CN114373551A - DRGs knowledge base construction method and application method - Google Patents

Abstract

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a construction method and a use method of a DRGs knowledge base, wherein the construction method of the DRGs knowledge base comprises the following steps: constructing a primary knowledge base according to the whole hospital data; updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base, so that the construction of the knowledge base is realized, the matching recommendation of DRGs combined data is conveniently carried out through the knowledge base subsequently, the derivation of the DRGs combined data is facilitated, and the management and the monitoring of management personnel are facilitated.

Description

DRGs knowledge base construction method and application method

Technical Field

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a construction method and a use method of a DRGs knowledge base.

Background

The conventional techniques are described, and conventional patent documents are given as examples of the conventional methods.

The technical problems of the prior art can be described in a targeted manner according to the technical solution of claim 1, and the technical problems to be solved by the present application are then brought out.

Disclosure of Invention

The invention aims to provide a construction method and a use method of a DRGs knowledge base.

In order to solve the above technical problems, the present invention provides a method for constructing a DRGs knowledge base, comprising:

constructing a primary knowledge base according to the whole hospital data;

updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and

and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base.

Further, the method for constructing the primary knowledge base according to the whole hospital data comprises the following steps:

counting basic data in all departments of the whole hospital, wherein a document of each basic data is shared by a plurality of departments;

the document comprises a plurality of first diagnoses, a plurality of first operations and a plurality of DRG names;

a plurality of departments share a plurality of documents;

matching all documents with department, diagnosis, operation and DRG group names to generate DRGs data combination, and storing the DRGs data combination in a database to form a primary knowledge base.

Further, the method for updating the primary knowledge base according to the DRGs combined data fed back by the DRGs to construct the secondary knowledge base comprises the following steps:

taking the attribute, other diagnosis, other operations or operations, sex, age, number of hospitalization days, discharge mode and transfer condition in DRGs combined data fed back by the DRGs as important fields;

combining the fed back DRGs combined data with the primary knowledge base to generate new data, and storing the new data in the database as the basic data of the secondary knowledge base.

Further, the method for generating new data comprises:

matching corresponding DRGs combined data in a primary knowledge base through diagnosis, operation and DRG group names of the fed back DRGs combined data;

if the matching is successful, the matched combined data is taken out, new important fields are added in the matched data, and the matched data is stored in the primary knowledge base again;

and if the matching fails, storing the fed back DRGs combined data in a primary knowledge base.

Further, the method for constructing the tertiary knowledge base according to the accumulated and corrected secondary knowledge base comprises the following steps:

and accumulating and correcting the combined data in the secondary knowledge base within a preset time to form a tertiary knowledge base.

In a second aspect, the present invention further provides a DRGs knowledge base using method using the DRGs knowledge base construction method, including:

recommending by a knowledge base when the DRG is customized; and

and exporting data in the DRGs knowledge base.

Further, the method for recommending the knowledge base in the self-defining of the DRG comprises the following steps:

and matching corresponding DRG combined data from the third-level knowledge base according to the operation and diagnosis when the DRG combined data is customized.

Further, the method for matching corresponding DRG combination data includes:

according to diagnosis, severe complications or complications are distinguished, then matching is carried out according to operation or operation, and more particularly, hospitalization days, sex and age intervals are divided so as to match corresponding DRG combination data.

Further, the method for deriving data in the DRGs knowledge base comprises:

and exporting the data in the derived DRGs knowledge base stored in the tertiary knowledge base according to the requirement.

The method has the advantages that a primary knowledge base is constructed according to the whole-hospital data; updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base, so that the construction of the knowledge base is realized, the matching recommendation of DRGs combined data is conveniently carried out through the knowledge base subsequently, the derivation of the DRGs combined data is facilitated, and the management and the monitoring of management personnel are facilitated.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a flow chart of the DRGs knowledge base construction method of the present invention;

fig. 2 is a specific flowchart of the method of using the DRGs knowledge base in the present invention.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment provides a method for constructing a DRGs knowledge base, which comprises the following steps: as shown in fig. 1, a primary knowledge base is constructed according to the whole-hospital data; updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base, so that the construction of the knowledge base is realized, the matching recommendation of DRGs combined data is conveniently carried out through the knowledge base subsequently, the derivation of the DRGs combined data is facilitated, and the management and the monitoring of management personnel are facilitated.

As shown in fig. 2, in this embodiment, the method for constructing the primary knowledge base according to the hospital-wide data includes: counting basic data in all departments of the whole hospital, wherein a document of each basic data is shared by a plurality of departments; the document comprises a plurality of first diagnoses, a plurality of first operations and a plurality of DRG names; a plurality of departments share a plurality of documents; matching all documents with department, diagnosis, operation and DRG group names to generate DRGs data combination, and storing the DRGs data combination in a database to form a primary knowledge base.

In this embodiment, the method for updating the primary knowledge base according to the DRGs combined data fed back by the DRGs to construct the secondary knowledge base includes: taking attributes (complication, serious complication and the like), other diagnoses, other operations or operations, sex, age, hospitalization days, discharge mode and transfer cases in DRGs combined data fed back by the DRGs as important fields; combining the fed back DRGs combined data with the primary knowledge base to generate new data, and storing the new data in the database as the basic data of the secondary knowledge base.

In this embodiment, the method for generating new data includes: matching corresponding DRGs combined data in a primary knowledge base through diagnosis, operation and DRG group names of the fed back DRGs combined data; if the matching is successful, the matched combined data is taken out, new important fields are added in the matched data, and the matched data is stored in the primary knowledge base again; and if the matching fails, storing the fed back DRGs combined data in a primary knowledge base.

In this embodiment, the method for building the tertiary knowledge base according to the accumulated and corrected secondary knowledge base includes: and accumulating and correcting the combined data in the secondary knowledge base within a preset time to form a tertiary knowledge base, wherein the tertiary knowledge base is more and more complete as more combined data in the secondary knowledge base are accumulated and accumulated over time.

In this embodiment, a method for using a DRGs knowledge base is further provided, which includes: recommending by a knowledge base when the DRG is customized; and exporting data in the DRGs knowledge base, so that managers such as a hospital leadership, a functional department, a clinical department and the like can conveniently and clearly know the comparison of statistical data such as DRGs core data, medical insurance settlement conditions, DRGs group structures, group entering practice case records and the like, and meanwhile, four disease categories of characteristics of the hospital are established.

In this embodiment, the method for recommending the knowledge base in the DRG customization includes: matching corresponding DRG combined data from the third-level knowledge base according to operation and diagnosis when the DRG combined data are customized; the matching rules can be based on the diagnosis of severe complications or complications, and further matching by operation or operation, then dividing according to the number of hospitalization days and gender and age interval, and finally recommending drg groups with high rw according to the high rw to low priority.

In this embodiment, the method for matching corresponding DRG combination data includes: according to diagnosis, severe complications or complications are distinguished, then matching is carried out according to operation or operation, and more particularly, hospitalization days, sex and age intervals are divided so as to match corresponding DRG combination data.

In this embodiment, the method for deriving data in the DRGs knowledge base includes: and exporting the data in the derived DRGs knowledge base stored in the tertiary knowledge base according to the requirement.

Specifically, an entry is additionally provided with a DRGs module at the current header, and DRGs management, grouping summary and DRGs diagnostic books are displayed in a pull-down mode; and clicking the DRGs diagnostic book to enter a diagnostic book function page.

DRGs core data (stored in a three-level knowledge base, namely DRGs combined data) summarizes the overall situation of the DRGs; description of functional points: the administrator can check the summary condition of the whole hospital or department in the background; using roles: courtyard and department; inputting: query conditions, total, time period (default to last month); and (3) treatment: 1. inquiring, namely inputting inquiry information by a courtyard manager for inquiring; and (3) query conditions: all, department, time period; the expandable display advanced queries: normalized case group entering rate, DRG group number, CMI, time consumption index, cost consumption index and payment rate interval query (query conditions are displayed according to list fields); and (5) inquiring results: hospital and department data of the hospital under the selected conditions; the manager inputs query information and queries; and (3) query conditions: department, time period; the expandable display advanced queries: normalized case group entering rate, DRG group number, CMI, time consumption index, cost consumption index and payment rate interval query (query conditions are displayed according to list fields); and (5) inquiring results: department data under selected conditions. 2. Setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; adding or hiding data, the data comprising: the number of actual cases, the number of cases entering groups, the group rate of normative cases, the number of DRG groups, the total weight, the number of settlers, the number of groups, the total medical cost (element), the average cost (element), the medicine proportion (%), the consumption proportion (%), the medical service cost proportion (%), the balance (element), CMI, the time consumption index, the cost consumption index, the profit and loss contribution, the case average balance, the payment rate, the worker payment rate, the resident payment rate, the normal group number, the basic group number, the ultrahigh group number, the ultralow group number, the four-stage operation or operation proportion, the difficult and difficult critical weight number proportion, and the single disease proportion; and the profit and loss contribution calculation formula: the sufficient departments account for the total profit percentage, and the deficient departments account for the total loss percentage. 3. Data is expanded, and when the hospital leadership layer has the rights of the whole hospital, the data of the whole hospital is displayed; when all departments are clicked and inquired, displaying 80 pieces of department data; querying a single department displays a single department data.

1, clicking a whole hospital to jump to a comparison graph page of each department to display the comparison graph of each department of the whole hospital; data switchable display (how many field contrasts to display as the header changes); the data source is data inquired in the previous page; the method comprises the following steps of displaying as a histogram, wherein the histogram supports custom display of all departments ranked at n (before (after); the function button as the figure is added, the default option of the pull-down list box is 'default', the selection of the department sorting of the front n bits or the back n bits of the ranking is supported, when the 'front' or the 'back' is selected, an input box appears, only the input of positive value numbers is supported, and after the input is determined, the department which meets the ranking condition is screened out to display the histogram. 2. And (3) clicking an entered histogram page on the core data and various data of 'all homes' in the DRG disease group to increase the analysis of the histogram and the trend chart of the hospital, and displaying the comparison chart of the hospital by the DRG disease group module under the condition that the screening condition is all homes. 3. Wherein the profit and loss contribution comparison graph is displayed as a loss ratio and an excess ratio graph; and the data is the balance + profit-loss contribution ratio, and the core data of the clicked breadcrumbs DRGs returns to the previous layer. 4. Clicking the department to display a department accumulated trend graph; data switchable display (how many field contrasts to display as the header changes); the data source is the data of the previous page query. 5. Clicking the DRG group number to jump to a DRG comparison graph page to display a DRG group number histogram (residents and employees), a DRG group number accumulation trend graph (residents, employees, all, accumulation), and subject development balance analysis (a scatter diagram displays the DRG group number, department name, CMI and case number); the data source is the data of the previous page query. 6. Clicking CMI to jump to a CMI comparison graph page to display a CMI histogram (residents and employees), a CMI cumulative trend graph (residents, employees, all and cumulative) and CMI profit and loss analysis (a scatter diagram displays profit and loss, department names and CMI); clicking breadcrumb DRGs core data to return to the previous layer; the data source is data inquired in the previous page; CMI profit and loss analysis scatter diagram data can display profit and loss, department names and CMI. 7. Clicking and scattering points, and jumping to the situation pages of each department of disease distribution; the data source is the data of the previous page query. 8. Clicks [ actual result medical record number, actual result group medical record number, DRG group number, Settlement people number, group number, total medical cost (Yuan), average cost (Yuan), drug proportion (%), proportion (%) for consumption (%), medical service cost proportion (%), balance (Yuan), CMI, time consumption index, cost consumption index, profit and loss contribution, balance for example, rate of payment, worker payment rate, rate of payment, normal group number, basic group number, super high group number, super low group number ] display [ actual result medical record number, actual result group medical record number, DRG group number, Settlement people number, group number, total medical cost (% Yuan), average cost (Yuan), drug proportion ratio, proportion ratio ], medical service cost proportion ratio ], balance (CMI), time consumption index, balance for consumption (%), balance for consumption (%, balance for average, balance (%), etc, Settlement rate, settlement rate of staff, settlement rate of residents, normal group number, basic group number, normal group case number, basic group case number, ultrahigh group case number, ultralow group case number ] histogram and cumulative trend curve chart number; data inquired for the previous page according to the source; wherein the profit and loss contributions only show histograms; profit-loss contributions can only be clicked in from the department. The data of the whole hospital only shows the profit and loss amount, and the profit and loss contribution comparison page cannot be clicked; the data are shown as all: balance + profit and loss contribution, residents: balance, staff: and (4) balance, clicking the core data of the breadcrumbs DRGs to return to the previous layer. 9. Clicking a fourth-level operation or operation to jump to a fourth-level operation or operation distribution page to display a fourth-level operation or operation histogram (residents and employees), a fourth-level operation or operation accumulation trend graph (residents, employees, all, accumulation), and a fourth-level operation or operation arrangement proportion (display example number and proportion); the data source is data inquired in the previous page; clicking breadcrumb DRGs core data to return to the previous layer; clicking a fourth-level operation or operation arrangement proportion middle module to display the ratio of 1 fourth-level operation/person, 2 fourth-level operations/person, 3 fourth-level operations/person, 4 fourth-level operations/person and more than 4 fourth-level operations/persons; the click module displays the operation details (the medical record can be viewed); click the hospital number to display the patient case; the case can be printed. 10. Clicking on the distribution page of the difficult and complicated critical quantity ratios, jumping to a difficult and complicated critical department distribution page, and displaying a difficult and complicated critical quantity ratio histogram (residents and employees), a difficult and complicated critical quantity ratio accumulation trend curve graph (residents, employees, all people and accumulation) and difficult and complicated critical quantity department distribution (displaying departments, examples, ratios and DRG groups); the data source is data inquired in the previous page; clicking breadcrumb DRGs core data to return to the upper layer, and clicking a hospital number to display the patient case; the case can be printed; clicking the cancel button or "x" closes the case history popup window. 11. Clicking single disease category proportion to display a histogram (residents and workers) of the number of difficult and complicated dangers, a cumulative trend curve graph (residents, workers, all and cumulative) of the single disease category proportion, and distribution (displaying departments, examples, proportion and DRG group number) of the single disease category departments; the data source is data inquired in the previous page; clicking breadcrumb DRGs core data to return to the previous layer; click the hospital number to display the patient case; the case can be printed; clicking the cancel button or "x" closes the case history popup window.

Monthly settlement is collected, and data such as monthly ultra-high payment (normal group), ultra-high payment (basic group), ultra-low payment (normal group) and the like are checked. Description of functional points: the master checks the monthly settlement condition of the whole hospital and the master checks the monthly settlement condition of the department; using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: the interval query of settlement number, total medical expense, uploaded fund total, accrual fund total, balance, case average balance and settlement rate is carried out; department selects a single department, and selecting a month can expand to display high-level queries: the interval query of settlement number, total medical expense, uploaded fund total, accrual fund total, balance, case average balance and settlement rate is carried out; and (3) outputting: 1. displaying the whole situation of the whole hospital, and clicking '>' to expand the resident information of the employees; clicking the next' >, expanding the information and hiding the resident information of the employees at the previous time. 2. Displaying the overall situation of the department, and clicking '>' to expand the resident information of the employees; clicking the next' >, expanding the information and hiding the resident information of the employees at the previous time. 3. Setting, the courtyard and the manager edit the header, and the display can be increased or hidden, and the data can be added or hidden: settlement number, total medical cost (yuan), uploading fund total (yuan), settlement fund total (yuan), balance (yuan), case average balance and settlement rate (%). The data chart display, 1, clicking the settlement times to jump to a settlement times comparison graph page to display a settlement times histogram (residents and employees) and a settlement times accumulation trend graph (residents, employees, all and accumulation); the data source is the data inquired in the previous page, and the clicking breadcrumbs are settled and summarized every month and returned to the previous layer. 2. Clicking the total medical expense, skipping to a page of a total medical expense comparison graph, displaying a column graph (residents and employees) of the total medical expense and a cumulative trend curve graph (residents, employees, all and cumulative) of the total medical expense, clicking the breadcrumbs, and settling and summarizing every month to return to the previous layer. 3. The large data items can be clicked into (upload fund total (element), settlement fund total (element), balance (element), case average balance and settlement rate (%)) to display (upload fund total (element), settlement fund total (element), balance (element), case average balance and settlement rate (%)) bar graphs and cumulative trend curve graphs. 4. Clicking the payment of the super-high limit (normal group), the payment of the super-low limit (normal group), the payment of the basic group into the payment of the super-high limit (normal group), the payment of the super-low limit (normal group), the payment distribution of the basic group and the settlement condition page, clicking the settlement summary of the breadcrumbs every month to return to the previous layer, and inquiring and listing the head.

And the DRG group is used for sorting the DRG group detailed data and RW interval distribution data. RW interval distribution table, function point description: the courtyard checks the RW interval distribution condition, and the manager checks the RW interval distribution condition of the department; using roles: courtyard and department; inputting: selecting all departments and selecting months; selecting a single department by department, and selecting a month; and (3) outputting: 1. displaying the whole situation of the whole hospital; the information mouse of the proportion graph is put into a certain module and displays the information of the RW interval. RW (0-1) indicates that 0< ═ RW <1, and clicking ">" can develop details of DRGs disease groups in the RW interval; clicking the next' >, expanding the information and hiding the DRGs disease group details in the RW interval. 2. Displaying the overall situation of the department; putting the proportion graph information mouse into a certain module, displaying RW interval information, and clicking '>' to expand worker and resident information; clicking the next' >, expanding the information and hiding the DRGs disease group details in the RW interval.

DRGs disease group details, functional point description: the universities check the DRGs disease group conditions of the whole universities, and the departments check the DRGs disease group conditions of the departments; using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: RW, disease type property, disease group attribute, settlement number, balance, case average balance and balance rate interval query; department selects a single department, and selecting a month can expand to display high-level queries: RW, disease category property, disease group attribute, number of settlers, balance, average balance, balance rate interval query (query conditions are listed according to list field display); and adding disease group types for screening on DRG disease group details and solid case pages, wherein the types are divided into total, basic and normal, all are displayed by default, and the diagnosis book relates to the added time consumption index and the cost consumption index of a specific disease group (a disease group detail page and four disease seed bank pages). And (3) outputting: 1. and displaying the whole hospital, wherein the nature of the disease is derived from the medical technology list. 2. The general situation of the department is displayed, wherein the nature of the disease is derived from the data elements in the medical technology list. 3. Setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; add or hide data: medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-grade operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of case, payment rate, ultrahigh case number and ultralow case number. 4. The data in the click list may show a histogram of the data and a cumulative trend graph. Comprises (settlement times, medicine ratio, medicine per index, consumption ratio, consumable material per index, medical service charge ratio, four-grade operation ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of examples and settlement rate). The data source is data inquired in the DRGs disease group details, and the breadcrumb DRGs disease group details are clicked to return to the previous layer; click the entered histogram page on the core data and various data of 'whole courtyard' in the DRG disease group, and increase the analysis of the courtyard area histogram and the trend chart; the DRG disease group module displays the courtyard comparison chart under the condition that the screening condition is the whole courtyard. 5. The information of the super-high number of instances is clicked to enter the information of the super-high number of instances; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; the patient can be inquired by the hospital number, the list data is exported, the patient case is checked, the data source is the data inquired in the DRGs disease group details, and the breadcrumb DRGs disease group details are clicked to return to the previous layer. 6. Clicking the ultra-low case number to enter the ultra-low case number information; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; hospitalization number query, export list data, review patient cases. The data source is data inquired in DRGs disease group details, and the breadcrumb DRGs disease group details are clicked to return to the previous layer. 8. And (4) clicking the name of the disease group to enter the distribution of the disease region, and displaying the condition of the department where the disease group is located. The data source is data inquired in DRGs disease group details; clicking the department to jump to the comparison graph page of each department to display the comparison graph of each department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the details of the breadcrumbs DRGs are clicked and returned to a DRGs detail page; clicking the distribution condition of the crumb ward to return to a ward distribution condition page; clicking the department to display the cumulative trend chart of the department; data switchable display (how many field contrasts to display as the header changes); the data source is the data of the previous page query, click: "x" closes the pop-up window; and clicking the data in the list to display a histogram and an accumulated trend curve chart of the data. Including (settlement times, balance, average profit and loss per case, drug ratio, drug per index, consumption ratio, consumable per index, medical service charge ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance rate); the data source is the data of the previous page query.

The DRG group corresponds to the actual medical record, and the functional points are described as follows: the hospital master checks the corresponding actual medical record condition of the DRG group of the whole hospital and the department master checks the corresponding actual medical record condition of the DRG group of the department; using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: the hospital number, the group name, the balance, the average profit and loss of each case, the medicine ratio, the medicine index and the consumption ratio interval are inquired; department selects a single department, and selecting a month can expand to display high-level queries: the method comprises the following steps of (1) inquiring (inquiring conditions are listed according to list field display), adding settlement classification on a corresponding actual case page, and displaying all the items by default, namely, the admission number, the group name, the balance, the average profit and loss of each case, the drug ratio, the drug index and the consumption ratio interval (inquiring conditions are listed), wherein the settlement classification is divided into all the items, namely, normal group payment, ultrahigh limit payment (normal group), ultralow limit payment (normal group), basic group payment, ultrahigh limit payment (basic) and ultralow limit payment (basic); the hard case part of the diagnostic book increases the time and cost expenditure index. And (3) outputting: 1. displaying the whole situation of the whole hospital; 2. displaying the overall situation of the department; 3. setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; add or hide data: group name, case number, group type, attending physician, date of discharge, month of settlement, category of settlement, weight, total medical cost (element), uploaded fund total (element), settlement rate, balance; 4. and (4) operating and checking medical records: the case can be printed; clicking a cancel button or closing a medical record popup window by 'x', and checking a settlement list: click "x" to close the settlement list popup.

The four disease seed libraries are used for checking information of the four disease seed libraries (strong disease seeds, dominant disease seeds, key disease seeds and high-difficulty disease seeds); the presentation was consistent with the details of the DRGs disease group, except for the data. The strong disease species, functional point description: the courtyard checks the situation of the intensive disease of the whole courtyard and the chief deputy checks the situation of the intensive disease of the department; using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: RW, disease type property, disease group attribute, settlement number, balance, case average balance and balance rate interval query; department selects a single department, and selecting a month can expand to display high-level queries: RW, disease category property, disease group attribute, number of settlers, balance, average balance, balance rate interval query (query conditions are listed according to list field display); and (3) outputting: 1. displaying the overall intensive disease situation (one hundred people before the hospital is discharged); 2. displaying the overall situation of the intensive disease of the department (the number of people discharged is three before the department); 3. and setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display. Add or hide data: medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases, balance rate, ultrahigh case number and ultralow case number; 4. the data in the click list may show a histogram of the data and a cumulative trend graph. The method comprises the following steps of (settlement of people, medicine ratio, medicine per index, consumption ratio, consumable material per index, medical service charge ratio, four-level operation ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases and balance rate), wherein the data source is data inquired in the details of a DRGs disease group, and the page for clicking bread crumb dominant disease seeds to return to the dominant disease seeds; 5. the information of the super-high number of instances is clicked to enter the information of the super-high number of instances; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; hospitalization number query, export list data, review patient cases. The data source is data inquired in the DRGs disease group details, and the bread crumb dominant disease seeds are clicked to return to a dominant disease seed page; 6. clicking the ultra-low case number to enter the ultra-low case number information; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; inquiring the hospitalization number, exporting list data and checking patient cases, wherein the data source is data inquired in DRGs disease group details, and the bread crumb dominant disease seeds are clicked to return to a dominant disease seed page; 7. and (4) clicking the name of the disease group to enter the distribution of the disease region, and displaying the condition of the department where the disease group is located. The data source is data inquired in the DRGs disease group details, and the bread crumb dominant disease seeds are clicked to return to a dominant disease seed page; clicking the department to jump to the comparison graph page of each department to display the comparison graph of each department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the bread crumb dominant disease seeds are clicked to return to the dominant disease seed page; clicking the distribution condition of the crumb ward to return to a ward distribution condition page; clicking the department to display the cumulative trend chart of the department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the popup window is closed by clicking 'x'; and clicking the data in the list to display a histogram and an accumulated trend curve chart of the data. Including (settlement times, balance, average profit and loss per case, drug ratio, drug per index, consumption ratio, consumable per index, medical service charge ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance rate); the data source is the data of the previous page query.

Dominant disease species, functional point description: the hospital chief can check the condition of the dominant disease species of the whole hospital, and the chief can check the condition of the dominant disease species of the department (the function is like the dominant disease species); using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: RW, disease type property, disease group attribute, settlement number, balance, case average balance and balance rate interval query; department selects a single department, and selecting a month can expand to display high-level queries: RW, disease category property, disease group attribute, number of settlers, balance, average balance, balance rate interval query (query conditions are listed according to list field display); and (3) outputting: 1. displaying the overall dominant disease condition of the whole hospital (the settlement rate is more than 100%); 2. displaying the overall dominant disease condition of the department (the settlement rate is more than 100%); 3. setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; add or hide data: medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases, balance rate, ultrahigh case number and ultralow case number; 4. the data in the click list may show a histogram of the data and a cumulative trend graph. The method comprises the steps of (settlement of people number, medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases and balance rate), wherein the data source is data inquired in the DRGs disease group details, and the bread crumb dominant disease species is clicked to return to a dominant disease species page; 5. the information of the super-high number of instances is clicked to enter the information of the super-high number of instances; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; hospitalization number query, export list data, review patient cases. The data source is data inquired in DRGs disease group details, and the dominant breadcrumb disease species is clicked to return to a dominant disease species page; 6. clicking the ultra-low case number to enter the ultra-low case number information; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; the patient can be inquired about the hospital number, the list data is exported, the patient case is checked, the data source is the data inquired about in the DRGs disease group details, and the bread crumb dominant disease species is clicked to return to the dominant disease species page; 7. and (4) clicking the name of the disease group to enter the distribution of the disease region, and displaying the condition of the department where the disease group is located. The data source is data inquired in DRGs disease group details, and the dominant breadcrumb disease species is clicked to return to a dominant disease species page; clicking the department to jump to the comparison graph page of each department to display the comparison graph of each department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the dominant disease page of the bread crumbs is clicked and returned; clicking the distribution condition of the crumb ward to return to a ward distribution condition page; clicking the department to display the cumulative trend chart of the department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the popup window is closed by clicking 'x'; and clicking the data in the list to display a histogram and an accumulated trend curve chart of the data. Including (settlement times, balance, average profit and loss per case, drug ratio, drug per index, consumption ratio, consumable per index, medical service charge ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance rate); the data source is the data of the previous page query.

The key disease species, functional point description: the chief can check the condition of the focus disease of the whole hospital and the chief can check the condition of the focus disease of the department (the function is like the strong disease); using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: RW, disease type property, disease group attribute, settlement number, balance, case average balance and balance rate interval query; department selects a single department, and selecting a month can expand to display high-level queries: RW, disease category property, disease group attribute, number of settlers, balance, average balance, balance rate interval query (query conditions are listed according to list field display); and (3) outputting: 1. displaying the overall key disease conditions of the whole hospital (data are exported from the medical technology list, and diagnosis operations of patients are needed to be compared one by one); 2. displaying the overall key disease types of departments (data are exported from a medical technology list, and diagnosis operations of patients need to be compared one by one); 3. setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; add or hide data: medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases, balance rate, ultrahigh case number and ultralow case number; 4. the data in the click list may show a histogram of the data and a cumulative trend graph. The method comprises the steps of (settlement of people times, medicine proportion, medicines per index, consumption proportion, consumable materials per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases and balance rate), wherein the data source is data inquired in the DRGs disease group details, and the bread crumb important disease species is clicked to return to an important disease species page; 5. the information of the super-high number of instances is clicked to enter the information of the super-high number of instances; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; hospitalization number query, export list data, review patient cases. The data source is data inquired in DRGs disease group details, and a bread crumb important disease species is clicked to return an important disease species page; 6. clicking the ultra-low case number to enter the ultra-low case number information; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; inquiring the hospitalization number, exporting list data and checking patient cases, wherein the data source is data inquired in DRGs disease group details, and the bread crumb repeat disease is clicked to return a repeat disease page; 7. clicking the name of a disease group to enter the distribution of the disease region, displaying the condition of a department where the disease group is located, wherein the data source is data inquired in DRGs disease group details, and clicking a crumb-based important disease seed to return to an important disease seed page; clicking the department to jump to the comparison graph page of each department to display the comparison graph of each department; data switchable display (how many field contrasts to display as the header changes); the data source is data inquired in the previous page, and the bread crumb important disease species is clicked to return an important disease species page; clicking the distribution condition of the crumb ward to return to a ward distribution condition page; clicking the department to display the cumulative trend chart of the department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the popup window is closed by clicking 'x'; and clicking the data in the list to display a histogram and an accumulated trend curve chart of the data. Including (settlement times, balance, average profit and loss per case, drug ratio, drug per index, consumption ratio, consumable per index, medical service charge ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance rate); the data source is the data of the previous page query.

High difficulty disease species, functional point description: the hospital chief can check the high-difficulty disease conditions of the whole hospital, and the conductor can check the high-difficulty disease conditions (functions such as strong disease) of the department; using roles: courtyard and department; inputting: department selects all, month selection can expand to display advanced queries: RW, disease type property, disease group attribute, settlement number, balance, case average balance and balance rate interval query; department selects a single department, and selecting a month can expand to display high-level queries: RW, disease category property, disease group attribute, number of settlers, balance, average balance, balance rate interval query (query conditions are listed according to list field display); and (3) outputting: 1. displaying the overall high-difficulty disease category condition of the whole hospital (RW is more than 2.0); 2. displaying the overall high-difficulty disease type condition of a department (RW is more than 2.0); 3. setting, editing the header by the courtyard leader and the manager, and increasing display or hiding display; adding hidden data: medicine proportion, medicine per index, consumption proportion, consumable material per index, medical service charge proportion, four-level operation proportion, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of cases, balance rate, ultrahigh case number and ultralow case number; 4. the data in the click list may show a histogram of the data and a cumulative trend graph. Comprises (settlement times, medicine ratio, medicine per index, consumption ratio, consumable material per index, medical service charge ratio, four-grade operation ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance, average balance of examples and settlement rate). The data source is data inquired in DRGs disease group details, and a high-difficulty disease page is returned by clicking breadcrumb high-difficulty disease; 5. the information of the super-high number of instances is clicked to enter the information of the super-high number of instances; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; the method can be used for inquiring the hospitalization number, exporting the list data and checking the patient case, wherein the data source is the data inquired in the DRGs disease group details, and the high-difficulty breadcrumb disease is clicked to return a high-difficulty disease page; 6. clicking the ultra-low case number to enter the ultra-low case number information; displaying information of a ward, a bed doctor, a hospital number, the number of hospitalization days, balance, a payment rate, a medicine ratio and a consumable ratio; hospitalization number query, export list data, review patient cases. The data source is data inquired in DRGs disease group details, and a high-difficulty disease page is returned by clicking breadcrumb high-difficulty disease; 7. and (4) clicking the name of the disease group to enter the distribution of the disease region, and displaying the condition of the department where the disease group is located. The data source is data inquired in DRGs disease group details, and a high-difficulty disease page is returned by clicking breadcrumb high-difficulty disease; clicking the department to jump to the comparison graph page of each department to display the comparison graph of each department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the high-difficulty disease seeds of the bread crumbs are clicked to return to the high-difficulty disease seed pages; clicking the distribution condition of the crumb ward to return to a ward distribution condition page; clicking the department to display the cumulative trend chart of the department; data switchable display (how many field contrasts to display as the header changes); the data source is the data inquired in the previous page, and the popup window is closed by clicking 'x'; and clicking the data in the list to display a histogram and an accumulated trend curve chart of the data. Including (settlement times, balance, average profit and loss per case, drug ratio, drug per index, consumption ratio, consumable per index, medical service charge ratio, total medical cost, average hospitalization day of the whole market, average hospitalization cost of the whole market, balance rate); the data source is the data of the previous page query. Analyzing a radar map under the expense condition, adding a 'radar map under the expense condition' analysis module in a diagnostic book, displaying radar map analysis of a whole hospital in a month at the initial page, supporting screening according to four conditions of time, department, DRG disease group and hospital number, displaying a radar map generated by data meeting the conditions after clicking a 'query' button, and supporting the viewing of accumulated radar map data and radar map data of a single month; 1. the accumulated radar map, the data category is consistent with the finance department (ask Sun worker), and the data of the total cost is increased; 2. the radar map of the single month is compared according to the month, when the time period is not selected, the month of the last year is provided for selection, the data of the current month and the previous month are selected by default, after the time period is selected, the month in the selected time period is provided for selection, and the total cost of the selected month is displayed above the radar map.

And a DRGs knowledge base for editing the reference of the primary DRGs knowledge base of the hospital and a medical technology list. Description of the function: editing the primary DRGs knowledge base reference and medical technology list of the second people's Hospital in Changzhou; using roles: an administrator; inputting: editing by an administrator; description of the drawings: (1) and displaying the content of the primary knowledge base, and displaying fields: department name, related DRG group code, RW node, DRG group property, disease type property, gender, age, days of hospitalization, manner of discharge, referral, first diagnosis, first operation or procedure, severe complication, other operation or procedure, procedure; (2) the operation supports editing and deleting; (3) supporting fuzzy query of diagnosis names, departments, DRG names, DRG codes and operation names; (4) support for new growth; description of the interface: 1. DRGs knowledge base, operation buttons: inquiring, inquiring key words, and displaying inquired data in a form; newly adding: newly adding content; editing: editing corresponding content; 2. newly adding content, clicking a newly added button to pop up, wherein the information required to be filled comprises: department name, related DRG group code, RW node, DRG group property, disease type property, gender, age, days of hospitalization, manner of discharge, referral, first diagnosis, first operation or procedure, severe complication, other operation or procedure; related DRG group code, related DRG group name, DRG group attributes, RW node data bundle, selection code or name automatically brings up other data, where department name, related DRG group name, nature of disease, first diagnosis, first surgery or operation is a mandatory item. Prompting to fill in the related name when not filled in; 3. editing content, clicking an editing button to pop up, wherein default data is data in a corresponding form and can be edited by a user; 4. rewriting data, and comparing and perfecting blank data by capturing data in later-stage daily self-defining feedback; and newly added attributes (sex, age, number of hospitalization days, discharge pattern, outcome, complications, severe complications, other operations); existing modification original data set, nonexistent new data set, mechanism of getting data: if the reasons and the contents are consistent, the doctor fills in the reasons and the contents, and does not extract the contents if the reasons and the contents are not filled in; inconsistency: and finally feeding back the data in the file.

The functional requirements, the interface operation requirements and the overall style are kept consistent, the function operation is completed on the same interface by using the buttons, the operation of the interface can be maximized, the dragging and the size change can be minimized, and the method is compatible with the resolution of 800X600 and above. The performance requirements are shown in the following table:

table 1: performance requirement table

The safety requirement is that a high-level administrator and ordinary personnel divide different operation menus with the authority.

Dimension other mechanisms and rules: 1. the search mechanism comprises the following steps: fuzzy word search, case and abbreviation search and pinyin mnemonic character search are supported. The search logic is click search, and after the key words are input, the 'confirm'/'enter key' on the keyboard is clicked to search. The recommendation query is carried out through association rules, the association rules reflect the interdependency and the association between one thing and other things, and if a certain association relationship exists between two or more things, one thing can be predicted through other things. For example, if a doctor writes a cold diagnosis for a patient, a cold medicine is generally prescribed, or a patient with fever is generally prescribed a blood routine test. Therefore, the combined data of the DRGs medical data of each patient can be inquired through background data to calculate the most suitable content.

Specific analysis results are as follows: first scanning: counting each candidate combination to obtain C1, deleting { D } to obtain a frequent 1-item set L1 because the support count of the candidate { D } is 1< the minimum support count of 2; and (3) second scanning: generating candidate C2 from L1 and counting the candidates C2, comparing the candidate support count with the minimum support count of 2-term frequent sets L2; and (3) third scanning: the process of concatenating and pruning L2 to produce the candidate 3 item set C3 is as follows:

1.: c3 (connection) L2L 2 { { a, C }, { B, E }, { C, E } } { { a, C }, { B, E }, { C, E } } { { a, B, C }, { a, C, E }, { B, C, E } };

2.: the 2 item subsets of { A, B, C } { A, B }, { A, C } and { B, C }, where { A, B } is not the 2 item subset L2 and is therefore not frequent, are deleted from C3; the 2 item subsets of { A, C, E } { A, C }, { A, E } and { C, E }, where { A, E } is not the 2 item subset L2 and is therefore not frequent, are deleted from C3; the 2 item subsets of { B, C, E } { B, C }, { B, E } and { C, E }, all of its 2 item subsets being elements of L2, are reserved in C3. After the L2 is connected and pruned, the set of candidate 3 item sets is generated as C3 ═ { B, C, E }. when the candidate set is counted, the 3-item set L3 is frequent since it is equal to the minimum support metric number of 2, and at the same time, C4 is an empty set since there are only 1 of 4-item sets, the algorithm terminates.

The frequent item set is divided into two subsets which are respectively used as a front piece and a back piece, and the frequent item set is converted into a rule with enough confidence; if the rule R: x ═ Y > Y satisfies support (X ═ Y) > ═ supmin (minimum support, which is used to measure the lowest importance that a rule needs to satisfy) and

the association rule X ═ Y is called a strong association rule, and otherwise, the association rule X ═ Y is called a weak association rule. The existing A, B, C, D, E patient record tables of five orders are used to find out all frequent item sets, assuming that the minimum support > is 50% and the minimum confidence > is 50%. For the association rule R: a > B, then: support (support): is the ratio of the number of combinations in the set that contain both A and B to all combinations.

Support (a ═ B) ═ P (atou B) ═ count (atou B)/| D | confidence (confidence): is the ratio of the number of combinations comprising A and B to the number of combinations comprising A. Configence (a ═ B) ═ P (B | a) ═ support (a ═ u B)/support (a).

The calculation procedure is as follows, and when K is 1, the term set { a } appears 2 times in T1 and T3 for 4 combinations, so that the support degree is 2/4 is 50%, which are calculated in sequence. Where the set of terms { D } occurs at T1 with a support of 1/4-25% and less than 50% of the minimum support, so removed to yield L1. And combining the item sets L1 pairwise, and calculating the support degrees respectively, wherein the item sets { A, B } appear 1 time in T3, the support degree is 1/4-25%, and is less than the minimum support degree by 50%, so that the L2 item sets are obtained by the same method. The set of items in L2 is combined, with more than three items being filtered, and the final calculation yields the L3 set of items { B, C, E }.

2. The sequencing mechanism is as follows: ordering according to the positive sequence of the phonetic letters by default; 3. a loading mechanism: when the system is opened, the system automatically loads the project content, and when the user slides to the bottom of the content, the loading mechanism is manually triggered. The system automatically loads or the user manually loads the prompt of 'acquiring data', and the content bottom prompts: in loading, after all the contents are loaded, prompting at the bottom of the contents: "not more". Multiple browsers may not be opened to operate the same page at the same time. 4. The export mechanism is as follows: what field is displayed on the header, and data below the field is derived; not all need be derived. 5. Graph display mechanism: and the mouse is placed under a certain module of the line graph, the proportion graph and the bar graph to automatically display the name and the data under the current module. 6. A data skipping mechanism: jumpable data is shown as #58BAB 9; suspension is shown as # e6f2f 2; the design drawing is the standard. 7. And (3) data display: displaying the last month data by default in each report and chart; the data retains the last two decimal places.

In this embodiment, the data required to be called for each function may be stored in the DRGs repository.

In conclusion, the primary knowledge base is constructed according to the whole hospital data; updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base, so that the construction of the knowledge base is realized, the matching recommendation of DRGs combined data is conveniently carried out through the knowledge base subsequently, the derivation of the DRGs combined data is facilitated, and the management and the monitoring of management personnel are facilitated.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (9)

1. A method for constructing a DRGs knowledge base is characterized by comprising the following steps:

constructing a primary knowledge base according to the whole hospital data;

updating the primary knowledge base according to DRGs combined data fed back by the DRGs to construct a secondary knowledge base; and

and constructing a third-level knowledge base according to the accumulated and corrected second-level knowledge base.

2. The method for constructing a knowledge base of DRGs as claimed in claim 1, wherein,

the method for constructing the primary knowledge base according to the hospital-wide data comprises the following steps:

counting basic data in all departments of the whole hospital, wherein a document of each basic data is shared by a plurality of departments;

the document comprises a plurality of first diagnoses, a plurality of first operations and a plurality of DRG names;

a plurality of departments share a plurality of documents;

matching all documents with department, diagnosis, operation and DRG group names to generate DRGs data combination, and storing the DRGs data combination in a database to form a primary knowledge base.

3. The DRGs knowledge base construction method of claim 2, wherein,

the method for updating the primary knowledge base according to the DRGs combined data fed back by the DRGs to construct the secondary knowledge base comprises the following steps:

taking the attribute, other diagnosis, other operations or operations, sex, age, number of hospitalization days, discharge mode and transfer condition in DRGs combined data fed back by the DRGs as important fields;

combining the fed back DRGs combined data with the primary knowledge base to generate new data, and storing the new data in the database as the basic data of the secondary knowledge base.

4. The DRGs knowledge base construction method of claim 3, wherein,

the method for generating new data comprises the following steps:

matching corresponding DRGs combined data in a primary knowledge base through diagnosis, operation and DRG group names of the fed back DRGs combined data;

if the matching is successful, the matched combined data is taken out, new important fields are added in the matched data, and the matched data is stored in the primary knowledge base again;

and if the matching fails, storing the fed back DRGs combined data in a primary knowledge base.

5. The DRGs knowledge base construction method of claim 4, wherein,

the method for constructing the tertiary knowledge base according to the accumulated and corrected secondary knowledge base comprises the following steps:

and accumulating and correcting the combined data in the secondary knowledge base within a preset time to form a tertiary knowledge base.

6. A method of using DRGs knowledge base using the DRGs knowledge base construction method according to any of claims 1-5, comprising:

recommending by a knowledge base when the DRG is customized; and

and exporting data in the DRGs knowledge base.

7. The method of using a DRGs knowledge base according to claim 6,

the method for recommending the knowledge base in the self-defining of the DRG comprises the following steps:

and matching corresponding DRG combined data from the third-level knowledge base according to the operation and diagnosis when the DRG combined data is customized.

8. The method of using a DRGs knowledge base according to claim 7,

the method for matching corresponding DRG combined data comprises the following steps:

according to diagnosis, severe complications or complications are distinguished, then matching is carried out according to operation or operation, and more particularly, hospitalization days, sex and age intervals are divided so as to match corresponding DRG combination data.

9. The method of using a DRGs knowledge base according to claim 6,

the method for exporting the data in the DRGs knowledge base comprises the following steps:

and exporting the data in the derived DRGs knowledge base stored in the tertiary knowledge base according to the requirement.

Images