Medical data intelligent processing and analysis method and system and medium

Abstract

A medical data intelligent processing and analysis method and system and a medium, comprising: S100, acquiring a medical service item price list and various medical data of a hospital, the medical data at least comprising one of outpatient expense detail list data, hospitalization expense detail list, consumable warehousing list, consumable ex-warehouse list, drug warehousing list and drug ex-warehouse list; s200, the medical service item price list and various medical data of the hospital are preprocessed respectively and stored in corresponding databases in a classified mode; s300, performing data analysis on the medical service item price list and various medical data of the hospital to obtain corresponding analysis results; the data analysis at least comprises one of price adding analysis, over-limit pricing analysis and charging analysis. By automatically acquiring, processing and analyzing the price list of the medical service items and various medical data of the hospital, the problems of low efficiency and high error rate of manual flipping check are solved, and the supervision efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing and analysis, and in particular to a method, system and medium for intelligent processing and analysis of medical data. Background Art

[0002] Existing technologies for monitoring the prices charged by medical institutions and the use of medical insurance funds mainly rely on manual account checking, which is unable to cope with the growing amount of medical data. Manual account checking is not only inefficient, but also leads to low efficiency and high error rate in the information processing process due to excessive reliance on manual operations.

[0003] In addition, the business data generated by medical institutions comes from various sources and formats, which makes unified management and analysis of data complicated. Data from different systems and formats are difficult to effectively integrate, which seriously limits the ability of regulatory authorities to conduct in-depth analysis and effective supervision of various medical systems in hospitals. This data island phenomenon hinders the in-depth supervision work and affects the accuracy and timeliness of regulatory decisions.

[0004] Therefore, existing technologies urgently need an automated and intelligent method to process and analyze various medical data to improve regulatory efficiency and accuracy. Summary of the invention

[0005] The purpose of the present invention is to propose a method, system and medium for intelligent processing and analysis of medical data, aiming to solve the technical problems of low automation and low efficiency in the prior art.

[0006] To achieve the above object, the present invention provides a method for intelligent processing and analysis of medical data, which comprises the following steps:

[0007] S100, obtaining a price list of medical service items and various medical data of the hospital, wherein the medical data includes at least one of a detailed list of outpatient expenses, a detailed list of inpatient expenses, a list of incoming consumables, a list of outgoing consumables, a list of incoming drugs, and a list of outgoing drugs;

[0008] S200, pre-processing the price list of medical service items and various medical data of the hospital respectively, and storing them in corresponding databases by categories;

[0009] S300, performing data analysis on the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

[0010] Optionally, it also includes: S400, displaying the analysis results in a visual form; preprocessing includes at least one of outlier processing, field name verification, data format unification, and data deduplication processing.

[0011] Optionally, the abnormal value processing includes at least the following steps: obtaining abnormal data that does not conform to the data format in the medical service item price list and various medical data of the hospital, and uniformly storing them in a designated location; performing abnormal value processing on the abnormal data, and if the abnormal data lacks necessary parameters, prompting the user to complete the information; and / or, performing intelligent identification on the abnormal data, obtaining useless data and discarding it; after the user completes the information and / or discards the useless data, re-importing the corresponding data;

[0012] The field name verification process includes at least the following steps: obtaining the price list of medical service items and the table header information of various medical data of the hospital; comparing the obtained table header information with the preset table header information. If the two are different, the data import is abandoned and a prompt is given.

[0013] Optionally, the data format unification processing includes at least the following steps: determining whether the medical service item price list and various medical data of the hospital include date, price, and quantity data; if date data is included, unifying it into a standard date format; if price format is included, unifying it into a standard price format; if quantity format is included, unifying it into a standard quantity format;

[0014] The data deduplication process includes at least the following steps: obtaining a price list of medical service items and unique identifiers corresponding to various types of medical data of the hospital; and removing duplicate data based on the unique identifiers.

[0015] Optionally, the markup analysis includes at least one of western medicine markup analysis, traditional Chinese medicine markup analysis, consumables markup analysis, and overstock markup analysis;

[0016] The markup analysis includes at least the following steps:

[0017] S310, identifying and screening the inbound and outbound records of items, and summarizing the data according to the batch number of the medicine or consumables, where the items include at least one of Chinese medicine, Western medicine, and consumables;

[0018] and / or,

[0019] Identify and filter out item usage records in hospitalization and outpatient expense details;

[0020] S311. Compare the incoming price and the outgoing price of items of the same batch number, and obtain data whose outgoing price is greater than the incoming price;

[0021] and / or,

[0022] Compare the usage quantity in the detailed list of hospitalization and outpatient expenses with the outbound quantity in the outbound list corresponding to the item, and obtain the data where the usage quantity is greater than the outbound quantity;

[0023] S312, summarizing the filtered and compared data according to preset identifiers;

[0024] S313. Calculate the total amount of illegal price increases.

[0025] Optionally, the over-limit pricing analysis includes at least the following steps:

[0026] S321. Associate the medical insurance item codes in the medical service item price list with the medical insurance item codes in the detailed list of hospitalization and outpatient expenses;

[0027] S322, comparing the charging unit price in the detailed list of hospitalization and outpatient expenses with the fixed price in the price list of medical service items, and obtaining charging records in which the charging unit price is greater than the fixed price;

[0028] S323. Based on the medical insurance item codes, summarize all charge records that are greater than the fixed price;

[0029] S324. For each medical insurance item code, calculate the total amount of the over-limit price;

[0030] S325. Calculate the total amount of over-limit prices for all medical insurance items.

[0031] Optionally, the charge analysis includes at least one of repeated charge analysis, decomposed charge analysis, and custom charge analysis;

[0032] The duplicate charge analysis includes at least the following steps: screening the charge records of the same patient within a first preset time interval in the detailed list of inpatient and outpatient charges; determining whether multiple items of charges are charged at the same time in the charge record, if so, there are duplicate charges, and calculating the total amount of duplicate charges in the charge record; calculating the total amount of duplicate charges for all patients;

[0033] The decomposed charge analysis includes at least the following steps: obtaining the charge records of the same patient in the detailed list of inpatient and outpatient charges within the first preset time interval; determining whether the same item or related items are charged at different time points in the charge record, if so, there are decomposed charges, and calculating the total decomposed charge of the charge record; calculating the total decomposed charge of all patients;

[0034] The custom charge analysis includes at least the following steps: obtaining the charge records in the detailed list of inpatient and outpatient expenses whose medical insurance item codes do not exist in the medical service item price list; and calculating the total amount of all custom charges.

[0035] Optionally, the preprocessed data is classified and stored in a corresponding database, which includes at least the following steps:

[0036] Convert the preprocessed data into CSV file format;

[0037] Provide a data import interface for users to set field correspondence, mapping the field names in the CSV file to the preset keys in the database;

[0038] Create a mapping table to associate the header field names of the CSV file with the preset keys in the database. The values ​​in the mapping table store specific information corresponding to each column of the imported data.

[0039] The data in the mapping table are grouped according to a preset number of entries to obtain multiple data sets;

[0040] Convert each data set into a text file;

[0041] The multi-threaded task scheduling technology is used to import the obtained text files into the database in batches in parallel.

[0042] Corresponding to the medical data intelligent processing and analysis method, the present invention provides a medical data intelligent processing and analysis system, which includes:

[0043] The data acquisition module is used to obtain the price list of medical service items and various medical data of the hospital, and the medical data includes at least one of the detailed list of outpatient expenses, the detailed list of inpatient expenses, the list of consumables entering the warehouse, the list of consumables leaving the warehouse, the list of medicines entering the warehouse, and the list of medicines leaving the warehouse;

[0044] The data processing module is used to pre-process the price list of medical service items and various medical data of the hospital, and store them in the corresponding database according to their categories;

[0045] The data analysis module is used to analyze the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

[0046] In addition, to achieve the above-mentioned objectives, the present invention also provides a computer-readable storage medium, on which a medical data intelligent processing and analysis program is stored. When the medical data intelligent processing and analysis program is executed by a processor, the steps of the medical data intelligent processing and analysis method described above are implemented.

[0047] The beneficial effects of the present invention are:

[0048] (1) Compared with the prior art, the present invention solves the problems of low efficiency and high error rate of manual account checking by automatically acquiring, processing and analyzing the price list of medical service items and various medical data of the hospital, thereby improving the efficiency and accuracy of supervision.

[0049] (2) Compared with the prior art, the present invention improves the quality and consistency of data through preprocessing steps such as outlier processing, field name verification, data format unification, and data deduplication, thereby providing a reliable data basis for subsequent data analysis.

[0050] (3) Compared with the prior art, the present invention ensures the accuracy and integrity of the data through outlier processing and field name verification steps, avoids the impact of erroneous data on the analysis results, and improves the reliability of the analysis.

[0051] (4) Compared with the prior art, the present invention enables unified management and analysis of data from different sources and formats through unified data format processing, thereby solving the data island problem and improving the efficiency of data integration.

[0052] (5) Compared with the prior art, the present invention identifies illegal price-raising behaviors through price-raising analysis, which helps regulatory agencies to discover and crack down on improper pricing behaviors and protect the rights and interests of patients.

[0053] (6) Compared with the prior art, the present invention identifies charging behaviors that exceed the prescribed prices through over-limit pricing analysis, which helps to maintain the fairness and rationality of medical service prices.

[0054] (7) Compared with the prior art, the present invention helps prevent improper charging by medical institutions, protects the rights and interests of patients, and improves the transparency of medical services by identifying duplicate charges, decomposing charges, and customizing charging behaviors.

[0055] (8) Compared with the prior art, the present invention improves the efficiency of data import through multi-threaded task scheduling technology, ensuring the integrity and accuracy of the data.

[0056] (9) Compared with the prior art, the present invention adopts a modular design of the medical data intelligent processing and analysis system, so that each module can operate independently and work together, thereby improving the flexibility and scalability of the system, and also improving the processing capability and analysis efficiency of the system.

[0057] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0059] In the figure:

[0060] Figure 1 This is a flow chart of an embodiment of the medical data intelligent processing and analysis method of the present invention.

[0061] Figure 2 This is a framework diagram of an embodiment of the medical data intelligent processing and analysis system of the present invention. DETAILED DESCRIPTION

[0062] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product or device that includes a series of steps or units.

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0067] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0068] See also Figure 1As shown, a medical data intelligent processing and analysis method provided by an embodiment of the present invention includes the following steps:

[0069] S100, obtaining a price list of medical service items and various medical data of the hospital, wherein the medical data includes at least one of a detailed list of outpatient expenses, a detailed list of inpatient expenses, a list of incoming consumables, a list of outgoing consumables, a list of incoming drugs, and a list of outgoing drugs;

[0070] S200, pre-processing the price list of medical service items and various medical data of the hospital respectively, and storing them in corresponding databases by categories;

[0071] S300, performing data analysis on the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

[0072] In this embodiment, the detailed list of outpatient expenses includes at least one of the following data: unique visit identification, outpatient number, name, visit date, visit department, document number, price code, hospital project code, hospital project name, specifications, batch number, unit price, unit of measurement, quantity, total amount, billing date, billing department, major expense category, and charging category.

[0073] The detailed list of hospitalization expenses shall include at least one of the following data: unique medical treatment identification, hospitalization number, hospitalization serial number, name, hospitalization date, discharge date, admission department, discharge department, document number, price code, hospital project code, hospital project name, specifications, batch number, unit price, unit of measurement, quantity, total amount, billing date, billing department, major expense category, and charging category.

[0074] The consumables inventory list shall include at least one of the following data: consumables code, consumables name, hospital charge item code, hospital charge item name, consumables category, specification, batch number, purchase price, unit of measurement, inventory amount, and inventory quantity.

[0075] The consumables outbound list shall include at least one of the following data: consumables code, consumables name, hospital charging item code, hospital charging item name, consumables category, issuing department, specifications, batch number, unit of measurement, outbound amount, outbound quantity, and outbound date.

[0076] The drug inventory list shall include at least one of the following data: drug code, drug name, hospital charge item code, hospital charge item name, drug category, specification, batch number, purchase price, unit of measurement, inventory amount, and inventory quantity.

[0077] The drug outbound list shall include at least one of the following data: drug code, drug name, hospital charge item code, hospital charge item name, drug category, specification, batch number, unit of measurement, outbound price, outbound quantity, and annual data.

[0078] The present invention solves the problems of low efficiency and high error rate of manual account checking by automatically acquiring, processing and analyzing the price list of medical service items and various medical data of the hospital, thereby improving the efficiency and accuracy of supervision.

[0079] In this embodiment, the format of the medical service item price list is specifically PDF or Excel. In view of the structure of the medical service item price list, in order to significantly improve the accuracy of reading data from the PDF table, the present invention deeply encapsulates the Tabula library for the second time. The Tabula library is an open source Python library that provides a read_pdf() function to read table data in PDF files. Users can optimize the table recognition effect by adjusting parameters, and can choose to export the table data to CSV, Excel or other formats; this improvement enables the present invention to more effectively deal with various complex situations that may appear in tables in PDF files, thereby ensuring the accuracy and reliability of data extraction.

[0080] In addition, the present invention uses the easyexcel library to read the data in the Excel file, and can also handle various situations that may occur in the table in the Excel file. EasyExcel is an open source Java library from Alibaba, which is specially used to handle the read and write operations of Excel files.

[0081] Since the data structures obtained by the above two methods are different, the present invention also preprocesses the data after obtaining the data. Preferably, the preprocessing includes at least one of abnormal value processing, field name verification, data format unification, and data deduplication processing.

[0082] The present invention improves the quality and consistency of data through preprocessing steps such as outlier processing, field name verification, data format unification, and data deduplication, thereby providing a reliable data basis for subsequent data analysis.

[0083] In this embodiment, the outlier processing includes at least the following steps: obtaining the abnormal data that does not conform to the data format in the price list of medical service items and various medical data of the hospital, and storing them in a designated location; performing outlier processing on the abnormal data, and if the abnormal data lacks necessary parameters, prompting the user to complete the information; and / or, intelligently identifying the abnormal data, obtaining useless data and discarding it; after the user completes the information and / or discards the useless data, re-importing the corresponding data.

[0084] Preferably, abnormal values ​​include at least incorrect field types and field values ​​that do not correspond to the data in the column; necessary parameters include project codes; useless data include descriptive text in project classifications, and data where the number of columns in a data row does not match the preset number of columns in the header.

[0085] In this embodiment, the field name verification process includes at least the following steps: obtaining the price list of medical service items and the table header information of various medical data of the hospital; comparing the obtained table header information with the preset table header information. If the two are different, the data import is abandoned and a prompt is given.

[0086] The present invention ensures the accuracy and integrity of data through abnormal value processing and field name verification steps, avoids the influence of erroneous data on analysis results, and improves the reliability of analysis.

[0087] In this embodiment, the data format unification processing includes at least the following steps: determining whether the medical service item price list and various medical data of the hospital include date, price, and quantity data; if date data is included, unifying it into a standard date format; if price format is included, unifying it into a standard price format; if quantity format is included, unifying it into a standard quantity format.

[0088] Preferably, the standard date format is YYYY-MM-DD (year-month-date) or YYYY-MM-DD HH:mm:ss (year-month-date hour:minute:second), and the standard price format and the standard quantity format uniformly retain four decimal places.

[0089] The present invention enables unified management and analysis of data from different sources and formats through unified data format processing, thereby solving the data island problem and improving the efficiency of data integration.

[0090] In this embodiment, the data deduplication process includes at least the following steps: obtaining a price list of medical service items and unique identifiers corresponding to various types of medical data of the hospital; and removing duplicate data according to the unique identifiers.

[0091] In this embodiment, the preprocessed data is classified and stored in the corresponding database, which at least includes the following steps:

[0092] Convert the preprocessed data into CSV file format;

[0093] Provide a data import interface for users to set field correspondence, mapping the field names in the CSV file to the preset keys in the database;

[0094] Create a mapping table (HashMap) to associate the header field names of the CSV file with the preset keys in the database. The values ​​in the mapping table store the specific information corresponding to each column of the imported data.

[0095] The data in the mapping table are grouped according to a preset number of entries to obtain multiple data sets;

[0096] Convert each data set into a text file;

[0097] The multi-threaded task scheduling technology is used to import the obtained text files into the database in batches in parallel.

[0098] Preferably, the preset number of items is 500.

[0099] It should be noted that CSV files are well-known for their high efficiency in processing large data, lightweight storage characteristics, and good readability. The present invention allows users to pre-set the corresponding relationship of data fields in the data import interface, thereby greatly facilitating users to check the content of the data to be imported.

[0100] In the data import process, a mapping table (HashMap) is constructed to store the imported data, and the header of the imported data is accurately associated with the preset key. The value in the mapping table is responsible for storing the specific information corresponding to each column of the imported data, and then the mapping table is inserted into the collection, each collection contains 500 mapping table data to ensure the efficiency of processing and the convenience of management. Next, these collections are converted into text files (txt format) recognizable by the database. This process not only ensures the readability of the data, but also greatly simplifies the subsequent data processing steps. In order to further improve the import efficiency, the present invention adopts multi-threaded task scheduling technology to import the generated text files into the database in batches in parallel. In this way, a large amount of data retrieved by the hospital can be quickly and accurately imported into the database, which not only significantly improves the speed of data processing, but also ensures the integrity and accuracy of the data.

[0101] Furthermore, after classifying and storing data from different data sources in different databases, database sharding and partitioning strategies are adopted to ensure orderly management and efficient access to data. At the same time, convenient data query functions are provided to support rapid retrieval of stored data. With the powerful capabilities of the ElasticSearch search engine, search results are quickly fed back based on the search conditions entered by the user, improving the user experience.

[0102] In this embodiment, the price markup analysis includes at least one of western medicine price markup analysis, traditional Chinese medicine price markup analysis, consumables price markup analysis, and overstock price markup analysis.

[0103] In this embodiment, the markup analysis includes at least the following steps:

[0104] S310, identifying and screening the inbound and outbound records of items, and summarizing the data according to the batch number of the medicine or consumables, where the items include at least one of Chinese medicine, Western medicine, and consumables;

[0105] and / or,

[0106] Identify and filter out item usage records in hospitalization and outpatient expense details;

[0107] S311. Compare the incoming price and the outgoing price of items of the same batch number, and obtain data whose outgoing price is greater than the incoming price;

[0108] and / or,

[0109] Compare the usage quantity in the detailed list of hospitalization and outpatient expenses with the outbound quantity in the outbound list corresponding to the item, and obtain the data where the usage quantity is greater than the outbound quantity;

[0110] S312, summarizing the filtered and compared data according to preset identifiers;

[0111] S313. Calculate the total amount of illegal price increases.

[0112] Preferably, the preset identifier is a batch number.

[0113] Specifically, the total amount of illegal price increases for Western medicines, T1, is calculated as follows:

[0114] T1=∑(P i -p i ) i ;

[0115] Among them, i is the batch number of western medicine, P i is the storage price of western medicine with the same batch number, p i is the price of western medicines with the same batch number, n i The number of western medicines with the same batch number shipped out of the warehouse.

[0116] The calculation formula for the total amount of illegal price increases for traditional Chinese medicines, T2, is as follows:

[0117] T2=∑(P z -p z ) z ;

[0118] Among them, z is the batch number of western medicine, P z is the storage price of Chinese medicine with the same batch number, p z is the ex-warehouse price of Chinese medicine with the same batch number, n z The number of Chinese medicines with the same batch number shipped out of the warehouse.

[0119] The calculation formula for the total amount of illegal price increases for consumables T3 is as follows:

[0120] T3=∑(P h -p h ) h ;

[0121] Where h is the batch number of consumables, P h is the inventory price of consumables with the same batch number, p h is the outbound price of consumables with the same batch number, n h The number of consumables shipped out of the same batch number.

[0122] The calculation formula for the total amount of illegal markup due to overstocking T4 is as follows:

[0123] T4=∑(d a -k a ) a ;

[0124] Among them, a) items with the same code and the same batch (including at least one of Chinese medicine, Western medicine, and consumables), d) a k is the number of items with the same code and the same batch used in the detailed list of hospitalization expenses and the detailed list of outpatient expenses, a is the outbound quantity of the goods (including at least one of Chinese medicine, Western medicine, and consumables) in the outbound list, p a It is the outbound amount in the outbound list of items (including at least one of Chinese medicine, Western medicine, and consumables).

[0125] The present invention identifies illegal price-raising behaviors through price-raising analysis, which helps regulatory agencies to discover and crack down on improper pricing behaviors and protect the rights and interests of patients.

[0126] In this embodiment, the over-limit pricing analysis includes at least the following steps:

[0127] S321. Associate the medical insurance item codes in the medical service item price list with the medical insurance item codes in the detailed list of hospitalization and outpatient expenses;

[0128] S322, comparing the charging unit price in the detailed list of hospitalization and outpatient expenses with the fixed price in the price list of medical service items, and obtaining charging records in which the charging unit price is greater than the fixed price;

[0129] S323. Based on the medical insurance item codes, summarize all charge records that are greater than the fixed price;

[0130] S324. For each medical insurance item code, calculate the total amount of the over-limit price;

[0131] S325. Calculate the total amount of over-limit prices for all medical insurance items.

[0132] Specifically, the total amount of over-limit pricing for all medical insurance items, T5, is calculated as follows:

[0133]

[0134] Among them: c is the medical insurance item code in the detailed list of hospitalization expenses and the detailed list of outpatient expenses, C is the medical insurance item code in the price list of medical service items, d c is the unit price in the detailed list of hospitalization expenses and the detailed list of outpatient expenses, w C is the fixed price in the price list of medical service items, n c It is the number of charges in the detailed list of inpatient expenses and the detailed list of outpatient expenses. The total amount of over-limit pricing coded for a particular health insurance item.

[0135] The present invention identifies charging behaviors exceeding the prescribed prices through over-limit pricing analysis, which helps to maintain the fairness and rationality of medical service prices.

[0136] In this embodiment, the charge analysis includes at least one of repeated charge analysis, decomposed charge analysis, and customized charge analysis.

[0137] In this embodiment, the duplicate charge analysis includes at least the following steps: screening the charge records of the same patient within the first preset time interval in the detailed list of inpatient and outpatient expenses; determining whether multiple items are charged at the same time in the charge record, if so, there are duplicate charges, and calculating the total amount of duplicate charges in the charge record; calculating the total amount of duplicate charges for all patients.

[0138] Specifically, the calculation formula for the total amount of repeated charges V1 for all patients is as follows:

[0139] V1={(p,(f l ,f j ),t)|p∈P,(f l ,f j )∈F p ,(c l ,c j )∈R1,|t l -t j |≤Δt1};

[0140] Where: P is the set of all patients, p is a certain patient, l and j are the codes of the charging items, f l and f j represents the charge items of patient p, F p All charges for patients, c l and c jare items that cannot be charged at the same time, R1 is the set of all items that cannot be charged at the same time, t l For charging items l The occurrence time, t j For charging items j The occurrence time of c l and c j The interval of occurrence time is the first preset time interval.

[0141] Preferably, the first preset time interval and the items that cannot be charged at the same time are both preset. The first preset time interval is set by the user when using the system, and is generally composed of the start time and end time of the collected charging item data; the items that cannot be charged at the same time are preset according to the "Price List of Medical Service Items of Public Medical Institutions" implemented by the medical institution and the contents of the relevant medical guidance manual files.

[0142] In this embodiment, the decomposed charge analysis includes at least the following steps: obtaining the charge records of the same patient within the first preset time interval in the detailed list of inpatient and outpatient expenses; determining whether the same item or related items in the charge record are charged at different time points, and if so, there are decomposed charges, and calculating the total decomposed charge of the charge record; calculating the total decomposed charge for all patients.

[0143] The calculation formula for the total amount of charges V2 for all patients is as follows:

[0144] V2={(p,(f e ,f g ),t)|p∈P,(f e ,f g )∈F p ,(o e ,o g )∈R2,|t e -t g |≤Δt2};

[0145] Where: P is the set of all patients, p is a certain patient, e and g are the codes of the charging items, f e and f g represents the charge items of patient p, F p All charges for patients, O e and O g is an item that cannot be decomposed into multiple charges, R2 is the set of all items that cannot be decomposed into multiple charges, t e For charging items e The occurrence time, t g For charging items g The occurrence time, Δt2 represents o e andg The interval of occurrence time is the second preset time interval.

[0146] Preferably, the second preset time interval and the items that cannot be decomposed into multiple charges are both preset. The second preset time interval is set by the user when using the system, and is generally composed of the start time and end time of the collected charge item data; the items that cannot be decomposed into charges are preset according to the "Price List of Medical Service Items of Public Medical Institutions" implemented by the medical institution and the contents of the relevant medical guidance manual files.

[0147] In this embodiment, the custom charge analysis includes at least the following steps: obtaining charge records in the detailed list of inpatient and outpatient expenses whose medical insurance item codes do not exist in the medical service item price list; and calculating the total amount of all custom charges.

[0148] The calculation formula for the total amount of all custom charges T6 is as follows:

[0149]

[0150] in, The total amount of over-limit pricing for a custom charge, k c It is the number of charges in the detailed list of inpatient expenses and the detailed list of outpatient expenses.

[0151] The present invention helps prevent improper charging by medical institutions, protects the rights and interests of patients, and improves the transparency of medical services by identifying duplicate charges, breaking down charges, and customizing charging behaviors.

[0152] Furthermore, the medical data intelligent processing and analysis method of the present invention further includes:

[0153] S400, display the analysis results in a visual form.

[0154] Specifically, the analysis results can be presented in the most intuitive way for different scenarios.

[0155] like Figure 2 As shown, the present invention also provides a medical data intelligent processing and analysis system, which includes:

[0156] The data acquisition module 10 is used to obtain the price list of medical service items and various medical data of the hospital, wherein the medical data includes at least one of the detailed list of outpatient expenses, the detailed list of inpatient expenses, the list of consumables entering the warehouse, the list of consumables leaving the warehouse, the list of medicines entering the warehouse, and the list of medicines leaving the warehouse;

[0157] The data processing module 20 is used to pre-process the medical service item price list and various medical data of the hospital, and store them in corresponding databases by categories;

[0158] The data analysis module 30 is used to perform data analysis on the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

[0159] Furthermore, the medical data intelligent processing and analysis system of the present invention also includes:

[0160] The visualization module is used to display the analysis results in a visual form.

[0161] Preferably, the visualization form can present the analysis results in the most intuitive way for different scenarios, support users to interact directly on the visualization form, view data analysis summary data (bar charts, pie charts, line charts), and support jumping to the data analysis module 30, making the originally boring data analysis work an easy task.

[0162] In the present invention, duplicate charge analysis: detects whether the hospital has duplicate charges. For example, according to relevant regulations, the number of times the same service or drug can be used in the same diagnosis and treatment process is limited, but the actual number of times used by the medical institution exceeds the prescribed number, resulting in multiple charges. This situation may bring unnecessary financial burdens to patients, and may also cause hospitals to generate illegal income.

[0163] Fee decomposition analysis: This focuses on whether the hospital breaks down fees that should have been charged once or a fixed number of times into multiple parts and charges them separately. This behavior may cause patients to pay unnecessary extra fees and may also cause the hospital to generate illegal income.

[0164] Excessive Charge Analysis: Checks whether hospitals charge more than the prescribed standard. Each region of the country has corresponding medical fee standards, and hospitals should charge according to these standards. If the standard charges are exceeded, it may raise issues of fairness and transparency.

[0165] Analysis of price markups for Western medicines: Check whether hospitals are selling Western medicines at a marked-up price without following the zero-margin sales principle, and conduct statistical analysis and display of related medicines.

[0166] TCM price increase analysis: Check whether hospitals have TCM price increase rates that exceed the prescribed price increase rate, and conduct statistical analysis and display of related drugs. The price increase rate can be set according to local policies during the analysis.

[0167] Analysis of excess inventory price increases: By comparing the in-hospital medical consumables data with the medical consumables data used in the HIS system, the problem of in-hospital price increases caused by inconsistencies between the in-hospital and in-hospital quantities and the quantities used is analyzed, and the excess inventory amount of related consumables is calculated.

[0168] Self-determined charging analysis: Based on the medical insurance catalogue, consumables catalogue, and special medical documents, comparisons are made with various databases to obtain self-determined charging items.

[0169] The present invention adopts modular design of medical data intelligent processing and analysis system, so that each module can operate independently and work together at the same time, which improves the flexibility and scalability of the system, and also improves the processing capacity and analysis efficiency of the system.

[0170] The embodiment of the present invention further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory in the above embodiment; or a computer-readable storage medium that exists independently and is not installed in a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement Figure 1 The medical data intelligent processing and analysis method shown in the figure. The computer readable storage medium can be a read-only memory, a disk or an optical disk, etc.

[0171] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system embodiment and the storage medium embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0172] Furthermore, in this document, the terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element.

[0173] The above description shows and describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention, through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not depart from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for intelligent processing and analysis of medical data, characterized in that: The following steps are involved: S100, obtaining a price list of medical service items and various medical data of the hospital, wherein the medical data includes at least one of a detailed list of outpatient expenses, a detailed list of inpatient expenses, a list of incoming consumables, a list of outgoing consumables, a list of incoming drugs, and a list of outgoing drugs; S200, pre-processing the price list of medical service items and various medical data of the hospital respectively, and storing them in corresponding databases by categories; S300, performing data analysis on the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

2. The medical data intelligent processing and analysis method according to claim 1, characterized in that: Also includes: S400, present the analysis results in a visual form; Preprocessing includes at least one of outlier processing, field name verification, data format unification, and data deduplication processing.

3. The medical data intelligent processing and analysis method according to claim 2, characterized in that: The outlier processing includes at least the following steps: obtaining the abnormal data that does not conform to the data format in the medical service item price list and various medical data of the hospital, and uniformly storing them in a designated location; performing outlier processing on the abnormal data, and if the abnormal data lacks necessary parameters, prompting the user to complete the information; and / or, performing intelligent identification on the abnormal data, obtaining useless data and discarding it; after the user completes the information and / or discards the useless data, re-importing the corresponding data; The field name verification process includes at least the following steps: obtaining the price list of medical service items and the table header information of various medical data of the hospital; comparing the obtained table header information with the preset table header information. If the two are different, the data import is abandoned and a prompt is given.

4. The medical data intelligent processing and analysis method according to claim 2, characterized in that: The data format unification process includes at least the following steps: Determine whether the price list of medical service items and various medical data of the hospital include date, price and quantity data; if date data is included, unify it into a standard date format; if price format is included, unify it into a standard price format; if quantity format is included, unify it into a standard quantity format; The data deduplication process includes at least the following steps: obtaining a price list of medical service items and unique identifiers corresponding to various types of medical data of the hospital; and removing duplicate data based on the unique identifiers.

5. The medical data intelligent processing and analysis method according to claim 1, characterized in that: The markup analysis includes at least one of the following: Western medicine markup analysis, Chinese medicine markup analysis, consumables markup analysis, and overstock markup analysis; The markup analysis includes at least the following steps: S310, identifying and screening the inbound and outbound records of items, and summarizing the data according to the batch number of the medicine or consumables, where the items include at least one of Chinese medicine, Western medicine, and consumables; and / or, Identify and filter out item usage records in hospitalization and outpatient expense details; S311. Compare the incoming price and the outgoing price of items of the same batch number, and obtain data whose outgoing price is greater than the incoming price; and / or, Compare the usage quantity in the detailed list of hospitalization and outpatient expenses with the outbound quantity in the outbound list corresponding to the item, and obtain the data where the usage quantity is greater than the outbound quantity; S312, summarizing the filtered and compared data according to preset identifiers; S313. Calculate the total amount of illegal price increases.

6. The medical data intelligent processing and analysis method according to claim 1, characterized in that: The over-limit pricing analysis includes at least the following steps: S321. Associate the medical insurance item codes in the medical service item price list with the medical insurance item codes in the detailed list of hospitalization and outpatient expenses; S322, comparing the charging unit price in the detailed list of hospitalization and outpatient expenses with the fixed price in the price list of medical service items, and obtaining charging records in which the charging unit price is greater than the fixed price; S323. Based on the medical insurance item codes, summarize all charge records that are greater than the fixed price; S324. For each medical insurance item code, calculate the total amount of the over-limit price; S325. Calculate the total amount of over-limit prices for all medical insurance items.

7. The medical data intelligent processing and analysis method according to claim 1, characterized in that: The charge analysis includes at least one of repeated charge analysis, decomposed charge analysis, and customized charge analysis; The duplicate charge analysis includes at least the following steps: screening the charge records of the same patient within a first preset time interval in the detailed list of inpatient and outpatient charges; determining whether multiple items of charges are charged at the same time in the charge record, if so, there are duplicate charges, and calculating the total amount of duplicate charges in the charge record; calculating the total amount of duplicate charges for all patients; The decomposed charge analysis includes at least the following steps: obtaining the charge records of the same patient in the detailed list of inpatient and outpatient charges within the first preset time interval; determining whether the same item or related items are charged at different time points in the charge record, if so, there are decomposed charges, and calculating the total decomposed charge of the charge record; calculating the total decomposed charge of all patients; The custom charge analysis includes at least the following steps: obtaining the charge records in the detailed list of inpatient and outpatient expenses whose medical insurance item codes do not exist in the medical service item price list; and calculating the total amount of all custom charges.

8. The medical data intelligent processing and analysis method according to claim 1, characterized in that: The preprocessed data is classified and stored in the corresponding database, which at least includes the following steps: Convert the preprocessed data into CSV file format; Provide a data import interface for users to set field correspondence, mapping the field names in the CSV file to the preset keys in the database; Create a mapping table to associate the header field names of the CSV file with the preset keys in the database. The values ​​in the mapping table store specific information corresponding to each column of the imported data. The data in the mapping table are grouped according to a preset number of entries to obtain multiple data sets; Convert each data set into a text file; The multi-threaded task scheduling technology is used to import the obtained text files into the database in batches in parallel.

9. A medical data intelligent processing and analysis system, characterized in that: include: The data acquisition module is used to obtain the price list of medical service items and various medical data of the hospital, and the medical data includes at least one of the detailed list of outpatient expenses, the detailed list of inpatient expenses, the list of consumables entering the warehouse, the list of consumables leaving the warehouse, the list of medicines entering the warehouse, and the list of medicines leaving the warehouse; The data processing module is used to pre-process the price list of medical service items and various medical data of the hospital, and store them in the corresponding database according to their categories; The data analysis module is used to analyze the price list of medical service items and various medical data of the hospital to obtain corresponding analysis results; the data analysis includes at least one of price increase analysis, over-limit pricing analysis, and charge analysis.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a medical data intelligent processing and analysis program, which, when executed by a processor, implements the steps of the medical data intelligent processing and analysis method as described in any one of claims 1 to 8.

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