WO2020087969A1 - Procédé d'identification de cas cliniques anormaux sur la base d'une analyse de données et dispositif informatique - Google Patents

Procédé d'identification de cas cliniques anormaux sur la base d'une analyse de données et dispositif informatique Download PDF

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WO2020087969A1
WO2020087969A1 PCT/CN2019/095009 CN2019095009W WO2020087969A1 WO 2020087969 A1 WO2020087969 A1 WO 2020087969A1 CN 2019095009 W CN2019095009 W CN 2019095009W WO 2020087969 A1 WO2020087969 A1 WO 2020087969A1
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case
disease
score
medical insurance
disease type
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PCT/CN2019/095009
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English (en)
Chinese (zh)
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刘俊芳
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平安医疗健康管理股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • 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.
  • 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.
  • ICD-10 International Classification of Diseases
  • 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.
  • 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.
  • the cost of medical services 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.
  • the embodiments of the present application provide an abnormal case identification method based on data analysis, which can realize the identification of abnormal cases.
  • 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 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.
  • 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.
  • 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:
  • the case data including the actual medical insurance cost
  • the disease type score dictionary table includes the correspondence between the classification of disease types and the scores of basic disease types;
  • 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
  • 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
  • an abnormal case identification method based on data analysis.
  • 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.
  • 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.
  • FIG. 1 is a functional architecture diagram of a medical insurance management platform provided by an embodiment of this application.
  • FIG. 3 is a flowchart of an abnormal case analysis method provided by an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a computing device provided by an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of yet another computing device provided by an embodiment of this application.
  • FIG. 6 is a schematic structural diagram of yet another computing device provided by an embodiment of this application.
  • FIG. 1 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:
  • 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.
  • 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
  • M is a positive integer.
  • 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.
  • 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.
  • 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.).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 2 is an abnormal case identification method provided by this application.
  • 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.
  • 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.
  • the diagnosis information may include a diagnosis identifier for identifying the classification of the insured person's disease type.
  • 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.
  • 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.
  • the disease category score dictionary table includes the correspondence between the disease category and the basic disease category score .
  • 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.
  • 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.
  • the disease classification code is an ICD code
  • the disease classification dictionary is an ICD dictionary
  • the disease type classification code is the first N digits of the ICD code, and N is a positive integer less than 6.
  • 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.
  • the diagnosis identifier in the first case is a diagnosis name or a diagnosis code
  • the disease category to which the first case belongs cannot be directly obtained through the diagnosis name or the diagnosis code.
  • 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.
  • 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.
  • 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.
  • 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.
  • the M code can be converted into an ICD code or a four-digit ICD code.
  • the MCD “M8140 / 6" corresponds to the ICD code "C78.7”
  • 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.
  • 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:
  • 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
  • E i is the additional disease Species score
  • i is the index of the additional disease species score
  • i is a positive integer.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the first threshold may be 10, 20, 35 or other numerical values.
  • 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.
  • 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.
  • the computing device may analyze the cause of the abnormal case.
  • 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.
  • 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.
  • the disease analysis model 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • step S93 is executed.
  • S96 Output prompt information for prompting the replacement of the primary and secondary diagnosis of the first case.
  • the method may further include:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • calculation unit 43 is specifically configured to:
  • 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 first disease type score is calculated according to the first basic disease type score by the following calculation formula:
  • 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:
  • 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
  • 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
  • D is the unit price of the score.
  • 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:
  • 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.
  • 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.
  • 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.
  • a baseband chip 610 e.g, a radio frequency (RF) module 6161, and / or communication Interface 6162
  • RF radio frequency
  • 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.
  • 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.
  • 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.
  • the radio frequency (RF) module 616161 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.
  • 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. .
  • social applications such as Facebook
  • image management applications such as albums
  • map applications such as Google Maps
  • browsers such as Safari, Google Chrome
  • 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.
  • 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:
  • the case data including the actual medical insurance cost
  • 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;
  • the processor 611 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.
  • 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:
  • 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 calculation method for calculating the first disease type score according to the first basic disease type score is:
  • 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:
  • 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
  • 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
  • D is the unit price of the score.
  • 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:
  • processor 611 is further used to execute:
  • 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.
  • processor 611 is further used to execute:
  • 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.
  • 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.
  • 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 modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.

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Abstract

Le mode de réalisation de la présente invention concerne un procédé d'identification de cas cliniques anormaux sur la base d'une analyse de données et un dispositif informatique. Le procédé comprend les étapes suivantes : le dispositif informatique reçoit des données de cas d'un premier cas, les données de cas comprenant les frais de couverture médicale effectifs ; le dispositif informatique recherche dans un dictionnaire de scores pathologiques un premier score pathologique de base correspondant à une première catégorie de maladie dont relève le premier cas, et calcule des frais de couverture médicale prédits pour le premier cas en fonction du premier score pathologique de base, le dictionnaire de scores pathologiques comprenant une relation de correspondance entre la catégorie de maladie et le score pathologique de base ; ensuite, le dispositif informatique détermine, en fonction des frais de couverture médicale effectifs et des frais de couverture médicale prédits, si le premier cas est un cas anormal ou pas ; si tel est le cas, le dispositif informatique délivre un message signalant l'anomalie du premier cas. Le procédé décrit peut être mis en œuvre pour identifier des cas anormaux.
PCT/CN2019/095009 2018-10-30 2019-07-08 Procédé d'identification de cas cliniques anormaux sur la base d'une analyse de données et dispositif informatique WO2020087969A1 (fr)

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CN112836500A (zh) * 2019-11-25 2021-05-25 泰康保险集团股份有限公司 识别病例的系统、方法、设备和计算机可读介质
CN111755076B (zh) * 2020-07-01 2024-08-09 北京小白世纪网络科技有限公司 基于空间可分离性的利用基因检测的疾病预测方法及系统
CN112016770A (zh) * 2020-10-21 2020-12-01 平安科技（深圳）有限公司 一种医保费用预测方法、装置、设备及存储介质
CN112992366B (zh) * 2021-03-01 2024-05-24 袁素华 基于医保病种付费制icd编码人工智能审核质控模式与系统
CN113780457B (zh) * 2021-09-18 2024-05-14 平安医疗健康管理股份有限公司 中医资源消耗的异常检测方法、装置、设备及介质
CN113869387B (zh) * 2021-09-18 2024-09-06 平安科技（深圳）有限公司 基于人工智能技术的异常医保报销识别方法及系统

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