An ophthalmology clinical nursing data acquisition system

Abstract

The present invention discloses an ophthalmic clinical nursing data acquisition system, which relates to the field of medical information processing technology. In order to solve the problems that the collection and management of ophthalmic clinical nursing data is relatively cumbersome, there is a lack of efficient and convenient information management means, and there are still deficiencies in the specific needs of ophthalmic nursing, such as equipment data integration, multimedia file support and evaluation scale application; through automated and intelligent data collection and analysis means, the efficiency and quality of ophthalmic nursing work are effectively improved, and the incidence of human errors is reduced; at the same time, by supporting the upload and sharing of multimedia files, evaluation scales and teaching videos, the patient's nursing files are enriched, providing medical staff with a more comprehensive and in-depth evaluation basis, which is helpful to formulate more scientific and reasonable nursing plans and treatment plans; in addition, the system also promotes the dissemination and popularization of ophthalmic nursing knowledge, and improves the health literacy and self-management ability of patients.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information processing, and in particular to an ophthalmology clinical nursing data acquisition system. Background Art

[0002] In the process of ophthalmic clinical care, medical staff need to record and analyze the patient's condition, treatment process and nursing situation in detail. For example, the patent application with publication number: CN116705256A discloses a medical care data acquisition system based on the Internet of Things, including four modules: nursing data extraction, cleaning and conversion, nursing data transmission and nursing data monitoring. The medical data of HISLIS, electronic medical records and other systems of various medical institutions are uploaded to the medical data information platform after cleaning and conversion, thereby realizing data collection and exchange between hospitals and platforms, data sharing and business collaboration between medical and health-related departments, and effectively integrating data from different medical care data acquisition systems. It can generate health records based on relevant medical care data, evaluate the user's physical health from multiple aspects, and output corresponding exercise and diet guidance suggestions based on the evaluation results.

[0003] In the existing technology, the collection and management of ophthalmic clinical nursing data is rather cumbersome, lacking efficient and convenient information management methods, and there are still deficiencies in the specific needs of ophthalmic care, such as equipment data integration, multimedia file support, and the application of assessment scales. Summary of the invention

[0004] The purpose of the present invention is to provide an ophthalmic clinical nursing data acquisition system, which enriches the patient's nursing files by supporting the uploading and sharing of multimedia files, assessment scales and teaching videos, provides medical staff with a more comprehensive and in-depth evaluation basis, and helps to formulate more scientific and reasonable nursing plans and treatment plans to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An ophthalmology clinical nursing data acquisition system, comprising:

[0007] The nursing data collection unit is used to establish a data interaction channel with various ophthalmic devices, actively collect clinical nursing information of ophthalmic patients based on the data interaction channel, classify and store the collected clinical nursing information, and build a patient clinical nursing file based on the patient information;

[0008] A multimedia data collection unit is used to update and integrate the constructed patient clinical nursing files based on multimedia files actively uploaded by users;

[0009] The multimedia data acquisition unit is also used to acquire and identify professional knowledge data actively uploaded by users, and to perform keyword tagging according to the category and main content of the professional knowledge data;

[0010] The teaching video sharing unit is used to construct a data sharing mechanism, establish a teaching video sharing port with the user's terminal based on the data sharing mechanism, and share the popular science knowledge data acquired by the multimedia data acquisition unit based on the teaching video sharing port;

[0011] The analysis report generating unit is used to generate an analysis report for the patient based on the acquired updated and integrated clinical nursing records of the patient, and to evaluate the treatment effect and the change in the quality of life of the patient based on the analysis report.

[0012] Furthermore, the nursing data collection unit includes:

[0013] The data acquisition module is used to build a data interface with various ophthalmic devices through the Internet of Things, establish a data transmission channel between the system and various ophthalmic devices, and actively collect the patient clinical care information obtained by various ophthalmic devices based on the data transmission channel;

[0014] The data integration module is used to establish an association between the collected patient clinical care information and the corresponding medical staff information, organize and classify the patient clinical care information based on the association, and generate and construct the patient clinical care file.

[0015] Furthermore, the data integration module is specifically:

[0016] Assign a unique first identifier to each patient, associate the patient's basic data with the first identifier, and associate the acquired eye examination results, medication status, treatment plan, and nursing record data of the patient with the patient's basic data to form a first identifier data set;

[0017] Assign a unique second identifier to each medical staff member, associate the basic data of the medical staff member with the identifier, and associate the treatment plan implementation process data with the basic data of the medical staff member to form a second identifier data set;

[0018] Based on the first identifier, the patient's basic data, eye examination results, medication status, treatment plan and nursing record data are matched in a correlation manner; based on the second identifier, the medical staff's basic data and the treatment plan implementation process data are matched in a correlation manner;

[0019] The first identification data set and the second identification data set are mapped and matched, and based on the mapping and matching result, the data of the first identification data set and the second identification data set are integrated into the patient database to generate the patient clinical nursing file.

[0020] Furthermore, the patient database also includes establishing a query system, specifically:

[0021] Based on the mapping matching results, when inputting the retrieval instruction and patient basic data:

[0022] Based on the patient basic data, query the first identification data set to obtain the first identification, and retrieve the patient's eye examination results, medication status, treatment plan and nursing record data based on the first identification;

[0023] At the same time, the second identifier associated with the first identifier is retrieved, and the corresponding medical staff information and treatment plan implementation process data are retrieved from the second identifier data set using the second identifier.

[0024] Furthermore, the multimedia data acquisition unit includes:

[0025] A file identification module is used to identify the category of multimedia files uploaded by users, wherein the identified images and videos are subjected to content identification to determine whether the images and videos are professional knowledge data;

[0026] A file classification module, for classifying multimedia files into preset categories, including medical data, patient life record data, health education materials, and popular science video categories, based on the file category and content judgment results of the multimedia files;

[0027] The professional knowledge data tagging module is used to extract multimedia files of health education materials and popular science videos according to the classification results, extract keywords according to the categories and main contents of the multimedia files, and tag the professional knowledge data according to the extracted keywords and the preset tagging system;

[0028] The data update module is used to match the corresponding patient clinical nursing files according to the user identity information, integrate the multimedia files and professional knowledge data uploaded by the user with the existing patient clinical nursing files, and record the corresponding timestamp.

[0029] Further, the teaching video sharing unit includes:

[0030] The teaching database access module is used to establish a professional knowledge database, import the professional knowledge data marked by the professional knowledge data marking module into the professional knowledge database, and classify and store them according to keywords;

[0031] The data sharing module is used to retrieve the corresponding professional knowledge data based on keyword search and category browsing, and share the professional knowledge data in the professional knowledge database based on the data sharing mechanism.

[0032] Furthermore, the professional knowledge data sharing in the data sharing module also includes:

[0033] Determine the advanced cache characteristics of the terminal where the user is located, obtain the data retrieval rules of the professional knowledge data, and generate a data transmission network protocol between the two according to the data retrieval rules and the advanced cache characteristics of the terminal where the user is located;

[0034] Retrieving a data resource sample from the professional knowledge data, running the data transmission network protocol based on the data resource sample, and obtaining an operation result;

[0035] Analyze the data transmission characteristic parameters of the data transmission network protocol, and perform correlation analysis with multiple working modes and behavioral characteristics of the user terminal to determine the efficient data transmission working mode and its target behavioral characteristics;

[0036] Generate a response code corresponding to the target behavior feature, generate a grid link between the user's terminal and the professional knowledge data based on the response code and the keywords of the professional knowledge data, and perform feature extraction and gradient inversion processing;

[0037] A data sharing mechanism is established based on the optimized grid chain characteristics, and professional knowledge data in the professional knowledge database is retrieved based on the user's keyword search and category browsing needs.

[0038] Furthermore, the analysis report generating unit includes:

[0039] The data analysis module is used to perform statistical analysis on the data collected from the nursing data collection unit, identify key trends and patterns in treatment and nursing care in the patient's clinical data, and generate analysis results including treatment effect, quality of life, disease prediction and risk factor analysis;

[0040] Patient feedback module, which is used to collect and analyze patient satisfaction feedback on treatment and care;

[0041] The report generation module is used to generate reports based on the analysis results output by the data analysis module and the feedback data from the patient feedback module.

[0042] Furthermore, after actively collecting clinical nursing information of ophthalmic patients based on the data interaction channel and before classifying and storing the collected clinical nursing information, it also includes:

[0043] Analyze the clinical nursing information of each ophthalmological patient, and obtain nursing behavior information and nursing status information based on the analysis results;

[0044] Determine the time series change sequence of eye health data of each ophthalmology patient according to nursing behavior information and nursing status information;

[0045] Determine the changes in the eye health status of each ophthalmology patient based on the time series change data, and determine the data retention value of clinical care information based on the changes in the eye health status;

[0046] Screening all clinical nursing information based on data retention value, and retaining the first clinical nursing information whose data retention value is greater than or equal to a preset threshold;

[0047] Acquire nursing stage description information of each first clinical nursing information, and divide each first clinical nursing information into multiple nursing stages according to the nursing stage description information;

[0048] Obtaining a nursing dictionary corresponding to each nursing stage, retrieving an eye recovery mapping index corresponding to the nursing dictionary, and calculating a content utilization rate of description information of each nursing stage according to the eye recovery mapping index;

[0049] Determine the content compliance of the description information of each nursing stage based on the content utilization rate, and determine the necessity of cleaning the description information of each nursing stage based on the content compliance;

[0050] Optimize and clean up the description information of each nursing stage based on the necessity of cleaning, and obtain the cleaned description information of the nursing stage;

[0051] Evaluate the nursing dispatch response level of the first clinical nursing information according to the multiple cleaned nursing stage description information of each first clinical nursing information;

[0052] The second clinical care information whose care dispatch response is graded above the target grade is regarded as qualified clinical care information and is used as an information sample for classification and storage.

[0053] Furthermore, after the collected clinical nursing information is classified and stored, the system is also used to: prioritize the classified clinical nursing information in each category, specifically:

[0054] Obtaining the nursing resource type and resource level of each classified clinical nursing information in each category, and determining the nursing resource cost coefficient of each classified clinical nursing information based on the resource type and resource level;

[0055] Obtaining the nursing period and period operation parameters of each classified clinical nursing information in each category, and determining the nursing time cost coefficient and the nursing operation cost coefficient of each classified clinical nursing information according to the nursing period and period operation parameters;

[0056] Obtain the medical and nursing collaboration parameters of each classified clinical nursing information in each category, determine the medical and nursing collaboration recovery mode according to the medical and nursing collaboration parameters, and determine the nursing manpower cost coefficient of each classified clinical nursing information based on the medical and nursing system recovery mode;

[0057] The recommended priority coefficient of each classified clinical nursing information is calculated based on the nursing time cost coefficient, nursing operation cost coefficient, nursing manpower cost coefficient and nursing resource cost coefficient of each classified clinical nursing information: in, It is expressed as the recommendation priority coefficient of the j-th classification clinical nursing information in the i-th category, It is expressed as the nursing time cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the first preset weight, which is 0.35. It is represented as the nursing operation cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the second preset weight, which is 0.25. It is expressed as the nursing manpower cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the third preset weight, which is 0.1. It is represented as the nursing resource cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the fourth preset weight, which is 0.3, and ln is represented as a logarithmic function. It is represented as the eye injury response difficulty index of the j-th classification clinical nursing information in the i-th category, denoted as the eye injury severity index of the j-th categorized clinical care information in the ith category, It is expressed as the physical mechanism health index of the patient corresponding to the j-th classification clinical care in the ith category;

[0058] The categorized clinical care information in the category is prioritized in descending order according to the recommended priority coefficient of each categorized clinical care information.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] Through automated and intelligent data collection and analysis methods, the efficiency and quality of ophthalmic care work have been effectively improved and the incidence of human errors has been reduced; at the same time, by supporting the uploading and sharing of multimedia files, assessment scales and teaching videos, the patient's care files have been enriched, providing medical staff with a more comprehensive and in-depth evaluation basis, which helps to formulate more scientific and reasonable care plans and treatment plans; in addition, the system has also promoted the dissemination and popularization of ophthalmic care knowledge and improved patients' health literacy and self-management ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the module of the ophthalmology clinical nursing data acquisition system of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In order to solve the cumbersome data collection and management of ophthalmic clinical nursing, the lack of efficient and convenient information management methods, and the technical problems of equipment data integration, multimedia file support and evaluation scale application for the specific needs of ophthalmic nursing, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0064] An ophthalmology clinical nursing data acquisition system, comprising:

[0065] The nursing data collection unit is used to establish a data interaction channel with various ophthalmic devices, and actively collect clinical nursing information of ophthalmic patients based on the data interaction channel, including basic patient data, eye examination results, medication status, treatment plan, nursing records and other aspects, and classify and store the collected clinical nursing information, build a patient clinical nursing file based on the patient information, realize remote connection and data transmission of various ophthalmic devices, and facilitate real-time update and summary analysis of data. Among them, the basic patient data includes name, gender, personal contact information, personal identity information, family and social relations, historical health information and medical information;

[0066] Multimedia data collection unit, used to update and integrate the constructed patient clinical care files based on multimedia files actively uploaded by users, who can be patients, their families, and medical staff. Multimedia files include photos, videos, and assessment scales to assess the patient's quality of life;

[0067] The multimedia data acquisition unit is also used to obtain and identify professional knowledge data actively uploaded by users, and to perform keyword tagging according to the category and main content of the professional knowledge data;

[0068] The teaching video sharing unit is used to build a data sharing mechanism, establish a teaching video sharing port with the user's terminal based on the data sharing mechanism, and share the popular science knowledge data acquired by the multimedia data acquisition unit based on the teaching video sharing port, so as to facilitate medical staff to share and learn ophthalmic professional knowledge, improve the diagnosis and treatment level, popularize scientific knowledge and enhance the skills of medical staff;

[0069] An analysis report generating unit is used to generate an analysis report of the patient based on the acquired updated and integrated clinical nursing records of the patient, and to evaluate the treatment effect and quality of life change of the patient based on the analysis report, including:

[0070] The data analysis module is used to perform statistical analysis on the data output by the nursing data acquisition unit, identify key trends and patterns in treatment and nursing care in the patient's clinical data, and generate analysis results including treatment effect, quality of life, disease prediction and risk factor analysis to support medical staff in optimizing diagnosis and treatment strategies;

[0071] Patient feedback module, which is used to collect and analyze patient satisfaction feedback on treatment and care;

[0072] The report generation module is used to generate reports based on the analysis results output by the data analysis module and the feedback data from the patient feedback module. It regularly and automatically generates drug usage reports, treatment effect evaluation reports, patient quality of life change reports, etc., to help optimize diagnosis and treatment strategies, thereby improving the efficiency and quality of clinical decision-making.

[0073] In this embodiment, through the seamless connection between ophthalmic equipment and the system, data can be synchronized in real time across regions and devices, greatly improving the efficiency and flexibility of medical services. Automated means reduce errors and time cost coefficients in manual entry, ensure the accuracy and real-time nature of data, and provide doctors with a comprehensive patient information database, which helps to formulate personalized treatment plans. By establishing a data sharing mechanism and sharing ports, professional knowledge data is effectively delivered to medical staff, thereby improving the quality and efficiency of medical services, enhancing patients' and their families' awareness and understanding of the disease, and promoting communication and trust between doctors and patients. Through evaluation scales and other data, attention is paid to changes in patients' quality of life, helping medical staff to objectively and accurately evaluate treatment effects and adjust treatment plans in a timely manner, providing strong support for the continuous improvement of ophthalmic medical services and the continuous improvement of patients' health levels.

[0074] In this embodiment, the nursing data collection unit includes:

[0075] The data acquisition module is used to build a data interface with various ophthalmic devices through the Internet of Things, establish a data transmission channel between the system and various ophthalmic devices, and actively collect the patient clinical care information obtained by various ophthalmic devices based on the data transmission channel;

[0076] The data integration module is used to establish an association between the collected patient clinical nursing information and the corresponding medical staff information, organize and classify the patient clinical nursing information based on the association, generate and construct the patient clinical nursing file, and ensure the accuracy and completeness of the data, specifically:

[0077] Assign a unique first identifier to each patient, associate the patient's basic data with the first identifier, and associate the acquired eye examination results, medication status, treatment plan, and nursing record data of the patient with the patient's basic data to form a first identifier data set;

[0078] Assign a unique second identifier to each medical staff member, associate the basic data of the medical staff member with the identifier, and associate the treatment plan implementation process data with the basic data of the medical staff member to form a second identifier data set;

[0079] Based on the first identifier, the patient's basic data, eye examination results, medication status, treatment plan and nursing record data are matched in a correlation manner; based on the second identifier, the medical staff's basic data and the treatment plan implementation process data are matched in a correlation manner;

[0080] Mapping and matching the first identification data set and the second identification data set, integrating the data of the first identification data set and the second identification data set into the patient database based on the mapping and matching result, and generating a clinical nursing file of the patient;

[0081] In this embodiment, the patient database also includes establishing a query system, specifically:

[0082] Based on the mapping matching results, when inputting the retrieval instruction and patient basic data:

[0083] Based on the patient basic data, query the first identification data set to obtain the first identification, and retrieve the patient's eye examination results, medication status, treatment plan and nursing record data based on the first identification;

[0084] At the same time, the second identifier associated with the first identifier is retrieved, and the corresponding medical staff information and treatment plan implementation process data are retrieved from the second identifier data set using the second identifier.

[0085] In this embodiment, through the establishment of identification and data sets, the information retrieval and access process is simplified, and the efficiency of medical information management is improved. Medical staff can quickly access comprehensive information of patients, which helps to make more accurate clinical decisions. Detailed and comprehensive patient information helps medical staff provide more personalized and targeted nursing services. Analysis of patient data and treatment process data can monitor and evaluate medical quality, and timely discover and solve problems. The query system in the patient database realizes the rapid retrieval and association of patient medical information. Medical staff can communicate treatment plans and nursing plans with patients more effectively, and can ensure continuous understanding and care of patients' conditions, which not only improves the quality of medical services, but also promotes the rational use of medical resources and continuous improvement of medical services.

[0086] In this embodiment, the multimedia data acquisition unit includes:

[0087] A file recognition module is used to identify the categories of multimedia files uploaded by users, such as photos, videos, assessment scales, medical record pictures, etc., wherein the identified images and videos are subjected to content recognition to determine whether the images and videos are professional knowledge data, such as charts in research papers, teaching videos, etc.;

[0088] A file classification module, for classifying multimedia files into preset categories, including medical data, patient life record data, health education materials, and popular science video categories, based on the file category and content judgment results of the multimedia files;

[0089] The professional knowledge data tagging module is used to extract multimedia files of health education materials and popular science videos according to the classification results, extract keywords according to the categories and main contents of the multimedia files, and tag the professional knowledge data according to the extracted keywords and the preset tagging system to facilitate subsequent retrieval and classification;

[0090] The data update module is used to match the corresponding patient clinical nursing files according to the user identity information, integrate the multimedia files and professional knowledge data uploaded by the user with the existing patient clinical nursing files, and record the corresponding timestamp.

[0091] In this embodiment, the professional knowledge data marking module extracts keywords and marks them according to a preset marking system, so that professional knowledge data such as health education materials and popular science videos can be effectively indexed and classified, thereby improving data accessibility and promoting knowledge sharing and dissemination. The data update module ensures the timeliness and integrity of the patient's clinical nursing files through dynamic updates, and provides medical staff with more comprehensive and accurate patient information. The multimedia data acquisition unit enables medical service agencies to collect, organize and analyze patient data more efficiently. By uploading multimedia files, patients and their families can more intuitively display the patient's health status and living conditions, which helps medical staff to understand the patient's condition more comprehensively, thereby formulating more personalized treatment plans, enhancing communication and trust between doctors and patients, and improving patient satisfaction and compliance.

[0092] In this embodiment, the teaching video sharing unit includes:

[0093] The teaching database access module is used to establish a professional knowledge database, import the professional knowledge data marked by the professional knowledge data marking module into the professional knowledge database, and classify and store them according to keywords;

[0094] The data sharing module is used to retrieve the corresponding professional knowledge data based on keyword search and category browsing, and share the professional knowledge data in the professional knowledge database based on the data sharing mechanism to facilitate users to learn ophthalmology-related knowledge, and also includes:

[0095] Determine the advanced cache characteristics of the terminal where the user is located, obtain the data retrieval rules of the professional knowledge data, and generate a data transmission network protocol between the two according to the data retrieval rules and the advanced cache characteristics of the terminal where the user is located;

[0096] Retrieving a data resource sample from the professional knowledge data, running the data transmission network protocol based on the data resource sample, and obtaining an operation result;

[0097] Analyze the data transmission characteristic parameters of the data transmission network protocol, and perform correlation analysis with multiple working modes and behavioral characteristics of the user terminal to determine the efficient data transmission working mode and its target behavioral characteristics;

[0098] Generate a response code corresponding to the target behavior feature, generate a grid link between the user's terminal and the professional knowledge data based on the response code and the keywords of the professional knowledge data, and perform feature extraction and gradient inversion processing;

[0099] A data sharing mechanism is established based on the optimized grid chain characteristics. Based on the user's keyword search and category browsing needs, professional knowledge data in the professional knowledge database is retrieved, and fast and stable data sharing is achieved.

[0100] In this embodiment, the data sharing mechanism is used to promote the widespread sharing of professional knowledge data, so that more users can access and learn these resources, providing users with an efficient, personalized and stable learning platform. Through the sharing of health education materials and popular science videos, patients and their families can also obtain more comprehensive and scientific health knowledge, thereby improving the overall quality of medical services.

[0101] Furthermore, after actively collecting clinical nursing information of ophthalmic patients based on the data interaction channel and before classifying and storing the collected clinical nursing information, it also includes:

[0102] Analyze the clinical nursing information of each ophthalmological patient, and obtain nursing behavior information and nursing status information based on the analysis results;

[0103] Determine the time series change sequence of eye health data of each ophthalmology patient according to nursing behavior information and nursing status information;

[0104] Determine the changes in the eye health status of each ophthalmology patient based on the time series change data, and determine the data retention value of clinical care information based on the changes in the eye health status;

[0105] Screening all clinical nursing information based on data retention value, and retaining the first clinical nursing information whose data retention value is greater than or equal to a preset threshold;

[0106] Acquire nursing stage description information of each first clinical nursing information, and divide each first clinical nursing information into multiple nursing stages according to the nursing stage description information;

[0107] Obtaining a nursing dictionary corresponding to each nursing stage, retrieving an eye recovery mapping index corresponding to the nursing dictionary, and calculating a content utilization rate of description information of each nursing stage according to the eye recovery mapping index;

[0108] Determine the content compliance of the description information of each nursing stage based on the content utilization rate, and determine the necessity of cleaning the description information of each nursing stage based on the content compliance;

[0109] Optimize and clean up the description information of each nursing stage based on the necessity of cleaning, and obtain the cleaned description information of the nursing stage;

[0110] Evaluate the nursing dispatch response level of the first clinical nursing information according to the multiple cleaned nursing stage description information of each first clinical nursing information;

[0111] The second clinical care information whose care dispatch response is graded above the target grade is regarded as qualified clinical care information and is used as an information sample for classification and storage.

[0112] The beneficial effects of the above technical solution are: by qualifying the clinical nursing information of patients, clinical nursing information with high data value can be effectively screened out for reference by medical staff and other patients, while also ensuring the high quality of clinical nursing information content, avoiding the occurrence of excessive storage resource occupation due to excessive invalid content, and improving the utilization rate of storage resources. Furthermore, by making qualification judgments based on the classification of nursing scheduling responses, reasonable and objective information qualification judgments can be made based on the level of scheduling response resources related to the nursing process, which can further ensure data quality and improve practicality.

[0113] In one embodiment, after the collected clinical nursing information is classified and stored, the system is further used to: prioritize the classified clinical nursing information in each category, specifically:

[0114] Obtaining the nursing resource type and resource level of each classified clinical nursing information in each category, and determining the nursing resource cost coefficient of each classified clinical nursing information based on the resource type and resource level;

[0115] Obtaining the nursing period and period operation parameters of each classified clinical nursing information in each category, and determining the nursing time cost coefficient and the nursing operation cost coefficient of each classified clinical nursing information according to the nursing period and period operation parameters;

[0116] Obtain the medical and nursing collaboration parameters of each classified clinical nursing information in each category, determine the medical and nursing collaboration recovery mode according to the medical and nursing collaboration parameters, and determine the nursing manpower cost coefficient of each classified clinical nursing information based on the medical and nursing system recovery mode;

[0117] The recommended priority coefficient of each classified clinical nursing information is calculated based on the nursing time cost coefficient, nursing operation cost coefficient, nursing manpower cost coefficient and nursing resource cost coefficient of each classified clinical nursing information: in, It is expressed as the recommendation priority coefficient of the j-th classification clinical nursing information in the i-th category, It is expressed as the nursing time cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the first preset weight, which is 0.35. It is represented as the nursing operation cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the second preset weight, which is 0.25. It is expressed as the nursing manpower cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the third preset weight, which is 0.1. It is represented as the nursing resource cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the fourth preset weight, which is 0.3, and ln is represented as a logarithmic function. It is represented as the eye injury response difficulty index of the j-th classification clinical nursing information in the i-th category, denoted as the eye injury severity index of the j-th categorized clinical care information in the ith category, It is expressed as the physical mechanism health index of the patient corresponding to the j-th classified clinical care in the ith category; the classified clinical care information in the category is prioritized in descending order according to the recommended priority coefficient of each classified clinical care information.

[0118] The beneficial effects of the above technical solution are: by prioritizing the classified clinical care information in each category, medical staff and other patients can make intuitive cost references and evaluations to choose their best eye recovery plan, which improves practicality and user experience. At the same time, the classified clinical information in each category can also be stored in an orderly manner, which is convenient for subsequent review and improves the display effect.

[0119] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An ophthalmology clinical nursing data acquisition system, characterized in that: include: The nursing data collection unit is used to establish a data interaction channel with various ophthalmic devices, actively collect clinical nursing information of ophthalmic patients based on the data interaction channel, classify and store the collected clinical nursing information, and build a patient clinical nursing file based on the patient information; A multimedia data collection unit is used to update and integrate the constructed patient clinical nursing files based on multimedia files actively uploaded by users; The multimedia data acquisition unit is also used to acquire and identify professional knowledge data actively uploaded by users, and to perform keyword tagging according to the category and main content of the professional knowledge data; The teaching video sharing unit is used to construct a data sharing mechanism, establish a teaching video sharing port with the user's terminal based on the data sharing mechanism, and share the popular science knowledge data acquired by the multimedia data acquisition unit based on the teaching video sharing port; An analysis report generating unit, used to generate an analysis report of the patient based on the acquired updated and integrated clinical nursing records of the patient, and to evaluate the treatment effect and the change in the quality of life of the patient based on the analysis report; After actively collecting clinical care information of ophthalmology patients based on the data interaction channel, and before classifying and storing the collected clinical care information, it also includes: Analyze the clinical nursing information of each ophthalmological patient, and obtain nursing behavior information and nursing status information based on the analysis results; Determine the time series change sequence of eye health data of each ophthalmology patient according to nursing behavior information and nursing status information; Determine the changes in the eye health status of each ophthalmology patient based on the time series change data, and determine the data retention value of clinical care information based on the changes in the eye health status; Screening all clinical nursing information based on data retention value, and retaining the first clinical nursing information whose data retention value is greater than or equal to a preset threshold; Acquire nursing stage description information of each first clinical nursing information, and divide each first clinical nursing information into multiple nursing stages according to the nursing stage description information; Obtaining a nursing dictionary corresponding to each nursing stage, retrieving an eye recovery mapping index corresponding to the nursing dictionary, and calculating a content utilization rate of description information of each nursing stage according to the eye recovery mapping index; Determine the content compliance of the description information of each nursing stage based on the content utilization rate, and determine the necessity of cleaning the description information of each nursing stage based on the content compliance; Optimize and clean up the description information of each nursing stage based on the necessity of cleaning, and obtain the cleaned description information of the nursing stage; Evaluate the nursing dispatch response level of the first clinical nursing information according to the multiple cleaned nursing stage description information of each first clinical nursing information; The second clinical care information whose care dispatch response is graded above the target grade is regarded as qualified clinical care information and is used as an information sample for classification and storage.

2. An ophthalmology clinical nursing data acquisition system as claimed in claim 1, characterized in that: Nursing data collection unit, including: The data acquisition module is used to build a data interface with various ophthalmic devices through the Internet of Things, establish a data transmission channel between the system and various ophthalmic devices, and actively collect the patient clinical care information obtained by various ophthalmic devices based on the data transmission channel; The data integration module is used to establish an association between the collected patient clinical care information and the corresponding medical staff information, organize and classify the patient clinical care information based on the association, and generate and construct the patient clinical care file.

3. An ophthalmology clinical nursing data acquisition system as claimed in claim 2, characterized in that: Data integration module, specifically: Assign a unique first identifier to each patient, associate the patient's basic data with the first identifier, and associate the acquired eye examination results, medication status, treatment plan, and nursing record data of the patient with the patient's basic data to form a first identifier data set; Assign a unique second identifier to each medical staff member, associate the basic data of the medical staff member with the identifier, and associate the treatment plan implementation process data with the basic data of the medical staff member to form a second identifier data set; Based on the first identifier, the patient's basic data, eye examination results, medication status, treatment plan and nursing record data are matched in a correlation manner; based on the second identifier, the medical staff's basic data and the treatment plan implementation process data are matched in a correlation manner; The first identification data set and the second identification data set are mapped and matched, and based on the mapping and matching result, the data of the first identification data set and the second identification data set are integrated into the patient database to generate the patient clinical nursing file.

4. An ophthalmology clinical nursing data acquisition system as claimed in claim 3, characterized in that: The patient database also includes establishing a query system, specifically: Based on the mapping matching results, when inputting the retrieval instruction and patient basic data: Based on the patient basic data, query the first identification data set to obtain the first identification, and retrieve the patient's eye examination results, medication status, treatment plan and nursing record data based on the first identification; At the same time, the second identifier associated with the first identifier is retrieved, and the corresponding medical staff information and treatment plan implementation process data are retrieved from the second identifier data set using the second identifier.

5. An ophthalmology clinical nursing data acquisition system as claimed in claim 4, characterized in that: Multimedia data acquisition unit, including: A file identification module is used to identify the category of multimedia files uploaded by users, wherein the identified images and videos are subjected to content identification to determine whether the images and videos are professional knowledge data; A file classification module, for classifying multimedia files into preset categories, including medical data, patient life record data, health education materials, and popular science video categories, based on the file category and content judgment results of the multimedia files; The professional knowledge data tagging module is used to extract multimedia files of health education materials and popular science videos according to the classification results, extract keywords according to the categories and main contents of the multimedia files, and tag the professional knowledge data according to the extracted keywords and the preset tagging system; The data update module is used to match the corresponding patient clinical nursing files according to the user identity information, integrate the multimedia files and professional knowledge data uploaded by the user with the existing patient clinical nursing files, and record the corresponding timestamp.

6. An ophthalmology clinical nursing data acquisition system as claimed in claim 5, characterized in that: Teaching video sharing unit, including: The teaching database access module is used to establish a professional knowledge database, import the professional knowledge data marked by the professional knowledge data marking module into the professional knowledge database, and classify and store them according to keywords; The data sharing module is used to retrieve the corresponding professional knowledge data based on keyword search and category browsing, and share the professional knowledge data in the professional knowledge database based on the data sharing mechanism.

7. An ophthalmology clinical nursing data acquisition system as claimed in claim 6, characterized in that: The professional knowledge data sharing in the data sharing module also includes: Determine the advanced cache characteristics of the terminal where the user is located, obtain the data retrieval rules of the professional knowledge data, and generate a data transmission network protocol between the two according to the data retrieval rules and the advanced cache characteristics of the terminal where the user is located; Retrieving a data resource sample from the professional knowledge data, running the data transmission network protocol based on the data resource sample, and obtaining an operation result; Analyze the data transmission characteristic parameters of the data transmission network protocol, and perform correlation analysis with multiple working modes and behavioral characteristics of the user terminal to determine the efficient data transmission working mode and target behavioral characteristics; Generate a response code corresponding to the target behavior feature, generate a grid link between the user's terminal and the professional knowledge data based on the response code and the keywords of the professional knowledge data, and perform feature extraction and gradient inversion processing; A data sharing mechanism is established based on the optimized grid chain characteristics, and professional knowledge data in the professional knowledge database is retrieved based on the user's keyword search and category browsing needs.

8. An ophthalmology clinical nursing data acquisition system as claimed in claim 7, characterized in that: Analysis report generation unit, including: The data analysis module is used to perform statistical analysis on the data collected from the nursing data collection unit, identify key trends and patterns in treatment and nursing care in the patient's clinical data, and generate analysis results including treatment effect, quality of life, disease prediction and risk factor analysis; Patient feedback module, which is used to collect and analyze patient satisfaction feedback on treatment and care; The report generation module is used to generate reports based on the analysis results output by the data analysis module and the feedback data from the patient feedback module.

9. An ophthalmology clinical nursing data acquisition system as claimed in claim 1, characterized in that: After the collected clinical nursing information is classified and stored, the system is also used to: prioritize the classified clinical nursing information in each category, specifically: Obtaining the nursing resource type and resource level of each classified clinical nursing information in each category, and determining the nursing resource cost coefficient of each classified clinical nursing information based on the resource type and resource level; Obtaining the nursing period and period operation parameters of each classified clinical nursing information in each category, and determining the nursing time cost coefficient and the nursing operation cost coefficient of each classified clinical nursing information according to the nursing period and period operation parameters; Obtain the medical and nursing collaboration parameters of each classified clinical nursing information in each category, determine the medical and nursing collaboration recovery mode according to the medical and nursing collaboration parameters, and determine the nursing manpower cost coefficient of each classified clinical nursing information based on the medical and nursing system recovery mode; The recommended priority coefficient of each classified clinical nursing information is calculated based on the nursing time cost coefficient, nursing operation cost coefficient, nursing manpower cost coefficient and nursing resource cost coefficient of each classified clinical nursing information: in, It is expressed as the recommendation priority coefficient of the j-th classification clinical nursing information in the i-th category, It is expressed as the nursing time cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the first preset weight, which is 0.

35. It is represented as the nursing operation cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the second preset weight, which is 0.

25. It is expressed as the nursing manpower cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the third preset weight, which is 0.

1. It is represented as the nursing resource cost coefficient of the j-th classification clinical nursing information in the i-th category, It is represented as the fourth preset weight, which is 0.3, and ln is represented as a logarithmic function. It is represented as the eye injury recovery difficulty index of the j-th classification clinical nursing information in the i-th category, denoted as the eye injury severity index of the j-th categorized clinical care information in the ith category, It is expressed as the physical mechanism health index of the patient corresponding to the j-th classification clinical care in the ith category; The categorized clinical care information in the category is prioritized in descending order according to the recommended priority coefficient of each categorized clinical care information.

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