Patient personalized nursing document generation method and device, equipment and medium

Abstract

The invention provides a patient personalized nursing document generation method and device, equipment and a medium, and relates to the technical field of nursing analysis, and the method comprises the steps: obtaining the age, disease data and health detection data of a target patient; matching the evaluation models corresponding to the age and the disease data from the configured data of different ages and different diseases and the corresponding evaluation models to obtain a target evaluation model; evaluating the health detection data by using the target evaluation model to obtain an evaluation result of the target patient; matching target nursing measures corresponding to the evaluation results from the configured different evaluation results and corresponding nursing measure comparison tables; and generating a nursing document of the target patient based on the evaluation result and the target nursing measure. According to the method, the patient is evaluated in multiple dimensions, and the document writing burden of nursing personnel is reduced by combining the overall evaluation result of the patient with the nursing document, so that the response speed and quality of the nursing service are improved.

Description

Technical Field

[0001] The present application relates to the field of nursing analysis technology, and in particular, to a method, device, equipment and medium for generating personalized nursing documents for patients. Background Art

[0002] The existing nursing plan is determined based on the disease and doctor's orders. Before determining the nursing plan, there is no comprehensive assessment of the patient. Instead, it relies on the results of admission assessment and risk assessment. It is not only lacking in comprehensiveness: the admission assessment focuses on the basic situation of the patient at admission, while the risk assessment focuses on identifying and preventing potential risks; it also ignores long-term follow-up: the admission assessment is usually a one-time thing and may not be able to detect the patient's potential complication risks in time. Systematic assessment can provide continuous and dynamic monitoring; it also ignores individual differences among patients and cannot provide personalized nursing services. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, equipment and medium for generating personalized nursing documents for patients, so as to solve the above-mentioned problems existing in the prior art and generate comprehensive and objective nursing documents for each patient.

[0004] In a first aspect, a method for generating a personalized nursing document for a patient is provided, and the method may include:

[0005] Obtaining the target patient's personal information, disease data, and health test data; wherein the personal information includes: age;

[0006] From the configured data of different ages and different diseases and the corresponding evaluation models, match the evaluation models corresponding to the age and disease data to obtain a target evaluation model;

[0007] Using the target evaluation model, the health test data is evaluated to obtain an evaluation result of the target patient;

[0008] Match the target nursing measures corresponding to the evaluation results from the configured comparison table of different evaluation results and corresponding nursing measures;

[0009] Based on the assessment results and the target nursing measures, a nursing document for the target patient is generated.

[0010] In an optional implementation, the target evaluation model includes: a quantitative target evaluation model and a qualitative target evaluation model; the quantitative target evaluation model includes a plurality of quantitative evaluation parameters and corresponding evaluation rules;

[0011] The health detection data includes health monitoring data and health collection data; wherein the health monitoring data is obtained based on the health monitoring equipment; the health collection data is the subjective perception data of the target patient;

[0012] The health monitoring data includes multiple indicators to be evaluated.

[0013] In an optional implementation, after obtaining the target evaluation model, the method further includes:

[0014] Extracting the indicator to be evaluated corresponding to the quantitative evaluation parameter from the health monitoring data to obtain the target indicator to be evaluated;

[0015] The health test data is evaluated using the target evaluation model to obtain an evaluation result of the target patient, including:

[0016] For any quantitative evaluation parameter, using the evaluation rule corresponding to the quantitative evaluation parameter, the target to-be-evaluated indicator corresponding to the quantitative evaluation parameter is evaluated to obtain the evaluation result of the target patient under the quantitative evaluation parameter;

[0017] Based on the evaluation results of the target patient under different quantitative evaluation parameters, a quantitative evaluation result of the target patient is obtained;

[0018] Inputting the health collection data into a pre-trained qualitative target assessment model to obtain a qualitative assessment result of the target patient;

[0019] The quantitative assessment result and the qualitative assessment result are integrated to obtain the assessment result of the target patient.

[0020] In an optional implementation, matching the target nursing measures corresponding to the evaluation results from the configured comparison table of different evaluation results and corresponding nursing measures includes:

[0021] Matching a first target nursing measure corresponding to the quantitative evaluation result from a comparison table of different configured quantitative evaluation parameters, different quantitative evaluation results, and corresponding nursing measures;

[0022] Matching the second target nursing measure corresponding to the qualitative evaluation result from the configured comparison table of different qualitative evaluation results and corresponding nursing measures;

[0023] The first target nursing measure and the second target nursing measure are integrated to obtain the target nursing measure.

[0024] In an optional implementation, a nursing document for a target patient is generated based on the assessment result and the target nursing measure, including:

[0025] Obtain target assessment instruments for target patients;

[0026] Determining nursing priorities of the first target nursing measure and the second target nursing measure based on the quantitative assessment result, the first target nursing measure, the qualitative assessment result, and the second target nursing measure;

[0027] Generate a nursing plan for a target patient based on the first target nursing measure, the second target nursing measure and corresponding nursing priorities;

[0028] A nursing instrument for the target patient is generated based on the nursing plan and the target assessment instrument.

[0029] In an optional implementation, the method for obtaining the target assessment document includes:

[0030] Matching the target assessment document template corresponding to the disease data of the target patient from the configured comparison table of different disease data and corresponding assessment document templates;

[0031] A target assessment instrument for the target patient is generated based on the target assessment instrument template and the assessment result.

[0032] In a second aspect, a device for generating a personalized nursing document for a patient is provided, and the device may include:

[0033] The acquisition unit is used to acquire the personal information, disease data and health test data of the target patient; wherein the personal information includes: age;

[0034] A matching unit, used to match the evaluation models corresponding to the age and disease data from the configured different age and disease data and the corresponding evaluation models, to obtain a target evaluation model;

[0035] An evaluation unit, configured to evaluate the health test data using the target evaluation model to obtain an evaluation result of a target patient;

[0036] The nursing unit is used to match the target nursing measures corresponding to the evaluation results from the configured comparison table of different evaluation results and corresponding nursing measures;

[0037] A generating unit is used to generate a nursing document for a target patient based on the evaluation result and the target nursing measure.

[0038] In a third aspect, an electronic device is provided, the electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0039] Memory, used to store computer programs;

[0040] The processor is used to implement any method step described in the first aspect when executing the program stored in the memory.

[0041] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0042] This application evaluates patients from multiple dimensions such as physiology, psychology, and socio-culture, and comprehensively grasps the patient's condition from all angles. By integrating the patient's overall assessment results with nursing documents, it reduces the paperwork burden of nursing staff, and helps improve the response speed and quality of nursing services. The overall assessment of this application enables the sharing of the patient's overall assessment information within the medical team, making it convenient for medical staff in all departments and at all levels to obtain the patient's complete information at any time, and doctors can also understand the patient's overall assessment in a timely and comprehensive manner. This efficient flow of information and collaborative mechanism effectively reduces communication costs and information errors, and improves overall medical work efficiency.

[0043] This application uses predefined templates to display nursing documents and evaluation results, reducing individual differences, standardizing the format and content of documents to facilitate communication and quality control, and realizing comprehensive sharing of medical data and rational allocation of medical resources. The nursing documents of this application provide decision support for nursing staff based on the overall evaluation results, which helps to improve nursing efficiency. This application obtains data from various medical devices and information systems without manual entry one by one, saving a lot of time.

[0044] The nursing plan of this application is determined based on the actual situation of the patient. The obtained nursing plan is more comprehensive and objective, which improves the scientific nature of nursing decisions and thus improves the overall quality of nursing services. At the same time, it enables nursing staff to more comprehensively and accurately identify the needs and risks of patients, so as to take more effective nursing measures and ultimately improve the safety and quality of care for patients. The unified evaluation standards provide nursing managers with reliable data support, enabling them to develop more effective nursing plans based on the results of the overall evaluation, thereby optimizing resource allocation and improving service quality.

[0045] The overall evaluation of this application can collect personalized data of patients, better understand patients' needs and preferences, provide data support for providing personalized nursing services, and provide data support for innovation of nursing service models, such as "Internet + nursing services" and continuing care, thereby providing more personalized and precise nursing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A flowchart of a method for generating a personalized nursing document for a patient provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a method for generating a personalized nursing document for a patient provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of a device for generating personalized nursing documents for patients provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The method for generating personalized nursing documents for patients provided in the embodiment of the present application can be applied in a server or in a terminal with strong computing power. The server can be a physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a user equipment (User Equipment, UE) such as a mobile phone, a smart phone, a laptop, a digital broadcast receiver, a personal digital assistant (PDA), a tablet computer (PAD), a handheld device, a vehicle-mounted device, a wearable device, a computing device or other processing equipment connected to a wireless modem, a mobile station (Mobile Station, MS), a mobile terminal (Mobile Terminal), etc. The terminal and the server can be directly or indirectly connected by a wired or wireless communication method, and this application is not limited here.

[0053] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0054] Figure 1 The following is a flow chart of a method for generating a personalized nursing document for a patient provided in an embodiment of the present application. Figure 1 As shown, the method may include:

[0055] Step S110, obtaining the personal information, disease data and health test data of the target patient; matching the evaluation model corresponding to the age and disease data from the configured different ages, different disease data and corresponding evaluation models to obtain the target evaluation model.

[0056] In the embodiment of the present application, the personal information of the target patient includes: age and in-hospital transfer status; among which, the in-hospital transfer status refers to whether the patient is currently in the process of admission, transfer to another department, leaving the ward, or being discharged, etc.

[0057] In the embodiment of the present application, the disease data includes: disease name, disease type, historical disease data, disease department, admission diagnosis and medical records, etc. Among them, the disease department can be determined by matching the disease name or disease type, or can be determined according to the patient's registered department; historical disease data includes: patient's past medical history, historical medical history, allergy history and surgical history, etc.

[0058] In the embodiment of the present application, health detection data includes: health monitoring data and health collection data; wherein, health monitoring data is obtained based on health monitoring equipment; health collection data is the subjective perception data of the target patient. Specifically, health monitoring data includes: data monitored by various bedside vital signs monitoring equipment (such as ECG monitors, blood glucose meters, etc.) and data collected by imaging equipment (CT, MRI, etc.); health collection data includes data input by patients, their families or medical staff through input devices, such as subjective description data of patients or medical staff on whether there is sputum, urination and defecation, sleep conditions, psychological state, etc.

[0059] In the embodiment of the present application, the health monitoring data is quantifiable and evaluable, and therefore various health monitoring data related to the patient's health status are used as indicators to be evaluated, such as blood pressure or respiratory rate.

[0060] This application obtains various types of health monitoring data and health collection data, so as to comprehensively, objectively and accurately obtain data on the patient's physiological, psychological, social and cultural aspects, thereby ensuring the richness and accuracy of the data.

[0061] In an embodiment of the present application, the target evaluation model includes: a quantitative target evaluation model and a qualitative target evaluation model; wherein the quantitative target evaluation model includes multiple quantitative evaluation parameters and corresponding evaluation rules; wherein the quantitative evaluation parameters correspond to the indicators to be evaluated.

[0062] In an embodiment of the present application, the evaluation rules include: evaluation scores corresponding to different evaluation contents, risk levels corresponding to score ranges, and evaluation frequencies corresponding to risk levels; using the evaluation scores to determine which risk level scores each indicator to be evaluated in the patient's health monitoring data meets; for example, when the ADL self-care ability evaluation score is lower than 40 points, the risk level is extremely high and requires daily evaluation; when the skin pressure injury evaluation score is higher than 9 points, the risk level is extremely high and requires evaluation every shift.

[0063] In the embodiment of the present application, different diseases or different departments require different attention content and fine granularity, so different quantitative target evaluation models are pre-defined for patients in different departments, different diseases, and different ages. The quantitative target evaluation model can be understood as an evaluation template, and the evaluation module is pre-defined according to department characteristics, disease characteristics, and age characteristics.

[0064] This application sets different quantitative evaluation models for different departments, ages or diseases to meet the different focus of different departments, different populations, different diseases, etc. in the evaluation process, and also facilitates automatic matching of evaluation templates for patients.

[0065] In the embodiment of the present application, the hospital or medical staff can adjust the quantitative target evaluation model according to actual needs; any quantitative evaluation model includes: core content such as patient basic information, diagnostic information, patient medical history, patient pain assessment, etc., and reserves a certain amount of expansion space to facilitate nursing staff to add special information or remarks according to actual conditions.

[0066] In the embodiment of the present application, the quantitative target assessment model is laid out in a "horizontal text" format by default. Different template paragraph sections have clear titles and separators, and are distinguished by text fonts, font sizes, colors, and other settings to make them clear and easy to read.

[0067] In actual applications, it supports clinicians to maintain assessment templates for special populations and special departments according to department characteristics, such as psychological assessment, radiotherapy assessment, etc.; the content of the assessment template varies according to the fine granularity of the overall assessment that different departments focus on. For example, according to department classification, the template of the internal medicine department may focus more on the assessment summary related to chronic disease management, while the surgical department focuses on the comparison of the patient's physical function before and after surgery, and the tracking of wound healing.

[0068] In actual applications, the content and corresponding scoring standards in the assessment modules corresponding to different age groups are different. It supports configuring the assessment template according to age range. For example, if a patient needs to undergo a "skin pressure injury" assessment, if the patient is under 14 years old, it is recommended to use the "Braden Children's Pressure Injury Assessment". If the patient is over 70 years old, the risk level of the assessment content can be directly judged as high risk.

[0069] In an embodiment of the present application, the quantitative target evaluation model of any department may include multiple ones, which are matched according to the frequency of use of the department or the patient's flow status in the hospital. That is, from the configured different ages, different flow status in the hospital, different disease data and corresponding evaluation models, the evaluation models corresponding to the age, flow status in the hospital and disease data are matched to obtain the target evaluation model.

[0070] In an embodiment of the present application, a user may generate feedback when or after using the quantitative target evaluation model, and the feedback is used to instruct adjustment of the format, content, and logical structure of the quantitative target evaluation model to ensure the scientificity, standardization, and practicality of the quantitative target evaluation model.

[0071] In an embodiment of the present application, after obtaining the target evaluation model, the method further includes: extracting the indicators to be evaluated corresponding to the quantitative evaluation parameters from the health monitoring data to obtain the target indicators to be evaluated.

[0072] Step S120: Use the target assessment model to evaluate the health test data to obtain the assessment results of the target patients; match the target nursing measures corresponding to the assessment results from the configured comparison table of different assessment results and corresponding nursing measures.

[0073] In the embodiment of the present application, the target evaluation model is used to evaluate the health test data to obtain the evaluation results of the target patient, including:

[0074] For any quantitative evaluation parameter, the target indicator to be evaluated corresponding to the quantitative evaluation parameter is evaluated using the evaluation rule corresponding to the quantitative evaluation parameter to obtain the evaluation result of the target patient under the quantitative evaluation parameter; based on the evaluation result of the target patient under different quantitative evaluation parameters, the quantitative evaluation result of the target patient is obtained; the health collection data is input into a pre-trained qualitative target evaluation model to obtain the qualitative evaluation result of the target patient; the quantitative evaluation result and the qualitative evaluation result are integrated to obtain the evaluation result of the target patient.

[0075] In the embodiment of the present application, the quantitative assessment result is quantifiable or defined, and the quantitative assessment result is based on the displayed assessment result, and can be displayed in different colors for reminders, such as "x points for moderate risk of falling out of bed"; the assessment results can be displayed in detail so that nurses can intuitively understand which assessment contents are more dangerous, such as "x points for moderate risk of falling out of bed; history of falling in the past six months, use of mobile assistive devices: crutches..."

[0076] In an embodiment of the present application, before inputting the health collection data into the pre-trained qualitative target assessment model, the method further includes:

[0077] Perform text preprocessing on the health collection data to obtain preprocessed health collection data; use the configured medical terminology library to map the medical terms contained in the preprocessed health collection data into standardized expressions, and add labels to each word after word segmentation in the preprocessed health collection data.

[0078] In the embodiment of the present application, the qualitative target assessment model is constructed based on the language classification model and the recognition rule engine; the health collection data is input into the pre-trained qualitative target assessment model to obtain the qualitative assessment results of the target patient, including:

[0079] Using a pre-trained entity recognition model, phrases describing symptoms and signs are extracted from each word to obtain a target phrase; the target phrase and the sentence containing the target phrase are used as target recognition data; using a pre-trained recognition rule engine to identify target recognition data that does not have abnormalities in each target recognition data, to obtain first target filtering data; using a pre-trained language classification model to identify target recognition data that does not have abnormalities in each target recognition data, to obtain second target filtering data; based on the first target filtering data and the second target filtering data, a qualitative evaluation result of the target patient is obtained.

[0080] In the embodiment of the present application, text preprocessing includes: removing punctuation marks, removing modal particles and stop words, and performing word segmentation; the label includes the part of speech and whether it is a negative word.

[0081] In an embodiment of the present application, signs and symptoms are described as entities, and entity recognition is performed using a pre-trained entity recognition model.

[0082] In an embodiment of the present application, the language classification model adopts a binary classification model, which is trained using symptom descriptions commonly used in hospitals, historical description data of patients, and other non-descriptive data; wherein the descriptive data includes two labels: abnormal and normal.

[0083] In an embodiment of the present application, a method for constructing and training a rule engine includes:

[0084] Obtain a predefined negative vocabulary and the negation scope of each negative word; obtain commonly used description texts of hospitals or historical health collection data of other patients; use dependency syntax analysis tools to parse the grammatical structure of commonly used description texts of hospitals or historical health collection data of other patients; generate recognition rules based on the acquired data and the analysis results; construct a recognition rule engine based on the recognition rules; use commonly used description texts of hospitals or historical health collection data of other patients to train and test the constructed recognition rule engine to obtain a trained recognition rule engine.

[0085] In the embodiment of the present application, the negative vocabulary includes all possible negative words, such as "none", "no", "non", "not", etc., as well as compound negative expressions, such as "not... anymore", "never... before", etc.; the negation scope of the negative word may include directly adjacent, within a phrase, or across sentences.

[0086] In the embodiment of the present application, when it is directly adjacent, that is, the negation word directly follows a symptom word, the symptom is considered to be negated; when it is within a phrase, that is, the negation word may be located in a phrase or clause before the symptom word, it is defined as a negation of the symptom.

[0087] Specifically, the recognition rules may include: if a symptom word is preceded by a negation word and there are no other intervening components between the two, the symptom should not be extracted; if the negation word appears in a clause or phrase, and the clause or phrase modifies a symptom word, the symptom should also not be extracted; for cross-sentence situations, whether to apply negation logic can be decided based on context clues.

[0088] In the embodiment of the present application, both the rule engine and the machine classification model may have deviations, so the final evaluation result is determined by combining the two to avoid missing abnormal statements; the overlapping parts of the first target filter data and the second target filter data are determined as the third filter data, and the third filter data is deleted from the target identification data; for the different parts of the first target filter data and the second target filter data, manual annotations can be introduced or they can be directly retained to obtain qualitative evaluation results.

[0089] In the embodiment of the present application, the qualitative assessment results focus on displaying positive signs and abnormal results, which is convenient for nurses to understand the key points and content references of this assessment content, and can set the prefix and suffix content of the generated content, such as "the patient has a dry cough, spontaneous sputum, a small amount of yellow-white..."; it supports the display of assessment content without abnormalities, and fully describes the results of the assessment, such as "the patient has a dry cough, no cough, spontaneous sputum, a small amount of yellow-white, no coughing up blood...".

[0090] In an embodiment of the present application, the quantitative assessment results and the qualitative assessment results can be placed in a preconfigured display area, and date and patient labels can be generated to facilitate viewing the assessment results of the current patient by date. For the quantitative assessment results, a chart can be generated, such as a trend chart, to facilitate nurses to continuously view changes over a period of time.

[0091] In practical applications, the evaluation results of target patients are associated with the target patients, which makes it convenient for doctors to view the evaluation results and provides a comprehensive reference for the patients' subsequent treatment and care.

[0092] In the embodiment of the present application, from the configured comparison table of different evaluation results and corresponding nursing measures, matching the target nursing measures corresponding to the evaluation results includes:

[0093] From the configured different quantitative evaluation parameters, different quantitative evaluation results and corresponding nursing measures comparison table, the first target nursing measures corresponding to the quantitative evaluation results are matched; from the configured different qualitative evaluation results and corresponding nursing measures comparison table, the second target nursing measures corresponding to the qualitative evaluation results are matched; the first target nursing measures and the second target nursing measures are integrated to obtain the target nursing measures.

[0094] In an embodiment of the present application, the first target nursing measure corresponding to the quantitative evaluation result can be pre-defined, for example, "if the score is greater than xx, nursing measure A needs to be implemented", such as "if the fall score is greater than 10 points, fall nursing measures need to be implemented", and the size relationship of the scores supported includes: greater than, less than, equal to, greater than or equal to, and less than or equal to.

[0095] In the embodiment of the present application, the second target nursing measure corresponding to the qualitative evaluation result may be: when the evaluation content includes xx, nursing measure B needs to be implemented; when the age is greater than 80, nursing measure C needs to be implemented.

[0096] In the embodiment of the present application, any nursing measure corresponds to a nursing problem, an influencing factor, and a nursing goal; the nursing measures corresponding to each evaluation result and the associated data of the nursing measures can be customized and modified.

[0097] In an embodiment of the present application, the first target nursing measure and the second target nursing measure are merged to obtain the target nursing measure, including: deduplicating the first target nursing measure and the second target nursing measure to obtain the target nursing measure.

[0098] For example, according to the assessment results, the patient's body temperature is greater than 38°C, the patient's age is greater than 80, and the patient's fall-out-of-bed assessment result is high risk. The nursing problems corresponding to the three assessment results are: high body temperature, risk of falling out of bed, and risk of falling out of bed. After the system removes duplicates, the recommended nursing plans corresponding to the nursing problems are: high body temperature, risk of falling out of bed. Manual adjustments are supported for nurses, including adjustments to the priority of the nursing plan, nursing measures, and also including ignoring suboptimal nursing plans.

[0099] From the configured comparison table of different assessment results and corresponding nursing measures, match the target nursing measures corresponding to the assessment results.

[0100] Step S130: Generate nursing documents for the target patient based on the evaluation results and the target nursing measures.

[0101] In the embodiment of the present application, based on the evaluation results and the target nursing measures, a nursing document for the target patient is generated, including:

[0102] Obtain the target assessment document for the target patient; determine the nursing priorities of the first target nursing measures and the second target nursing measures based on the quantitative assessment results, the first target nursing measures, the qualitative assessment results and the second target nursing measures; generate a nursing plan for the target patient based on the first target nursing measures, the second target nursing measures and the corresponding nursing priorities; generate a nursing document for the target patient based on the nursing plan and the target assessment document.

[0103] In an embodiment of the present application, a method for obtaining a target evaluation document includes:

[0104] From the configured comparison table of different disease data and corresponding assessment document templates, match the target assessment document template corresponding to the target patient's disease data; based on the target assessment document template and the assessment results, generate the target assessment document for the target patient.

[0105] In an embodiment of the present application, a method for determining nursing priority includes:

[0106] Conduct a correlation analysis on the nursing problems corresponding to each nursing measure and the health and safety of the target patients to obtain the correlation analysis results; input each nursing measure and the corresponding associated data into a pre-built deep learning model to obtain the initial priority of each nursing measure; use the correlation analysis results to adjust the initial priority of each nursing measure to obtain the nursing priority of each nursing measure.

[0107] In an embodiment of the present application, the deep learning model is trained using a pre-constructed data set, which contains multiple nursing measures and corresponding data, and includes the priorities of the nursing measures annotated manually.

[0108] In the embodiment of the present application, the determination of nursing priority is to first solve those problems that directly threaten life safety, such as dyspnea, severe bleeding, cardiac arrest, etc.; secondly, consider those conditions that are not immediately life-threatening but may worsen if not handled in time; finally, arrange routine nursing tasks.

[0109] In the embodiment of the present application, the evaluation document template is constructed according to pre-set formats, prefixes, suffixes, splicing rules, sentence formation rules, etc., and its purpose is to display the evaluation results in a manner that conforms to clinical writing habits.

[0110] In an embodiment of the present application, a nursing document for a target patient is generated based on a nursing plan and a target assessment document, including: the target assessment document can be automatically or manually synchronized into a nursing record sheet.

[0111] In actual applications, automatic synchronization is suitable for quantitative assessments conducted at a certain frequency. After the assessment, the assessment results and risk levels are automatically written into the nursing record according to the assessment time. While writing, it is determined whether the nursing record has been created and whether there is detailed data at the same time point. If neither exists, the document and detailed data are automatically created; if there is data with the same detailed time, the assessment results are appended to the existing content.

[0112] In actual applications, manual synchronization is suitable for cases with complex conditions or qualitative assessment results with large amounts of content. Nurses need to make secondary edits to the assessment results after an overall assessment, and focus on the assessment results or selectively quote the content into the nursing record.

[0113] In an embodiment of the present application, if the patient has historical medical records, the evaluation model can also be matched according to the historical medical records, and the matched evaluation model and the determined evaluation model can be merged so that the evaluation process includes data such as the patient's historical medical condition. At the same time, when matching nursing measures, it is also necessary to match the historical nursing measures corresponding to the sudden abnormalities, so that the final nursing plan covers the nursing measures for the historical sudden abnormalities to avoid the recurrence of sudden abnormalities.

[0114] like Figure 2 As shown, the method for generating a personalized nursing document for a patient provided in an embodiment of the present application includes:

[0115] (1) Define the overall assessment template

[0116] The overall assessment template is the first step in the system to standardize and customize the overall assessment data, and is the basis for overall assessment. The overall assessment template takes into account the maintenance and formulation of nursing document templates, and supports the release of the basic version of the overall assessment template and the adjustment of template content through the definition of the scope of use and the control of template attributes.

[0117] (2) Collecting evaluation data

[0118] The overall assessment is the entry point for the business data collection of the entire system. Its role is to collect a wide range of relevant information about patients and provide a basic data source for the subsequent document generation. It uses multiple channels and methods, including docking with vital sign equipment, nurse inquiries, observations, etc., to comprehensively obtain information on the patient's physiology, psychology, and social culture to ensure the richness and accuracy of the data.

[0119] (3) Generate evaluation results

[0120] According to the collected data, the corresponding assessment results are generated in the writing format configured in the system background. At the same time, according to the risk level of the assessment results of different contents, the knowledge base of positive characteristics, etc., the assessment results are marked with different colors to distinguish them, and the assessment and analysis results are presented in a clear and standardized form, providing an important reference for medical staff to intuitively and comprehensively understand the patient's assessment status.

[0121] (4) Linked care plan

[0122] The linkage care plan is a key part that assists decision-making and optimizes processes. It provides timely, accurate and targeted prompt information to medical staff through intelligent analysis of evaluation results and preset linkage care plan rules, aiming to improve the safety, timeliness and effectiveness of medical care.

[0123] (5) Generate nursing documents

[0124] The system uses predefined document templates that comply with the writing standards and quality requirements of nursing documents. The overall assessment results can be synchronized to the nursing documents as the complete content of the patient's condition changes and overall assessment.

[0125] Corresponding to the above method, the embodiment of the present application also provides a device for generating personalized nursing documents for patients, such as Figure 3 As shown, the device for generating the patient personalized nursing document includes:

[0126] The acquisition unit 310 is used to acquire the personal information, disease data and health test data of the target patient; wherein the personal information includes: age;

[0127] A matching unit 320 is used to match the evaluation models corresponding to the age and disease data from the configured different age and different disease data and the corresponding evaluation models to obtain a target evaluation model;

[0128] An evaluation unit 330 is used to evaluate the health test data using the target evaluation model to obtain an evaluation result of the target patient;

[0129] Nursing unit 340, used to match target nursing measures corresponding to the evaluation results from a comparison table of different evaluation results and corresponding nursing measures configured;

[0130] The generating unit 350 is used to generate a nursing document for a target patient based on the evaluation result and the target nursing measure.

[0131] The functions of each functional unit of the device for generating personalized nursing documents for patients provided in the above-mentioned embodiments of the present application can be realized through the above-mentioned method steps. Therefore, the specific working process and beneficial effects of each unit in the device for generating personalized nursing documents for patients provided in the embodiments of the present application will not be repeated here.

[0132] The present application also provides an electronic device, such as Figure 4 As shown, it includes a processor 410 , a communication interface 420 , a memory 430 and a communication bus 440 , wherein the processor 410 , the communication interface 420 , and the memory 430 communicate with each other via the communication bus 440 .

[0133] Memory 430, for storing computer programs;

[0134] The processor 410 is used to execute the program stored in the memory 430 to implement the following steps:

[0135] Obtain the target patient’s personal information, disease data, and health test data; personal information includes: age;

[0136] From the configured data of different ages and diseases and the corresponding evaluation models, match the evaluation models corresponding to the age and disease data to obtain the target evaluation model;

[0137] Use the target assessment model to evaluate the health test data and obtain the assessment results of the target patients;

[0138] From the configured comparison table of different assessment results and corresponding nursing measures, match the target nursing measures corresponding to the assessment results;

[0139] Generate nursing instruments for target patients based on assessment results and target nursing measures.

[0140] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0141] The communication interface is used for communication between the above electronic device and other devices.

[0142] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0143] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0144] The implementation methods and beneficial effects of the components of the electronic device in the above embodiments to solve the problems can be found in Figure 1The various steps in the illustrated embodiment are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0145] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer executes the method for generating personalized patient care documents described in any of the above embodiments.

[0146] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the method for generating personalized patient care documents described in any of the above embodiments.

[0147] Those skilled in the art will appreciate that the embodiments in the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt a complete hardware embodiment, a complete software embodiment, or a form of an embodiment combining software and hardware. Moreover, the present application may adopt a form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0152] Obviously, those skilled in the art can make various changes and modifications to the embodiments in the present application without departing from the spirit and scope of the embodiments in the present application. Thus, if these modifications and variations of the embodiments in the present application fall within the scope of the claims and their equivalents in the embodiments of the present application, the embodiments of the present application are also intended to include these modifications and variations.

Claims

1. A method for generating personalized nursing documents for patients, characterized in that: The method comprises: Obtaining the target patient's personal information, disease data, and health test data; wherein the personal information includes: age; From the configured data of different ages and different diseases and the corresponding evaluation models, match the evaluation models corresponding to the age and disease data to obtain a target evaluation model; Using the target evaluation model, the health test data is evaluated to obtain an evaluation result of the target patient; Match the target nursing measures corresponding to the evaluation results from the configured comparison table of different evaluation results and corresponding nursing measures; Based on the assessment results and the target nursing measures, a nursing document for the target patient is generated.

2. The method according to claim 1, characterized in that The target evaluation model includes: a quantitative target evaluation model and a qualitative target evaluation model; the quantitative target evaluation model includes a plurality of quantitative evaluation parameters and corresponding evaluation rules; The health detection data includes health monitoring data and health collection data; wherein the health monitoring data is obtained based on the health monitoring equipment; the health collection data is the subjective perception data of the target patient; The health monitoring data includes multiple indicators to be evaluated.

3. The method according to claim 2, characterized in that After obtaining the target evaluation model, the method further includes: Extracting the indicator to be evaluated corresponding to the quantitative evaluation parameter from the health monitoring data to obtain the target indicator to be evaluated; The health test data is evaluated using the target evaluation model to obtain an evaluation result of the target patient, including: For any quantitative evaluation parameter, using the evaluation rule corresponding to the quantitative evaluation parameter, the target to-be-evaluated indicator corresponding to the quantitative evaluation parameter is evaluated to obtain the evaluation result of the target patient under the quantitative evaluation parameter; Based on the evaluation results of the target patient under different quantitative evaluation parameters, a quantitative evaluation result of the target patient is obtained; Inputting the health collection data into a pre-trained qualitative target assessment model to obtain a qualitative assessment result of the target patient; The quantitative assessment result and the qualitative assessment result are integrated to obtain the assessment result of the target patient.

4. The method according to claim 3, characterized in that From the configured comparison table of different assessment results and corresponding nursing measures, match the target nursing measures corresponding to the assessment results, including: Matching a first target nursing measure corresponding to the quantitative evaluation result from a comparison table of different configured quantitative evaluation parameters, different quantitative evaluation results, and corresponding nursing measures; Matching the second target nursing measure corresponding to the qualitative evaluation result from the configured comparison table of different qualitative evaluation results and corresponding nursing measures; The first target nursing measure and the second target nursing measure are integrated to obtain the target nursing measure.

5. The method according to claim 4, characterized in that Based on the assessment results and the target nursing measures, a nursing document for the target patient is generated, including: Obtain target assessment instruments for target patients; Determining nursing priorities of the first target nursing measure and the second target nursing measure based on the quantitative assessment result, the first target nursing measure, the qualitative assessment result, and the second target nursing measure; Generate a nursing plan for a target patient based on the first target nursing measure, the second target nursing measure and corresponding nursing priorities; A nursing instrument for the target patient is generated based on the nursing plan and the target assessment instrument.

6. The method according to claim 5, characterized in that The method for obtaining the target assessment document includes: Matching the target assessment document template corresponding to the disease data of the target patient from the configured comparison table of different disease data and corresponding assessment document templates; A target assessment instrument for the target patient is generated based on the target assessment instrument template and the assessment result.

7. A device for generating personalized nursing documents for patients, characterized in that: The device comprises: The acquisition unit is used to acquire the personal information, disease data and health test data of the target patient; wherein the personal information includes: age; A matching unit, used to match the evaluation models corresponding to the age and disease data from the configured different age and disease data and the corresponding evaluation models, to obtain a target evaluation model; An evaluation unit, configured to evaluate the health test data using the target evaluation model to obtain an evaluation result of a target patient; The nursing unit is used to match the target nursing measures corresponding to the evaluation results from the configured comparison table of different evaluation results and corresponding nursing measures; A generating unit is used to generate a nursing document for a target patient based on the evaluation result and the target nursing measure.

8. An electronic device, characterized in that: The electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, used to implement any method described in claims 1-6 when executing a program stored in a memory.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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