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CN109544377B - Disease seed score calculating method and calculating equipment based on big data - Google Patents

Disease seed score calculating method and calculating equipment based on big data Download PDF

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Publication number
CN109544377B
CN109544377B CN201811282999.8A CN201811282999A CN109544377B CN 109544377 B CN109544377 B CN 109544377B CN 201811282999 A CN201811282999 A CN 201811282999A CN 109544377 B CN109544377 B CN 109544377B
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disease
case
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CN109544377A (en
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刘俊芳
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Ping An Medical and Healthcare Management Co Ltd
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Abstract

The embodiment of the invention discloses a disease seed score calculating method and calculating equipment based on big data, comprising the following steps: the computing device receives a set of cases, the case data for each case in the set of cases including an actual medical insurance cost and a diagnostic identifier; for any case in a case set, determining the disease classification of the case according to the diagnosis identification of the case; furthermore, a base disease score for a disease class i is calculated from the medical insurance costs for each case in the first disease example set, wherein the base disease score is used to calculate a predicted medical insurance cost for the disease class i, the first case subset is a set of cases in the case set that belong to the disease class i, and the disease class i is any one of the disease classes to which all cases in the case set belong. Therefore, the embodiment of the invention sets the basic disease score of the disease classification based on the actual medical insurance expense in the case big data, provides the standard of paying according to the basic disease score, and has more reasonable setting of the basic disease score.

Description

Disease seed score calculating method and calculating equipment based on big data
Technical Field
The invention relates to the technical field of medical management, in particular to a disease seed score calculating method and calculating 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 calculation method based on big data, basic disease type scores of disease type classification are set based on actual medical insurance expense in case big data, a standard for paying according to the basic disease type scores is provided, and the basic disease type scores are set more reasonably.
In a first aspect, an embodiment of the present invention provides a disease seed score calculating method based on big data, including:
The computing device receives a case set comprising a plurality of cases, case data for a single case of the plurality of cases comprising an actual medical insurance cost and a diagnostic identification;
for any case in the case set, the computing device determines a category classification for the case based on a diagnostic identification of the case;
the computing device calculates a basic disease score of a disease class i according to the medical insurance cost of each case in a first disease example set, wherein the basic disease score is used for calculating the predicted medical insurance cost of the disease class i, the first case subset is a set of cases belonging to the disease class i in the case set, and the disease class i is any one of the disease classes to which all cases in the case set belong.
In one implementation of the present application, the disease classification is an item in a disease classification dictionary, the disease classification dictionary includes names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one to one, the disease classification codes are first N bit codes of ICD codes or ICD codes, N is a positive integer less than 6, and M is a positive integer.
In yet another implementation of the present application, the calculating the base disease score for the disease classification i based on the medical insurance costs for each case in the first disease example set includes:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0.
In yet another implementation of the present application, the method further includes:
and establishing a disease type score dictionary, wherein the disease type score dictionary comprises a one-to-one correspondence between the identification of the M disease type classifications and M basic disease type scores.
In yet another implementation of the present application, the method further includes:
and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
Optionally, the calculating the predicted medical insurance costs of the disease classification i according to the basic disease score of the disease classification i includes:
Si＝Yi*D
Wherein D is the unit price of the score, and S i is the predicted medical insurance cost of the disease classification i.
Optionally, the calculating method of D includes:
D＝S Total (S) /Y
wherein S Total (S) is total disease seed control cost; y is the total score, Y is = Σ k∑iYi*Qi,k*Ck, k is the index of the hospital, k is a positive integer and k is smaller than the total number of hospitals, and Q i,k is the total number of cases of the disease classification i in hospital k; c k is the hospital level coefficient of said hospital k.
In a second aspect, an embodiment of the present application further provides a computing device, including:
A receiving unit configured to receive a case set including a plurality of cases, case data of a single case of the plurality of cases including an actual medical insurance cost and a diagnosis identifier;
the classification and identification unit is used for determining the disease classification of any case in the case set according to the diagnosis identification of the case;
The calculating unit is used for calculating a basic disease score of a disease classification i according to the medical insurance cost of each case in a first disease example set, wherein the basic disease score is used for calculating the predicted medical insurance cost of the disease classification i, the first case subset is a set of cases belonging to the disease classification i in the case set, and the disease classification i is any one of the disease classifications to which all cases belong in the case set.
Optionally, the disease classification is an item in a disease classification dictionary, the disease classification dictionary includes names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one to one, the disease classification codes are ICD codes or first N bit codes of ICD codes, N is a positive integer less than 6, and M is a positive integer.
Optionally, the calculation unit specifically calculates by the following formula:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0.
Optionally, the computing device further comprises: the dictionary establishing unit is used for establishing a disease type score dictionary which comprises one-to-one correspondence between the identification of the M disease type classifications and M basic disease type scores.
Optionally, the computing unit is further configured to: and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
Optionally, the calculating unit performs the calculating of the predicted medical insurance expense of the disease classification i according to the basic disease score of the disease classification i, and specifically includes:
Si＝f(Yi)*D
Wherein D is the unit price of the score, S i is the predicted medical insurance cost of the disease classification i, and f (Y i). Score for the disease type of a case
Optionally, the calculating method of D includes:
D＝S Total (S) /Y
wherein S Total (S) is total disease seed control cost; y is the total score, Y is = Σ k∑iYi*Qi,k*Ck, k is the index of the hospital, k is a positive integer and k is smaller than the total number of hospitals, and Q i,k is the total number of cases of the disease classification i in hospital k; c k is the hospital level coefficient of said hospital k.
In a third aspect, embodiments of the present application further provide a computing device, the computing device including a processor, a memory, and a communication module, the processor being coupled to the memory, the communication module, the processor configured to invoke program code stored in the memory to execute:
Receiving, by the communication module, a case set, the case set comprising a plurality of cases, case data for a single case of the plurality of cases comprising an actual medical insurance expense and a diagnostic identification;
determining the disease classification of any case in the case set according to the diagnosis mark of the case;
calculating a basic disease score of a disease class i according to the medical insurance costs of each case in a first disease example set, wherein the basic disease score is used for calculating the predicted medical insurance costs of the disease class i, the first case subset is a set of cases belonging to the disease class i in the case set, and the disease class i is any one of the disease classes to which all cases in the case set belong.
Optionally, the disease classification is an item in a disease classification dictionary, the disease classification dictionary includes names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one to one, the disease classification codes are ICD codes or first N bit codes of ICD codes, N is a positive integer less than 6, and M is a positive integer.
Optionally, the processor performs the calculating of a base disease score for the disease classification i based on the medical insurance costs for each case in the first disease example set, including:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0.
Optionally, the processor is further configured to perform: and establishing a disease type score dictionary, wherein the disease type score dictionary comprises a one-to-one correspondence between the identification of the M disease type classifications and M basic disease type scores.
Optionally, the processor is further configured to perform: and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
Optionally, the calculating the predicted medical insurance costs of the disease classification i according to the basic disease score of the disease classification i includes:
Si＝f(Yi)*D
Wherein D is the unit price of the score, S i is the predicted medical insurance cost of the disease classification i, and f (Y i). Score for the disease type of a case
Optionally, the calculating method of D includes:
D＝S Total (S) /Y
wherein S Total (S) is total disease seed control cost; y is the total score, Y is = Σ k∑iYi*Qi,k*Ck, k is the index of the hospital, k is a positive integer and k is smaller than the total number of hospitals, and Q i,k is the total number of cases of the disease classification i in hospital k; c k is the hospital level coefficient of said hospital k.
In a fourth aspect, embodiments of the present application also 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 calculation methods of the first aspect.
In a fifth aspect, embodiments of the present application also 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 score calculation methods of the first aspect.
To sum up, the computing device receives a case set comprising a plurality of cases, the case data for each of the plurality of cases comprising an actual medical insurance cost and a diagnostic identifier; for any case in a case set, determining the disease classification of the case according to the diagnosis identification of the case; furthermore, a base disease score for a disease class i is calculated from the medical insurance costs for each case in the first disease example set, wherein the base disease score is used to calculate a predicted medical insurance cost for the disease class i, the first case subset is a set of cases in the case set that belong to the disease class i, and the disease class i is any one of the disease classes to which all cases in the case set belong. Therefore, the embodiment of the invention sets the basic disease score of the disease classification based on the actual medical insurance expense in the case big data, provides the standard of paying according to the basic disease score, and has more reasonable setting of the basic disease score.
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 calculating disease score according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a computing device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a further computing device according to an embodiment of the present invention;
Fig. 5 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.
Referring to fig. 2, fig. 2 is a method for calculating disease score according to the present application. In the embodiment of fig. 2, the main body of the disease score calculation 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 calculation may also be performed by other devices having a data processing function, such as a terminal or a server, which is not limited to 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:
S1: a case set is received, the case set including a plurality of cases, case data for a single case of the plurality of cases including an actual medical insurance cost and a diagnostic identifier.
The first case is any case in the case set, and the case data of the first case comprises a diagnosis mark for identifying classification of the types of the attendees in the first case and actual medical insurance cost.
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 case set is a set of cases of each hospital in a first region in a time interval, the first region can be an area determined by an operator of a medical insurance management platform, such as Beijing city, shenzhen city, guangdong province, and the first case is any case in the case set. It will be appreciated that in embodiments of the present application, the computing device establishes a disease type coding dictionary that distinguishes from ICDs in the prior art based on case data in the case set.
It will be appreciated that the time interval may be the time of the first region before the payment of the disease is made, ensuring the reliability of the case data in the case set.
S2: for any case in the case set, determining the disease classification of the case according to the diagnosis identification of the case.
It can be understood that the first case is any case in the case set, and the specific implementation manner of step S2 is described by taking the first case as an example: the computing device may determine a category of disease for the first case based on the diagnostic identification in the first case.
In an embodiment of the present application, the diagnosis identifier is a disease classification name or a disease classification code in a disease classification dictionary, and the disease classification can be directly determined according to the diagnosis identifier.
The disease classification dictionary comprises one-to-one correspondence between names of M disease classifications and M disease classification codes, and optionally, the disease classification codes are ICD codes, and the disease classification dictionary is an ICD dictionary; or the disease classification code is the first N bit code of ICD code, N is a positive integer less 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 first four cases of the disease classification of the case set as 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.
In one embodiment of the application, because the region where the hospital is located is different and the personal habits of doctors are different, the expression of the disease type is different from the standard disease type when the diagnosis information and the treatment information are registered and recorded. Or the disease classification names or disease classification codes in the disease classification dictionary cannot be directly obtained through the diagnosis marks, so that the diagnosis names in the case data and the names of the disease classifications in the disease classification dictionary are required to be matched, or the diagnosis codes of the first disease are converted into ICD codes which can be matched with the disease classification codes in the disease classification dictionary.
One implementation of step S2 may be: the diagnostic identifier of the first disease may include a diagnostic name, and the computing device may pre-store a disease name lookup table including M disease classifications and one or more diagnostic names corresponding to each of the M disease classifications. Furthermore, the computing device may determine a disease classification corresponding to the diagnostic name of the first disease according to the disease name comparison table, and further determine a disease classification code corresponding to the disease classification of the second case according to the disease classification dictionary.
Another implementation of step S2 may be: the diagnostic marker of the first disease may include a diagnostic code, which may be an ICD-10 code, an ICD-9-CM3 surgical code, a tumor morphology code (also known as M code), a TCM disease code, or the like,
For ICD-10 codes or ICD-9-CM3 surgical codes, the computing device may look up the disease classification code matching the diagnostic code in the first case directly in the disease classification dictionary.
For M-codes, the M-codes may be converted into ICD codes or four-bit ICD codes according to an M-code conversion table. For example, the ICD code "C78.7" corresponding to the M code "M8140/6" and the ICD code "C34.9" corresponding to the M code "M8140/3". The computing device may convert the M-code in the first case into an ICD code (the ICD code may be a disease classification code in the disease classification dictionary, or may be an ICD code with six-bit codes, etc.) according to the M-code conversion table, and then find out a disease classification code matching the ICD code obtained by the conversion in the disease classification dictionary.
S3: calculating a basic disease score of a disease classification i according to the medical insurance cost of each case in the first disease example set, wherein the basic disease score of the disease classification i is used for calculating the predicted medical insurance cost of the disease classification i, the first case subset is a set of cases belonging to the disease classification i in the case set, and the disease classification i is any one of the disease classifications to which all cases belong in the case set.
Alternatively, calculating the underlying disease seed score for disease seed class i based on the medical insurance costs for each case in the first disease example set may be calculated by the following formula:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0. The constant δ may be configurable, and may be a value of 100, 10, or 20, and embodiments of the present application are not limited.
Further, after step S3, the method may further include: and establishing a disease type score dictionary, wherein the disease type score dictionary comprises a one-to-one correspondence relation between the identifications of M disease type classifications and M basic disease type scores.
In an embodiment of the present invention, the method may further include: and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i. The predictive medical insurance expense calculation mode of the disease classification i can comprise but is not limited to the following two calculation modes:
The first calculation mode:
Si＝Yi*D
Wherein D is the unit price of the score, and S i is the predicted medical insurance cost of the disease type classification i. Where D may be a fixed value, the setting of D is related to the magnitude of δ, and where d=δ, it is understood that D may be other setting manners, and embodiments of the present application are not limited.
The second calculation mode:
the hospital level indicates the comprehensive medical level of the hospital, and can reflect the scale, technical level, medical equipment, management level, hospital quality and other conditions of the hospital. For hospitals in China, the hospitals are evaluated and determined to be three-level, and each level is divided into a first level, a second level, a third level and the like, wherein the third-level hospitals are additionally provided with special-level hospitals, so that the hospitals are divided into ten levels. The hospital level coefficients are different for different levels of hospitals. For example, when a hospital belongs to a trimethyl hospital, the hospital level factor may be 1.5; when the hospital belongs to a triethylhospital, the hospital level coefficient may be 1.4; when the hospital belongs to a tripartite hospital, the hospital level coefficient may be 1.3; when the hospital belongs to a dimethyl hospital, the hospital level coefficient may be 1.2; when the hospital belongs to a first-class hospital, the hospital level coefficient may be 1 or the like, and the embodiment of the present application is not limited. The computing device may pre-store a correspondence table of hospital levels and hospital level coefficients, and search for the hospital level coefficients corresponding to the hospital levels in each case through the correspondence table.
Hospitals of different levels may include hospital level coefficients such that hospitals of different levels have different disease scores for the same disease category classification. Wherein, the total score Y of the region is:
Wherein Y is the total score, k is the index of the regional hospitals, k is a positive integer and k is smaller than the total number of the regional hospitals, and Q i,k is the total number of cases of the disease classification i in the hospital k; c k is the hospital level coefficient for hospital k.
At this time, the unit price D may be the ratio of the total disease control fee S Total (S) to the total differential value Y, that is:
D＝S Total (S) /Y
Further, the predicted medical insurance costs of each disease category can be calculated according to the first calculation mode of the predicted medical insurance costs of the disease category i, and the predicted medical insurance costs are used as the standard for paying the medical insurance costs to the hospital.
Therefore, by setting the total disease control cost S Total (S) , the value unit price D is calculated according to the total value Y, and the value unit is a non-fixed value, so that the predicted medical cost of each disease classification is changed along with the total value and the total disease control cost. D obtained through back calculation is a non-fixed value, a hospital does not know the value of D, and after the hospital is placed and knows the disease type score, the hospital calculates the predicted medical insurance expense according to the disease type score, and the diagnosis and treatment of the patient are carried out according to the predicted medical insurance expense.
Alternatively, the case set1 for calculating the disease score may be a set of cases obtained by screening the set2 of cases occurring in the time interval of each hospital in the first region. The first level is any one of a plurality of levels of hospitals, and the screening of the cases of the first level of hospitals in the case set2 is described below as an example, and the method for screening includes the following steps:
(1) And calculating the average actual medical insurance cost of the disease classification i under the first level of hospitals, namely the ratio of the total actual medical insurance cost of the cases of the disease classification i in the first level of hospitals to the number of cases of the disease classification i in the first level of hospitals.
(2) And calculating the proportional relation between the actual medical insurance cost of the cases of the first-level hospital lower disease classification i and the average actual medical insurance cost of the disease classification of the first-level hospital lower disease classification i.
(3) Further, the case data with the proportion relation outside the preset range (for example, 0.4-2.5) in the case set2 is removed, namely, the case with ultra-high actual medical insurance cost and ultra-low actual medical insurance cost is deducted.
The application scenario of the disease score or predicted medical insurance costs is described below. It can be understood that after the disease score dictionary is established, when the computing device receives the second case based on the medical insurance management platform, the disease classification of the second case is identified, then the basic disease score corresponding to the disease classification of the second case is determined according to the disease score dictionary, then the grade of the hospital is determined according to the hospital information of the medical behaviors of the participants in the second case, the hospital grade coefficient adopted by the second case is determined according to the grade of the hospital, and then the disease score adopted by the second case when the calculation of the predicted medical insurance expense is performed is obtained, and finally the predicted medical insurance expense of the second case is obtained.
It will be appreciated that the predicted medical insurance costs or disease scores may be different for different cases for the same disease classification, but the underlying disease scores are the same.
To sum up, the computing device receives a case set comprising a plurality of cases, the case data for each of the plurality of cases comprising an actual medical insurance cost and a diagnostic identifier; determining the disease classification of each case in the case set according to the diagnosis mark of each case in the case set; furthermore, a base disease score for a disease class i is calculated from the medical insurance costs for each case in the first disease example set, wherein the base disease score is used to calculate a predicted medical insurance cost for the disease class i, the first case subset is a set of cases in the case set that belong to the disease class i, and the disease class i is any one of the disease classes to which all cases in the case set belong. Therefore, the embodiment of the invention sets the basic disease score of the disease classification based on the actual medical insurance expense in the case big data, provides the standard of paying according to the basic disease score, and has more reasonable setting of the basic disease score.
The following describes the apparatus according to the embodiment of the invention.
Referring to fig. 3, computing device 30 includes, but is not limited to: a receiving unit 31, a classification and identification unit 32, a calculation unit 33, and the like. Wherein,
A receiving unit 31 for receiving a case set including a plurality of cases, case data of a single case of the plurality of cases including an actual medical insurance cost and a diagnosis identifier;
A classification recognition unit 32 for determining, for any case in the case set, a disease category classification of the case according to a diagnosis identification of the case;
A calculating unit 33, configured to calculate a basic disease score of a disease class i according to a medical insurance cost of each case in a first disease example set, where the first case subset is a set of cases in the case set belonging to the disease class i, and the disease class i is any one of the disease classes to which all cases in the case set belong.
Optionally, the disease classification is an item in a disease classification dictionary, the disease classification dictionary includes names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one to one, the disease classification codes are ICD codes or first N bit codes of ICD codes, N is a positive integer less than 6, and M is a positive integer.
Alternatively, the calculation unit 33 calculates specifically by the following formula:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0.
Referring to fig. 4, the computing device 40 shown in fig. 4 may include, in addition to the various units of the computing device shown in fig. 3: and a dictionary establishing unit 34, configured to establish a disease score dictionary, where the disease score dictionary includes a one-to-one correspondence between the identifiers of the M disease classifications and M basic disease scores.
Optionally, the computing unit 33 is further configured to: and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
Optionally, the calculating unit 33 performs the calculating of the predicted medical insurance costs of the disease classification i according to the basic disease score of the disease classification i, specifically including:
Si＝f(Yi)*D
Wherein D is the unit price of the score, S i is the predicted medical insurance cost of the disease classification i, and f (Y i). Score for the disease type of a case
Optionally, the calculating method of D includes:
D＝S Total (S) /Y
wherein S Total (S) is total disease seed control cost; y is the total score, Y is = Σ k∑iYi*Qi,k*Ck, k is the index of the hospital, k is a positive integer and k is smaller than the total number of hospitals, and Q i,k is the total number of cases of the disease classification i in hospital k; c k is the hospital level coefficient of said hospital k.
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. 5, the computing device 500 may include: baseband chip 510, memory 515 (one or more computer-readable storage media), communication module 516 (e.g., radio Frequency (RF) module 5161 and/or communication interface 5162), peripheral system 517. These components may communicate over one or more communication buses 514.
The peripheral system 517 is primarily intended to implement interactive functionality between the computing device 510 and the user/external environment, and primarily comprises the input/output means of the computing device 500. In particular implementations, the peripheral system 517 may include: a touch screen controller 518, a camera controller 519, an audio controller 520, and a sensor management module 521. Wherein each controller may be coupled to a respective peripheral device (e.g., touch screen 523, camera 524, audio circuit 525, and sensor 526). It should be noted that the peripheral system 517 may also include other I/O peripherals.
The baseband chip 510 may be integrated to include: one or more processors 511, a clock module 522, and a power management module 513. The clock module 522 integrated in the baseband chip 510 is mainly used to generate clocks required for data transmission and timing control for the processor 511. The power management module 513 integrated in the baseband chip 510 is mainly used for providing stable and high-precision voltage to the processor 511, the radio frequency module 5161 and the peripheral system.
A Radio Frequency (RF) module 5161 is used to receive and transmit radio frequency signals, primarily integrating the receiver and transmitter of the computing device 500. The Radio Frequency (RF) module 5161 communicates with a communication network and other communication devices through radio frequency signals. In particular implementations, the Radio Frequency (RF) module 5161 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 5161 may be implemented on a separate chip.
The communication module 516 is used for data exchange between the computing device 500 and other devices.
A memory 515 is coupled to the processor 511 for storing various software programs and/or sets of instructions. In particular implementations, memory 515 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory 515 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 515 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 515 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 515 may also store one or more application programs. As shown in fig. 5, 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 511 may be used to read and execute computer readable instructions. In particular, the processor 511 may be configured to invoke a program stored in the memory 515, for example, a program for implementing the disease seed score calculation method based on big data provided in the present application, and execute instructions included in the program.
Specifically, the processor 511 may be configured to call a program stored in the memory 515, such as a program for implementing the disease seed score calculating method based on big data provided in the present application, and execute the following procedures:
receiving, by the communication module 516, a case set comprising a plurality of cases, case data for a single case of the plurality of cases comprising an actual medical insurance cost and a diagnostic identifier;
determining the disease classification of any case in the case set according to the diagnosis mark of the case;
calculating a basic disease score of a disease class i according to the medical insurance costs of each case in a first disease example set, wherein the basic disease score is used for calculating the predicted medical insurance costs of the disease class i, the first case subset is a set of cases belonging to the disease class i in the case set, and the disease class i is any one of the disease classes to which all cases in the case set belong.
Optionally, the disease classification is an item in a disease classification dictionary, the disease classification dictionary includes names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one to one, the disease classification codes are ICD codes or first N bit codes of ICD codes, N is a positive integer less than 6, and M is a positive integer.
Optionally, the processor 511 performs the calculating of a base disease score for the disease classification i based on the medical insurance costs for each case in the first disease example set, including:
wherein Y i is the basic disease score of the disease classification i, j is the index of the cases of the disease classification i in the case set, j is a positive integer and the total number of cases Q i,Sj smaller than the disease classification i in the case set is the actual medical insurance cost in the case j, delta is a constant, and delta is larger than 0.
Optionally, the processor 511 is further configured to perform: and establishing a disease type score dictionary, wherein the disease type score dictionary comprises a one-to-one correspondence between the identification of the M disease type classifications and M basic disease type scores.
Optionally, the processor 511 is further configured to perform: and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
Optionally, the calculating the predicted medical insurance costs of the disease classification i according to the basic disease score of the disease classification i includes:
Si＝f(Yi)*D
Wherein D is the unit price of the score, S i is the predicted medical insurance cost of the disease classification i, and f (Y i). Score for the disease type of a case
Optionally, the calculating method of D includes:
D＝S Total (S) /Y
wherein S Total (S) is total disease seed control cost; y is the total score, Y is = Σ k∑iYi*Qi,k*Ck, k is the index of the hospital, k is a positive integer and k is smaller than the total number of hospitals, and Q i,k is the total number of cases of the disease classification i in hospital k; c k is the hospital level coefficient of said hospital k.
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 500 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 method for calculating the disease seed score based on the big data is characterized by comprising the following steps of:
the computing device receives a first case set, the first case set being a set of cases occurring at hospitals within a first region, the first case set comprising a plurality of cases, case data for a single case of the plurality of cases comprising an actual medical insurance cost and a diagnostic identifier;
calculating average actual medical insurance costs of a first disease class under a first level of hospitals in the first case set;
Calculating the proportional relation between the actual medical insurance cost of each case belonging to the disease classification i under the first-level hospital and the average actual medical insurance cost of the disease classification i under the first-level hospital; the first grade is any grade in a plurality of grades of hospitals;
Removing cases with proportion relation outside a preset range from the first case set to obtain a second case set;
For any case in the second set of cases, the computing device determines a category classification for the case based on a diagnostic identification of the case;
The computing device calculates a basic disease score of a disease class i according to the medical insurance cost of each case in a first disease example set, wherein the basic disease score is used for calculating the predicted medical insurance cost of the disease class i, the first case subset is a set of cases belonging to the disease class i in the second case set, and the disease class i is any disease class in the disease class of all cases in the second case set; the disease classification is a term in a disease classification dictionary, the disease classification dictionary comprises names of M disease classifications and M disease classification codes corresponding to the names of the M disease classifications one by one, the disease classification codes are first N bit codes coded by ICD, N is a positive integer less than 6, and M is a positive integer;
The calculating a base disease seed score for the disease seed classification i based on the medical insurance costs for each case in the first disease example set includes:
wherein, For the base disease score of the disease class i, j is an index of cases of the second case set disease class i, j is a positive integer and less than the total number of cases of the second case set disease class i，For the actual medical insurance costs in case j,Is a constant value, and is used for the treatment of the skin,；
The method further comprises the steps of:
and establishing a disease type score dictionary, wherein the disease type score dictionary comprises a one-to-one correspondence between the identification of the M disease type classifications and M basic disease type scores.
2. The method of claim 1, wherein the method further comprises:
and calculating the predicted medical insurance cost of the disease classification i according to the basic disease score of the disease classification i.
3. The method of claim 1, wherein said calculating a predicted medical insurance charge for said disease class i based on said base disease score for said disease class i comprises:
wherein D is the unit price of the score, The predicted medical insurance costs for the disease category i,Is the disease seed score of the case.
4. A method as claimed in claim 3, wherein the calculation method of D comprises:
D=S Total (S) /Y
Wherein S Total (S) is total disease seed control cost; y is the total score value of the total score, K is an index of hospitals, k is a positive integer and k is less than the total number of hospitals,The total number of cases for the disease category i in hospital k; is a hospital level coefficient for said hospital k.
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 method of disease seed score calculation of any of claims 1-4.
6. A computing device comprising functional units for implementing the disease seed score calculation method of any one of claims 1-4.
7. A computer storage medium storing computer software instructions which, when executed by a computer, cause the computer to perform the disease seed score calculation method of any one of claims 1-4.
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