WO2020087969A1

WO2020087969A1 - Method for identifying abnormal clinical cases based on data analysis and computing device

Info

Publication number: WO2020087969A1
Application number: PCT/CN2019/095009
Authority: WO
Inventors: 刘俊芳
Original assignee: 平安医疗健康管理股份有限公司
Priority date: 2018-10-30
Filing date: 2019-07-08
Publication date: 2020-05-07
Also published as: CN109544376A

Abstract

The embodiment of the present application discloses a method for identifying abnormal clinical cases based on data analysis and a computing device. The method comprises the following steps: the computing device receives case data of a first case, and the case data contains the actual medical insurance fee; the computing device looks up in a disease score dictionary a first basic disease score corresponding to a first disease category to which the first case belongs, and computes a predicted medical insurance fee of the first case according to the first basic disease score, wherein the disease score dictionary includes a corresponding relationship between the disease category and the basic disease score; and then, the computing device determines, according to the actual medical insurance fee and the predicted medical insurance fee, whether the first case is an abnormal case; if yes, the computing device outputs a prompt message on abnormity of the first case. The descried method can be executed to identify abnormal cases.

Description

Abnormal case recognition method and computing equipment based on data analysis

This application requires the priority of the Chinese patent application submitted to the China Patent Office on October 30, 2018, with the application number 201811282997.9 and the application name as "an abnormal case identification method and computing device based on data analysis", all of which are approved by The reference is incorporated in this application.

Technical field

The present application relates to the technical field of medical insurance, in particular to a method and computing device for identifying abnormal cases based on data analysis.

Background technique

With the continuous deepening of the national public medical layout and medical reforms, the payment system reform is a reflection of a major change in the system engineering, the concept of medical insurance management, and the role of medical insurance agencies. The implementation of payment by disease type fully embodies the payment system is the key to comprehensive medical reform.

The so-called payment by disease refers to scientifically formulating a fixed payment standard for each disease through a unified disease diagnosis classification, and the social security institution pays the hospitalization fee to the designated medical institution according to the standard and the number of hospitalizations, so that the use of medical resources is standardized , That is, the resource consumption of medical institutions is directly proportional to the number of inpatients treated, the complexity of the disease and the intensity of services. In short, it is to clearly stipulate how much a certain disease should cost, so as to avoid the medical unit from abusing medical service items, repeating items and disassembling items, preventing serious illnesses in hospitals, and ensuring the quality of medical services.

Standardized medical information is very important for the application of medical information big data to the payment method based on the type of disease. The standardization of medical information is the prerequisite for the application of medical big data. The classification of disease types currently generally adopts the International Classification of Diseases (ICD). ICD-10 divides the disease into 21 chapters, more than 26000 kinds of diseases according to the characteristics of etiology, location, pathology and clinical manifestations, and encodes each disease type. However, for the overall medical environment in China, the number of common diseases in various regions is far less than 26,000, and when medical staff record cases, due to the diversity and complexity of the diseases in the existing technology, medical staff often do not Ranked according to international standards, each region has a localized language description, which brings certain difficulties to the implementation of fee-based disease.

According to the regulations, when determining the payment standard for payment according to the type of disease, the cost of medical services, the actual actual cost incurred, the affordability of the medical insurance fund and the burden level of the insured should be fully considered, and the main operation and treatment methods of the disease should be combined with the medical institution. Consultation and negotiation are reasonably determined. At present, how to determine the payment standard according to the medical situation in various places, how to manage the medical expenses paid by the type of disease, detection and analysis of cases, medical payment standards, etc. are all technical problems that need to be solved urgently.

Summary of the invention

The embodiments of the present application provide an abnormal case identification method based on data analysis, which can realize the identification of abnormal cases.

In a first aspect, an embodiment of the present application provides an abnormal case recognition method based on data analysis, including:

The computing device receives the case data of the first case, and the case data includes the actual medical insurance cost;

The computing device searches the disease type score dictionary table for the first basic disease type score corresponding to the first disease type classification to which the first case belongs, and calculates the first basic disease type score according to the first basic disease type score The predicted medical insurance cost of a case, the first disease type classification is the disease type classification to which the first case belongs, and the disease type score dictionary table includes the correspondence between the disease type classification and the basic disease type score;

The computing device determines whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, outputs prompt information for prompting the abnormality of the first case.

In a second aspect, an embodiment of the present application further provides a computing device, including:

A receiving unit, configured to receive case data of the first case, the case data including actual medical insurance expenses;

The searching unit is used to search for the first basic disease type score corresponding to the first disease type classification to which the first case belongs in the disease type score dictionary table, and the disease type score dictionary table includes the disease type classification and the basic Correspondence of disease scores;

A calculation unit, configured to calculate the predicted medical insurance cost of the first case according to the score of the first basic disease;

A first determining unit, configured to determine whether the first case is an abnormal case based on the actual medical insurance cost and the predicted medical insurance cost;

The output unit is configured to output prompt information for prompting the abnormality of the first case when the judgment result of the judgment unit is yes.

In a third aspect, an embodiment of the present application further provides a computing device, the computing device includes a processor, a memory, and a communication module, the processor is coupled to the memory, the communication module, and the processor is used to call The program code stored in the memory executes:

Receiving the case data of the first case through the communication module, the case data including the actual medical insurance cost;

Find the first basic disease type score corresponding to the first disease type classification to which the first case belongs in the disease type score dictionary table, and calculate the prediction of the first case according to the first basic disease type score Medical insurance costs, the disease type score dictionary table includes the correspondence between the classification of disease types and the scores of basic disease types;

Determine whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, output prompt information for prompting the abnormality of the first case.

According to a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which is used for computer software instructions, which when executed by a computer causes the computer to execute as the first According to any aspect, an abnormal case identification method based on data analysis.

According to a fifth aspect, an embodiment of the present application further provides a computer program, the computer program includes computer software instructions, which when executed by a computer causes the computer to execute any one of the first aspect Recognition method of abnormal cases based on data analysis.

In summary, the computing device receives the case data of the first case, the case data includes the actual medical insurance cost; look up the first basic disease type score corresponding to the first disease type classification in the disease type score dictionary table, and according to the The first basic disease type score calculates the predicted medical insurance cost of the first case. The first disease type classification is the disease type classification of the first case. The disease type score dictionary table includes the correspondence between the disease type classification and the basic disease type score Furthermore, according to the actual medical insurance cost and the predicted medical insurance cost, it is determined whether the first case is an abnormal case. If it is, it outputs a prompt message for prompting the abnormality of the first case. By executing the above method, the identification of abnormal cases can be realized.

BRIEF DESCRIPTION

In order to more clearly explain the technical solutions in the embodiments or the prior art of the present application, the drawings required in the description of the embodiments or the prior art will be briefly introduced below.

1 is a functional architecture diagram of a medical insurance management platform provided by an embodiment of this application;

2 is a flowchart of an abnormal case identification method provided by an embodiment of the present application;

3 is a flowchart of an abnormal case analysis method provided by an embodiment of the present application;

4 is a schematic structural diagram of a computing device provided by an embodiment of the present application;

5 is a schematic structural diagram of yet another computing device provided by an embodiment of this application;

6 is a schematic structural diagram of yet another computing device provided by an embodiment of this application.

detailed description

It should be noted that the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include the majority forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more associated listed items.

In order to better understand the embodiments of the present application, the following first describes the various functions of the medical insurance management platform to which the embodiments of the present application apply. Please refer to FIG. 1, which is a functional architecture diagram of a medical insurance management platform provided by the embodiments of the present application. The medical insurance management platform can run on computing devices, and provides a series of functions related to cases, medical insurance, and disease scores for the operators of the medical insurance management platform. The medical insurance management platform includes but is not limited to some or all of the following functions achieve:

Disease code, the medical insurance management platform can encode the disease type obtained by the main diagnosis of the case according to the case data of the input case, and the disease type coding method can use ICD-10 code (also called six-digit code code in this application), Other encoding methods may also be used, such as four-bit code encoding (ie, the first 4 bits of the six-bit code), three-bit code encoding (ie, the first three bits of the six-bit code), and so on. It can be understood that a disease classification dictionary applicable to the region can be established through a disease coding method through a case set of a certain region, and the disease classification dictionary includes names of M disease categories and names of the M disease category classifications One-to-one correspondence of M disease classification codes, M is a positive integer. Optionally, the computing device can identify the classification of the disease corresponding to the case based on the diagnosis name, disease code and other information filled in by medical personnel in the case data of the medical insurance management platform, and then the disease classification code corresponding to the classification of the disease Added to the case data, in order to further calculate the score of the disease type of the case, and then realize the functions of paying by disease type and detecting the authenticity of the case data based on the disease type score.

For the calculation of disease scores, the medical insurance management platform can store a correspondence table of disease types and disease scores or include a program for calculating the scores of disease types, and can determine the insured person in the case by the name of the disease type and the code of the disease type (Ie the patient) the classification of the diseased type, and then the process of determining the disease type score based on the disease type classification according to the corresponding relationship between the disease type classification and the disease type score value or the disease type value calculation program, etc. value. Among them, the disease score is the standard score of the region (for example, country, province or city, etc.) that is determined based on the big data of the case and used to calculate medical expenses (such as predicted medical insurance expenses, predicted total expenses, etc.). Specifically, a disease type score table may be established, and the disease type score table includes M disease type classification marks and basic disease type score values corresponding to the M disease type classification marks one-to-one, and then according to the actual case The situation (such as the age of the insured person, the severity of the disease, the hospital, the department, etc.) is adjusted on the basis of the score of the basic disease to obtain the score of the disease suitable for the case. The score of disease type has a positive correlation with medical expenses, that is, the higher the score of disease type, the higher the medical cost of the disease type.

For statistical analysis of case data, the medical insurance management platform can perform statistical analysis on the cases reported by various hospitals in the area according to the evaluation cycle (for example, monthly, quarterly, annual, etc.). The above statistical analysis of cases can support statistical analysis by month, quarter, year, etc., and support the occurrence of the number of cases, total cost, actual cost of one or more combinations of different hospitals, different cost ranges, different diseases, etc. Statistical analysis of medical insurance costs, forecasted medical insurance costs, etc., to adjust the disease scores for each disease type to be used in the next evaluation cycle based on the statistical analysis results. It should be understood that other functions can be implemented based on the statistical analysis results, such as adjusting the hospital level coefficient of the hospital based on statistics on the income and expenditure of each hospital, and this embodiment of the present application is not limited.

For the authenticity detection of a case, the medical insurance management platform can detect the authenticity of the case based on the case data in the case. When it is detected that the case contains false data, the case is marked and the case is output with false data or is abnormal Case prompt messages, etc., so that the operator of the medical insurance management platform can identify the problem case in time and analyze the cause of the problem case.

Data visualization, the medical insurance management platform can visualize the statistical analysis results obtained by the statistical analysis function of case data, and can also visualize the results of statistical analysis of problem cases, so as to facilitate the statistical analysis of the operators of the medical insurance management platform.

In this application, computing devices may include, but are not limited to, mobile phones, mobile computers, tablet computers, media players, computers, servers, and other devices that include data processing functions. Computing devices running various functions of the medical insurance management platform can receive cases reported from institutions and individuals such as hospitals.

Not limited to that shown in FIG. 1, the medical insurance management platform provided by the present application may also include the implementation of other functions, for example, optimization of disease scores, etc., and this embodiment of the present application is not limited.

Please refer to FIG. 2, which is an abnormal case identification method provided by this application. In the embodiment of FIG. 2, the execution subject of the calculation of the disease score is described as a computing device (a device that runs various functions of the case management platform) as an example. It can be understood that the abnormal case identification method can also be performed by other terminals or servers, etc. For devices with data processing functions, this embodiment of the present application is not limited. As shown in FIG. 2, the method may include, but is not limited to, some or all of the following steps:

S2: Receive the case data of the first case, which includes actual medical insurance costs.

Among them, the case is the patient diagnosis and treatment process recorded by the hospital for the patient. Case data may include, but is not limited to, one or more combinations of the patient's personal information, diagnostic information, treatment information, cost information, actual medical insurance costs, and so on. Among them, the diagnosis information may include a diagnosis identifier for identifying the classification of the insured person's disease type. Among them, the diagnosis identifier may be a diagnosis name, such as a main diagnosis name; it may also be a diagnosis code, such as an ICD diagnosis code, etc .; or it may be a surgery ID, which may be a surgery name, a surgery code, etc. It should be understood that personal information may include, but is not limited to, the age, sex, medical history and other information of the insured person. The treatment information is the process information of the insured person's treatment recorded in the case. The fee information includes, but is not limited to, one or more combinations of the surgical expenses, hospitalization fees, testing fees, registration fees, drug fees, total costs, and actual medical insurance costs incurred by the insured during the treatment of this disease.

S4: Look up the first basic disease category score corresponding to the first disease category to which the first case belongs in the disease category score dictionary table. The disease category score dictionary table includes the correspondence between the disease category and the basic disease category score .

Optionally, S4 includes: calculating the disease score of the first case according to the score of the first basic disease; further, calculating the unit price based on the disease score of the first case and a preset score Describe the predicted medical insurance cost of the first case.

The case data of the first case includes a diagnostic mark for identifying the classification of the disease. In one implementation of the present application, the first case has recognized the disease classification of the first case by the disease classification recognition method and added the identification of the identified disease classification to the case data of the first case. The diagnosis identification is the identification of the disease classification (the classification name or classification code). It can be understood that the identification of the disease classification is the name or classification code of the disease classification included in the disease classification dictionary, wherein the disease classification dictionary includes the names of the M disease classifications and the names of the M disease classifications One-to-one correspondence of M disease classification codes, M is a positive integer. Optionally, the disease classification code is an ICD code, and the disease classification dictionary is an ICD dictionary; or, the disease type classification code is the first N digits of the ICD code, and N is a positive integer less than 6.

For the disease classification code in the disease classification dictionary is the first N digits of the ICD code, choose the first four digits, the first three digits or the first two digits of the ICD code of the disease, depending on the case classification of the cases in the concentrated case The number of the first four digits of the ICD code, such as greater than 10 cases, choose "four-digit code" as the disease classification code; if less than 10 cases, choose "three-digit code" as the disease classification code.

In another implementation of the present application, the diagnosis identifier in the first case is a diagnosis name or a diagnosis code, and the disease category to which the first case belongs cannot be directly obtained through the diagnosis name or the diagnosis code. At this time, before S4, the computing device may determine the disease type classification of the first case according to the diagnostic identifier for identifying the disease type classification of the insured person in the first case.

The computing device may determine that the classification of the disease type to which the first case belongs based on the diagnosis identifier of the first case may be: the diagnosis ID of the first disease type may include the diagnosis name, and the calculation device may pre-store the disease name comparison table The disease name comparison table includes identifications of M disease classifications and one or more diagnostic names corresponding to the identifications of each of the M disease classifications. Furthermore, the computing device may determine the disease category corresponding to the diagnosis name of the first disease according to the disease category name comparison table, and further determine the disease category code corresponding to the disease category of the second case according to the disease category dictionary.

The computing device may determine that the classification of the disease category to which the first case belongs according to the diagnosis identifier of the first case may be: the diagnosis identifier of the first disease type may include a diagnosis code, and the diagnosis code may be ICD-10 Code, ICD-9-CM3 surgical code, tumor morphology code (also called M code) or Chinese medicine disease code, etc.

For ICD-10 codes or ICD-9-CM3 surgical codes, the computing device can directly look up the disease classification code in the disease classification dictionary that matches the diagnostic code in the first case.

For the M code, according to the M code conversion table, the M code can be converted into an ICD code or a four-digit ICD code. For example, the MCD "M8140 / 6" corresponds to the ICD code "C78.7", and the M code "M8140 / 3" corresponds to the ICD code "C34.9". The computing device can convert the M code in the first case to an ICD code according to the M code conversion table (the ICD code can be a disease classification code in a disease classification dictionary, or an ICD code with a six-digit code, etc.), Then look up the disease classification code in the disease classification dictionary that matches the converted ICD code.

S6: Calculate the predicted medical insurance cost of the first case according to the score of the first basic disease.

In an embodiment of the present application, an implementation manner in which the computing device calculates the second disease type score of the first case according to the first basic disease type score may be:

Where Y is the score of the second disease of the first case, A ₁ is the score of the first basic disease, C ₁ is the hospital level coefficient of the hospital where the first case is located, and E _i is the additional disease Species score, i is the index of the additional disease species score, i is a positive integer.

Among them, if the first case satisfies one or more of preset surgery, preset complication, preset secondary disease, preset hospitalization information, pediatric case, etc., the second disease score needs to be appended with the first The additional score of the disease type corresponding to the item satisfied by the case. It can be understood that for the first case, the additional scores of the disease types corresponding to different items may be different. For the same item, the additional score of the disease type corresponding to cases of different disease types may be different.

It can be understood that the first disease type score may also include other calculation methods, for example, Y = ∑ _i A ₁ * C ₁ , which is not limited in the embodiments of the present application.

The predicted medical insurance cost S of the first case is the product of the score Y of the first disease and the score unit price D, that is, S = Y * D

Among them, the score unit price D is a constant, and the score unit can be set based on the total score of the area and / or the total control medical insurance cost of the area, that is, the total control medical insurance cost of the area divided by the total score of the area.

S8: Determine whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, output prompt information for prompting the abnormality of the first case.

It can be understood that the medical payment method of paying by disease type, by identifying the disease classification of the case, and then looking up the disease classification value corresponding to the disease classification from the disease classification dictionary table, calculate the predicted medical insurance cost, and report to the hospital Pay the predicted medical insurance costs. However, when the case data of the case may have false data, the predicted medical insurance cost calculated by the case from the disease category score table found in the disease category score dictionary table is far from the actual medical insurance cost of the case. For example, in the case of the disease type "diabetes", the treatment method used is only anti-inflammatory drugs, then the score of the disease type determined in the case is high, and the case data may be false.

Optionally, an implementation manner of the computing device determining whether the first case is an abnormal case may be: the computing device determines whether the difference between the actual medical insurance cost and the predicted medical insurance cost is greater than a first threshold, and if so, Then the first case is an abnormal case. Wherein, the first threshold may be 10, 20, 35 or other numerical values. Alternatively, the first threshold Q may be set according to the first disease type score Y ′ ₁ or the second disease type score Y ₁ , for example, Q = Y ′ ₁ * μ, or Q = Y ₁ * μ, where , 0 <μ <1.

Optionally, another implementation manner in which the computing device determines whether the first case is an abnormal case may be: the computing device determines whether the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold or less than the third The threshold, if yes, then the first case is an abnormal case. The value range of the second threshold may be 1.1-3, for example, 1.5, 2, 2.4 or other values, which are not limited in the embodiments of the present application. The third threshold may be 0.1-0.9, such as 0.4, 0.5, 0.7 or other values, which is not limited in the embodiments of the present application.

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold, a prompt message is displayed to indicate that the disease score of the first case is too low; when the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the first threshold and When it is greater than the second threshold, a prompt message is displayed to indicate that the disease score of the first case is within the normal range; when the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the second threshold, the output is used to prompt the first case The prompt message of the disease type score is too high.

In an embodiment of the present application, after step S8, the computing device may analyze the cause of the abnormal case. As shown in the flowchart of the abnormal case analysis method shown in FIG. 3, the method may further include but not limited to: S91, S92, and S93.

S91: Input multiple case characteristics of the first case into a disease type analysis model to obtain a predicted disease type classification of the first case.

Among them, the disease analysis model is used to identify the disease classification of a case based on the multiple case characteristics of the case. The computing device can identify and extract the case characteristics of the case data based on the case data, and the case characteristics can include, but are not limited to, diagnostic identification, drug identification, drug dosage, drug cost, detection item identification, surgical identification, hospitalization days, insured person One or more combinations of age, insured person's gender, etc.

Before analyzing and classifying disease types, the disease analysis model needs to train the disease analysis model through sample data to learn to recognize the disease classification of the case based on multiple case characteristics of the case. Optionally, the sample data may be data obtained before calculating the medical insurance cost without implementing the score of the disease type to ensure the accuracy of the sample data. It is understandable that the case data obtained before the calculation of the medical insurance cost without implementing the score of the disease type does not suspect that the medical staff makes false information such as the replacement of the main diagnosis and the subdiagnosis to increase the score of the disease type, and increases the hospitalization items. The data has good reliability. The actual classification of cases in the sample data can be determined by medical staff. The disease classification model can be trained by supervising and predicting disease classification and real disease classification.

S92: Determine whether the predicted disease category and the first disease category are the same disease category.

When the judgment result of S92 is yes, it is considered that the disease classification of the first case is correct, and the computing device may mark the disease classification as correct, that is, the diagnosis information (primary diagnosis) is correct, or may perform step S94, or may not perform the operation The embodiment of the present application is not limited. When the judgment result of S92 is NO, step S93 is executed.

S93: Output prompt information for prompting that the diagnosis of the first case is wrong or the classification of the disease is wrong.

Further, when the first case includes the primary diagnosis identifier and the secondary diagnosis identifier, the first disease category is the disease category determined by the primary diagnosis identifier, and the method may further include: S94, S95, and S96.

S94: Determine the second disease category of the first case according to the identifier of the secondary diagnosis.

Wherein, the realization principle of determining the second disease classification of the first case according to the identification of the secondary diagnosis is similar to that of determining the first disease classification according to the diagnosis identification. For details, please refer to The diagnostic identifier that identifies the insured person's disease category classification determines the relevant description in the disease category of the first case, which will not be repeated in the embodiments of the present application.

S95: Determine whether the predicted disease category and the second disease category are the same disease category.

When the judgment result of S94 is yes, it is considered that the primary and secondary diagnosis of the first case has been switched, and the computing device may execute S96. When the judgment result of S94 is NO, step S93 is executed.

S96: Output prompt information for prompting the replacement of the primary and secondary diagnosis of the first case.

In an embodiment of the present application, the method may further include:

Receiving a case set, the case set includes multiple cases, and the first case may be any one of the multiple cases;

The first image is displayed according to the case data of the abnormal case in the case set, and the first image includes at least one of the following: the correspondence between the hospital where the abnormal case in the case set is located and the number of cases, and the abnormal case in the case set The corresponding relationship between the number of attending physicians and the number of cases, the classification of the disease types of the abnormal cases in the case set and the number of cases.

It can be seen that the abnormal cases are analyzed by visualizing the case data of the abnormal cases, so that the management personnel of the medical insurance management platform can quickly find the problem of the abnormal case according to the visualized first image.

In summary, the computing device in the embodiment of the present application receives the case data of the first case, and the case data includes the actual medical insurance cost; look up the score of the first basic disease category corresponding to the first disease category in the disease category dictionary table , And calculate the predicted medical insurance cost of the first case according to the score of the first basic disease, the first disease category is the disease category of the first case, and the disease category dictionary table includes the disease category and the underlying disease Correspondence relationship between the scores; further, according to the actual medical insurance cost and the predicted medical insurance cost to determine whether the first case is an abnormal case, if it is, then output a prompt message to prompt the abnormality of the first case. By executing the above method, abnormal Case identification.

The devices involved in the embodiments of the invention are described below.

Please refer to the computing device 40 in FIG. 4, including but not limited to: a receiving unit 41, a searching unit 42, a judging unit 43, a determining unit 44, a computing unit 45, and the like. among them,

The receiving unit 41 is configured to receive case data of the first case, and the case data includes actual medical insurance expenses;

The searching unit 42 is used to search for the first basic disease type score corresponding to the first disease type classification to which the first case belongs in the disease type score dictionary table. The disease type score dictionary table includes the disease type classification and Correspondence between scores of basic diseases;

The calculation unit 43 is configured to calculate the predicted medical insurance cost of the first case according to the score of the first basic disease;

The first determining unit 44 is configured to determine whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost;

The output unit 45 is configured to output prompt information for prompting the abnormality of the first case when the judgment result of the judgment unit is yes.

Please refer to the computing device shown in FIG. 5. In addition to the various units in the computing device 40 shown in FIG. 4, the computing device 50 may also include: a classification determination unit 46 and / or a prediction unit 47, and a second judgment unit 48.

The classification determining unit 46 is configured to determine the classification of the disease type of the first case according to the diagnostic identifier for identifying the classification of the insured person in the first case, wherein the classification of the disease type is the classification of the disease type An item in the dictionary, the disease category score dictionary table includes the correspondence between the disease category and the basic disease category score, the disease category code is an ICD code or the first N digits of the ICD code, and the N is less than A positive integer of 6.

In another implementation of the present application, the calculation unit 43 is specifically configured to:

Calculating the first disease type score according to the first basic disease type score;

The predicted medical insurance cost of the first case is calculated according to the first disease type score and the preset score unit price.

Optionally, the first disease type score is calculated according to the first basic disease type score by the following calculation formula:

Y = ∑ _i A ₁ * C ₁ + E _i ;

The calculation formula for calculating the predicted medical insurance cost of the first case according to the first disease type score and the preset score unit price is:

S ＝ Y * D

Where Y is the score of the first disease type, A ₁ is the score of the first basic disease type, C ₁ is the hospital level coefficient of the hospital where the first case is located, and E _i is the score of the additional disease type, i is the index of the score of the additional disease, i is a positive integer, S is the predicted medical insurance cost, and D is the unit price of the score.

In yet another implementation of the present application, the first determining unit 44 is specifically configured to: determine whether the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold or less than the third threshold;

The first output unit 45 is specifically used to:

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold, output prompt information for prompting that the disease type score of the first case is too low;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the first threshold and greater than the second threshold, output prompt information for prompting that the disease score of the first case is within a normal range;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the second threshold, output prompt information for prompting that the disease type score of the first case is too high.

In another implementation of the present application, the computing device 50 further includes:

The predicting unit 47 is configured to input multiple case characteristics of the first case into a disease type analysis model to obtain a predicted disease type classification of the first case;

The second judgment unit 48 is used to judge whether the first case is a predicted disease category and whether the first disease category is the same disease category,

The output unit 45 is further configured to: when the judgment result of the second judgment unit is yes, output prompt information for prompting that the diagnosis of the first case is wrong.

In still another implementation of the present application, the receiving unit 41 is further configured to: receive a case set, the case set includes multiple cases, and the first case is any one of the multiple cases;

The output unit 45 is further configured to display a first image according to the case data of the abnormal case in the case set, the first image including at least one of the following: correspondence between the hospital where the abnormal case in the case set is located and the number of cases The relationship, the correspondence relationship between the attending physicians of the abnormal cases in the case set and the number of cases, and the correspondence relationship between the classification of the disease types of the abnormal cases in the case set and the number of cases.

It should be noted that, for specific implementation of each unit of the foregoing computing device, reference may be made to related descriptions in the foregoing method embodiments, and details are not described in this application.

As shown in FIG. 6, the computing device 600 may include: a baseband chip 610, a memory 615 (one or more computer-readable storage media), a communication module 616 (eg, a radio frequency (RF) module 6161, and / or communication Interface 6162), peripheral system 617, communication interface 623. These components can communicate on one or more communication buses 614.

The peripheral system 617 is mainly used to realize the interactive function between the computing device 610 and the user / external environment, mainly including the input / output device of the computing device 600. In a specific implementation, the 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 with their corresponding peripheral devices (such as 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 integrate 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 a clock 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 to provide a stable, high-precision voltage for the processor 611, the radio frequency module 6161, and peripheral systems.

The radio frequency (RF) module 6161 is used to receive and transmit radio frequency signals, and mainly integrates the receiver and transmitter of the computing device 600. The radio frequency (RF) module 6161 communicates with the communication network and other communication devices through radio frequency signals. In a specific implementation, 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, and Storage media, etc. 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.

The memory 615 is coupled to the processor 611 and is used to store various software programs and / or multiple sets of instructions. In a specific implementation, the memory 615 may include a high-speed random access memory, and may also include a 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 615 may store an operating system (hereinafter referred to as a system), such as an embedded operating system such as ANDROID, IOS, WINDOWS, or LINUX. The memory 615 may also store a network communication program, which may be used to communicate with one or more additional devices, one or more computing device devices, or one or more network devices. The memory 615 can also store a user interface program, which can display the content of the application program vividly through a graphical operation interface, and receive user control operations on the application program through input controls such as menus, dialog boxes, and keys. .

The memory 615 may also store one or more application programs. As shown in FIG. 6, these applications may include: social applications (such as Facebook), image management applications (such as albums), map applications (such as Google Maps), browsers (such as Safari, Google Chrome), etc. .

In this application, the processor 611 may be used to read and execute computer-readable instructions. Specifically, the processor 611 may be used to call a program stored in the memory 615, for example, an implementation program of a method for calculating a disease score provided by the present application, and execute instructions contained in the program.

Specifically, the processor 611 may be used to call a program stored in the memory 615, such as an implementation program of a method for calculating a disease score provided by the present application, and execute the following process:

Receiving the case data of the first case through the communication module 616, the case data including the actual medical insurance cost;

Look up the first basic disease category score corresponding to the first disease category in the disease category score dictionary table, and calculate the predicted medical insurance cost of the first case according to the first basic disease category score, the first The disease category is the disease category to which the first case belongs, and the disease category score dictionary table includes the correspondence between the disease category and the basic disease category score;

In an implementation of the present application, before the processor 611 executes the search for the first basic disease score corresponding to the first disease category in the disease category dictionary table, it is also used to execute:

The disease classification of the first case is determined according to the diagnostic identifier used to identify the disease classification of the insured in the first case, wherein the disease classification is an item in the disease classification dictionary, the The disease type score dictionary table includes the correspondence relationship between the disease type classification and the basic disease type score. The disease type classification code is an ICD code or the first N digit codes of the ICD code, where N is a positive integer less than 6.

In yet another implementation of the present application, the processor 611 executing the calculation of the predicted medical insurance cost of the first case based on the score of the first basic disease includes executing:

Optionally, the calculation method for calculating the first disease type score according to the first basic disease type score is:

Y = ∑ _i A ₁ * C ₁ + E _i ;

The calculation method for calculating the predicted medical insurance cost of the first case based on the first disease type score and the preset score unit price is:

S ＝ Y * D

In yet another implementation of the present application, the processor 611 executes the judging whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, outputs the Describe the abnormal information of the first case, including implementation:

Determine whether the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold or less than the third threshold;

In yet another implementation of the present application, the processor 611 is further used to execute:

Inputting multiple case characteristics of the first case into a disease analysis model to obtain the predicted disease classification of the first case;

It is determined whether the first case is a predicted disease type classification and the first disease type classification is the same disease type classification, and if not, output prompt information for prompting that the diagnosis of the first case is incorrect.

Receiving a case set through the communication module 616, the case set includes multiple cases, and the first case is any one of the multiple cases;

It can be understood that, for specific implementation of the foregoing processes and functional units, reference may be made to related descriptions in the foregoing method embodiments, and the embodiments of the present application are not described in detail.

It should be understood that the computing device 600 is only an example provided by the embodiments of the present application, and the computing device 600 may have more or less components than those shown, may combine two or more components, or may have Different configurations of components are implemented.

In the above embodiments, the description of each embodiment has its own emphasis. For a part that is not detailed in an embodiment, you can refer to related descriptions in other embodiments.

A person of ordinary skill in the art may understand that all or part of the processes in the method of the foregoing embodiments may be completed by instructing relevant hardware through a computer program, and the program may be stored in a computer-readable storage medium. During execution, the process of the above method embodiments may be included. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM), etc.

The steps in the method of the embodiment of the present application may be adjusted, merged, and deleted sequentially according to actual needs.

The modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.

Claims

An abnormal case identification method based on data analysis, which is characterized by:

The computing device receives the case data of the first case, and the case data includes the actual medical insurance cost;

The computing device searches the disease type score dictionary table for the first basic disease type score corresponding to the first disease type classification to which the first case belongs, and calculates the first basic disease type score according to the first basic disease type score The predicted medical insurance cost of a case, the disease type score dictionary table includes the correspondence between the disease type classification and the basic disease type score;

The computing device determines whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, outputs prompt information for prompting the abnormality of the first case.
The method according to claim 1, wherein the computing device searches the disease type score dictionary table before searching for the first basic disease type score corresponding to the first disease type classification to which the first case belongs, The method also includes:

The computing device determines the disease category of the first case according to the diagnostic identifier for identifying the disease category of the insured person in the first case, where the disease category is in the disease category dictionary Item, the disease classification dictionary includes the correspondence between the name of the disease classification and the classification code of the disease, the classification code of the disease is an ICD code or the first N digits of the ICD code, and the N is a positive integer less than 6 .
The method of claim 1, wherein the calculating the predicted medical insurance cost of the first case based on the score of the first basic disease includes:

Calculating the first disease type score according to the first basic disease type score;

The predicted medical insurance cost of the first case is calculated according to the first disease type score and the preset score unit price.
The method of claim 3, wherein calculating the first disease type score based on the first basic disease type score includes:

Y = ∑ i A 1 * C 1 + E i ;

The calculation of the predicted medical insurance cost of the first case based on the first disease type score and the preset score unit price includes:

S ＝ Y * D

Where Y is the score of the first disease type, A 1 is the score of the first basic disease type, C 1 is the hospital level coefficient of the hospital where the first case is located, and E i is the score of the additional disease type, i is the index of the score of the additional disease, i is a positive integer, S is the predicted medical insurance cost, and D is the unit price of the score.
The method according to claim 1, wherein the computing device determines whether the first case is an abnormal case based on the actual medical insurance cost and the predicted medical insurance cost, and if so, outputs a prompt for the The prompt information for the first case abnormality includes:

The computing device determines whether the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold or less than the third threshold;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold, output prompt information for prompting that the disease type score of the first case is too low;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the first threshold and greater than the second threshold, output prompt information for prompting that the disease score of the first case is within a normal range;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the second threshold, output prompt information for prompting that the disease type score of the first case is too high.
The method according to any one of claims 1 to 5, wherein the method further comprises:

Inputting multiple case characteristics of the first case into a disease analysis model to obtain the predicted disease classification of the first case;

It is judged whether the first case is a predicted disease type classification and the first disease type classification is the same disease type classification, and if not, a prompt message for prompting that the diagnosis of the first case is wrong is output.
The method according to any one of claims 1 to 5, wherein the method further comprises:

Receiving a case set, the case set includes multiple cases, and the first case is any one of the multiple cases;

The first image is displayed according to the case data of the abnormal case in the case set, and the first image includes at least one of the following: the correspondence between the hospital where the abnormal case in the case set is located and the number of cases, and the abnormal case in the case set The corresponding relationship between the number of attending physicians and the number of cases, the classification of the disease types of the abnormal cases in the case set and the number of cases.
A computing device is characterized by comprising a processor, a memory, and a communication module. The processor is coupled to the memory and the communication module. The processor is used to invoke program code stored in the memory for execution:

Receiving the case data of the first case through the communication module, the case data including the actual medical insurance cost;

Find the first basic disease type score corresponding to the first disease type classification to which the first case belongs in the disease type score dictionary table, and calculate the prediction of the first case according to the first basic disease type score Medical insurance costs, the disease type score dictionary table includes the correspondence between the classification of disease types and the scores of basic disease types;

Determine whether the first case is an abnormal case according to the actual medical insurance cost and the predicted medical insurance cost, and if so, output prompt information for prompting the abnormality of the first case.
The computing device according to claim 8, wherein before the processor executes the search for the first basic disease category score corresponding to the first disease category in the disease category score dictionary table, the processor To execute:

The disease classification of the first case is determined according to the diagnostic identifier used to identify the disease classification of the insured in the first case, wherein the disease classification is an item in the disease classification dictionary, the The disease type score dictionary table includes the correspondence relationship between the disease type classification and the basic disease type score. The disease type classification code is an ICD code or the first N digit codes of the ICD code, where N is a positive integer less than 6.
The computing device of claim 8, wherein the processor executing the calculation of the predicted medical insurance cost of the first case based on the score of the first basic disease includes executing:

Calculating the first disease type score according to the first basic disease type score;

The predicted medical insurance cost of the first case is calculated according to the first disease type score and the preset score unit price.
The computing device according to claim 10, wherein the processor calculates the first disease type score by the following formula:

Y = ∑ i A 1 * C 1 + E i ;

The calculation method for calculating the predicted medical insurance cost of the first case based on the first disease type score and the preset score unit price is:

S ＝ Y * D

Where Y is the score of the first disease type, A 1 is the score of the first basic disease type, C 1 is the hospital level coefficient of the hospital where the first case is located, and E i is the score of the additional disease type, i is the index of the score of the additional disease, i is a positive integer, S is the predicted medical insurance cost, and D is the unit price of the score.
The computing device according to claim 8, wherein the processor executes the process of determining whether the first case is an abnormal case based on the actual medical insurance cost and the predicted medical insurance cost, and if so, output The prompt information for prompting the abnormality of the first case includes executing:

Determine whether the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold or less than the third threshold;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold, output prompt information for prompting that the disease type score of the first case is too low;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the first threshold and greater than the second threshold, output prompt information for prompting that the disease score of the first case is within a normal range;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the second threshold, output prompt information for prompting that the disease type score of the first case is too high.
The computing device according to any one of claims 8-12, wherein the processor is further configured to execute:

Inputting multiple case characteristics of the first case into a disease analysis model to obtain the predicted disease classification of the first case;

It is determined whether the first case is a predicted disease type classification and the first disease type classification is the same disease type classification, and if not, output prompt information for prompting that the diagnosis of the first case is incorrect.
A computing device, characterized in that it includes:

A receiving unit, configured to receive case data of the first case, the case data including actual medical insurance expenses;

The searching unit is used to search for the first basic disease type score corresponding to the first disease type classification to which the first case belongs in the disease type score dictionary table, and the disease type score dictionary table includes the disease type classification and the basic Correspondence of disease scores;

A calculation unit, configured to calculate the predicted medical insurance cost of the first case according to the score of the first basic disease;

A first determining unit, configured to determine whether the first case is an abnormal case based on the actual medical insurance cost and the predicted medical insurance cost;

The output unit is configured to output prompt information for prompting the abnormality of the first case when the judgment result of the judgment unit is yes.
The computing device of claim 14, wherein the computing device further comprises:

A classification determination unit, configured to determine the classification of the disease type of the first case according to the diagnostic identifier used to identify the classification of the insured person in the first case, wherein the classification of the disease type is a classification dictionary The item in the disease category score dictionary table includes the correspondence between the disease category and the basic disease category score, the disease category code is the ICD code or the first N digits of the ICD code, and the N is less than 6 Positive integer.
The computing device according to claim 14, wherein the computing unit is specifically configured to:

Calculating the first disease type score according to the first basic disease type score;

The predicted medical insurance cost of the first case is calculated according to the first disease type score and the preset score unit price.
The computing device according to claim 16, wherein the calculation unit is specifically configured to calculate the first disease type score according to the first basic disease type score by the following calculation formula:

Y = ∑ i A 1 * C 1 + E i ;

The calculation formula for calculating the predicted medical insurance cost of the first case according to the first disease type score and the preset score unit price is:

S ＝ Y * D

Where Y is the score of the first disease type, A 1 is the score of the first basic disease type, C 1 is the hospital level coefficient of the hospital where the first case is located, and E i is the score of the additional disease type, i is the index of the score of the additional disease, i is a positive integer, S is the predicted medical insurance cost, and D is the unit price of the score.
The computing device according to claim 14, wherein the first determining unit is specifically configured to: determine whether a ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than a second threshold or less than a third threshold;

The first output unit is specifically used for:

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is greater than the second threshold, output prompt information for prompting how low the disease score of the first case is;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the first threshold and greater than the second threshold, output prompt information for prompting that the disease score of the first case is within a normal range;

When the ratio of the actual medical insurance cost to the predicted medical insurance cost is less than the second threshold, output prompt information for prompting that the disease type score of the first case is too high.
The computing device according to any one of claims 14 to 18, wherein the computing device further comprises:

A prediction unit, configured to input multiple case characteristics of the first case into a disease type analysis model to obtain a predicted disease type classification of the first case;

A second judgment unit, configured to judge whether the first case is a predicted disease category and the first disease category is the same disease category,

The output unit is further configured to: when the judgment result of the second judgment unit is yes, output prompt information for prompting that the diagnosis of the first case is incorrect.
A computer-readable storage medium, characterized in that the computer-readable storage medium is used for computer software instructions, which when executed by a computer causes the computer to execute any one of claims 1-7 The abnormal case identification method based on data analysis as claimed in claim.