Medical record quality monitoring method and system based on big data

Abstract

The invention discloses a medical record quality monitoring method and system based on big data, and the method comprises the steps: creating a medical record connotation quality control system database, formulating a quality control rule, collecting medical record data, and inputting the data into the medical record connotation quality control system database; scoring the medical record data through a preset medical record quality scoring system, determining a qualified attribute of the medical record data according to a scoring result, and determining medical record availability based on the qualified attribute; effective medical records are screened out according to the medical record availability, time specification parameters and content specification parameters are determined based on a quality control rule, and quality control processing is conducted on the effective medical records through the time specification parameters and the content specification parameters; medical record defects are determined according to the quality control result, feedback modification processing is carried out based on the medical record defects, and second-order end quality control is carried out on the effective medical records. Effective geological control work can be quickly and accurately performed on each medical record according to quality control requirements, the practicability is improved while the working efficiency is improved, medical behaviors are standardized, and the medical record quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical quality management, and particularly to a method and system for monitoring the quality of medical records based on big data. Background Art

[0002] At present, in the medical system of our country, medical records, as the original medical documents recording the complete processes such as the occurrence, development, diagnosis, treatment and prognosis of patients' diseases, have an important and unique status. On the one hand, their quality largely reflects the level of doctors' diagnosis and treatment, and is an important evidence for handling medical accidents and legal disputes; on the other hand, a large amount of diagnosis and treatment data integrated in medical records is the core resource for assisting clinical decision-making and medical research, affecting the effectiveness and final effect of data secondary utilization. Therefore, for a long time, each hospital has invested a large amount of manpower and material resources in the problem of medical record quality control, but the results have been very little. The reasons are as follows: first, the efficiency of manual review is low, the accuracy is poor, and the time consumption is too long, making it difficult to cover all discharged medical records, resulting in the omission of problems; second, the quality control process is lagging, the management is weak, and it is impossible to track and feedback in real time, resulting in the backlog of old problems and the frequent occurrence of new problems, achieving half the result with twice the effort. Summary of the Invention

[0003] In view of the problems shown above, the present invention provides a method and system for monitoring the quality of medical records based on big data to solve the problems of low efficiency, poor accuracy, and too long time consumption in manual review mentioned in the background art, making it difficult to cover all discharged medical records and resulting in the omission of problems.

[0004] A method for monitoring the quality of medical records based on big data includes the following steps:

[0005] Create a database for the in-depth quality control system of medical records and formulate quality control rules, collect medical record data and input it into the database of the in-depth quality control system of medical records;

[0006] Score the medical record data through a preset medical record quality scoring system, determine the qualified attributes of the medical record data according to the scoring results, and determine the usability of the medical record based on the qualified attributes;

[0007] Select valid medical records according to the usability of the medical records, determine the time specification parameters and content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and content specification parameters;

[0008] Determine the medical record defects according to the quality control results, perform feedback and modification processing based on the medical record defects, and perform second-order final quality control on the valid medical records.

[0009] Preferably, the creating a database for the in-depth quality control system of medical records and formulating quality control rules includes:

[0010] Determine the standard data structure of medical record data, and design data table fields and data table relationships according to the standard data structure;

[0011] Create a database and table structure using SQL statements according to the data table fields and data table relationships, and construct a database for the medical record content quality control system based on the database and table structure;

[0012] Determine the quality control objectives based on the medical record writing specifications and medical record quality evaluation criteria;

[0013] Formulate quality control rules according to the quality control objectives and the professional review opinions of senior quality control doctors in tertiary hospitals as the standard.

[0014] Preferably, collecting medical record data and inputting it into the medical record content quality control system database includes:

[0015] Determine the data sources of multiple medical business systems, and collect medical data from multiple data sources using data extraction technology according to the communication interoperability standard;

[0016] Perform data cleaning and quantitative integration on the medical data, determine the data items of the diagnosis and treatment plan and treatment process, screen and classify the medical data according to the data items, and obtain high-quality medical record data;

[0017] Load the high-quality pathological data into the medical record content quality control system database.

[0018] Preferably, scoring the medical record data through a preset medical record quality scoring system, determining the qualified attributes of the medical record data according to the scoring results, and determining the usability of the medical record based on the qualified attributes, including:

[0019] Obtain multiple types of scoring criteria according to the hospital's medical record scoring work mode, and construct a preset medical record quality scoring system according to the multiple types of scoring criteria;

[0020] Compare and score the medical record data through the preset medical record quality scoring system, obtain the scoring results, and determine the passing score distribution and failing score distribution of the medical record data according to the scoring results;

[0021] Determine the qualified attributes of the medical record data according to the passing score distribution and failing score distribution, and the qualified attributes include: fully qualified, multi-part qualified, few-part qualified, and fully unqualified;

[0022] Determine the data value and the degree of compliance of the diagnosis and treatment process of the medical record data according to the qualified attributes, and determine the usability of the medical record according to the data value and the degree of compliance of the diagnosis and treatment process.

[0023] Preferably, before screening out valid medical records according to the usability of the medical record, determining the time specification parameters and content specification parameters based on the quality control rules, and performing quality control processing on the valid medical records through the time specification parameters and content specification parameters, it also includes:

[0024] Obtain the free text of the medical record, extract the descriptive content in the free text, and structurally process the free text according to the treatment process through NIP technology;

[0025] Determine multiple data nodes according to the processing results. Extract important parameters in each data node by using the predefined text extraction technology based on the professional medical terminology database;

[0026] Perform context matching and semantic analysis processing on the important parameters to obtain the entity concepts corresponding to each data node;

[0027] Determine the current quality control content of each data node according to the entity concept corresponding to each data node.

[0028] Preferably, screen out valid medical records according to the availability of medical records, determine the time specification parameters and content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and content specification parameters, including:

[0029] Screen out reliable medical records, medical records to be verified and unreliable medical records according to the availability of medical records, obtain the manual verification results of the medical records to be verified, screen out the medical records that pass the verification according to the manual verification results, and confirm the medical records that pass the verification and reliable medical records as valid medical records;

[0030] Determine the standard registration duration parameter and standard execution duration parameter of each treatment process according to the quality control rules, and determine the time specification parameter according to the standard registration duration parameter and standard execution duration parameter;

[0031] Obtain the statistical behavior parameters and result status description parameters of each treatment process, and determine the content specification parameters of each treatment process according to the statistical behavior parameters and result status description parameters;

[0032] Determine the quality control standard according to the time specification parameter and content specification parameter, and determine the data quality control requirements for each data node according to the quality control standard;

[0033] Perform quality control processing on the valid medical records according to the data quality control requirements for each data node, and perform defect anomaly location and feedback.

[0034] Preferably, determine the medical record defects according to the quality control results, perform feedback modification processing based on the medical record defects, and perform second-order final quality control on the valid medical records, including:

[0035] Determine the link quality parameters according to the quality control results, determine the medical record defects based on the link quality parameters, determine the modification parameters according to the medical record defects and perform reminder feedback;

[0036] Receive the modification content of the attending physician for the medical record defects and perform replacement processing on the original medical record content to generate the quality-controlled medical record;

[0037] Obtain effective quality evaluation indicators for the second-order final quality control, and perform second-order final quality control on the medical records after quality control based on the effective quality evaluation indicators;

[0038] Determine the final qualified judgment result of the medical records after quality control according to the second-order quality control result, and generate a data view for display based on the final qualified judgment result.

[0039] Preferably, determining the data value and the compliance degree of the diagnosis and treatment process of the medical record data according to the qualified attribute, and determining the usability of the medical record according to the data value and the compliance degree of the diagnosis and treatment process, includes:

[0040] Determine the diagnosed disease of the patient according to the medical record data, obtain the medical record sequence corresponding to the diagnosed disease, and obtain the diagnosis and treatment process of the diagnosed disease according to the medical record sequence;

[0041] Determine the examination items based on the diagnosis and treatment process, and determine the complete examination result parameters of each examination item according to the hierarchical model of the examination items;

[0042] Determine the diagnostic vocabulary feature vector and the diagnostic vocabulary feature frequency according to the complete examination result parameters through the bag-of-words model;

[0043] Determine the current vocabulary feature total amount and the vocabulary feature scanning frequency according to the qualified attribute and the item result description parameters of each examination item;

[0044] Determine the vocabulary feature clustering difference and the vocabulary feature frequency distribution difference according to the diagnostic vocabulary feature vector, the diagnostic vocabulary feature frequency, the current vocabulary feature total amount, and the vocabulary feature scanning frequency;

[0045] Determine the data value of each examination item according to the vocabulary feature clustering difference and the vocabulary feature frequency distribution difference;

[0046] Determine the total data value of the medical record data according to the single-item weight of each examination item for the diagnosis and treatment process;

[0047] Obtain the clinical path and the review path of the diagnosis and treatment process related to the diagnosed disease, and determine the scheduling of medical resources and the timing control features according to the clinical path and the review path;

[0048] Construct a workflow model of the diagnosis and treatment process related to the diagnosed disease according to the scheduling of medical resources and the timing control features;

[0049] Determine the fixed diagnosis and treatment parameters and the changeable diagnosis and treatment parameters according to the workflow model, and respectively obtain the corresponding data of the fixed diagnosis and treatment parameters and the changeable diagnosis and treatment parameters in the medical record data;

[0050] Determine the record missing attributes of the corresponding data of the fixed diagnosis and treatment parameters and the changeable diagnosis and treatment parameters in the medical record data according to the qualified attribute;

[0051] Determine the compliance of the diagnosis and treatment process of medical record data based on the missing attributes of the record, and determine the complete medical records and missing medical records according to the total data value and the compliance of the diagnosis and treatment process of the medical record data;

[0052] Confirm the complete medical records as available medical records and the missing medical records as unavailable medical records.

[0053] Preferably, after creating the database of the medical record content quality control system and formulating the quality control rules, it further includes:

[0054] Determine multiple quality control items and the first-level quality control logic and the second-level quality control logic for each quality control item according to the quality control rules;

[0055] Respectively obtain the quality control forms and quality control data characteristics of the first-level quality control logic and the second-level quality control logic, and determine the quality control factors of the first-level quality control logic and the second-level quality control logic according to the quality control forms and quality control data characteristics;

[0056] Based on the quality control factors, determine the common quality control factors and independent quality control factors, and construct a general expression according to the common quality control factors;

[0057] Construct characteristic expressions for the first-level quality control logic and the second-level quality control logic respectively according to the independent quality control factors;

[0058] Obtain the rule configuration parameters of the quality control rules, and substitute the rule configuration parameters into the general expression and the characteristic expressions to generate coarse-grained quality control rules and fine-grained quality control rules;

[0059] Determine the post-quality control form of the quality control object through the coarse-grained quality control rules and the fine-grained quality control rules, and determine the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules respectively based on the post-quality control form. The rule attributes include: rules for intervening during the improvement of medical records, rules for reminding before filling in medical records, and rules for post-quality control of medical record content;

[0060] Set the quality control feedback form according to the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules respectively, and associate the quality control feedback form with the quality control rules to feedback various medical record defects during the quality control process of medical record data.

[0061] A medical record quality monitoring system based on big data, the system includes:

[0062] A creation module for creating a database of the medical record content quality control system and formulating quality control rules, collecting medical record data and inputting it into the database of the medical record content quality control system;

[0063] A determination module for scoring the medical record data through a preset medical record quality scoring system, determining the qualified attributes of the medical record data according to the scoring results, and determining the availability of the medical record based on the qualified attributes;

[0064] The first quality control module is used to screen out valid medical records according to the availability of medical records, determine time specification parameters and content specification parameters based on quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and content specification parameters;

[0065] The second quality control module is used to determine medical record defects according to the quality control results, perform feedback modification processing based on the medical record defects, and perform second-order final quality control on the valid medical records.

[0066] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0067] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0068] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention.

[0069] Figure 1 It is a working flowchart of a method for monitoring the quality of medical records based on big data provided by the present invention;

[0070] Figure 2 It is another working flowchart of a method for monitoring the quality of medical records based on big data provided by the present invention;

[0071] Figure 3 It is yet another working flowchart of a method for monitoring the quality of medical records based on big data provided by the present invention;

[0072] Figure 4 It is a schematic structural diagram of a system for monitoring the quality of medical records based on big data provided by the present invention. Detailed Embodiments

[0073] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0074] Currently, in China's medical system, medical records, as the original medical documents recording the complete process of a patient's disease occurrence, development, diagnosis, treatment, and prognosis, hold an important and unique position. On the one hand, their quality largely reflects the level of doctors' diagnosis and treatment, and they are important vouchers for handling medical accidents and legal disputes; on the other hand, the large amount of diagnosis and treatment data integrated in medical records is the core resource for assisting clinical decision-making and medical research, affecting the effectiveness and ultimate results of data secondary utilization. Therefore, for a long time, hospitals have invested a large amount of manpower and material resources in the quality control of medical records, but with little effect. The reasons are as follows: First, manual review is inefficient, inaccurate, and time-consuming, making it difficult to cover all discharged medical records and resulting in omissions of problems; second, the quality control process is lagging and management is weak, unable to track and feedback in real time, leading to the backlog of old problems and the frequent emergence of new problems, achieving little with much effort. To solve the above problems, this embodiment discloses a method for monitoring the quality of medical records based on big data.

[0075] A method for monitoring the quality of medical records based on big data, as Figure 1 shown, includes the following steps:

[0076] Step S101, create a database for the quality control system of medical record connotations and formulate quality control rules, collect medical record data and input it into the database for the quality control system of medical record connotations;

[0077] Step S102, score the medical record data through a preset medical record quality scoring system, determine the qualified attributes of the medical record data according to the scoring results, and determine the usability of the medical record based on the qualified attributes;

[0078] Step S103, screen out valid medical records according to the usability of the medical records, determine the time specification parameters and content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and content specification parameters;

[0079] Step S104, determine the medical record defects according to the quality control results, perform feedback and modification processing based on the medical record defects, and perform second-order final quality control on the valid medical records.

[0080] The working principle of the above technical solution is as follows: Create a database for the quality control system of medical record connotations and formulate quality control rules, collect medical record data and input it into the database for the quality control system of medical record connotations; score the medical record data through a preset medical record quality scoring system, determine the qualified attributes of the medical record data according to the scoring results, and determine the usability of the medical record based on the qualified attributes; screen out valid medical records according to the usability of the medical records, determine the time specification parameters and content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and content specification parameters; determine the medical record defects according to the quality control results, perform feedback and modification processing based on the medical record defects, and perform second-order final quality control on the valid medical records.

[0081] The beneficial effects of the above technical solution are as follows: By using the braking quality control rules and creating a database, the collected medical record data can be effectively and intelligently automatically quality controlled. All medical records can be quality controlled without omission based on the big data collection method, avoiding problem omissions. Further, by using an intelligent model instead of manual work for quality control, the quality control of each medical record can be quickly and accurately carried out according to the quality control requirements, improving work efficiency and practicality, standardizing medical behaviors, and enhancing the quality of medical records. It solves the problems of low efficiency, poor accuracy, long time consumption in manual review in the existing technology, and difficulty in covering all discharged medical records, resulting in problem omissions.

[0082] In one embodiment, as Figure 2 shown, creating the database of the medical record content quality control system and formulating quality control rules includes:

[0083] Step S201: Determine the standard data structure of the medical record data, and design the data table fields and data table relationships according to the standard data structure;

[0084] Step S202: Use SQL statements to create a database and table structure according to the data table fields and data table relationships, and construct the database of the medical record content quality control system based on the database and table structure;

[0085] Step S203: Determine the quality control objectives based on the medical record writing specifications and medical record quality evaluation criteria;

[0086] Step S204: Formulate quality control rules according to the quality control objectives and based on the professional review opinions of senior quality control doctors in the top three level hospitals as the standard.

[0087] The beneficial effects of the above technical solution are as follows: Using SQL statements to create a database and table structure to construct the database of the medical record content quality control system, and formulating quality control rules by combining the quality control objectives and the professional review opinions of senior quality control doctors in the top three level hospitals as the standard can ensure the professionalism and standardization of the rules and improve the credibility of the quality control rules.

[0088] In one embodiment, collecting the medical record data and inputting it into the database of the medical record content quality control system includes:

[0089] Determine the data sources of multiple medical business systems, and collect medical data from multiple data sources using data extraction technology according to the communication interoperability standard;

[0090] Perform data cleaning and quantitative integration on the medical data, determine the data items of the diagnosis and treatment plan and treatment process, and screen and classify the medical data according to the data items to obtain high-quality medical record data;

[0091] Load the high-quality pathological data into the database of the medical record content quality control system.

[0092] The beneficial effects of the above technical solution are as follows: collecting medical data from the data source of the medical service system according to the communication interoperability standard and performing preprocessing can improve the consistency and accuracy of the data. Further, screening and classifying the medical data according to data items can improve the data quality of the medical record content quality control system database and ensure that the data is representative.

[0093] In one embodiment, as Figure 3 shown, scoring the medical record data through a preset medical record quality scoring system, determining the qualified attributes of the medical record data according to the scoring results, and determining the usability of the medical record based on the qualified attributes, including:

[0094] Step S301: Obtain multiple types of scoring criteria according to the hospital medical record scoring work mode, and construct a preset medical record quality scoring system according to the multiple types of scoring criteria;

[0095] Step S302: Compare and score the medical record data through the preset medical record quality scoring system, obtain the scoring results, and determine the passing score distribution and failing score distribution of the medical record data according to the scoring results;

[0096] Step S303: Determine the qualified attributes of the medical record data according to the passing score distribution and the failing score distribution. The qualified attributes include: fully qualified, mostly qualified, partially qualified, and fully unqualified;

[0097] Step S304: Determine the data value and the compliance degree of the diagnosis and treatment process of the medical record data according to the qualified attributes, and determine the usability of the medical record according to the data value and the compliance degree of the diagnosis and treatment process.

[0098] The beneficial effects of the above technical solution are as follows: constructing a preset medical record quality scoring system according to multiple types of scoring criteria, scoring the medical record quality, determining the passing score distribution and the failing score distribution of the medical record data and the qualified attributes, can quickly understand the medical record data, improve the diagnosis and treatment efficiency. At the same time, it improves the usability of the medical record and the adaptability to other medical records, and reduces medical errors caused by manual recording errors and information missing.

[0099] In one embodiment, before screening out valid medical records according to the usability of the medical record, determining the time specification parameter and the content specification parameter based on the quality control rule, and performing quality control processing on the valid medical record through the time specification parameter and the content specification parameter, it further includes:

[0100] Obtain the free text of the medical record, extract the description content in the free text, and perform structured processing on the free text according to the treatment process through NIP technology;

[0101] Determine multiple data nodes according to the processing results. Extract important parameters in each data node by using the pre-defined text extraction technology based on the professional medical terminology library;

[0102] Perform context matching and semantic analysis on important parameters to obtain the entity concepts corresponding to each data node;

[0103] Determine the current quality control content of each data node according to the entity concepts corresponding to each data node.

[0104] The beneficial effects of the above technical solution are as follows: obtain the description content of the medical record free text, perform structured processing, extract important parameters in each data node and perform semantic analysis processing, which can more accurately understand the medical record content, determine the current quality control content of each data node, quickly locate problems and make corrections, reduce redundant steps and errors in data processing, and thus improve the overall data processing efficiency.

[0105] In one embodiment, effective medical records are screened according to the availability of medical records, time specification parameters and content specification parameters are determined based on quality control rules, and quality control processing is performed on the effective medical records through the time specification parameters and content specification parameters, including:

[0106] Reliable medical records, medical records to be verified and unreliable medical records are screened according to the availability of medical records, the manual verification results of the medical records to be verified are obtained, the verified medical records are screened according to the manual verification results, and the verified medical records and reliable medical records are confirmed as effective medical records;

[0107] Determine the standard registration duration parameter and standard execution duration parameter of each treatment process according to the quality control rules, and determine the time specification parameters according to the standard registration duration parameter and standard execution duration parameter;

[0108] Obtain the statistical behavior parameters and result status description parameters of each treatment process, and determine the content specification parameters of each treatment process according to the statistical behavior parameters and result status description parameters;

[0109] Determine the quality control standard according to the time specification parameters and content specification parameters, and determine the data quality control requirements for each data node according to the quality control standard;

[0110] Perform quality control processing on the effective medical records according to the data quality control requirements for each data node, and perform defect anomaly location and feedback.

[0111] The beneficial effects of the above technical solution are as follows: determine the quality control standard according to the time specification parameters and content specification parameters, so as to determine the data quality control requirements for each data node, which can ensure the accuracy and consistency of the data. At the same time, it makes the data source and processing process more transparent. Further, performing quality control processing on the effective medical records and performing defect anomaly location and feedback can enhance the integrity and standardization of the medical records.

[0112] In one embodiment, the process of determining medical record defects according to the quality control results, performing feedback modification processing based on the medical record defects, and performing second-order final quality control on the valid medical records includes:

[0113] Determine the quality parameters of each link based on the quality control results, determine the medical record defects based on the quality parameters of each link, determine the modification parameters based on the medical record defects and provide reminder feedback;

[0114] Receive the attending physician's modification of the disease defects and replace the original medical record content to generate a quality-controlled medical record;

[0115] Obtain effective quality evaluation indicators for the second-order final quality control, and conduct second-order final quality control on the medical records after quality control based on the effective quality evaluation indicators;

[0116] The final qualified judgment result of the medical record after quality control is determined according to the second-order quality control result, and a data view is generated based on the final qualified judgment result for display.

[0117] The beneficial effects of the above technical solution are: determining medical record defects based on quality control results, and determining modification parameters and reminder feedback, which can ensure the accuracy and completeness of medical records. Furthermore, the medical records after quality control are subjected to second-order final quality control based on the effective quality evaluation indicators of the second-order final quality control, so as to determine the final qualified judgment results of the medical records and perform a visual display, which can improve the qualification and credibility of the medical records.

[0118] In one embodiment, determining the data value of the medical record data and the degree of consistency with the diagnosis and treatment process according to the qualified attributes, and determining the availability of the medical record according to the data value and the degree of consistency with the diagnosis and treatment process, includes:

[0119] Determine the patient's diagnosed disease based on the medical record data, obtain the medical record sequence corresponding to the diagnosed disease, and obtain the diagnosis and treatment process of the diagnosed disease based on the medical record sequence;

[0120] Determine the examination items based on the diagnosis and treatment process, and determine the perfect examination result parameters for each examination item based on the hierarchical model of the examination items;

[0121] The diagnostic vocabulary feature vector and diagnostic vocabulary feature frequency are determined by using the bag-of-words model according to the parameters of the improved examination results;

[0122] Determine the current vocabulary feature summary amount and vocabulary feature scanning frequency according to the qualified attributes and the project result description parameters of each inspection project;

[0123] Determine the differences in vocabulary feature clustering and vocabulary feature frequency distribution according to the diagnostic vocabulary feature vectors and diagnostic vocabulary feature frequencies and the current vocabulary feature summary and vocabulary feature scanning frequencies;

[0124] Determine the data value of each inspection item based on the differences in vocabulary feature clustering and vocabulary feature frequency distribution;

[0125] Determine the total data value of the medical record data according to the single-item weight of each inspection item for the diagnosis and treatment process;

[0126] Obtain the clinical pathway and review pathway of the diagnosis and treatment process related to the diagnosed disease, and determine the scheduling of medical resources and timing control features according to the clinical pathway and review pathway;

[0127] Construct a workflow model for the diagnosis and treatment process related to the diagnosed disease according to the scheduling of medical resources and timing control features;

[0128] Determine the fixed diagnosis and treatment parameters and changeable diagnosis and treatment parameters according to the workflow model, and respectively obtain the corresponding data of the fixed diagnosis and treatment parameters and changeable diagnosis and treatment parameters in the medical record data;

[0129] Determine the record missing attributes of the corresponding data of the fixed diagnosis and treatment parameters and changeable diagnosis and treatment parameters in the medical record data according to the qualified attributes;

[0130] Determine the compliance degree of the diagnosis and treatment process of the medical record data according to the record missing attributes, and determine the complete medical record and the missing medical record according to the total data value and the compliance degree of the diagnosis and treatment process of the medical record data;

[0131] Confirm the complete medical record as an available medical record, and confirm the missing medical record as an unavailable medical record.

[0132] The beneficial effects of the above technical solutions are as follows: By determining the data value of each inspection item based on the clustering difference of lexical features and the frequency distribution difference of lexical features, and then determining the total value of the medical record data, it is possible to perform feature comparison and word frequency vector on the recorded data in the medical record text based on the result parameters of the standard inspection items for value evaluation, so as to judge the inspection applications of the patient in each inspection item, improving the determination accuracy and reliability. Further, by checking the record missing attributes in the case data, it is possible to directly determine the missing diagnosis and treatment steps of the patient during the diagnosis and treatment process, and then judge the compliance degree with the diagnosis and treatment process, laying a reference basis for the subsequent qualitative determination of the availability of the medical record and improving the practicality.

[0133] In one embodiment, after creating the medical record content quality control system database and formulating quality control rules, it further includes:

[0134] Determine multiple quality control items and the primary quality control logic and secondary quality control logic for each quality control item according to the quality control rules;

[0135] Respectively obtain the quality control forms and quality control data features of the primary quality control logic and the secondary quality control logic, and determine the quality control factors of the primary quality control logic and the secondary quality control logic according to the quality control forms and quality control data features;

[0136] Determine the common quality control factors and independent quality control factors based on quality control factors, and construct a general expression according to the common quality control factors;

[0137] Construct the characteristic expressions of the first-level quality control logic and the second-level quality control logic respectively according to the independent quality control factors;

[0138] Obtain the rule configuration parameters of the quality control rules, and substitute the rule configuration parameters into the general expression and the characteristic expression to generate the coarse-grained quality control rules and the fine-grained quality control rules;

[0139] Determine the post-quality control form of the quality control object through the coarse-grained quality control rules and the fine-grained quality control rules, and determine the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules respectively based on the post-quality control form. The rule attributes include: rules for intervening in the process of medical record completion, rules for reminding in advance of medical record filling, and rules for post-quality control of medical record content;

[0140] Set the quality control feedback form according to the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules respectively, and associate the quality control feedback form with the quality control rules to feedback various medical record defects in the quality control process of medical record data.

[0141] The beneficial effects of the above technical solution are: by qualitatively determining the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules and performing quality control feedback, it is possible to track and feedback the real-time filling of medical records by doctors in a timely manner, penetrate from the data appearance to the cause of the problem to avoid the low-quality problem of medical records, and avoid the backlog of old problems and the frequent occurrence of new problems, improving the practicability and reliability.

[0142] In one embodiment, this embodiment also discloses a medical record quality monitoring system based on big data, as Figure 4 shown. This system includes:

[0143] A creation module 401, configured to create a database of the medical record content quality control system and formulate quality control rules, and collect medical record data and input it into the database of the medical record content quality control system;

[0144] A determination module 402, configured to score the medical record data through a preset medical record quality scoring system, determine the qualified attribute of the medical record data according to the scoring result, and determine the usability of the medical record based on the qualified attribute;

[0145] A first quality control module 403, configured to screen out valid medical records according to the usability of the medical records, determine the time specification parameters and content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and the content specification parameters;

[0146] A second quality control module 404, configured to determine medical record defects according to the quality control results, perform feedback modification processing based on the medical record defects, and perform second-order end-of-term quality control on the valid medical records.

[0147] The working principle and beneficial effects of the above technical solution have been described in the method embodiments and will not be elaborated here.

[0148] Those skilled in the art should understand that the first and second in the present invention only refer to different application stages.

[0149] After considering the specification and practicing the disclosure herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are to be considered as exemplary only, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0150] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for monitoring the quality of medical records based on big data, characterized in that: The following steps are involved: Create a medical record content quality control system database and formulate quality control rules, collect medical record data and input it into the medical record content quality control system database; Score the medical record data through a preset medical record quality scoring system, determine the qualified attributes of the medical record data based on the scoring results, and determine the medical record availability based on the qualified attributes; Filter out valid medical records according to their availability, determine time specification parameters and content specification parameters based on quality control rules, and perform quality control on valid medical records using time specification parameters and content specification parameters; Determine medical record defects based on quality control results, conduct feedback and modification based on medical record defects, and conduct second-level final quality control on valid medical records.

2. The method for monitoring the quality of medical records based on big data according to claim 1, characterized in that: The creation of a medical record content quality control system database and formulation of quality control rules include: Determine the standard data structure of medical record data, and design data table fields and data table relationships based on the standard data structure; Use SQL statements to create database and table structures based on data table fields and data table relationships, and build a medical record content quality control system database based on the database and table structure; Determine quality control objectives based on medical record writing standards and medical record quality evaluation standards; Quality control rules are formulated based on quality control objectives and the professional review opinions of senior qualified quality control doctors from tertiary hospitals.

3. The method for monitoring the quality of medical records based on big data according to claim 1 is characterized in that: The collecting of medical record data and inputting it into the medical record content quality control system database includes: Determine the data sources of multiple medical business systems and collect medical data from multiple data sources using data extraction technology in accordance with communication and interoperability standards; Clean and quantify medical data, determine the data items of diagnosis and treatment plan and treatment process, screen and classify medical data according to the data items, and obtain high-quality medical record data; Load high-quality pathology data into the medical record content quality control system database.

4. The method for monitoring the quality of medical records based on big data according to claim 1 is characterized in that: Scoring the medical record data by using a preset medical record quality scoring system, determining the qualified attributes of the medical record data according to the scoring results, and determining the medical record availability based on the qualified attributes, includes: Obtain multiple scoring standards based on the hospital medical record scoring work model, and build a preset medical record quality scoring system based on multiple scoring standards; Compare and score the medical record data through a preset medical record quality scoring system, obtain the scoring results, and determine the passing score distribution and failing score distribution of the medical record data according to the scoring results; Determine the qualified attributes of the medical record data according to the passing score distribution and the failing score distribution, wherein the qualified attributes include: fully qualified, mostly qualified, slightly qualified, and completely unqualified; The data value of medical record data and the degree of consistency with the diagnosis and treatment process are determined based on qualified attributes, and the availability of medical records is determined based on the data value and the degree of consistency with the diagnosis and treatment process.

5. The method for monitoring medical records quality based on big data according to claim 1 is characterized in that: Before selecting valid medical records according to the availability of medical records, determining the time specification parameters and the content specification parameters based on the quality control rules, and performing quality control processing on the valid medical records by using the time specification parameters and the content specification parameters, the following is also included: Obtain the free text of the medical record, extract the descriptive content in the free text, and use NIP technology to structure the free text according to the treatment process; Determine multiple data nodes according to the processing results, and extract important parameters in each data node using a predefined text extraction technology based on a professional medical terminology library; Perform context matching and semantic analysis on important parameters to obtain the entity concepts corresponding to each data node; The current quality control content of each data node is determined according to the entity concept corresponding to each data node.

6. The method for monitoring the quality of medical records based on big data according to claim 5 is characterized in that: Filter out valid medical records based on their availability, determine time specification parameters and content specification parameters based on quality control rules, and perform quality control on valid medical records using time specification parameters and content specification parameters, including: Screen out reliable medical records, pending medical records and unreliable medical records based on the availability of medical records, obtain manual verification results of pending medical records, screen out verified medical records based on the manual verification results, and confirm verified medical records and reliable medical records as valid medical records; Determine the standard registration time parameters and standard execution time parameters of each treatment process according to the quality control rules, and determine the time specification parameters according to the standard registration time parameters and standard execution time parameters; Obtaining statistical behavior parameters and result status description parameters of each treatment process, and determining content specification parameters of each treatment process according to the statistical behavior parameters and result status description parameters; Determine the quality control standards based on the time specification parameters and content specification parameters, and determine the data quality control requirements for each data node based on the quality control standards; According to the data quality control requirements for each data node, the valid medical records are quality controlled and defects and anomalies are located and fed back.

7. The method for monitoring the quality of medical records based on big data according to claim 1, characterized in that: Determining medical record defects based on quality control results, performing feedback and modification based on medical record defects, and performing second-order final quality control on valid medical records include: Determine the quality parameters of each link based on the quality control results, determine the medical record defects based on the quality parameters of each link, determine the modification parameters based on the medical record defects and provide reminder feedback; Receive the attending physician's modification of the disease defects and replace the original medical record content to generate a quality-controlled medical record; Obtain effective quality evaluation indicators for the second-order final quality control, and conduct second-order final quality control on the medical records after quality control based on the effective quality evaluation indicators; The final qualified judgment result of the medical record after quality control is determined according to the second-order quality control result, and a data view is generated based on the final qualified judgment result for display.

8. The method for monitoring the quality of medical records based on big data according to claim 4 is characterized in that: Determining the data value of the medical record data and the degree of consistency with the diagnosis and treatment process according to the qualified attributes, and determining the availability of the medical record according to the data value and the degree of consistency with the diagnosis and treatment process, includes: Determine the patient's diagnosed disease based on the medical record data, obtain the medical record sequence corresponding to the diagnosed disease, and obtain the diagnosis and treatment process of the diagnosed disease based on the medical record sequence; Determine the examination items based on the diagnosis and treatment process, and determine the perfect examination result parameters for each examination item based on the hierarchical model of the examination items; The diagnostic vocabulary feature vector and diagnostic vocabulary feature frequency are determined by using the bag-of-words model according to the parameters of the improved examination results; Determine the current vocabulary feature summary amount and vocabulary feature scanning frequency according to the qualified attributes and the project result description parameters of each inspection project; Determine the differences in vocabulary feature clustering and vocabulary feature frequency distribution according to the diagnostic vocabulary feature vectors and diagnostic vocabulary feature frequencies and the current vocabulary feature summary and vocabulary feature scanning frequencies; Determine the data value of each inspection item based on the differences in vocabulary feature clustering and vocabulary feature frequency distribution; Determine the total data value of medical record data based on the individual weight of each examination item for the diagnosis and treatment process; Obtain the clinical pathways and review pathways for the diagnosis and treatment processes related to the disease, and determine the scheduling of medical resources and timing control characteristics based on the clinical pathways and review pathways; Construct a workflow model for the diagnosis and treatment process related to the diagnosis of diseases based on the scheduling of medical resources and timing control characteristics; Determine fixed diagnosis and treatment parameters and changeable diagnosis and treatment parameters according to the workflow model, and obtain corresponding data of the fixed diagnosis and treatment parameters and the changeable diagnosis and treatment parameters in the medical record data respectively; Determine the missing record attributes of the corresponding data of the fixed diagnosis and treatment parameters and the changeable diagnosis and treatment parameters in the medical record data according to the qualified attributes; Determine the consistency of medical record data with the diagnosis and treatment process based on the missing attributes of the records, and determine complete medical records and missing medical records based on the total data value of the medical record data and the consistency of the diagnosis and treatment process; Complete medical records are identified as usable medical records, and missing medical records are identified as unusable medical records.

9. The method for monitoring medical records quality based on big data according to claim 1, characterized in that: After creating the medical record content quality control system database and formulating quality control rules, it also includes: Determine multiple quality control items and the primary quality control logic and secondary quality control logic for each quality control item according to quality control rules; The quality control forms and quality control data characteristics of the primary quality control logic and the secondary quality control logic are obtained respectively, and the quality control factors of the primary quality control logic and the secondary quality control logic are determined according to the quality control forms and the quality control data characteristics; Determine common quality control factors and independent quality control factors based on quality control factors, and construct a universal expression based on the common quality control factors; Construct the characteristic expressions of the primary quality control logic and the secondary quality control logic according to the independent quality control factors; Obtaining the rule configuration parameters of the quality control rules, and substituting the rule configuration parameters into the general expression and the characteristic expression to generate the coarse-grained quality control rules and the fine-grained quality control rules; Determine the post-quality control form of the quality control object through coarse-grained quality control rules and fine-grained quality control rules, and determine the rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules based on the post-quality control form. The rule attributes include: medical record improvement in-process intervention rules, medical record filling pre-reminder rules, and medical record content post-quality control rules; The quality control feedback form is set according to the respective rule attributes of the coarse-grained quality control rules and the fine-grained quality control rules, and the quality control feedback form is associated with the quality control rules to provide feedback on various medical record defects in the quality control process of the medical record data.

10. A medical record quality monitoring system based on big data, characterized in that: The system includes: A creation module is used to create a medical record content quality control system database and formulate quality control rules, collect medical record data and input it into the medical record content quality control system database; A determination module, used to score the medical record data through a preset medical record quality scoring system, determine the qualified attributes of the medical record data according to the scoring results, and determine the availability of the medical record based on the qualified attributes; The first quality control module is used to screen out valid medical records according to the availability of medical records, determine the time specification parameters and the content specification parameters based on the quality control rules, and perform quality control processing on the valid medical records through the time specification parameters and the content specification parameters; The second quality control module is used to determine medical record defects based on quality control results, perform feedback and modification based on medical record defects, and conduct second-order final quality control on valid medical records.

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