CN109544374B - Disease seed score adjusting method based on big data and computing equipment - Google Patents
Disease seed score adjusting method based on big data and computing equipment Download PDFInfo
- Publication number
- CN109544374B CN109544374B CN201811282849.7A CN201811282849A CN109544374B CN 109544374 B CN109544374 B CN 109544374B CN 201811282849 A CN201811282849 A CN 201811282849A CN 109544374 B CN109544374 B CN 109544374B
- Authority
- CN
- China
- Prior art keywords
- disease
- score
- medical insurance
- case
- classification
- 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.)
- Active
Links
- 201000010099 disease Diseases 0.000 title claims abstract description 423
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 423
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000011156 evaluation Methods 0.000 claims abstract description 80
- 238000004364 calculation method Methods 0.000 claims description 23
- 238000004891 communication Methods 0.000 claims description 14
- 208000030990 Impulse-control disease Diseases 0.000 description 18
- 230000006870 function Effects 0.000 description 10
- 238000007619 statistical method Methods 0.000 description 10
- 230000002093 peripheral effect Effects 0.000 description 7
- 230000003902 lesion Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 208000024891 symptom Diseases 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 3
- 239000003814 drug Substances 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 206010025482 malaise Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The embodiment of the invention discloses a disease seed score adjusting method and computing equipment based on big data, wherein the method comprises the following steps: the method comprises the steps that a computing device receives a case set, wherein the case set is a set of cases, which are generated in a first evaluation period in a first region, of a disease classification belonging to a first disease classification, and the cases in the case set comprise actual medical insurance costs; calculating the disease type score of each case in the case set according to the basic disease type score of the first disease classification; calculating a first total medical insurance cost of the case set according to the disease seed scores of all cases in the case set, and calculating a second total medical insurance cost of the case set according to the actual medical insurance cost of all cases in the case set; and further, adjusting the first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense so as to calculate the disease score of the case belonging to the first disease classification generated in the second evaluation period through the adjusted first basic disease score and improve the accuracy of the disease score.
Description
Technical Field
The invention relates to the technical field of medical management, in particular to a disease seed score adjusting method and computing equipment based on big data.
Background
With the continuous deep arrangement and medical reform of national public medical treatment, the payment system reform is the embodiment that the system engineering is the important transformation of medical insurance management concept and medical insurance manager roles. Pay according to the disease seeds is carried out, and the key point that the payment system is comprehensive medical reform is fully embodied.
The term pay-per-disease type refers to scientifically preparing rated reimbursement standard of each disease through unified disease diagnosis classification, and social security institutions pay hospitalization fees to fixed-point medical institutions according to the standard and hospitalization times, so that medical resource utilization is standardized, namely medical institution resource consumption is in direct proportion to the number of hospitalized patients treated, the complexity of the disease and the service intensity. In short, the cost of a certain disease is clearly specified, so that medical institutions are prevented from abusing medical service projects, repeating projects and decomposing projects, hospital sickness is prevented from being treated greatly, and medical service quality is guaranteed.
The standardized medical information is very important for the application of the medical information big data in the payment mode of charging according to the disease types, and the standardized medical information is a precondition for realizing the application of the medical big data. Classification of disease species currently generally employs international disease classification (international Classification of diseases, ICD). ICD-10 divides diseases into 21 sections and 26000 kinds of diseases according to the characteristics of etiology, part, pathology, clinical manifestations and the like, and codes each disease kind. However, for the whole medical environment of China, the number of common disease types in each region is far less than 26000, and when medical staff records cases, the medical staff often does not grade according to the international standard due to the diversity and complexity of the disease types in the prior art, and each region has regional language description, so that a certain difficulty is brought to the implementation of paying according to the disease types.
According to regulations, when determining payment standards according to the disease types in various places, the factors such as medical service cost, actual occurrence cost, medical insurance fund bearing capacity, and negative carry water of the paramedics should be fully considered, and by combining the main operation and treatment modes of the disease types, the payment standards are reasonably determined through negotiations with medical institutions, the medical cost according to the medical conditions in various places is managed, and the detection and analysis of cases, medical payment standards and the like are all technical problems which need to be solved urgently at present.
Disclosure of Invention
The embodiment of the invention provides a disease type score adjusting method and computing equipment based on big data, aiming at the big data of cases, the disease type score of a first disease type classification is adjusted through the first total medical insurance cost of the cases belonging to the first disease type classification calculated by the disease type score and the second total medical insurance cost of the cases belonging to the first disease type classification calculated by the actual medical insurance cost in the cases, so that the disease type score can be adjusted according to the change of the current actual medical conditions, and the accuracy of the disease type score is improved.
In a first aspect, an embodiment of the present invention provides a disease seed score adjustment method based on big data, including:
The method comprises the steps that a computing device receives a case set, wherein the case set is a set of cases, generated in a first evaluation period, of a first region, and the disease classification belongs to a first disease classification, and any case in the case set at least comprises actual medical insurance cost;
The computing device computes a disease score for each case in the case set according to the base disease score for the first disease classification;
The computing equipment calculates first total medical insurance costs of the case set according to disease seed scores of all cases in the case set, and calculates second total medical insurance costs of the case set according to actual medical insurance costs of all cases in the case set;
The computing device adjusts a first base disease score for the first disease classification based on the first total medical insurance expense and the second total medical insurance expense, the adjusted first base disease score being used to calculate a disease score for cases in the first disease classification generated in the second evaluation period for the first region.
In one implementation of the application, the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
In yet another implementation of the present application, the first disease classification is an item in a disease classification dictionary, the disease classification dictionary including a plurality of disease classifications and disease classification codes in one-to-one correspondence with the plurality of disease classifications, the disease classification codes being ICD codes or first N-bit codes of ICD codes, the N being a positive integer less than 6.
In yet another implementation of the present application, the case set includes a case i, where a calculation formula of a disease seed score of the case i is: y i＝A*Ci+Ei;
Wherein i is the index of the cases in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the first sub-case set, A is the first basic disease score, C i is the hospital grade coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i.
Optionally, the method for calculating the first total medical insurance expense includes: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
In yet another implementation of the present application, before the computing device adjusts the first base disease score for the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, the method further includes:
judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold and smaller than a second threshold, and if not, triggering the computing equipment to execute the step of adjusting the first basic disease type score corresponding to the first disease type classification according to the first total medical insurance expense and the second total medical insurance expense.
Optionally, the computing device adjusting the first base disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense comprises:
A′＝A*σ
Wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is the adjustment parameter, and sigma > 0.
Optionally, the adjustment parameter σ is such that a ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score and the second total medical insurance expense is greater than a first threshold and less than a second threshold.
In a second aspect, embodiments of the present application also provide a computing device, including:
the system comprises a receiving unit, a judging unit and a judging unit, wherein the receiving unit is used for receiving a case set, the case set is a set of cases, wherein the disease classification generated in a first evaluation period in a first region belongs to a first disease classification, and any case in the case set at least comprises actual medical insurance expense;
a first calculation unit, configured to calculate a disease score of each case in the case set according to the basic disease score of the first disease classification;
the second calculation unit is used for calculating the first total medical insurance cost of the case set according to the disease seed scores of all cases in the case set;
a third calculation unit, configured to calculate a second total medical insurance cost of the case set according to actual medical insurance costs of all cases in the case set;
The adjusting unit is used for adjusting a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, and the adjusted first basic disease score is used for calculating the disease score of the case, which is generated in the second evaluation period, of the first region and is classified as the first disease classification.
In one implementation of the application, the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
In yet another implementation of the present application, the first disease classification is an item in a disease classification dictionary, the disease classification dictionary including a plurality of disease classifications and disease classification codes in one-to-one correspondence with the plurality of disease classifications, the disease classification codes being ICD codes or first N-bit codes of ICD codes, the N being a positive integer less than 6.
In yet another implementation of the present application, the case set includes a case i, where a calculation formula of a disease seed score of the case i is: y i＝A*Ci+Ei;
Wherein i is the index of the cases in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the first sub-case set, A is the first basic disease score, C i is the hospital grade coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i.
Optionally, the method for calculating the first total medical insurance expense includes: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
In yet another implementation of the present application, the computing device further includes:
The judging unit is used for judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold value and smaller than a second threshold value, and if not, triggering the adjusting unit to execute the first basic disease type score corresponding to the first disease type classification according to the first total medical insurance expense and the second total medical insurance expense.
Optionally, the adjusting unit is specifically configured to adjust the first basic disease seed score of the first disease seed classification by the following formula, including:
A′＝A*σ
Wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is the adjustment parameter, and sigma > 0.
Optionally, the adjustment parameter σ is such that a ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score and the second total medical insurance expense is greater than a first threshold and less than a second threshold.
In a third aspect, an embodiment of the present application further provides a computing device, including a processor, a memory, and a communication module, where the processor is coupled to the memory, the communication module, and the processor is configured to invoke program code stored in the memory to execute:
receiving a case set through the communication module, wherein the case set is a set of cases, which are generated in a first evaluation period in a first region, of which the disease classification belongs to a first disease classification, and any case in the case set at least comprises actual medical insurance expense;
Calculating the disease score of each case in the case set according to the basic disease score of the first disease classification;
Calculating a first total medical insurance cost of the case set according to the disease seed scores of all cases in the case set, and calculating a second total medical insurance cost of the case set according to the actual medical insurance cost of all cases in the case set;
And adjusting a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, wherein the adjusted first basic disease score is used for calculating the disease score of the case, which is generated in the second evaluation period, of the first region and is classified as the first disease classification, according to the disease classification of the first region.
In one implementation of the application, the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
In yet another implementation of the present application, the first disease classification is an item in a disease classification dictionary, the disease classification dictionary including a plurality of disease classifications and disease classification codes in one-to-one correspondence with the plurality of disease classifications, the disease classification codes being ICD codes or first N-bit codes of ICD codes, the N being a positive integer less than 6.
In yet another implementation of the present application, the case set includes a case i, where a calculation formula of a disease seed score of the case i is: y i＝A*Ci+Ei;
Wherein i is the index of the cases in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the first sub-case set, A is the first basic disease score, C i is the hospital grade coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i.
Optionally, the method for calculating the first total medical insurance expense includes: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
In yet another implementation of the present application, before the processor executes the adjusting the first base lesion score of the first lesion classification according to the first total medical insurance expense and the second total medical insurance expense, the processor is further configured to execute:
Judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold and smaller than a second threshold, and if not, triggering the processor to execute the step of adjusting the first basic disease classification value corresponding to the first disease classification according to the first total medical insurance expense and the second total medical insurance expense.
Optionally, the processor executing the adjusting the first base lesion score of the first lesion classification according to the first total medical insurance expense and the second total medical insurance expense comprises:
A′＝A*σ
Wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is the adjustment parameter, and sigma > 0.
Optionally, the adjustment parameter σ is such that a ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score and the second total medical insurance expense is greater than a first threshold and less than a second threshold.
In a fourth aspect, embodiments of the present application further provide a computer storage medium for computer software instructions which, when executed by a computer, cause the computer to perform any of the big data based disease seed score adjustment methods according to the first aspect.
In a fifth aspect, embodiments of the present application further provide a computer program comprising computer software instructions which, when executed by a computer, cause the computer to perform any of the big data based disease seed score adjustment methods of the first aspect.
In summary, the embodiment of the invention provides a disease type score adjustment method and computing equipment based on big data, aiming at case big data generated in a first evaluation period (such as the last evaluation period of the current evaluation period), a first basic disease type score of the first disease type classification is adjusted through a first total medical insurance cost of the first disease type classification calculated by the disease type score and a second total medical insurance cost of the first disease type classification calculated by the actual medical insurance cost in the cases, and the purpose is to calculate the disease type score of the cases generated in a second evaluation period (such as the current evaluation period) through the adjusted first basic disease type score so as to realize adjustment of the disease type score of the cases generated in the second evaluation period, so that the disease type score can be adjusted according to the change of the current actual medical condition, and the accuracy of the disease type score is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a functional architecture diagram of a medical insurance management platform provided by an embodiment of the present invention;
FIG. 2 is a flowchart of a method for adjusting disease score according to an embodiment of the present invention;
FIG. 3 is a flowchart of another method for adjusting disease score according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a computing device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a further computing device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of another computing device according to an embodiment of the present invention.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that the terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
For better understanding of the embodiments of the present invention, the following describes the functions of the medical insurance management platform applicable to the embodiments of the present invention, please refer to fig. 1, fig. 1 is a functional architecture diagram of the medical insurance management platform provided by the embodiments of the present invention, where the medical insurance management platform may be operated in a computing device, and a series of functions related to cases, medical insurance, disease scores, etc. provided for an operator of the medical insurance management platform, where the medical insurance management platform includes, but is not limited to, implementation of some or all of the following functions:
The disease type coding, the medical insurance management platform can code the disease type obtained by main diagnosis in the case according to the case data of the input case, and the disease type coding method can adopt ICD-10 coding (also called six-bit code coding in the application), can also adopt other coding methods, such as four-bit code coding (namely the first 4 bits of the six-bit code), three-bit code coding (namely the first 3 bits of the six-bit code) and the like. It can be understood that a disease classification dictionary applicable to a certain region can be established through a disease coding method by a case set occurring in the region, the disease classification dictionary comprises M disease classification names and M disease classification codes corresponding to the M disease classification names one by one, and M is a positive integer. Optionally, the computing device may identify, based on the information such as the diagnosis name and the disease code filled by the medical staff in the case data, to identify the disease classification corresponding to the case, so as to supplement the disease classification code corresponding to the disease classification to the case data, so as to further calculate the disease score of the case, thereby realizing the functions of paying according to the disease, detecting the authenticity of the case data based on the disease score, and the like.
The medical insurance management platform can store a corresponding relation table of disease types and disease type scores or comprises a disease type score calculation program, and can determine the disease type classification of a participant (namely a patient) in a case through the name of the disease type, the code of the disease type and the like in the case, so that the disease type score of the case is determined according to the corresponding relation of the disease type classification and the disease type scores or the realization process of the disease type score calculation program and the like based on the disease type classification. Wherein the disease score is a standard score for calculating medical costs (e.g., predicted medical insurance costs, predicted total costs, etc.) determined based on case big data for a region (e.g., country, province, city, etc.). Specifically, a disease score dictionary may be established, where the disease score dictionary includes a one-to-one correspondence between the identifiers of M disease classifications and M basic disease scores, and then the disease scores are adjusted based on the basic disease scores according to the actual conditions of the cases (such as the age of the patient, the severity of the disease, the information of the hospital, the department, etc.), so as to obtain the disease score suitable for the case. The disease seed score and the medical cost are in positive correlation, namely, the higher the disease seed score is, the higher the medical cost of the disease seed is.
Statistical analysis of case data the medical insurance management platform may perform statistical analysis on the cases reported by various hospitals in the region according to the evaluation period (e.g., month, quarter, year, etc.). The statistical analysis of cases can support statistical analysis according to month, quarter, year and the like, and support statistical analysis of one or more of different hospitals, different expense intervals, different disease types and the like, such as occurrence number, total expense, actual medical insurance expense, predicted medical insurance expense and the like, so as to adjust disease type scores of each disease type adopted in the next evaluation period based on the statistical analysis result. It should be understood that other functions may be implemented based on the statistical analysis result, such as adjusting hospital level coefficients of hospitals based on the statistics of income and expense of each hospital, which is not limited in this embodiment of the present application.
The authenticity detection of the case can be carried out by the medical insurance management platform based on the case data in the case, when the case is detected to contain false data, the case is marked, a prompt message that the case contains false data is output, and the like, so that an operator of the medical insurance management platform can identify the problem case in time, and analyze the cause of the problem case.
The data visualization is realized, and the medical insurance management platform can visualize the statistical analysis result obtained by the statistical analysis function of the case data and also can visualize the result of the statistical analysis of the problem cases, so that the operator of the medical insurance management platform can conveniently calculate the analysis result.
In the present application, computing devices may include, but are not limited to, mobile phones, mobile computers, tablet computers, media players, computers, servers, etc. that contain data processing functionality. The computing device running the various functions of the medical insurance management platform may receive cases reported from institutions or individuals such as hospitals.
The medical insurance management platform provided by the application is not limited to that shown in fig. 1, and can also comprise implementation of other functions, such as optimization of disease type scores, and the like, so that the embodiment of the application is not limited.
The embodiment of the invention provides a disease type score adjustment method and calculation equipment based on big data, aiming at a case set of a first disease type classification generated in a first evaluation period (such as the last evaluation period of the current evaluation period), the first basic disease type score of the first disease type classification is adjusted through the first total medical insurance cost of the first disease type classification calculated by the disease type score and the second total medical insurance cost of the first disease type classification calculated by the actual medical insurance cost in cases, and the purpose is to calculate the disease type score of the case generated in a second evaluation period (such as the current evaluation period) through the adjusted first basic disease type score so as to realize adjustment of the disease type score of the case generated in the second evaluation period, so that the disease type score can be adjusted according to the change of the current actual medical condition, and the accuracy of the disease type score is improved.
Referring to fig. 2, fig. 2 is a flow chart of a disease seed score adjusting method based on big data provided by the application. In the embodiment of fig. 2, the main body of execution of the disease score adjustment method is taken as an example of a computing device (a device running each function of the case management platform), and it is understood that the disease score adjustment method may also be implemented by other devices having a data processing function, such as a terminal or a server, which is not limited in this embodiment of the present application. As shown in fig. 2, the method may include, but is not limited to, some or all of the following steps:
s2: and receiving a case set, wherein the case set is a set of cases, which are generated in a first evaluation period in a first region, of a disease classification belonging to a first disease classification, and any case in the case set at least comprises actual medical insurance expense.
The case set is a set of cases in which the disease classification of each hospital in the first evaluation period belongs to the first disease classification, and the first region may be an area determined by an operator of a medical insurance management platform, such as Beijing city, shenzhen city, guangdong province, and the like. The evaluation period may be a weekly, monthly, quarterly, or annual time interval, and the first evaluation period may be the last evaluation period of the evaluation period in which the current time is located.
Wherein the case is a patient diagnostic treatment course recorded by a hospital for the patient. The case data may include, but is not limited to, one or more of personal information of the patient, diagnostic information, treatment information, cost information, actual medical insurance costs, and the like. Wherein the diagnostic information may include a diagnostic identifier for identifying a classification of the disease species of the attendee. Wherein the diagnostic identifier may be a diagnostic name, such as a master diagnostic name; diagnostic codes, such as ICD diagnostic codes, etc.; the surgical identifier may also be a surgical name, surgical code, etc. It should be understood that personal information may include, but is not limited to, information of the age, sex, medical history, etc. of the participant. The treatment information is the process information of the treatment of the underwriting person recorded in the case. The fee information includes, but is not limited to, one or more of a surgical fee, a hospital fee, a detection fee, a registration fee, a drug fee, a total fee, etc., generated by the participant during the course of the current disease treatment.
In particular, the computing device may identify and extract features of the case data from the case data, which may include, but are not limited to, one or more of diagnostic identification, drug dosage, drug cost, identification of test items, test item cost, surgical identification, surgical cost, number of days in hospital, hospitalization cost, complication identification, secondary symptom identification, participant age, participant gender, and the like.
Optionally, the second evaluation period is the first evaluation period, or a next evaluation period to the first evaluation period. It will be appreciated that the computing device may effect adjustment of the underlying disease score used by the first evaluation period or the current evaluation period through cases generated by the first evaluation period (e.g., the last evaluation period of the current evaluation period).
In the embodiment of the application, the computing device may receive all cases generated in the first evaluation period in the first region, where the cases have been added with the identification of the disease classification, that is, the disease classification of each case has been identified, and the case set is a case set in which the disease classification obtained by screening from all cases generated in the first evaluation period in the first region is the first disease classification. In another embodiment of the present application, the case in the case set may not add a disease classification identification, and the computing device may identify a disease classification for the case based on the disease classification dictionary and the diagnostic identification in the case.
The first disease classification is a term in a disease classification dictionary, the disease classification dictionary comprises a plurality of disease classifications and disease classification codes corresponding to the plurality of disease classifications one to one, the disease classification codes are ICD codes or first N bit codes of the ICD codes, and N is a positive integer smaller than 6.
For the first N-bit codes of the disease classification codes in the disease classification dictionary, selecting the first four, the first three or the first two of the ICD codes of the disease, and selecting the 'four-bit code' as the disease classification code depending on the case number of the disease classification of the cases in the sample case set as the first four of the ICD codes, for example, more than 10 cases; if the number is less than 10, a three-bit code is selected as a disease classification code. The encoding mode of the disease classification can reduce the disease classification, and the medical environment which is more close to the current medical area is obtained, so that the classification mode of the disease classification and the disease payment mode can be better applied to local places.
S4: and calculating the disease type score of each case in the case set according to the basic disease type score of the first disease classification.
Specifically, the computing device may pre-store a disease score dictionary, where the disease score dictionary includes a plurality of disease classifications and basic disease scores corresponding to the plurality of disease classifications one-to-one, and the computing device may find a basic disease score corresponding to the first disease classification, that is, the first disease score, according to the disease score dictionary.
The basic disease score is used for calculating the disease score of the case, however, the severity of the illness of the participants in the multiple cases of the same disease classification may be different, and the adopted treatment means may have larger differences, so that the disease scores of the multiple cases of the same disease classification may be different, i.e. special cases may be included, and the difference of the disease scores among the multiple cases of the same disease classification may be represented by additional disease scores.
For example, when the case data of the case i includes one or more of a preset operation, a preset complication, a preset secondary complication, a preset hospitalization information, etc., the disease score of the case i needs to be increased by an additional disease score corresponding to the item included in the case i.
Optionally, the case set includes a case i, where the calculation formula of the disease score of the case i is:
Yi＝A*Ci+Ei；
wherein i is the index of the cases in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the case set, A is the first basic disease score, C i is the hospital level coefficient of the hospital where the case i is located, E i is the additional disease score of the case i, S 1 is the first total medical insurance cost, and D is the score unit price.
Optionally, the preset procedure is a procedure specified by a procedure identifier in a procedure list (e.g., a procedure with a greater difficulty, a procedure with a procedure cost greater than a preset amount (e.g., 3 ten thousand yuan), etc.), the procedure list including one or more procedure identifiers; the preset complications are disease classification specified by disease classification identifiers in a complications list, and the complications list comprises one or more disease classification identifiers; the preset secondary symptoms are disease classification specified by disease classification identifiers in a secondary symptom list, and the secondary symptom list comprises one or more disease classification identifiers; the preset hospitalization information comprises a hospitalization day longer than a first time period. The first period may be 7 days, 10 days, 22 days, 30 days, or other days, which is not limited to the present application.
It should be noted that, the disease score may also include other calculation methods, such as Y i＝A*Ci, etc., which are not limited by the embodiment of the present application.
S6: calculating the first total medical insurance cost of the case set according to the disease type scores of all cases in the case set, and calculating the second total medical insurance cost of the case set according to the actual medical insurance cost of all cases in the case set.
It can be understood that the predicted medical insurance cost of the case i is the product of the disease type score and the score unit price of the case i, the first total medical insurance cost is the sum of the predicted medical insurance costs of all cases in the case set, and the second total medical insurance cost is the sum of the actual medical insurance costs of all cases in the case set.
Optionally, the method for calculating the first total medical insurance expense includes:
S1＝∑iYi*D；
Wherein S 1 is the first total medical insurance expense, and D is the unit price of the score.
It will be appreciated that D may be a fixed value or may vary with the total score. One implementation of determining D is: calculating a total disease score Y Total (S) of the cases occurring in the first evaluation period in the first region, and calculating a score unit price D according to the total control cost (namely, the total cost S Total (S) of the first region for medical insurance in the first evaluation period), namely:
YTotal (S) ＝∑jYj，
Wherein Y j is the disease score of the j-th case in the set of cases occurring in the first evaluation period in the first region, j is the index of the disease, and j is a positive integer.
The calculation method of the second total medical insurance expense comprises the following steps:
Wherein S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of case i.
S8: and adjusting a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, wherein the adjusted first basic disease score is used for calculating the disease score of the case, which is generated in the second evaluation period in the first region, of the disease classification of the first disease classification.
It will be appreciated that when the first total medical insurance cost is greater than the second total medical insurance cost, it is indicated that the medical insurance cost divided into the first disease classification in the total control cost of the first region is greater than the medical insurance cost actually reimbursed for the cases of the first disease classification, and that all hospitals in the first region are integrally profitable on the cases of the first disease classification, at this time, the underlying disease score of the first disease classification needs to be reduced. However, when the first total medical insurance cost is smaller than the second total medical insurance cost, the cost of classifying the first disease type in the total control cost of the whole city is smaller than the medical insurance cost actually reimbursed by the first disease type classification, and all hospitals in the first area have deficiency in the cases of the first disease type classification, and then the basic disease type score of the first disease type classification needs to be increased. When the first total medical insurance cost is equal to the second total medical insurance cost or the difference is smaller, the cost divided into the first disease classification in the total control cost of the first area is equal to or about equal to the medical insurance cost actually reimbursed by the first disease classification, and the basic disease classification value is reasonably priced corresponding to the first disease classification, so that the basic disease classification value of the first disease classification can be unchanged.
Optionally, the computing device may also output a prompt indicating that the first underlying disease seed score is set too high, too low, or appropriate.
In an embodiment of the present invention, before step S8, the method may further include S71 or S72. Please refer to the flowchart of another disease score adjustment method shown in fig. 3.
S71: and judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold value and smaller than a second threshold value.
Wherein the first threshold is smaller than the second threshold, and the first threshold may be 0.1-0.9, for example, 0.4, 0.5, 0.7, or other values, which are not limited in the embodiments of the present application. The second threshold may have a value ranging from 1.1 to 3, for example, 1.5, 2, 2.4, or other values, which are not limited in this embodiment.
S72: and judging whether the difference value between the first total medical insurance expense and the second total medical insurance expense is larger than a third threshold value.
Wherein, the third threshold value can be 1 ten thousand, 2 ten thousand, 3.5 ten thousand or other numerical values, etc. Alternatively, the third threshold P may be set according to the first total medical insurance expense or the second total medical insurance expense, for example, p=s 1 ×μ, where 0 < μ < 1, for example, is 0.1, 0.2, 0.25, or other values, and the embodiment of the present application is not limited.
And when the judgment result of the step S71 is NO or the judgment result of the step S72 is YES, triggering the computing equipment to execute the step S8, otherwise, ending the flow.
One implementation of adjusting the first underlying disease score of the first disease classification may be:
A′＝A*σ
wherein A' is the first basic disease seed score after adjustment, A is the first basic disease seed score before adjustment, sigma is the adjustment parameter, sigma > 0.
Optionally, the parameter σ is adjusted such that a ratio of the total medical insurance expense calculated from the adjusted first base disease seed score to the second total medical insurance expense is greater than the first threshold Q 1 and less than the second threshold Q 2. That is, the parameter σ is adjusted so that the following condition holds:
Wherein S 1′＝∑iYi′*D,Yi′＝A′*Ci+Ei,Yi ' is the disease score of the disease i calculated according to the adjusted first basic disease score, S 1 ' is the total medical insurance expense of the first disease classification calculated again according to the adjusted basic disease score A ', i is the index of the case in the case set, i is a positive integer, case i is the case in the case set, C i is the hospital grade coefficient of the hospital where case i is located, E i is the additional disease score of case i, and D is the score unit price.
Optionally, the adjustment parameter σ may be a value such that a difference between the total medical insurance expense calculated from the adjusted first base disease seed score and the second total medical insurance expense is smaller than a third threshold value.
Alternatively, the process may be carried out in a single-stage,I.e. Σ i(A*σ*Ci+Ei)*D＝∑iTi.
It should be noted that, the embodiment of the present invention may further include other adjustment manners, for example, adjusting the total control cost of the first area, and recalculating the unit price of the score, so that the ratio of the total medical insurance cost calculated by the first basic disease score according to the adjusted unit price of the score to the second total medical insurance cost is greater than the first threshold and less than the second threshold.
In an embodiment of the present invention, the computing device may further recalculate a first base disease score of the first disease classification based on the case set, where the recalculated first base disease score is the adjusted first base disease score. It will be appreciated that the adjusted first underlying disease seed score A' is:
Wherein P is the number of cases in the case set, D' may be equal to D, and the unit price may be recalculated based on the set of cases obtained in the first evaluation period in the first region and the total cost.
Therefore, the embodiment of the invention aims at the case set of the first disease classification, which is generated in the first evaluation period (such as the last evaluation period of the current evaluation period), and adjusts the first basic disease score of the first disease classification through the first total medical insurance expense of the case set calculated by the disease score and the second total medical insurance expense of the case set calculated by the actual medical insurance expense in the case, so as to calculate the disease score of the case generated in the second evaluation period (such as the current evaluation period) through the adjusted first basic disease score, thereby realizing the adjustment of the disease score of the case generated in the second evaluation period, enabling the disease score to be adjusted according to the change of the current actual medical condition and improving the accuracy of the disease score.
The following describes the apparatus according to the embodiment of the invention.
Referring to fig. 4, computing device 40 includes, but is not limited to: a receiving unit 41, a first calculating unit 42, a second calculating unit 43, a third calculating unit 44, an adjusting unit 45, and the like. Wherein,
A receiving unit 41, configured to receive a case set, where the case set is a set of cases in which a disease classification generated in a first evaluation period in a first region belongs to a first disease classification, and any case in the case set includes at least an actual medical insurance cost;
a first calculation unit 42 for calculating a disease score for each case in the case set based on the base disease score for the first disease class;
a second calculation unit 43 for calculating a first total medical insurance cost of the case set according to disease score of all cases in the case set;
A third calculation unit 44 for calculating a second total medical insurance cost for the case set based on actual medical insurance costs for all cases in the case set;
The adjusting unit 45 is configured to adjust a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, where the adjusted first basic disease score is used to calculate a disease score of a case in which the disease classification generated in the first region in the second evaluation period is the first disease classification.
In one implementation of the application, the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
In yet another implementation of the present application, the first disease classification is an item in a disease classification dictionary, the disease classification dictionary including a plurality of disease classifications and disease classification codes in one-to-one correspondence with the plurality of disease classifications, the disease classification codes being ICD codes or first N-bit codes of ICD codes, the N being a positive integer less than 6.
In yet another implementation of the present application, the case set includes a case i, where a calculation formula of a disease seed score of the case i is: y i＝A*Ci+Ei;
Wherein i is the index of the case in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the first sub-case set, A is the first basic disease score, C i is the hospital grade coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i.
Optionally, the method for calculating the first total medical insurance expense includes: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
Referring to fig. 5, a computing device is shown that may include a determination unit 46 in addition to the various units shown for computing device 40 in fig. 4. Wherein,
And the judging unit 46 is configured to judge whether a ratio of the first total medical insurance expense to the second total medical insurance expense is greater than a first threshold and smaller than a second threshold, and if not, trigger the adjusting unit to execute the adjustment of the first basic disease score corresponding to the first disease classification according to the first total medical insurance expense and the second total medical insurance expense.
Optionally, the adjusting unit 45 is specifically configured to adjust the first base disease score of the first disease classification by the following formula, including:
A′＝A*σ
wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is the adjustment parameter, and sigma >0.
Optionally, the adjustment parameter σ is such that a ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score and the second total medical insurance expense is greater than a first threshold and less than a second threshold.
It should be noted that, specific implementations of each unit of the above computing device may be referred to the related descriptions in the above method embodiments, and the disclosure is not repeated.
As with the computing device shown in fig. 6, the computing device 600 may include: baseband chip 610, memory 615 (one or more computer-readable storage media), communication module 616 (e.g., radio Frequency (RF) module 6161 and/or communication interface 6162), peripheral system 617, and communication interface 623. These components may communicate over one or more communication buses 614.
The peripheral system 617 is primarily intended to implement interactive functionality between the computing device 610 and a user/external environment and primarily comprises input/output means of the computing device 600. In particular implementations, peripheral system 617 may include: a touch screen controller 618, a camera controller 619, an audio controller 620, and a sensor management module 621. Wherein each controller may be coupled to a respective peripheral device (e.g., touch screen 623, camera 624, audio circuit 625, and sensor 626). It should be noted that the peripheral system 617 may also include other I/O peripherals.
The baseband chip 610 may integrally include: one or more processors 611, a clock module 622, and a power management module 613. The clock module 622 integrated in the baseband chip 610 is mainly used to generate clocks required for data transmission and timing control for the processor 611. The power management module 613 integrated in the baseband chip 610 is mainly used for providing stable and high-precision voltage to the processor 611, the rf module 6161 and the peripheral system.
The Radio Frequency (RF) module 6161 is used to receive and transmit radio frequency signals, and is primarily integrated with the receiver and transmitter of the computing device 600. The Radio Frequency (RF) module 6161 communicates with a communication network and other communication devices through radio frequency signals. In particular implementations, the Radio Frequency (RF) module 6161 may include, but is not limited to: an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chip, a SIM card, a storage medium, and so forth. In some embodiments, the Radio Frequency (RF) module 6161 may be implemented on a separate chip.
The communication module 616 is used for data exchange between the computing device 600 and other devices.
Memory 615 is coupled to processor 611 for storing various software programs and/or sets of instructions. In particular implementations, memory 615 may include high-speed random access memory, and may also include non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory 615 may store an operating system (hereinafter referred to as a system), such as ANDROID, IOS, WINDOWS, or an embedded operating system, such as LINUX. Memory 615 may also store network communication programs that may be used to communicate with one or more additional devices, one or more computing device devices, and one or more network devices. The memory 615 may also store a user interface program that can vividly display the content image of the application program through a graphical operation interface, and receive control operations of the application program by a user through input controls such as menus, dialog boxes, buttons, and the like.
Memory 615 may also store one or more application programs. As shown in fig. 6, these applications may include: social applications (e.g., facebook), image management applications (e.g., album), map class applications (e.g., google map), browsers (e.g., safari, google Chrome), and so forth.
In the present application, processor 611 is operative to read and execute computer readable instructions. In particular, the processor 611 may be configured to invoke a program stored in the memory 615, for example, a program for implementing the disease seed score calculation method provided by the present application, and execute instructions included in the program.
Specifically, the processor 611 may be configured to call a program stored in the memory 615, such as a program for implementing the disease seed score calculating method provided in the present application, and execute the following procedures:
Receiving, via the communication module 616, a set of cases, the set of cases being a set of cases in which a disease classification generated in a first evaluation period in a first region belongs to a first disease classification, any one of the cases including at least an actual medical insurance charge;
Calculating the disease score of each case in the case set according to the basic disease score of the first disease classification;
Calculating a first total medical insurance cost of the case set according to the disease seed scores of all cases in the case set, and calculating a second total medical insurance cost of the case set according to the actual medical insurance cost of all cases in the case set;
And adjusting a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, wherein the adjusted first basic disease score is used for calculating the disease score of the case, which is generated in the second evaluation period, of the first region and is classified as the first disease classification, according to the disease classification of the first region.
In one implementation of the application, the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
In yet another implementation of the present application, the first disease classification is an item in a disease classification dictionary, the disease classification dictionary including a plurality of disease classifications and disease classification codes in one-to-one correspondence with the plurality of disease classifications, the disease classification codes being ICD codes or first N-bit codes of ICD codes, the N being a positive integer less than 6.
In yet another implementation of the present application, the case set includes a case i, where a calculation formula of a disease seed score of the case i is: y i＝A*Ci+Ei;
Wherein i is the index of the case in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the first sub-case set, A is the first basic disease score, C i is the hospital grade coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i.
Optionally, the method for calculating the first total medical insurance expense includes: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
In yet another implementation of the present application, before the processor 611 executes the adjusting the first base disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, the processor 611 is further configured to execute:
Judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold and smaller than a second threshold, and if not, triggering the processor 611 to execute the step of adjusting the first basic disease type score corresponding to the first disease type classification according to the first total medical insurance expense and the second total medical insurance expense.
Optionally, the processor 611 executing the adjusting the first base lesion score of the first lesion classification according to the first total medical insurance expense and the second total medical insurance expense includes:
A′＝A*σ
Wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is the adjustment parameter, and sigma > 0.
Optionally, the adjustment parameter σ is such that a ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score and the second total medical insurance expense is greater than a first threshold and less than a second threshold.
It may be understood that the specific implementation of each flow and each functional unit may refer to the related description in the above method embodiment, and the embodiment of the present application is not repeated.
It should be understood that computing device 600 is only one example provided for embodiments of the invention, and that computing device 500 may have more or fewer components than shown, may combine two or more components, or may have different configuration implementations of the components.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs.
The modules in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. The disease seed score adjusting method based on big data is characterized by comprising the following steps:
The method comprises the steps that a computing device receives a case set, wherein the case set is a set of cases, generated in a first evaluation period, of a first region, and the disease classification belongs to a first disease classification, and any case in the case set at least comprises actual medical insurance cost;
The computing device computes a disease score for each case in the case set according to the base disease score for the first disease classification;
The computing equipment calculates first total medical insurance costs of the case set according to disease seed scores of all cases in the case set, and calculates second total medical insurance costs of the case set according to actual medical insurance costs of all cases in the case set;
The computing equipment adjusts a first basic disease score of the first disease classification according to the first total medical insurance expense and the second total medical insurance expense, and the adjusted first basic disease score is used for calculating the disease score of the case, which is generated in the first region in a second evaluation period, of the disease classification of the first disease classification;
the first disease classification is a term in a disease classification dictionary, the disease classification dictionary comprises a plurality of disease classifications and disease classification codes which are in one-to-one correspondence with the plurality of disease classifications, the disease classification codes are first N bit codes coded by ICD, and N is a positive integer less than 6;
The case set comprises a case i, and the calculation formula of the disease score of the case i is as follows: y i＝A*Ci+Ei;
Wherein i is the index of the cases in the case set, i is a positive integer, Y i is the disease score of the case i, the case i is the case in the case set, A is the first basic disease score, C i is the hospital level coefficient of the hospital where the case i is located, and E i is the additional disease score of the case i;
the computing device adjusting a first base disease seed score for the first disease seed classification according to the first total medical insurance expense and the second total medical insurance expense includes:
A′＝A*σ
wherein A' is the adjusted first basic disease seed score, A is the first basic disease seed score, sigma is an adjustment parameter, and sigma is more than 0;
The adjustment parameter sigma enables the ratio of the third total medical insurance expense calculated according to the adjusted first basic disease seed score to the second total medical insurance expense to be larger than a first threshold value and smaller than a second threshold value.
2. The method of claim 1, wherein the second evaluation period is the first evaluation period or a next evaluation period to the first evaluation period.
3. The method of claim 1, wherein the first total medical insurance cost is calculated by: s 1＝∑iYi x D;
The calculation method of the second total medical insurance expense comprises the following steps: s 2＝∑iTi
Wherein S 1 is the first total medical insurance cost, D is the unit price of the score, S 2 is the second total medical insurance cost, and T i is the actual medical insurance cost of the case i.
4. The method of any one of claims 1-3, wherein prior to the computing device adjusting the first base disease score for the first disease classification based on the first total medical insurance expense and the second total medical insurance expense, the method further comprises:
judging whether the ratio of the first total medical insurance expense to the second total medical insurance expense is larger than a first threshold and smaller than a second threshold, and if not, triggering the computing equipment to execute the step of adjusting the first basic disease type score corresponding to the first disease type classification according to the first total medical insurance expense and the second total medical insurance expense.
5. A computing device comprising a processor, a memory, and a communication module, the processor coupled to the memory, the communication module, the processor operable to invoke program code stored in the memory to perform the big data based disease seed score adjustment method of any of claims 1-4.
6. A computing device comprising functional units for implementing the big data based disease seed score adjustment method of any of claims 1-4.
7. A computer storage medium for computer software instructions which, when executed by a computer, cause the computer to perform the big data based disease seed score adjustment method of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282849.7A CN109544374B (en) | 2018-10-30 | 2018-10-30 | Disease seed score adjusting method based on big data and computing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282849.7A CN109544374B (en) | 2018-10-30 | 2018-10-30 | Disease seed score adjusting method based on big data and computing equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109544374A CN109544374A (en) | 2019-03-29 |
| CN109544374B true CN109544374B (en) | 2024-06-25 |
Family
ID=65845584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811282849.7A Active CN109544374B (en) | 2018-10-30 | 2018-10-30 | Disease seed score adjusting method based on big data and computing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109544374B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110288484B (en) * | 2019-04-02 | 2022-12-13 | 上海瀚之友信息技术服务有限公司 | Insurance classification user recommendation method and system based on big data platform |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IL176540A0 (en) * | 2006-06-25 | 2006-10-05 | Oded Sarel | Case based means and associated method of data analysis for use in risk assessment |
| CN107123072A (en) * | 2017-05-11 | 2017-09-01 | 杭州逸曜信息技术有限公司 | Medical expense restriction rule generation method |
| CN107481151A (en) * | 2017-08-01 | 2017-12-15 | 广州医博信息技术有限公司 | A kind of medical insurance settlement method and device based on simulated annealing |
| CN107578339A (en) * | 2017-09-11 | 2018-01-12 | 宁波市众鑫保网络技术有限公司 | A kind of method and apparatus of Insurance Analysis processing |
-
2018
- 2018-10-30 CN CN201811282849.7A patent/CN109544374B/en active Active
Non-Patent Citations (3)
| Title |
|---|
| 南昌市城镇职工基本医疗保险支付方式改革研究;褚蕾;《中国优秀硕士学位论文全文数据库 医药卫生科技辑》(第7期);E053-10 * |
| 按病种分值结算的实施效果分析;陶春莲 等;《中国医疗保险》(第8期);48-52 * |
| 褚蕾.南昌市城镇职工基本医疗保险支付方式改革研究.《中国优秀硕士学位论文全文数据库 医药卫生科技辑》.2017,(第7期),E053-10. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109544374A (en) | 2019-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109545387B (en) | Abnormal case recognition method and computing equipment based on neural network | |
| CN109544376A (en) | A kind of abnormal case recognition methods and calculating equipment based on data analysis | |
| Surján | Questions on validity of International Classification of Diseases-coded diagnoses | |
| CN110910976A (en) | Medical record detection method, device, equipment and storage medium | |
| US20220044328A1 (en) | Machine learning systems and methods to evaluate a claim submission | |
| CN108765176A (en) | Settlement of insurance claim case processing method, device, computer equipment and storage medium | |
| CN111881991A (en) | Method and device for identifying fraud and electronic equipment | |
| CN109545297A (en) | A kind of disease coding method and calculating equipment based on big data | |
| CA3110455A1 (en) | Visualization of social determinants of health | |
| CN108648091A (en) | Declaration form methods of risk assessment, device, equipment and computer storage media | |
| CN112397171A (en) | DRG-based method, device, processor and storage medium for realizing monitoring of critical path for diagnosis and treatment items and materials | |
| CN110288488A (en) | Medical insurance Fraud Prediction method, apparatus, equipment and readable storage medium storing program for executing | |
| CN116013505A (en) | Medical expense management system based on DRG | |
| CN112560400A (en) | Medical data processing method and device and storage medium | |
| CN112599227A (en) | Hospital resource consumption control method and system, corresponding equipment and storage medium | |
| CN112163962A (en) | Method and device for model training and business wind control | |
| CN110265127B (en) | Disease charge calculation method and device and terminal equipment | |
| CN109544374B (en) | Disease seed score adjusting method based on big data and computing equipment | |
| CN117370331B (en) | Method and device for cleaning total water consumption data of cell, terminal equipment and storage medium | |
| WO2020087970A1 (en) | Neural network-based disease type score verification method and computing device | |
| CN109523406A (en) | A kind of disease score value calculation method and calculating equipment based on data analysis | |
| CN109544377B (en) | Disease seed score calculating method and calculating equipment based on big data | |
| CN109544375A (en) | A kind of hospital's gain and loss analysis method and calculating equipment based on big data | |
| CN109523407B (en) | Disease seed score adjusting method based on big data and computing equipment | |
| CN109545298A (en) | A kind of disease score value calculation method and calculating equipment based on data analysis |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |