CN112992366A - ICD (identity control document) code artificial intelligence audit quality control mode and system based on medical insurance disease payment system - Google Patents
ICD (identity control document) code artificial intelligence audit quality control mode and system based on medical insurance disease payment system Download PDFInfo
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Abstract
The invention discloses an ICD (identity control document) code artificial intelligence auditing quality control mode and system based on medical insurance disease payment, which comprises a data acquisition and processing unit, a knowledge base unit, a rule base unit, an early warning unit, an artificial connotation quality control unit, a cloud quality control unit, an interaction unit and an analysis unit. The invention belongs to the technical field of medical insurance disease payment, and particularly relates to an ICD (identification control code) artificial intelligence audit quality control mode and system based on medical insurance disease payment system ICD, which forms an ICD code related identification library from historical data by applying a big data mining technology, and obtains an ICD code knowledge factor hidden in data resources and an incidence relation thereof by applying knowledge fusion extraction and conversion; establishing a dynamic ICD coding table and a structured auditing logic judgment database of each related knowledge base by a natural language processing technology, wherein the idea is to dialectically combine full quality control of special medical records with other random spot checks; and the deep mining of big data, the advanced technology of the artificial intelligence technology and the coding experience are combined.
Description
Technical Field
The invention relates to the technical field of medical insurance payment ICD quality control and medical information, in particular to an artificial intelligent quality control auditing mode and system based on medical insurance disease payment ICD codes.
Background
With the increasing of the national basic medical insurance fund risk year by year, the rapid rise of medical expenses caused by the aging population brings huge pressure to the operation of the medical insurance fund. The paying medical insurance payment of the disease species is an important effective means for controlling unreasonable increase of medical expenses and reducing the burden of patients. The disease type payment system takes an ICD-10 code of main diagnosis and an ICD-9-CM-3 code of operation and operation diagnosis in a first page of a medical record as a basis for calculating a score, the score is determined according to the average cost of disease types in a total budget, and medical expenses are settled, namely, the ICD code of diagnosis and treatment information is a key basis for grouping the disease types, and the code quality not only directly influences each hospital to obtain a medical insurance fund, but also influences the clearance and payment rejection rate of medical insurance year; the accuracy of ICD codes has attracted high attention of hospital management departments, and hospitals must be provided with code auditing and quality control posts to ensure the accuracy of the codes. Generally, a small part of codes are wrong, because the quality control codes in China are in shortage, the current hospital code quality control working mode is random spot check and audit, manual quality control is the main mode, few advanced hospitals have simple logic condition audit as the auxiliary mode, the working mode can not accurately judge the medical record with wrong codes or wrong questions, and the quality control is not in the key direction, so that part of coding cases needing quality control can not be subjected to quality control, and the correct coding cases are subjected to unnecessary quality control.
With the gradual advance of the artificial intelligence technology, the artificial intelligence technology is widely applied to the medical field, and in order to solve the dilemma, an artificial intelligence auditing and quality control new mode is researched and explored, so that cases needing encoding quality control are intelligently and accurately judged, the quality control direction is prompted, unnecessary work is reduced, the work efficiency is improved, and the encoding accuracy is improved.
Disclosure of Invention
Aiming at the situation and overcoming the defects of the prior art, the invention provides an ICD coding artificial intelligence auditing quality control mode and system based on medical insurance type payment, an ICD coding related identification base is formed from historical data by applying a big data mining technology, and an ICD coding knowledge factor hidden in data resources and an incidence relation thereof are obtained by applying knowledge fusion extraction and conversion; through a natural language processing technology, a dynamic ICD coding table and a structured audit logic judgment database of each related knowledge base are established, the idea is to combine full quality control of special medical records and other random spot checks dialectically, intelligent audit is taken as a main part, manual quality control is taken as an auxiliary part, the manpower of quality control is concentrated on a blade to the maximum extent, the efficiency is improved, and the effect is improved; and the deep mining of big data, the advanced technology of the artificial intelligence technology and the coding experience are combined.
The ICD coding artificial intelligent auditing quality control system based on medical insurance disease payment comprises a data acquisition and processing unit, a knowledge base unit, a rule base unit, an early warning unit, an artificial inclusion quality control unit, a cloud quality control unit, an interaction unit and an analysis unit, wherein the knowledge base unit and the rule base unit are respectively connected with the data acquisition and processing unit, the early warning unit is connected with the knowledge base unit and the rule base unit, the artificial inclusion quality control unit and the cloud quality control unit are respectively connected with the early warning unit, the interaction unit is connected with the early warning unit, and the analysis unit is connected with the interaction unit.
The invention relates to an ICD (interface control document) code artificial intelligence audit quality control mode based on medical insurance disease charge system, which comprises the following steps:
(1) and (3) clinical data acquisition: the data acquisition and processing unit is in butt joint with a database of a related data system of the hospital to extract historical data, the data of the system in the hospital is associated by using a main index patient ID through connecting databases of systems such as hospital province medical record statistical software, EMR (electronic medical record), HIS (medical information system), and the like according to an interface specification by adopting a view method, the related historical data such as the information of the first page of the last five years, charging information, diagnosis and treatment terms written by a doctor are extracted, and the data are cleaned, converted and loaded to a data warehouse in order to ensure the quality of the data;
(2) establishing a basic ICD code matching knowledge (P) and a logic audit basic library (L): the method combines the modes of practical experience of coding quality control, document retrieval, expert consultation and the like, and comprises an ICD coding knowledge rule table, an audit logic table, four versions of ICD coding mapping integrated table and a national unified clinical terminology knowledge table;
(3) establishing an ICD (identity control document) audit data quality control discriminant library (S):
1, performing statistical analysis on historical data in a data warehouse, and forming a related knowledge table, wherein the related knowledge table comprises an operation expense and high-value consumable charging item table needing to be filled in an operation column, a standard clinical diagnosis and treatment term table in a hospital, a diagnosis and treatment term mapping table in the hospital and the state, and the like;
2, obtaining coding knowledge factors hidden in an ICD disease diagnosis operation table and a related knowledge table and association and logic relations thereof by using knowledge fusion extraction and conversion, and establishing various relation models: the system comprises a national unified clinical diagnosis and treatment term table and ICD (interface control document) relational model, an operation and high-value consumable charging and diagnosis and treatment term relational model, an operation and high-value consumable charging item and ICD coding relational model, an operation coding and disease coding mutual corresponding relational model, a disease category and average cost hospitalization day and other key index relational models, a DRG ungrouped and QY grouping model and an age and disease category grouping relational model;
(4) and (3) applying AI to form a dynamic ICD cloud quality control library (Y): the cloud quality control unit comprises various logic relation tables and the like, each table at least comprises auditing conditions, quality control contents and quality control case types, various logic relations are dynamic, are updated regularly and irregularly, and are updated regularly when the policy is stable, the policy change can be adjusted or updated in real time, the relation range is generally counted by regional hospital coding quality control data, and a small-probability event is assumed to be abnormal;
(5) establishing a quality control library (F) for maintaining medical insurance payment: policies related to medical insurance payment of various cities, special examination items and the like are integrated, and quality control early warning problem medical records are synthesized from dimensions such as payment mode matching, medical insurance settlement calculation, diagnosis and treatment charging standard scanning, in-hospital ICD disease coding and object price charging relation models and the like.
(6) And (3) quality control early warning of common medical records: according to the ICD audit quality control logic discrimination library and the code related knowledge library, a computer technology is applied to intelligently discriminate quality control categories and prompt quality control content of information such as ICD codes, ages and expenses of cases needing quality control coding, and the ordinary cases are subjected to spot check quality control in a proper sampling mode;
(7) the encoder spot check algorithm: selecting quality control data of each coding person in nearly five years, counting the coding accuracy of common and frequently-occurring cases of each coding person every year, collecting key factor data of the coding person influencing the coding accuracy by adopting a multiple linear regression method, and performing single-factor and multi-factor regression analysis by using SPSS21.0 software to determine a proper sampling mode, including a spot check rate and a spot check mode;
(8) applying an ICD code artificial intelligence audit quality control system based on medical insurance disease payment system: compiling an API universal interface to fuse different ICD coding systems, fusing an intelligent auditing quality control mode to a hospital through the universal interface, and performing real-time spot check quality control on the system with the ICD coding function;
(9) coding quality control effect analysis: and (4) carrying out comparison and evaluation on the primary achievement indexes (coding accuracy) of the ICD coding intelligent auditing quality control mode in the first and the last half years by an analysis unit.
Further, the four versions ICD codes in the step (2) are home release, provincial release, medical insurance release and national clinical release.
Further, the hospital standard clinical diagnosis and treatment terminology table in the step (3) is a disease diagnosis table, a pathological diagnosis table and an operation name table.
Further, the relation mode in the step (3) relates to a plurality of diagnosis descriptions corresponding to a plurality of ICD diseases and operations, and associated medicine/material/diagnosis and treatment item charge codes, and is stored in a database in a treatment combination mode, such as combinations Sx1-1, Sx1-2, Sx1-3 and the like, and correspondingly triggers diagnosis descriptions and key parameter labels K1.1, K1.11 and K1.12; the association is disclosed as (Sx1 + K1 + weight)/trigger ═ warning coefficient.
Further, the disease species in the step (3) are municipal medical insurance list disease species and provincial edition DRGs disease species.
Further, the AI cloud quality control library in step (4) includes, but is not limited to, a medical term set, a regional code set, a disease category operation set, a diagnosis and treatment combination set, a coding basis set, an ICD code adding set, a charging item set, a difficult and complicated coding set, and the like, and is mapped by analyzing an ICD coding quality control data model of multiple hospitals in a manner of combining regional quality control coefficients and national quality control specifications.
Further, the quality control categories in the step (6) are special case quality control and common case quality control.
Further, the key factor data of the encoding personnel in the step (7) are medical background, academic calendar, expert, working age, main chapter codes in work, error-prone code categories and the like.
Further, the API described in step (8) includes a data interface and a front-end encapsulation module, which supports one-key calling of other systems and enables the user quality control experience to be consistent.
The invention with the structure has the following beneficial effects: according to the scheme, based on an ICD (interface control document) code artificial intelligence auditing and quality control mode of medical insurance disease payment system, an ICD code related identification library is formed from historical data by applying a big data mining technology, and ICD code knowledge factors and the association relation thereof hidden in data resources are obtained by applying knowledge fusion extraction and conversion; through a natural language processing technology, a dynamic ICD coding table and a structured audit logic judgment database of each related knowledge base are established, the idea is to combine full quality control of special medical records and other random spot checks dialectically, intelligent audit is taken as a main part, manual quality control is taken as an auxiliary part, the manpower of quality control is concentrated on a blade to the maximum extent, the efficiency is improved, and the effect is improved; and the deep mining of big data, the advanced technology of the artificial intelligence technology and the coding experience are combined.
Drawings
FIG. 1 is a framework structure diagram of an artificial intelligent audit quality control mode based on ICD codes of medical insurance disease payment system.
FIG. 2 is a system configuration of an artificial intelligent auditing and quality control system based on ICD codes of medical insurance disease payment system.
FIG. 3 is a flow chart of the present invention for checking quality control mode and system matching based on ICD code of medical insurance system for paying.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The ICD coding artificial intelligent auditing quality control system based on medical insurance disease payment comprises a data acquisition and processing unit, a knowledge base unit, a rule base unit, an early warning unit, an artificial inclusion quality control unit, a cloud quality control unit, an interaction unit and an analysis unit, wherein the knowledge base unit and the rule base unit are respectively connected with the data acquisition and processing unit, the early warning unit is connected with the knowledge base unit and the rule base unit, the artificial inclusion quality control unit and the cloud quality control unit are respectively connected with the early warning unit, the interaction unit is connected with the early warning unit, and the analysis unit is connected with the interaction unit.
The invention relates to an ICD (interface control document) code artificial intelligence audit quality control mode based on medical insurance disease charge system, which comprises the following steps:
(1) and (3) clinical data acquisition: the data acquisition and processing unit is in butt joint with a database of a related data system of the hospital to extract historical data, the data of the system in the hospital is associated by using a main index patient ID through connecting databases of systems such as hospital province medical record statistical software, EMR (electronic medical record), HIS (medical information system), and the like according to an interface specification by adopting a view method, the related historical data such as the information of the first page of the last five years, charging information, diagnosis and treatment terms written by a doctor are extracted, and the data are cleaned, converted and loaded to a data warehouse in order to ensure the quality of the data;
(2) establishing a basic ICD code matching knowledge (P) and a logic audit basic library (L): the method combines the modes of practical experience of coding quality control, document retrieval, expert consultation and the like, and comprises an ICD coding knowledge rule table, an audit logic table, four versions of ICD coding mapping integrated table and a national unified clinical terminology knowledge table;
(3) establishing an ICD (identity control document) audit data quality control discriminant library (S):
3, performing statistical analysis on historical data in the data warehouse, and forming a related knowledge table, wherein the related knowledge table comprises an operation expense and high-value consumable charging item table needing to be filled in an operation column, a standard clinical diagnosis and treatment term table in a hospital, a diagnosis and treatment term mapping table in the hospital and the country and the like;
4, obtaining coding knowledge factors hidden in an ICD disease diagnosis operation table and a related knowledge table and association and logic relations thereof by using knowledge fusion extraction and conversion, and establishing various relation models: the system comprises a national unified clinical diagnosis and treatment term table and ICD (interface control document) relational model, an operation and high-value consumable charging and diagnosis and treatment term relational model, an operation and high-value consumable charging item and ICD coding relational model, an operation coding and disease coding mutual corresponding relational model, a disease category and average cost hospitalization day and other key index relational models, a DRG ungrouped and QY grouping model and an age and disease category grouping relational model;
(4) and (3) applying AI to form a dynamic ICD cloud quality control library (Y): the cloud quality control unit comprises various logic relation tables and the like, each table at least comprises auditing conditions, quality control contents and quality control case types, various logic relations are dynamic, are updated regularly and irregularly, and are updated regularly when the policy is stable, the policy change can be adjusted or updated in real time, the relation range is generally counted by regional hospital coding quality control data, and a small-probability event is assumed to be abnormal;
(5) establishing a quality control library (F) for maintaining medical insurance payment: policies related to medical insurance payment of various cities, special examination items and the like are integrated, and quality control early warning problem medical records are synthesized from dimensions such as payment mode matching, medical insurance settlement calculation, diagnosis and treatment charging standard scanning, in-hospital ICD disease coding and object price charging relation models and the like.
(6) And (3) quality control early warning of common medical records: according to the ICD audit quality control logic discrimination library and the code related knowledge library, a computer technology is applied to intelligently discriminate quality control categories and prompt quality control content of information such as ICD codes, ages and expenses of cases needing quality control coding, and the ordinary cases are subjected to spot check quality control in a proper sampling mode;
(7) the encoder spot check algorithm: selecting quality control data of each coding person in nearly five years, counting the coding accuracy of common and frequently-occurring cases of each coding person every year, collecting key factor data of the coding person influencing the coding accuracy by adopting a multiple linear regression method, and performing single-factor and multi-factor regression analysis by using SPSS21.0 software to determine a proper sampling mode, including a spot check rate and a spot check mode;
(8) applying an ICD code artificial intelligence audit quality control system based on medical insurance disease payment system: compiling an API universal interface to fuse different ICD coding systems, fusing an intelligent auditing quality control mode to a hospital through the universal interface, and performing real-time spot check quality control on the system with the ICD coding function;
(9) coding quality control effect analysis: and (4) carrying out comparison and evaluation on the primary achievement indexes (coding accuracy) of the ICD coding intelligent auditing quality control mode in the first and the last half years by an analysis unit.
And (3) the four versions ICD codes in the step (2) are home version, provincial version, medical insurance version and national clinical version.
And (3) the hospital normative clinical diagnosis and treatment glossary is a disease diagnosis table, a pathological diagnosis table and an operation name table.
The relation mode in the step (3) relates to a plurality of diagnosis descriptions corresponding to a plurality of ICD diseases and operations, and associated medicine/material/diagnosis and treatment item charge codes, and is stored in a database in a treatment combination mode, such as combinations Sx1-1, Sx1-2, Sx1-3 and the like, and the diagnosis descriptions and key parameter labels K1.1, K1.11 and K1.12 are correspondingly triggered; the association is disclosed as (Sx1 + K1 + weight)/trigger ═ warning coefficient.
The disease seeds in the step (3) are municipal medical insurance list disease seeds and provincial version DRGs disease seeds.
The AI cloud quality control library in the step (4) comprises but is not limited to a medical term set, a regional coding set, a disease operation set, a diagnosis and treatment combination set, a coding basis set, an ICD coding set, a charging item set, a difficult coding set and the like, and mapping is carried out by adopting a mode of combining regional quality control coefficients and national quality control specifications through analyzing a multi-hospital ICD coding quality control data model.
And (4) the quality control categories in the step (6) are special case quality control and common case quality control.
The key factor data of the encoding personnel in the step (7) are medical background, academic calendar, specialties, working age, main chapter codes in work, error-prone code categories and the like.
The API in the step (8) comprises a data interface and a front-end packaging module, and supports one-key calling of other systems, and the user quality control experience is consistent.
When the system is used specifically, according to coding related knowledge and coding quality control practical experience, a development environment is Win10, a background database is MySQL, PHP + Python + Apache is matched and developed to carry out big data mining, knowledge integration, natural language processing and other intelligent technologies to research ICD coding intelligent audit quality control modes; determining the most appropriate sampling mode by adopting SPSS21.0 multiple linear regression method to analyze key factors of encoding personnel having influence on encoding accuracy; in practical use, there are tens of thousands of ICD quality control rules in the mode and system of the present invention that cannot be set forth one by one, and an embodiment of the present invention is described in detail with reference to fig. 1, 2 and 3, which illustrate artificial intelligent audit quality control based on ICD codes for medical insurance patient payment. In the manual intelligent auditing and quality control mode flows B1-B6, the ICD coding manual intelligent auditing and quality control mode is formed by combining corresponding unit modules such as improvement of clinical code matching (P), quality control (S) of medical record coding homepage and medical record key index data, ICD logic quality control (L), rules and algorithms of associated market medical insurance payment quality control (F), auxiliary regional cloud quality control (A) and the like, and the specific steps are as follows: and B1, data acquisition is to be butted with the existing clinical database of the medical institution, EMR/HIS data in an Oracle database, data of the medical record in an Sql Server and the like are acquired, the EMR/HIS data and the data of the medical record in the Oracle database comprise the hospitalization information of the patient, the advice charge details, outpatient/hospitalization/pathological diagnosis, doctor operation, course of illness/operation/nursing record, the medical record HIS _ ba 1-9, a return table of a municipal social security system and the like, and the main index association is carried out on the hospitalization number, the hospitalization times and the serial number of the patient. The data cleaning mode comprises punctuation mark correction, data type correction, combination/deletion of different data in the same field and the like; the system regularly collects and arranges data every day to form a local basic data warehouse for related data analysis and quality control work; the system will automatically perform icd matching for doctor diagnosis and surgery through P2 and perform P1 calculations. The doctor diagnoses D1, divides the word into D1 words (D1.1, D1.2, D1.3. cndot. cndot.), and confirms that the dominant word is matched with the ICD disease with high conformity after the association is the term relationship of S1; clinical users can log in the module for manual matching, relevant code reference instructions are checked, the system can be moved forward to the period that patients are in a hospital to carry out expected evaluation on cost and diagnosis based on a medical insurance disease payment system, treatment means in the hospital are managed, P1 values are checked in real time, and the medical quality of the patients is reasonably guaranteed. B2: when the user performs the first page encoding, the relevant rule of L, S will be triggered, such as when the codes E04 and E05 are in the category, the L4 rule will be triggered, which suggests that the codes are mutually exclusive. When Q42.1 and Q43.6 codes appear, triggering an L3 rule to prompt that the codes need to be combined into Q42.0; when the operation code 52.4 appears, an S3 rule is triggered, and the main diagnosis is only K86.2/K86.3/Q45.2; the encoding quality control level comprises but is not limited to warning/missing/suggestion/prompt/suspicious and the like, wherein the third level triggers the AI cloud quality control rule again, desensitizes and checks the key parameters of the patient, judges AI according to signs, check and check, charging items and the like, and pushes the result to B5 to assist manual quality control. B3/B4, performing primary processing on a secondary result of quality control early warning, directly correcting or refuting to a clinical end for re-recording and updating, and calculating a diagnosis and treatment charging rule of F3 in this link, wherein the key description in the text is analyzed through medical advice, disease course record, operation record, nursing record and the like of the medical record, whether illegal behaviors exist is judged, and if yes, the original text is labeled and pushed to B5 for manual review, and the "pulp opening drainage" and the "pulp inactivation" are simultaneously collected, "pressure sore nursing" is carried out for exceeding daily limit cost, "blood oxygen saturation monitoring" is collected for exceeding times, "preoperative enema" is serially switched to "colon water therapy" for charging and the like. And B5, collecting all the early warning information, displaying according to early warning level severity, medical record type, encoder and other dimensions, and performing review quality control by combining a common medical record sampling inspection mode and an encoder error-prone encoding sampling inspection mode. And (4) recording new rules during quality control, and maintaining and updating the new rules to a corresponding rule base according to classification. B6, comparing the data of quality control effect regularly, such as the first quality control accuracy and filing early warning rate, and counting the error rate of the encoder to optimize the corresponding sampling inspection algorithm; and analyzing the corrected encoding rate of each large section of the ICD, optimizing the hospital medical record type weight by the ratio of 1% of cases, and increasing the quality control scanning rule and the trigger level of the medical record with high team weight.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The present invention and the embodiments thereof have been described above, but the description is not limited to the embodiments, and the actual configuration is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. ICD coding artificial intelligence audit quality control system based on medical insurance disease payment system is characterized by comprising a data acquisition processing unit, a knowledge base unit, a rule base unit, an early warning unit, an artificial connotation quality control unit, a cloud quality control unit, an interaction unit and an analysis unit, wherein the knowledge base unit and the rule base unit are respectively connected with the data acquisition processing unit, the early warning unit is connected with the knowledge base unit and the rule base unit, the artificial connotation quality control unit and the cloud quality control unit are respectively connected with the early warning unit, the interaction unit is connected with the early warning unit, and the analysis unit is connected with the interaction unit.
2. ICD coding artificial intelligence audit quality control mode based on medical insurance disease payment system, which is characterized by comprising the following steps:
(1) and (3) clinical data acquisition: the data acquisition and processing unit is in butt joint with a database of a related data system of the hospital to extract historical data, the data of the system in the hospital is associated by using a main index patient ID through connecting databases of systems such as hospital province medical record statistical software, EMR (electronic medical record), HIS (medical information system), and the like according to an interface specification by adopting a view method, the related historical data such as the information of the first page of the last five years, charging information, diagnosis and treatment terms written by a doctor are extracted, and the data are cleaned, converted and loaded to a data warehouse in order to ensure the quality of the data;
(2) establishing a basic ICD code matching knowledge (P) and a logic audit basic library (L): the method combines the modes of practical experience of coding quality control, document retrieval, expert consultation and the like, and comprises an ICD coding knowledge rule table, an audit logic table, four versions of ICD coding mapping integrated table and a national unified clinical terminology knowledge table;
(3) establishing an ICD (identity control document) audit data quality control discriminant library (S):
1, performing statistical analysis on historical data in a data warehouse, and forming a related knowledge table, wherein the related knowledge table comprises an operation expense and high-value consumable charging item table needing to be filled in an operation column, a standard clinical diagnosis and treatment term table in a hospital, a diagnosis and treatment term mapping table in the hospital and the state, and the like;
2, obtaining coding knowledge factors hidden in an ICD disease diagnosis operation table and a related knowledge table and association and logic relations thereof by using knowledge fusion extraction and conversion, and establishing various relation models: the system comprises a national unified clinical diagnosis and treatment term table and ICD (interface control document) relational model, an operation and high-value consumable charging and diagnosis and treatment term relational model, an operation and high-value consumable charging item and ICD coding relational model, an operation coding and disease coding mutual corresponding relational model, a disease category and average cost hospitalization day and other key index relational models, a DRG ungrouped and QY grouping model and an age and disease category grouping relational model;
(4) and (3) applying AI to form a dynamic ICD cloud quality control library (Y): the cloud quality control unit comprises various logic relation tables and the like, each table at least comprises auditing conditions, quality control contents and quality control case types, various logic relations are dynamic, are updated regularly and irregularly, and are updated regularly when the policy is stable, the policy change can be adjusted or updated in real time, the relation range is generally counted by regional hospital coding quality control data, and a small-probability event is assumed to be abnormal;
(5) establishing a quality control library (F) for maintaining medical insurance payment: policies related to medical insurance payment of various cities, special examination items and the like are integrated, and quality control early warning problem medical records are synthesized from dimensions such as payment mode matching, medical insurance settlement calculation, diagnosis and treatment charging standard scanning, in-hospital ICD disease coding and object price charging relation models and the like.
(6) And (3) quality control early warning of common medical records: according to the ICD audit quality control logic discrimination library and the code related knowledge library, a computer technology is applied to intelligently discriminate quality control categories and prompt quality control content of information such as ICD codes, ages and expenses of cases needing quality control coding, and the ordinary cases are subjected to spot check quality control in a proper sampling mode;
(7) the encoder spot check algorithm: selecting quality control data of each coding person in nearly five years, counting the coding accuracy of common and frequently-occurring cases of each coding person every year, collecting key factor data of the coding person influencing the coding accuracy by adopting a multiple linear regression method, and performing single-factor and multi-factor regression analysis by using SPSS21.0 software to determine a proper sampling mode, including a spot check rate and a spot check mode;
(8) applying an ICD code artificial intelligence audit quality control system based on medical insurance disease payment system: compiling an API universal interface to fuse different ICD coding systems, fusing an intelligent auditing quality control mode to a hospital through the universal interface, and performing real-time spot check quality control on the system with the ICD coding function;
coding quality control effect analysis: and performing comparative evaluation on the primary effect indexes of the ICD code intelligent auditing and quality control mode in the first and second half years by the analysis unit. The ICD codes of the four versions in the step 2) are home release, provincial release, medical insurance release and national clinical release.
3. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: and (3) the four versions ICD codes in the step (2) are home version, provincial version, medical insurance version and national clinical version.
4. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: and (3) the hospital normative clinical diagnosis and treatment glossary is a disease diagnosis table, a pathological diagnosis table and an operation name table.
5. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: the relation mode in the step (3) relates to a plurality of diagnosis descriptions corresponding to a plurality of ICD diseases and operations, and associated medicine/material/diagnosis and treatment item charge codes, and is stored in a database in a treatment combination mode, such as combinations Sx1-1, Sx1-2, Sx1-3 and the like, and the diagnosis descriptions and key parameter labels K1.1, K1.11 and K1.12 are correspondingly triggered; the association is disclosed as (Sx1 + K1 + weight)/trigger ═ warning coefficient.
6. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: the disease seeds in the step (3) are municipal medical insurance list disease seeds and provincial version DRGs disease seeds.
7. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: the AI cloud quality control library in the step (4) comprises but is not limited to a medical term set, a regional coding set, a disease operation set, a diagnosis and treatment combination set, a coding basis set, an ICD coding set, a charging item set, a difficult coding set and the like, and mapping is carried out by adopting a mode of combining regional quality control coefficients and national quality control specifications through analyzing a multi-hospital ICD coding quality control data model.
8. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: and (4) the quality control categories in the step (6) are special case quality control and common case quality control.
9. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: the key factor data of the encoding personnel in the step (7) are medical background, academic calendar, specialties, working age, main chapter codes in work and error-prone code categories.
10. The ICD code artificial intelligence audit quality control model based on medical insurance system payment system of claim 2, wherein: the API in the step (8) comprises a data interface and a front-end packaging module, and supports one-key calling of other systems, and the user quality control experience is consistent.
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