CN114913950A - Full DIP group standardization medical information analysis method and system - Google Patents
Full DIP group standardization medical information analysis method and system Download PDFInfo
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Abstract
A full DIP group standardization medical information analysis method and a system thereof are provided, wherein a patient basic information base table is constructed according to first patient information and second patient information, and comprises an operation code and a diagnosis code; acquiring a disease category score table of a specified region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information; generating a unique key for each record in a disease score table, and generating a score matching table by taking the unique key as a DIP score group identification field; putting the value of the DIP score group identification field in the score matching table into a set of Redis, and traversing a patient basic information base table; splicing character string information hash codes of the diagnosis codes and the operation codes of the patient basic information base table; whether the member element is a member of the set is judged through a Redis Simemeber command, and if so, a DIP group table record of the patient is generated. The invention realizes the analysis and processing of the standardized critical weight rate of the full DIP group and is beneficial to the implementation of branch payment standardization work.
Description
Technical Field
The invention belongs to the technical field of medical information processing, and particularly relates to a full DIP group-standardized medical information analysis method and system.
Background
According to a complete management system established by utilizing the advantages of big data, the general characteristics of 'disease Diagnosis + treatment modes' are explored to objectively classify medical record data according to the disease category score pay (DIP), and the standardized location of each disease and treatment mode combination is formed in the whole sample medical record data in a certain area range, so that the disease severity, the treatment complexity, the resource consumption level and the clinical behavior standard are objectively reflected.
The DIP score (RW) is a Weight given according to the resource consumption degree of each disease combination, and reflects the severity of the disease and the complexity and difficulty of the treatment method. The DIP directory library is a basic application system which determines stable grouping and brings the stable grouping into unified directory management and supports grouping application normalization on the basis of combined exhaustion and clustering of disease diagnosis and treatment modes.
In the prior art, the statistical analysis of the critical rate of the DIP value groups is lacked, the reasonable, effective and clear cognition of the critical rate of each DIP combination cannot be realized, and the quality management of critical medical records in the process of landing the DIP value payment by a hospital is not facilitated.
Disclosure of Invention
Therefore, the invention provides a full DIP group standardized medical information analysis method and system, which solve the problems that the statistical analysis of the critical rate cannot be carried out according to the medical information and the quality management of critical medical records is not facilitated.
In order to achieve the above purpose, the invention provides the following technical scheme: a full DIP group standardized medical information analysis method comprises the following steps:
(1) acquiring a critical document from a hospital critical platform, and extracting first patient information in the critical document; acquiring a first page document from a hospital HIS platform, and extracting second patient information in the first page document; constructing a patient basic information base table according to the first patient information and the second patient information, wherein the patient basic information base table comprises operation codes and diagnosis codes;
(2) acquiring a disease category score table of a specified region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information;
(3) preprocessing the disease category score table, generating a unique key for each record in the disease category score table, and generating a score matching table by taking the unique key as a DIP score group identification field;
(4) putting the value of the DIP score group identification field in the score matching table into a set of Redis, and traversing the patient basic information base table;
(5) splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a Hash code of the character string of the diagnosis code and the operation code;
(6) whether the member element is a member of the set is judged through a Redis Simemeber command, and if so, a DIP group table record of the patient is generated.
As a preferred embodiment of the full DIP group standardized medical information analysis method, in the step (1), the first patient information extracted from the critical document includes a hospital serial number, a patient name and whether or not critical;
in the step (1), the second patient information extracted from the homepage document comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient main diagnosis name, a patient main diagnosis code, a patient main operation name, a patient main operation code and a total payment cost of the patient;
the patient base information library includes a set of the first patient information and the second patient information.
As a preferred embodiment of the full DIP group standardized medical information analysis method, in the step (2), the disease types include core disease types and comprehensive disease types.
As a preferred scheme of the full-DIP group standardized medical information analysis method, in the step (3), the unique key is formed by a hash code of a character string formed by splicing a diagnosis code and a surgery code;
in the step (4), the set of Redis is realized through a hash table, and the complexity of addition, deletion and search is O (1).
As a preferred embodiment of the full DIP group standardized medical information analysis method, in step (6), the patient DIP group entry table includes a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a DIP score group identification field, and a critical or not field;
analyzing a full DIP group risk ratio, a DIP group risk ratio per department, and a DIP group risk ratio per medical group according to the patient DIP cohort chart.
The invention also provides a full DIP group standardized medical information analysis system, which comprises:
the first patient information acquisition module is used for acquiring a critical document from a hospital critical platform and extracting first patient information in the critical document;
the second patient information acquisition module is used for acquiring the homepage document from the HIS platform of the hospital and extracting the second patient information in the homepage document;
the basic information base building module is used for building a basic information base table of the patient according to the first patient information and the second patient information, and the basic information base table of the patient comprises an operation code and a diagnosis code;
the disease score table acquisition module is used for acquiring a disease score table of a specified region, wherein the disease score table comprises operation codes, diagnosis codes, disease types and score field information;
the disease score table preprocessing module is used for preprocessing the disease score table, generating a unique key for each record in the disease score table, and generating a score matching table by taking the unique key as a DIP score group identification field;
the traversal analysis module is used for putting the value of the DIP score group identification field in the score matching table into a set of Redis and traversing the patient basic information base table;
the character splicing processing module is used for splicing the character string information of the diagnosis codes and the operation codes in the patient basic information base table to form hash codes of the character strings of the diagnosis codes and the operation codes;
and the patient DIP grouping table generation module is used for judging whether the member element is a member of the set through a Redis Simmemer command, and if so, generating a patient DIP grouping table record.
As a preferred embodiment of the full DIP group standardized medical information analysis system, in the first patient information acquisition module, the first patient information extracted from the critical document includes a hospital serial number, a patient name, and whether or not it is critical;
in the second patient information acquisition module, the second patient information extracted from the homepage document comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient main diagnosis name, a patient main diagnosis code, a patient main operation name, a patient main operation code and a patient payment total fee;
the basic information base building module is used for building a basic information base of the patient, wherein the basic information base of the patient comprises the first patient information and the second patient information.
As a preferred scheme of the full DIP group standardized medical information analysis system, in the disease category score table acquisition module, the disease category types comprise core disease categories and comprehensive disease categories.
As a preferred scheme of a full-DIP group-standardized medical information analysis system, in the disease score table preprocessing module, a unique key is formed by a hash code of a character string formed by splicing a diagnosis code and a surgery code;
in the traversal analysis module, a set of Redis is realized through a hash table, and the complexity of addition, deletion and search is O (1).
As a preferred scheme of the full DIP group standardized medical information analysis system, the patient DIP group table generation module generates a patient DIP group table, wherein the patient DIP group table comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a DIP score group identification field and a critical or not field;
analyzing a full DIP group risk ratio, a DIP group risk ratio per department, and a DIP group risk ratio per medical group according to the patient DIP cohort chart.
The invention has the following advantages: acquiring a critical document from a hospital critical platform, and extracting first patient information in the critical document; acquiring a first page document from a hospital HIS platform, and extracting second patient information in the first page document; constructing a patient basic information base table according to the first patient information and the second patient information, wherein the patient basic information base table comprises operation codes and diagnosis codes; acquiring a disease category score table of a specified region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information; preprocessing the disease category score table, generating a unique key for each record in the disease category score table, and generating a score matching table by taking the unique key as a DIP score group identification field; putting the value of the DIP score group identification field in the score matching table into a set of Redis, and traversing the patient basic information base table; splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a Hash code of the character string of the diagnosis code and the operation code; whether the member element is a member of the set is judged through a Redis Simemeber command, and if so, a DIP group table record of the patient is generated. The invention realizes the analysis and processing of the standardized critical weight rates of the full DIP group, improves the relationship cognition of the critical weight rates of the DIP combination and is beneficial to the implementation of the standardized work of branch payment.
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. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a schematic flow chart of a full DIP group standardized medical information analysis method provided in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a full DIP group standardized medical information analysis system architecture provided in embodiment 2 of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
Example 1
Referring to fig. 1, embodiment 1 of the present invention provides a full DIP group standardized medical information analysis method, including the following steps:
s1, acquiring a critical document from a hospital critical platform, and extracting first patient information in the critical document; acquiring a first page document from a hospital HIS platform, and extracting second patient information in the first page document; constructing a patient basic information base table according to the first patient information and the second patient information, wherein the patient basic information base table comprises operation codes and diagnosis codes;
s2, acquiring a disease category score table of the designated region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information;
s3, preprocessing the disease score table, generating a unique key for each record in the disease score table, taking the unique key as a DIP score group identification field, and generating a score matching table;
s4, putting the values of the DIP score group identification fields in the score matching table into a set of Redis, and traversing the patient basic information base table;
s5, splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a hash code of the character string of the diagnosis code and the operation code;
and S6, judging whether the member element is a member of the set through a Redis Simmemer command, and if so, generating a DIP (DIP in patient) group table record of the patient.
In this embodiment, in step S1, the first patient information extracted from the critical document includes the hospitalization serial number, the patient name, and whether or not the critical document is critical; the second patient information extracted from the homepage document comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient primary diagnosis name, a patient primary diagnosis code, a patient primary operation name, a patient primary operation code and a total patient payment fee; the patient base information base includes a set of the first patient information and the second patient information.
In this embodiment, in step S2, the disease types include a core disease type and a comprehensive disease type. The main directory of the DIP directory library is a general rule of diagnosis and treatment, which reflects the common characteristics of disease and treatment modes by a standardized method formed by big data, and is the basis of DIP, and the main directory is divided into core disease types and comprehensive disease types. The core disease species and the comprehensive disease species are distinguished in a case quantity critical value mode, natural clustering based on disease diagnosis and treatment modes is directly adopted for the core disease species, and secondary clustering is carried out on the comprehensive disease species to improve data comparability and application applicability. The main directory is divided into three levels of directories according to the principle that the finest directory is clustered and converged layer by layer upwards.
For example, a diagnosis code of a04.9, a diagnosis name of unspecified bacterial intestinal infection, a non-surgical code of a disease type as a core disease type corresponds to a score of 31.02; the diagnosis code is a09, the diagnosis names are gastroenteritis and colitis which are infectious and unspecified as etiological factors, the operation code is ZDXCZ, and the score corresponding to the comprehensive disease category of the operation name diagnostic operation group is 115.26.
In this embodiment, in step S3, the unique key is formed by a hash code of a character string formed by splicing the diagnostic code and the surgical code; in step S4, the set of Redis is implemented by a hash table, and the complexity of addition, deletion, and lookup is all O (1). In step S6, the patient DIP grouping table includes a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a DIP score group identification field, and a critical or not field; analyzing a full DIP group risk ratio, a DIP group risk ratio per department, and a DIP group risk ratio per medical group according to the patient DIP cohort chart.
Specifically, when full DIP group standardization critical ratio analysis is performed, firstly, a disease category score table is preprocessed, a unique key is generated for each disease category score table record and serves as a DIP score group identification field, the unique key is formed by hash codes of character strings formed by splicing diagnosis codes plus '|' + operation codes, a score matching table is generated, and the DIP score group identification field has the diagnosis codes, the operation codes and scores. In order to accelerate the matching speed, the DIP unique identification field value of the score matching table is placed into a set of Redis, the set in the Redis is realized through a hash table, so the adding, deleting and searching complexity is O (1), through traversing a patient basic information base table, a hash code of a character string formed by splicing diagnosis codes plus "|" + surgical codes is formed through the diagnosis codes and the surgical code information of the patient basic information base table, and then a Redis Sismember command is used for judging whether a member element is a member of the set. If so, a patient DIP grouping entry form record is generated, and the patient DIP grouping entry form record comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a DIP score group identification field and a critical or non-critical field.
Thereby, it is possible to obtain:
first, full DIP group critical weight rate: numerator select count (1) from patient DIP entry table where if is critical, denominator select count (1) from patient DIP entry table;
the total DIP group risk score group identification field, the numerator/denominator from patient DIP group table where 1 is 1group by score group identification field.
Second, DIP group critical rate for each department:
the numerator (1) from patient DIP group table where is critical or not is and the department of discharging hospital is cardiovascular, the denominator (1) from patient DIP group table where is cardiovascular;
the total DIP group risk score group identification field, the numerator/denominator from patient DIP group table where 1 is 1group by score group identification field.
Third, DIP group critical rate for each treatment group:
numerator select count (1) from patient DIP cohort table where is critical or not, and medical cohort is 'cardiovascular 1 cohort', denominator select count (1) from patient DIP cohort table where is 'cardiovascular 1 cohort';
the total DIP group risk score group identification field, the numerator/denominator from patient DIP group table where 1 is 1group by score group identification field.
In conclusion, the critical document is acquired from the hospital critical platform, and the first patient information in the critical document is extracted; acquiring a first page document from a hospital HIS platform, and extracting second patient information in the first page document; constructing a patient basic information base table according to the first patient information and the second patient information, wherein the patient basic information base table comprises operation codes and diagnosis codes; acquiring a disease category score table of a specified region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information; preprocessing the disease category score table, generating a unique key for each record in the disease category score table, and generating a score matching table by taking the unique key as a DIP score group identification field; putting the value of the DIP score group identification field in the score matching table into a set of Redis, and traversing the patient basic information base table; splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a Hash code of the character string of the diagnosis code and the operation code; whether the member element is a member of the set is judged through a Redis Simemeber command, and if so, a DIP group table record of the patient is generated. The invention realizes the analysis and processing of the standardized critical weight rates of the full DIP group, improves the relationship cognition of the critical weight rates of the DIP combination and is beneficial to the implementation of the standardized work of branch payment.
Example 2
Referring to fig. 2, embodiment 2 of the present invention further provides a full DIP group standardized medical information analysis system, including:
the first patient information acquisition module 1 is used for acquiring a critical document from a hospital critical platform and extracting first patient information in the critical document;
the second patient information acquisition module 2 is used for acquiring a homepage document from a hospital HIS platform and extracting second patient information in the homepage document;
a basic information base construction module 3, configured to construct a basic patient information base table according to the first patient information and the second patient information, where the basic patient information base table includes a surgical code and a diagnostic code;
the disease category score table acquisition module 4 is used for acquiring a disease category score table of a specified region, wherein the disease category score table comprises operation codes, diagnosis codes, disease category types and score field information;
the disease score table preprocessing module 5 is used for preprocessing the disease score table, generating a unique key for each record in the disease score table, and generating a score matching table by using the unique key as a DIP score group identification field;
the traversal analysis module 6 is used for putting the value of the DIP score group identification field in the score matching table into a set of Redis and traversing the patient basic information base table;
the character splicing processing module 7 is used for splicing the character string information of the diagnosis code and the operation code in the patient basic information base table to form a hash code of the character string of the diagnosis code and the operation code;
and the patient DIP grouping table generating module 8 is used for judging whether the member element is a member of the set through a Redis Simmemer command, and if so, generating a patient DIP grouping table record.
In this embodiment, in the first patient information obtaining module 1, the first patient information extracted from the critical document includes a hospital serial number, a patient name, and whether or not the critical document is critical;
in the second patient information acquiring module 2, the second patient information extracted from the homepage document includes a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient main diagnosis name, a patient main diagnosis code, a patient main operation name, a patient main operation code, and a total payment fee of the patient;
in the basic information base construction module 3, the patient basic information base includes the set of the first patient information and the second patient information.
In this embodiment, in the disease classification table obtaining module 4, the disease types include core disease types and comprehensive disease types.
In this embodiment, in the disease score table preprocessing module 5, the unique key is formed by a hash code of a character string formed by splicing a diagnostic code and an operation code;
in the traversal analysis module, a set of Redis is realized through a hash table, and the complexity of addition, deletion and search is O (1).
In this embodiment, in the patient DIP grouping table generating module 8, the patient DIP grouping table includes a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a DIP score group identification field, and a critical or not field;
analyzing a full DIP group risk ratio, a DIP group risk ratio per department, and a DIP group risk ratio per medical group according to the patient DIP cohort chart.
It should be noted that, for the information interaction, execution process, and other contents between the modules/sub-modules of the system, since the same concept is based on the method embodiment in embodiment 1 of the present application, the technical effect brought by the information interaction, execution process, and other contents are the same as those of the method embodiment of the present application, and specific contents may refer to the description in the foregoing method embodiment of the present application, and are not described herein again.
Example 3
Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium having stored therein program code for a full DIP group standardized medical information analysis method, the program code including instructions for performing the full DIP group standardized medical information analysis method of embodiment 1 or any possible implementation thereof.
The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Example 4
An embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;
the processor and the memory are communicated with each other through a bus; the memory stores program instructions executable by the processor to invoke the program instructions to enable execution of the full DIP group standardized medical information analysis method of embodiment 1 or any possible implementation thereof.
Specifically, the processor may be implemented by hardware or software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor implemented by reading software code stored in a memory, which may be integrated in the processor, located external to the processor, or stand-alone.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.).
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A full DIP group standardized medical information analysis method is characterized by comprising the following steps:
(1) acquiring a critical document from a hospital critical platform, and extracting first patient information in the critical document; acquiring a first page document from a hospital HIS platform, and extracting second patient information in the first page document; constructing a patient basic information base table according to the first patient information and the second patient information, wherein the patient basic information base table comprises operation codes and diagnosis codes;
(2) acquiring a disease seed score table of a specified region, wherein the disease seed score table comprises operation codes, diagnosis codes, disease types and score field information;
(3) preprocessing the disease category score table, generating a unique key for each record in the disease category score table, and generating a score matching table by taking the unique key as a DIP score group identification field;
(4) putting the value of the DIP score group identification field in the score matching table into a set of Redis, and traversing the patient basic information base table;
(5) splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a Hash code of the character string of the diagnosis code and the operation code;
(6) whether the member element is a member of the set is judged through a Redis Simemeber command, and if so, a DIP group table record of the patient is generated.
2. The method according to claim 1, wherein in step (1), the first patient information extracted from the critical document includes a hospital serial number, a patient name, and whether the patient is critical;
in the step (1), the second patient information extracted from the homepage document comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient main diagnosis name, a patient main diagnosis code, a patient main operation name, a patient main operation code and a total payment cost of the patient;
the patient base information base includes a set of the first patient information and the second patient information.
3. The method according to claim 1, wherein in step (2), the disease types include core disease types and comprehensive disease types.
4. The method according to claim 1, wherein in step (3), the unique key is formed by a hash code of a character string formed by splicing a diagnostic code and a surgical code;
in the step (4), the set of Redis is realized through a hash table, and the complexity of addition, deletion and search is O (1).
5. The method according to claim 1, wherein in step (6), the patient DIP admission table comprises patient water number, patient name, discharge time, discharge department, medical group, DIP score group identification field, and critical or not field;
the full DIP cohort risk, DIP cohort risk per department and DIP cohort risk per medical cohort were analyzed according to the patient DIP cohort table.
6. A full DIP group-tagged medical information analysis system, comprising:
the first patient information acquisition module is used for acquiring a critical document from a hospital critical platform and extracting first patient information in the critical document;
the second patient information acquisition module is used for acquiring the homepage document from the HIS platform of the hospital and extracting the second patient information in the homepage document;
the basic information base building module is used for building a basic information base table of the patient according to the first patient information and the second patient information, and the basic information base table of the patient comprises an operation code and a diagnosis code;
the disease score table acquisition module is used for acquiring a disease score table of a specified region, wherein the disease score table comprises operation codes, diagnosis codes, disease types and score field information;
the disease score table preprocessing module is used for preprocessing the disease score table, generating a unique key for each record in the disease score table, and generating a score matching table by taking the unique key as a DIP score group identification field;
the traversal analysis module is used for putting the value of the DIP score group identification field in the score matching table into a set of Redis and traversing the patient basic information base table;
the character splicing processing module is used for splicing the character string information of the diagnosis code and the operation code of the patient basic information base table to form a hash code of the character string of the diagnosis code and the operation code;
and the patient DIP grouping table generation module is used for judging whether the member element is a member of the set through a Redis Simmemer command, and if so, generating a patient DIP grouping table record.
7. The system according to claim 6, wherein the first patient information extracted from the critical document in the first patient information obtaining module comprises a hospital serial number, a patient name, and whether the patient is critical;
in the second patient information acquisition module, the second patient information extracted from the homepage document comprises a patient serial number, a patient name, a discharge time, a discharge department, a medical group, a patient age, a patient sex, a patient main diagnosis name, a patient main diagnosis code, a patient main operation name, a patient main operation code and a patient payment total fee;
in the basic information base construction module, the patient basic information base includes a set of the first patient information and the second patient information.
8. The system of claim 6, wherein the disease classification table acquisition module is configured to acquire the disease types including a core disease type and a comprehensive disease type.
9. The system according to claim 6, wherein in the disease score table preprocessing module, the unique key is composed of a hash code of a character string formed by splicing a diagnostic code and a surgical code;
in the traversal analysis module, a set of Redis is realized through a hash table, and the complexity of addition, deletion and search is O (1).
10. The system according to claim 6, wherein the patient DIP subgroup identification medical information analysis module comprises a patient Water number, a patient name, a discharge time, a discharge department, a medical subgroup, a DIP subgroup identification field, and a critical or not field;
analyzing a full DIP group risk ratio, a DIP group risk ratio per department, and a DIP group risk ratio per medical group according to the patient DIP cohort chart.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117149780A (en) * | 2023-10-31 | 2023-12-01 | 北京创智和宇科技有限公司 | Method and device for constructing DIP local disease seed catalog library |
| CN117219288A (en) * | 2023-10-27 | 2023-12-12 | 上海金仕达卫宁软件科技有限公司 | Case classification method and system based on treatment mode of medical insurance cases |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117219288A (en) * | 2023-10-27 | 2023-12-12 | 上海金仕达卫宁软件科技有限公司 | Case classification method and system based on treatment mode of medical insurance cases |
| CN117149780A (en) * | 2023-10-31 | 2023-12-01 | 北京创智和宇科技有限公司 | Method and device for constructing DIP local disease seed catalog library |
| CN117149780B (en) * | 2023-10-31 | 2024-01-05 | 北京创智和宇科技有限公司 | Method and device for constructing DIP local disease seed catalog library |
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