sample verified by normal distribution, utilize central limit theorem, repeatedly sample to the sample under this classification, fit to perfect sample distribution form, sample estimates expense is interval again, shows the tendency form after matching;
If when sample size is less than or equal to 50 and is greater than 10, carry out t distribution and estimate, try to achieve fiducial interval expense and estimate;
Further, described influence factor comprises the formation of sex, age, disease code (ICD-10 coding), length of stay, mechanism's grade, state of being admitted to hospital, consultation time, complication complication, Hospitalization expenses and total expenses, and the formation of wherein said total expenses comprises medicine expense, cost of hospitalization, general inspection expense, large-scale Laboratory Fee, Operation Fee, treatment cost, diagnosis and treatment expense, other fees.
Further, the described estimated result to expense interval carries out modelling verification and specifically comprises:
Stochastic variable X after standardization has average μ=0, variance ￠ 2=1, substitutes into the density formula of X
in, its standard normal density is
;
K-S checks, and P>0.05, meets normal distribution, is distributed by normal distribution fiducial interval, calculates the expense border in fiducial interval;
Choose reserved checking sample, validation test sample falls into expense interval probability,
If test probability meets the description of fiducial interval substantially, then expense interval estimation is reasonable, otherwise it is interval then to need to re-start the new expense of sample transformation calculations.
Further, the method for described normalize has: the non-linear transfer such as root, logarithm of making even.
beneficial effect:
Compared with existing correlation technique, the present invention has following beneficial effect:
The hospitalization cost of Single diseases of the present invention estimates decision method, the analysis process such as data cleansing, mathematical analysis, matching estimation are organically combined, sampling of data, Factor Selection that a traditional manual type is carried out are abandoned, go back the influence degree of quantization influence factor simultaneously----adopt the reason of grouping factor, provide the expense interval prediction model of Single diseases under Different Effects condition.
Embodiment
The present invention will be further described below.
The present invention is directed to current medical insurance control expense demand, for local medical insurance reimbursement policy maker can immediately formulate and adjust medical insurance Claims Resolution policy, improves the service efficiency of medical insurance fund and provide the reference analysis index of the interval and influence factor of a set of Single diseases expense.
The hospitalization cost of Single diseases of the present invention estimates that decision method mainly integrates out a set of Single diseases cost analysis flow process from rejecting abnormal data, the sick sample size of planting of solution part compared with aspects such as minor issue, disease cost analysis of Influential Factors, total expenses interval estimations.
Abnormal data: mainly refer to obvious irrational data such as diagnosis, length of stay, hospitalization cost, length of stay, therapeutic scheme that the individual in medical insurance settlement system or hospital information system goes to a doctor in data, indivedual diagnosis records etc. that these abnormal datas mainly input (necessary information containing not filling out) by mistake or obviously exceed general range cause.
Sample size is less: in Mathematical Statistics Analysis, the general demand fulfillment of analyzing samples number more than 50 of same subject.Part sample is because the peculiar property of time span or sick kind itself is difficult to meet the smallest sample number analyzed.
The influence factor of disease cost: the factor affecting final disease cost is a lot, and influence degree is different sizes also.
Total expenses interval estimation: by rational analytical model, estimates the reasonable fee scope of Single diseases.
The program carries out threshold interval estimation, analysis of Influential Factors mainly through the analytical approachs such as the multifactor analysis of variance, normal distribution estimation and technology to medical hospitalization cost, utilize central limit theorem repeatedly to sample to sample simultaneously, expand actual sample capacity, the true expense distribution of matching sample, sample estimates reasonable expense is interval; Before finally checking matching expense interval and matching, the expense of authentic specimen is estimated, comprehensively infers that the reasonable expense of Single diseases under different affecting factors is interval.
Embodiment
The hospitalization cost of the present embodiment Single diseases estimates decision method, specifically comprises the steps:
1, data cleansing
In sample extraction process, indivedual sample may because input carelessness, and information may occur input error, and dislocation is filled in such as diagnosis, fastens one person's story upon another person; Once medical is not complete therapeutic process, may be a rehabilitation or just simple ask to examine cause whole medical expense on the low side; Also or long-term nursing for treating, cause length of stay on the high side.These or more situation cause data exception, if cleaning treatment can directly improve our analysis cost not in time, reduce and analyze accuracy rate, even directly lead to errors conclusion.
So most important foundation a set ofly can identify that disease plants relevant basic attribute framework, and this framework comprises the general therapeutic modality, medical material operation, length of stay, medical expense etc. of disease kind.
Then, by above-mentioned framework knowledge, filter the sample not meeting knowledge, the sample that rule are closed in screening carries out more analyzing providing sample support for follow-up.
2, factor confirms
The sample of involutory rule carries out warehouse-in and arranges, and every bar sample case can extract following information: the formation of sex, age, disease code (ICD-10 coding), length of stay, mechanism's grade, state of being admitted to hospital, consultation time, complication complication, total expenses and total expenses.Wherein total expenses is made up of 8 parts such as medicine expense, cost of hospitalization, general inspection expense, large-scale Laboratory Fee, Operation Fee, treatment cost, diagnosis and treatment expense, other fees.By correlation analysis and in conjunction with clinical priori, these factor to affect on the impact of total expenses significantly (variance analysis, F inspection), have the clinical experience (influence factor) of statistical significance.Here emphasize statistical significance, although be because some factors meets clinical knowledge, overall expenses there is no impact, so temporarily we do not consider these factors.Wherein general inspection expense, large-scale Laboratory Fee, Operation Fee, treatment cost, diagnosis and treatment expense, other fees, determine according to actual conditions are actual.
3, classified calculating
According to the analysis of above influence factor, extract the influence factor case sample under different water condition larger to disease kind medical expense.In analytic process, more cost of sample is discontinuous discrete value.In the face of these discontinuous data, can be observed by frequency plot, divide data interval equally some equal portions, then add up the cumulative frequencies of each segment data.
When sample size is enough large (general more than 50), because general sampled data all may occur that class condition has deviation or singular value point (too large or too little), make the line peak of distribution to the left or to the right.Some inclined peak is that singular value has appearred in data, could process after also must rejecting.Attempt rejecting maximal value or minimum value at algorithm iteration for this reason, then verify sample distribution form and limit the retention ratio (prevent from rejecting too much sample data, cause sample size on the low side) of sample.Can by data normalize being utilized the most of data of residue after abnormal value elimination.The common method of data normalize has: the process of the non-linear transfer such as root, logarithm of making even, but makes the distributional pattern that can change raw data like this, therefore the accuracy of result will reduce.Therefore, by the inference will obtained after data normalize process, through the verification process of data decimation.The fiducial interval of the standard variance estimated cost of general employing 2 times.If data cannot be verified by normal distribution or sample still " exceptional value " on the high side, may be so current sample distribution not pure normal distribution (single Gaussian distribution), there is our the affecting cost element of the unknown and the gauss hybrid models formed.For gauss hybrid models, we can pass through maximum expected value algorithm classification sample, then find out the difference between inhomogeneity sample, and verifying and being formed final affects cost element, thus subseries is that sample distribution is tending towards normalize again.For the sample verified by normal distribution, utilize central limit theorem, repeatedly sample to the sample under this classification, fit to perfect sample distribution form, sample estimates expense is interval again, shows the fitting result after optimizing.
The sample size of planting when certain is sick is less than 50 and is greater than 10, does not namely need to carry out analysis of variance (also not needing classification) to it, transfers directly to carry out t distribution and estimates, try to achieve fiducial interval expense and estimate.
Sample size is less than to the disease kind of 10, it has not possessed in statistical significance, interval without the need to estimated cost.
4, modelling verification
Stochastic variable X after standardization has average μ=0, variance ￠ 2=1, substitutes into the density formula of X
in, its standard normal density is
.K-S checks, and P>0.05, meets normal distribution.Distributed by normal distribution fiducial interval, calculate the expense border in certain fiducial interval.Choose reserved checking sample, validation test sample falls into expense interval probability.If test probability meets the description of fiducial interval substantially, then this expense interval estimation is reasonable.Otherwise it is interval then to need to re-start the new expense of sample transformation calculations.
For certain districts and cities' diabetes sample, choose 1028 with the case sample under classification, the general analyzes result before and after sample process is as shown in the table:
Before and after process, the eigenwert of sample finds: the kurtosis of the latter and degree of bias index are closer to 0(normal distribution).
The present invention be should be understood that; above-described embodiment; further detailed description has been carried out to object of the present invention, technical scheme and beneficial effect; these are only embodiments of the invention; be not intended to limit the present invention, every within spiritual principles of the present invention, done any amendment, equivalent replacement, improvement etc.; all should be included within protection scope of the present invention, the protection domain that protection scope of the present invention should define with claim is as the criterion.
Claims (4)
1. the hospitalization cost of Single diseases estimates a decision method, and it is characterized in that, described method comprises:
Abnormal data cleans, and filters out qualified sample, sets up sample database;
Described sample is arranged, extracts and estimate to judge relevant influence factor to Single diseases hospitalization cost;
Carry out the estimation in expense interval respectively according to the size of sample size, wherein sample size must be greater than 10,
If when sample size is greater than 50, first attempt rejecting maximal value or minimum value by algorithm iteration, remaining sample is reserved checking sample;
Then verify sample distribution form and limit the retention ratio of sample, by sample normalize estimated cost is interval;
Finally, carry out the verification process that expense is chosen, adopt the fiducial interval of the standard variance estimated cost of 2 times;
If sample cannot be verified by normal distribution or sample still exceptional value be greater than 80%, we can reclassify sample by greatest hope value-based algorithm, then find out the difference (cost impacts factor) between inhomogeneity sample, normalize sample distribution,
For the sample verified by normal distribution, utilize central limit theorem, repeatedly sample to the sample under this classification, fit to perfect sample distribution form, sample estimates expense is interval again, shows the tendency form after matching; If when sample size is less than or equal to 50 and is greater than 10, carry out t distribution and estimate, try to achieve fiducial interval expense and estimate.
2. the hospitalization cost of Single diseases according to claim 1 estimates decision method, it is characterized in that, described influence factor comprises the formation of sex, age, disease code (ICD-10 coding), length of stay, mechanism's grade, state of being admitted to hospital, consultation time, complication complication, Hospitalization expenses and total expenses, and the formation of wherein said total expenses comprises medicine expense, cost of hospitalization, general inspection expense, large-scale Laboratory Fee, Operation Fee, treatment cost, diagnosis and treatment expense, other fees.
3. the hospitalization cost of Single diseases according to claim 1 estimates decision method, and it is characterized in that, the described estimated result to expense interval carries out modelling verification and specifically comprises:
Stochastic variable X after standardization has average μ=0, variance ￠ 2=1, substitutes into the density formula of X
in, its standard normal density is
;
K-S checks, and P>0.05, meets normal distribution, is distributed by normal distribution fiducial interval, calculates the expense border in fiducial interval;
Choose reserved checking sample, validation test sample falls into expense interval probability,
If test probability meets the description of fiducial interval substantially, then expense interval estimation is reasonable, otherwise it is interval then to need to re-start the new expense of sample transformation calculations.
4. the hospitalization cost of Single diseases according to claim 1 estimates decision method, and it is characterized in that, the method for described normalize has: the non-linear transfer such as root, logarithm of making even.
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Cited By (11)
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| CN108875303A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Dendrobidium huoshanness |
| CN108876634A (en) * | 2018-06-14 | 2018-11-23 | 四川久远银海软件股份有限公司 | A kind of cost information screening technique and device |
| CN108875309A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Dendrobium loddigesii |
| CN108875304A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Herba Dendrobii |
| CN109242709A (en) * | 2018-10-27 | 2019-01-18 | 平安科技（深圳）有限公司 | The method and apparatus for estimating medical expense |
| CN109544235A (en) * | 2018-05-28 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Disease cost measuring method, device, computer equipment and storage medium |
| CN109545371A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Hyperplasia of prostate quality certification method, equipment and server based on data processing |
| CN109636643A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Recognition methods, device, terminal and the computer readable storage medium of abnormal purchase medicine |
| CN110265127A (en) * | 2019-06-25 | 2019-09-20 | 河北省科学院应用数学研究所 | Disease cost measuring method, device and terminal device |
| WO2020087970A1 (en) * | 2018-10-30 | 2020-05-07 | 平安医疗健康管理股份有限公司 | Neural network-based disease type score verification method and computing device |
| CN112599227A (en) * | 2020-11-30 | 2021-04-02 | 望海康信(北京)科技股份公司 | Hospital resource consumption control method and system, corresponding equipment and storage medium |
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108875303A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Dendrobidium huoshanness |
| CN108875309A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Dendrobium loddigesii |
| CN108875304A (en) * | 2017-05-11 | 2018-11-23 | 北京蓝标成科技有限公司 | A kind of foundation, judgment criteria and the judgment method of the method judging the purebred phase recency of Herba Dendrobii |
| CN109544235A (en) * | 2018-05-28 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Disease cost measuring method, device, computer equipment and storage medium |
| CN108876634A (en) * | 2018-06-14 | 2018-11-23 | 四川久远银海软件股份有限公司 | A kind of cost information screening technique and device |
| CN109242709A (en) * | 2018-10-27 | 2019-01-18 | 平安科技（深圳）有限公司 | The method and apparatus for estimating medical expense |
| CN109545371A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | Hyperplasia of prostate quality certification method, equipment and server based on data processing |
| CN109242709B (en) * | 2018-10-27 | 2024-02-13 | 平安科技（深圳）有限公司 | Method and device for estimating medical cost |
| WO2020087970A1 (en) * | 2018-10-30 | 2020-05-07 | 平安医疗健康管理股份有限公司 | Neural network-based disease type score verification method and computing device |
| CN109636643A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Recognition methods, device, terminal and the computer readable storage medium of abnormal purchase medicine |
| CN110265127A (en) * | 2019-06-25 | 2019-09-20 | 河北省科学院应用数学研究所 | Disease cost measuring method, device and terminal device |
| CN112599227A (en) * | 2020-11-30 | 2021-04-02 | 望海康信(北京)科技股份公司 | Hospital resource consumption control method and system, corresponding equipment and storage medium |
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Application publication date: 20151118 |