CN116627988A - Patient main index system based on rule configuration - Google Patents
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The invention relates to the technical field of a patient main index calculation method based on a custom model, in particular to a patient main index system based on rule configuration, which comprises the following modules: the system comprises a list module, a query module, a cross index viewing module, a cross index unifying module, a main index splitting module, a color distinguishing module, a project management module, a weight setting module, a matching mode selecting module, a threshold setting module, a rule management module, a similar patient processing module, a similar record group query module, a sorting module, a similar patient relieving module, a data quality control module, a combined query module, an irregular field recording module, a test cleaning module, a formal rule verification module, a foreground operation module and a cleaning report generating module. The invention adopts a self-defined calculation model mode, solves the problem that the actual medical service is complex and changeable, adopts different calculation rules aiming at different identity recognition attributes, and solves the problem of automatic correction of partial misoperation data.
Description
Technical Field
The invention relates to the technical field of a patient main index calculation method based on a custom model, in particular to a patient main index system based on rule configuration.
Background
The system comprises a hospital information system (his), an electronic medical record (emr), a checking management system (lis), an inspection management system (pacs), a physical examination system, a hand and hemp system, a performance management system, a material system, a mobile medical care system and other three-party systems, wherein a unified patient main index is not established, and unique patient identification ids exist in each system, so that the same patient is scattered in different clinical systems. In addition, in the use process of the same system, multiple repeated records of the same patient can appear due to various factors (repeated gear establishment, non-acquisition of the identity card, change of the identity card number and the like), so that the information of the patient is more dispersed.
Most of the existing main index systems of patients in the market are judged through fixed identity identification information, the calculation method is single, complex service scenes cannot be met, and the defects of data redundancy and low calculation efficiency in big data scenes exist.
The main index calculation method based on the self-defined model supports the functions of configuring weights through a visual interface, precisely matching, self-defining calculation rules aiming at different identity identification attributes, carrying out identity identification on basic information of a patient through a more intelligent algorithm, dynamically generating a complete set of standardized algorithm and data matching algorithm by combining with a probability statistics theory, carrying out main index merging, splitting, auxiliary manual processing and the like on main index information. And simultaneously, the HL7 data standard and IHE integration specification are also supported to be interconnected and communicated with other application systems.
The prior art has the following disadvantages:
1. the system has a single computing mode, and the computing model cannot be customized according to complex business scenes mainly through names and identity cards
2. The calculation accuracy of the system is not high, and if part of patient information is input incorrectly, the system cannot automatically identify
3. When the system faces massive archival data, the computing performance is reduced
4. Lack of national standard, lack of standard value domain information issued by Ministry of health, and incapability of automatically converting information of value domain type in files into standard value domain information
5. Docking with a platform or with an integrated platform is not supported.
Disclosure of Invention
In view of the above, the present invention is directed to a patient main indexing system based on a rule configuration.
Based on the above objects, the present invention provides a patient master indexing system based on a rule configuration.
A patient primary indexing system based on a rule configuration, comprising the following modules:
(1) The list module is used for intensively storing and managing resident registration data, including statistical information such as headcount, similarity ratio, combined population ratio and the like;
(2) The inquiry module supports acquisition of resident index information through basic inquiry conditions, wherein the resident index information comprises a master index number, a patient name, a certificate number, a telephone number, a birth date, gender and marital status;
(3) The cross index checking module is used for checking the main index details and the cross index information of the source of the main index details;
(4) The cross index unification module unifies patient identifications from a plurality of systems into one, so that each patient is ensured to have only one main index number;
(5) The main index splitting module supports the split of the main index introduction line, generates a new main index, splits the main index line by line and combines the split;
(6) The color distinguishing module is used for distinguishing main indexes with a plurality of pieces of source information through colors;
(7) The project management module supports newly adding and editing projects to be matched;
(8) The weight setting module is used for supporting setting of the weight of the matching item and taking the weight as the basis of matching score calculation;
(9) The matching mode selection module supports two modes of complete matching and fuzzy matching, selects a corresponding algorithm according to the field types to calculate similarity, and combines weights to obtain matching scores;
(10) The threshold setting module is used for supporting the setting of a similar threshold and the same threshold, and judging a matching result according to the interval of the matching score;
(11) The rule management module supports setting a plurality of rules and adjusts the priority of the rules through upward movement and downward movement;
(12) The similar patient processing module is used for independently storing the patient information with the score in the similar interval, so that the user can conveniently combine or release the similar operation;
(13) The similar record group inquiry module supports inquiring information of the similar record group through basic inquiry conditions such as resident names, certificate numbers and the like;
(14) The ordering module supports ordering according to the similarity total score, resident name score, certificate number score, birth date score and mobile phone number score;
(15) The similar patient removing module is used for supporting similar patient removing, updating data and maintaining the accuracy of the data;
(16) The data quality control module is used for independently storing records which do not accord with the standard in the original data, so that the user can check and correct the records conveniently;
(17) The combined inquiry module supports combined inquiry according to names, certificate numbers, birth dates, sexes, mobile phone numbers and the like;
(18) The nonstandard field recording module records nonstandard field information, including a certificate number, a birth date and gender errors;
(19) The test cleaning module is used for measuring and calculating the matching degree of the matching rule and the quality of hospital data and adjusting the matching rule according to the result;
(20) The formal rule verification module supports the introduction of formal rules to carry out test cleaning rule verification;
(21) The foreground operation module provides a foreground page to perform test cleaning operation, including random test cleaning in a random time period;
(22) The cleaning report generation module generates a cleaning report including a sample size, a newly added main index number, a similar resident number and an irregular record number.
Further, the query module supports accurate query according to patient name and certificate number.
Furthermore, the matching mode selection module supports the selection of different algorithms, such as cosine similarity algorithm, minimum editing distance algorithm, literal similarity algorithm and pinyin similarity algorithm.
Further, the ranking module supports ascending and descending ranking.
Further, the data quality control module supports the problems of nonstandard detection of the length of the identification card number and inconsistent of the identification card number and the birth date.
Further, the non-standard field recording module supports recording other field information which does not meet the standard, including other field information related to resident registration data which does not meet the standard, contains illegal characters or is incorrect in format.
Further, the test cleaning module supports random test cleaning in a time period and displays the completion progress condition of the test cleaning.
Further, the cleaning report generating module may include statistical information of cleaning results, such as a sample size, a number of newly added main indexes, a number of similar residents, and a number of irregular records.
The invention has the beneficial effects that:
1. the invention adopts a self-defined calculation model mode, and solves the problem that the actual medical service is complex and changeable.
2. Different calculation rules are adopted for different identity recognition attributes, and the problem of automatic correction of partial misoperation data is solved.
3. A self-cleaning function is provided to help system implementation personnel randomly extract archival data to verify the rationality of the computing model.
4. The method has the advantages that the interval threshold value is set based on the model scores, the file data with different scores are automatically subjected to operations such as file adding and combining, the workload of medical staff is greatly reduced, and the working efficiency is improved.
5. The method and the system provide logs generated by automatic and manual operation, support the operation process of tracing through the logs and support the recovery of data through the logs.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
A patient primary indexing system based on a rule configuration, comprising the following modules:
(1) The list module is used for intensively storing and managing resident registration data, including statistical information such as headcount, similarity ratio, combined population ratio and the like;
(2) The inquiry module supports acquisition of resident index information through basic inquiry conditions, wherein the resident index information comprises a master index number, a patient name, a certificate number, a telephone number, a birth date, gender and marital status;
(3) The cross index checking module is used for checking the main index details and the cross index information of the source of the main index details;
(4) The cross index unification module unifies patient identifications from a plurality of systems into one, so that each patient is ensured to have only one main index number;
(5) The main index splitting module supports the split of the main index introduction line, generates a new main index, splits the main index line by line and combines the split;
(6) The color distinguishing module is used for distinguishing main indexes with a plurality of pieces of source information through colors;
(7) The project management module supports newly adding and editing projects to be matched;
(8) The weight setting module is used for supporting setting of the weight of the matching item and taking the weight as the basis of matching score calculation;
(9) The matching mode selection module supports two modes of complete matching and fuzzy matching, selects a corresponding algorithm according to the field types to calculate similarity, and combines weights to obtain matching scores;
(10) The threshold setting module is used for supporting the setting of a similar threshold and the same threshold, and judging a matching result according to the interval of the matching score;
(11) The rule management module supports setting a plurality of rules and adjusts the priority of the rules through upward movement and downward movement;
(12) The similar patient processing module is used for independently storing the patient information with the score in the similar interval, so that the user can conveniently combine or release the similar operation;
(13) The similar record group inquiry module supports inquiring information of the similar record group through basic inquiry conditions such as resident names, certificate numbers and the like;
(14) The ordering module supports ordering according to the similarity total score, resident name score, certificate number score, birth date score and mobile phone number score;
(15) The similar patient removing module is used for supporting similar patient removing, updating data and maintaining the accuracy of the data;
(16) The data quality control module is used for independently storing records which do not accord with the standard in the original data, so that the user can check and correct the records conveniently;
(17) The combined inquiry module supports combined inquiry according to names, certificate numbers, birth dates, sexes, mobile phone numbers and the like;
(18) The nonstandard field recording module records nonstandard field information, including a certificate number, a birth date and gender errors;
(19) The test cleaning module is used for measuring and calculating the matching degree of the matching rule and the quality of hospital data and adjusting the matching rule according to the result;
(20) The formal rule verification module supports the introduction of formal rules to carry out test cleaning rule verification;
(21) The foreground operation module provides a foreground page to perform test cleaning operation, including random test cleaning in a random time period;
(22) The cleaning report generation module generates a cleaning report including a sample size, a newly added main index number, a similar resident number and an irregular record number.
In a specific embodiment, the query module supports accurate queries according to patient name, certificate number.
Specifically, the matching mode selection module supports the selection of different algorithms, such as cosine similarity algorithm, minimum editing distance algorithm, literal similarity algorithm and pinyin similarity algorithm.
Specifically, the ranking module supports ascending and descending ranks.
Specifically, the data quality control module supports the problems of nonstandard detection of the length of the identification card number and inconsistent of the identification card number and the birth date.
Specifically, the non-standard field recording module supports recording other field information which does not meet the standard, including other field information related to resident registration data which does not meet the standard, contains illegal characters or has incorrect format.
Specifically, the test cleaning module supports random test cleaning in a time period and displays the completion progress of the test cleaning.
Specifically, the cleaning report generating module may include statistical information of cleaning results, such as a sample size, a number of newly added main indexes, a number of similar residents, and a number of irregular records.
Working principle: the DataStudio function provides a development environment in which a user can configure the rules of the patient's primary indexing system.
The functions of automatic prompt and completion, grammar highlighting, sentence beautifying, grammar checking and the like improve the development efficiency and the code quality of a user.
The user can verify the correctness of rule configuration through the function of debugging and previewing the result, and the accuracy and reliability of the system are ensured.
The definition and use of global variables facilitates parameter delivery and sharing.
MetaStore is used to store and manage metadata information of a system, including data sources, data types, etc., and provides description and management functions of data.
The field-level blood-margin analysis function tracks and analyzes the relationship and dependence among the fields, and helps the user to know the flow direction and the association relationship of the data.
The metadata query function allows a user to query and retrieve metadata information in the system, providing a convenient metadata management function.
The FlinkSQL generating function automatically generates corresponding FlinkSQL sentences according to rule configuration, and the establishment and operation processes of the system are simplified.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (8)
1. A patient primary indexing system based on a regular arrangement, characterized by being composed of the following modules:
(1) The list module is used for intensively storing and managing resident registration data, including statistical information such as headcount, similarity ratio, combined population ratio and the like;
(2) The inquiry module supports acquisition of resident index information through basic inquiry conditions, wherein the resident index information comprises a master index number, a patient name, a certificate number, a telephone number, a birth date, gender and marital status;
(3) The cross index checking module is used for checking the main index details and the cross index information of the source of the main index details;
(4) The cross index unification module unifies patient identifications from a plurality of systems into one, so that each patient is ensured to have only one main index number;
(5) The main index splitting module supports the split of the main index introduction line, generates a new main index, splits the main index line by line and combines the split;
(6) The color distinguishing module is used for distinguishing main indexes with a plurality of pieces of source information through colors;
(7) The project management module supports newly adding and editing projects to be matched;
(8) The weight setting module is used for supporting setting of the weight of the matching item and taking the weight as the basis of matching score calculation;
(9) The matching mode selection module supports two modes of complete matching and fuzzy matching, selects a corresponding algorithm according to the field types to calculate similarity, and combines weights to obtain matching scores;
(10) The threshold setting module is used for supporting the setting of a similar threshold and the same threshold, and judging a matching result according to the interval of the matching score;
(11) The rule management module supports setting a plurality of rules and adjusts the priority of the rules through upward movement and downward movement;
(12) The similar patient processing module is used for independently storing the patient information with the score in the similar interval, so that the user can conveniently combine or release the similar operation;
(13) The similar record group inquiry module supports inquiring information of the similar record group through basic inquiry conditions such as resident names, certificate numbers and the like;
(14) The ordering module supports ordering according to the similarity total score, resident name score, certificate number score, birth date score and mobile phone number score;
(15) The similar patient removing module is used for supporting similar patient removing, updating data and maintaining the accuracy of the data;
(16) The data quality control module is used for independently storing records which do not accord with the standard in the original data, so that the user can check and correct the records conveniently;
(17) The combined inquiry module supports combined inquiry according to names, certificate numbers, birth dates, sexes, mobile phone numbers and the like;
(18) The nonstandard field recording module records nonstandard field information, including a certificate number, a birth date and gender errors;
(19) The test cleaning module is used for measuring and calculating the matching degree of the matching rule and the quality of hospital data and adjusting the matching rule according to the result;
(20) The formal rule verification module supports the introduction of formal rules to carry out test cleaning rule verification;
(21) The foreground operation module provides a foreground page to perform test cleaning operation, including random test cleaning in a random time period;
(22) The cleaning report generation module generates a cleaning report including a sample size, a newly added main index number, a similar resident number and an irregular record number.
2. The rule-based configured patient master indexing system of claim 1, wherein the query module supports accurate queries by patient name, certificate number.
3. The rule-based configuration patient master indexing system of claim 2, wherein the matching pattern selection module supports selection of different ones of cosine similarity algorithm, minimum edit distance algorithm, literal similarity algorithm, pinyin similarity algorithm.
4. A regularly configured patient primary indexing system in accordance with claim 3, wherein the ordering module supports ascending and descending order.
5. The rule-based configured patient primary indexing system of claim 4, wherein the data quality control module supports detection of problems of identity card number length non-standard, identity card number non-uniformity with birth date.
6. The rule-based configured patient master indexing system of claim 5, wherein the non-canonical field recording module supports recording other non-canonical field information, including other non-canonical field information related to resident registration data, including illegal characters, or incorrectly formatted.
7. The rule-based configured patient primary indexing system of claim 6, wherein the trial cleaning module supports random trial cleaning over a time period and presents a trial cleaning completion schedule.
8. The rule-based configured patient primary index system of claim 7, wherein the cleansing report generating module includes statistics of cleansing results such as sample size, number of new primary indices, number of similar residents, number of irregular records.
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| CN118248297B (en) * | 2024-05-28 | 2024-08-20 | 山东佰泰丰信息科技有限公司 | Diversified information management system |
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