Clinical nursing assessment risk control method, storage medium and electronic equipment

Abstract

The invention provides a clinical nursing assessment risk control method, a storage medium and electronic equipment. The method comprises the following steps: acquiring various types of clinical assessment data; matching risk types and risk application conditions based on the clinical assessment data of each category, and respectively combining the risk types and the risk application conditions to form a plurality of combined screening items; a corresponding risk scale template is matched for each combined screening item from a pre-configured risk scale template library, the initial risk scale is controlled to enter an editing mode, and the initial risk scale provides a plurality of assessment risk factors which can be edited and input in the editing mode; matching assessment risk factors in the risk scale template based on clinical assessment data, and determining a risk score and a risk assessment level based on each matched assessment risk factor to form an initial risk scale; and generating a final risk scale in response to a received confirmation instruction after adjustment and confirmation of each assessment risk factor. According to the invention, the accuracy and integrity of risk management can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and in particular to the field of risk management technology related to clinical care. Background Art

[0002] In clinical nursing, risk management is a key component to ensure patient safety. However, nurses face many challenges in implementing risk management, especially in using risk scales, conducting risk assessments, identifying risks, and implementing targeted risk interventions.

[0003] 1) Challenges of using risk scales

[0004] Different patients' conditions and backgrounds (such as age, underlying diseases, living habits, etc.) require different risk scales. This diversity requires nurses to have an in-depth understanding of each scale and the ability to correctly apply it. In actual operations, it is often difficult to meet the diverse personalized needs of patients. In addition, a variety of risk assessment tools are currently being promoted and used in clinical practice, but not all tools are suitable for all situations. Nurses need to face the problem of scale selection and applicability. It requires professional judgment to choose the right scale for different patients to assess specific risks (such as VTE, pressure sores, falls, etc.).

[0005] 2) Barriers to risk assessment

[0006] When conducting risk assessment, it is necessary to collect comprehensive information about the patient, including the patient's lifestyle, medical history, latest physical data, test report data, and medical and surgical intervention information. However, information acquisition may be affected by incomplete nurse assessments, time constraints, or insufficient resources, resulting in incomplete information. Moreover, different nurses may have different risk assessment results for the same patient, and the subjectivity of this assessment may affect the accuracy and consistency of the assessment.

[0007] 3) Difficulty in risk identification

[0008] New nurses or nurses with less experience may not be aware of certain risk factors and may not fully assess patients. This may lead to the neglect of important risk factors. In addition, due to the dynamic changes in risk factors: the continuous changes in the patient's condition require nurses to be able to identify emerging risks in real time, which is particularly difficult in a busy clinical environment.

[0009] 4) Difficulties of risk intervention

[0010] Effective risk intervention may require additional manpower, material resources, and time. In situations where resources are limited, it may not be feasible to implement comprehensive risk intervention measures. Moreover, ideal risk intervention should be personalized based on the patient's specific risk level and related factors. However, in practice, it is often difficult to design a completely personalized intervention plan for each patient.

[0011] In the face of the above difficulties faced by clinical nurses in patient risk management, it is necessary to solve the problem of appropriate risk screening scales for patients through intelligent and information technology, and to master the comprehensiveness and timeliness of risk assessment, so that it is possible to use these assessment information for risk screening, and at the same time, to provide patients with specific intervention measures for the impact of each risk factor. It can also be achieved through continuous education, skills training, more effective workflow design, and the use of advanced nursing technology. Through these measures, the quality of nursing services can be effectively improved, thereby improving the overall treatment effect and satisfaction of patients.

[0012] It can be seen that the clinical nursing risk management in the existing technology has technical problems such as various risk screening tools used in clinical practice are suitable for different patient populations, nurses are prone to incorrect use of appropriate risk screening scales, and incomplete selection of various risk screening scales in clinical use and evaluation. Summary of the invention

[0013] The present application provides a clinical nursing assessment risk control method, storage medium and electronic device for improving the accuracy and completeness of risk management.

[0014] In a first aspect, an embodiment of the present application provides a clinical nursing assessment risk control method, comprising: obtaining clinical assessment data of patients of multiple categories, and storing the clinical assessment data of patients of multiple categories into a preset patient assessment database; matching risk types and risk applicable conditions based on the clinical assessment data of each category, and combining the risk types and the risk applicable conditions respectively to form multiple combination screening items; matching corresponding risk scale templates for each of the combination screening items from a pre-configured risk scale template library; controlling the risk scale template to enter an editing mode, wherein the risk scale template provides multiple assessment risk factors for editing and entry in the editing mode, matching the assessment risk factors in the risk scale template based on the clinical assessment data, and determining a risk score and a risk assessment level based on each matched assessment risk factor to form an initial risk scale; and generating a final risk scale in response to receiving a confirmation instruction after confirming the adjustment of each of the assessment risk factors.

[0015] In an implementation of the first aspect, obtaining multiple categories of patient clinical evaluation data includes: collecting patient medical data from a data source, and matching corresponding CCC classification standard codes based on the type of the patient medical data; and evaluating and processing the patient medical data based on the CCC classification standard codes to form the patient clinical evaluation data.

[0016] In an implementation of the first aspect, it also includes: constructing the patient assessment database: collecting patient medical data from multiple data sources, and obtaining patient clinical assessment data based on the patient medical data and the CCC classification standard code corresponding to the patient medical data; storing the patient clinical assessment data based on the data association and mutually exclusive logical attributes between each of the patient clinical assessment data to form a patient assessment database; updating the patient assessment database based on the patient clinical assessment data acquired in real time and data timeliness.

[0017] In an implementation of the first aspect, matching the assessment risk factors in the risk scale template based on the clinical assessment data includes: decomposing the assessment risk factors in the risk scale template into a structured data set containing at least one minimum structured data factor; matching each of the minimum structured data factors in the structured data set with the clinical assessment data one by one.

[0018] In an implementation of the first aspect, decomposing the assessment risk factors in the risk scale template into a structured data set containing at least one minimum structured data factor includes: performing semantic analysis on the assessment risk factors to extract multiple factor indicators; calculating and processing the factor indicators in turn based on any one or more combinations of preset calculation formulas, reference tables, and factor association relationships to generate a structured data set containing at least one minimum structured data factor.

[0019] In an implementation of the first aspect, it also includes: generating a risk management page based on the final risk scale; the risk management page includes risk management sub-interface areas for multiple patients, each of the risk management sub-interface areas includes at least one corresponding matching display of the risk assessment level and risk type, and different risk assessment levels are displayed in different colors.

[0020] In an implementation of the first aspect, it also includes: matching a nursing plan including bundled nursing measures based on the final risk scale, and generating a nursing execution interface including bundled nursing measures based on the nursing plan.

[0021] In an implementation of the first aspect, the method further includes: updating the final risk scale in real time based on the patient clinical assessment data collected in real time.

[0022] In a second aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the clinical nursing assessment risk control method described in any one of the first aspects of the present application.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing the clinical nursing assessment risk control method described in any one of the first aspects of the present application when the computer program is called.

[0024] The clinical nursing assessment risk control method, storage medium and electronic device provided in the embodiments of the present application have the following beneficial effects:

[0025] This application can automatically generate risk scales for various types of assessments based on clinical assessment data, automatically calculate risk levels, and effectively obtain the patient's latest comprehensive, complete, and effective overall clinical assessment data content to ensure the integrity of risk assessment data. Therefore, this application can effectively improve the accuracy and completeness of risk management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Shown is a flow chart of a clinical nursing assessment risk control method according to an embodiment of the present application.

[0027] Figure 2 Shown is a flow chart of forming patient clinical assessment data in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0028] Figure 3 Shown is a flowchart of constructing a patient assessment database in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0029] Figure 4 Shown is a schematic diagram of the principle architecture of constructing a patient assessment database in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0030] Figure 5 Shown is a schematic diagram of applicable dimensions for risk scale screening in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0031] Figure 6 Shown is a flow chart of matching the assessment risk factors in the risk scale template with the clinical assessment data in the clinical nursing assessment risk control method according to one embodiment of the present application.

[0032] Figure 7 Shown is a flow chart of decomposing assessment risk factors into minimum structured data factors in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0033] Figure 8 Shown is a schematic diagram of the risk assessment principle of a clinical nursing assessment risk control method according to an embodiment of the present application.

[0034] Fig. 9 Shown is a schematic diagram of primary risk assessment screening in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0035] Fig.10 and Fig.11 Shown is a schematic diagram of possible diagnostic judgments and lists in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0036] Fig.12 Shown is a schematic diagram of analysis, reanalysis and exclusion in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0037] Fig.13 Shown is a confirmation diagram of risk information assessment in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0038] Fig.14 Shown is a schematic diagram of a risk closed-loop management model in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0039] Fig.15 Shown is a schematic diagram of a fully intelligent big data risk assessment recording system in a clinical nursing assessment risk control method according to an embodiment of the present application.

[0040] Fig.16 Shown is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0042] From assessment to diagnosis, nurses must synthesize and analyze all relevant information to form an accurate nursing diagnosis. This requires: Clinical judgment ability: Accurate nursing diagnosis depends on the nurse's clinical judgment, which is a comprehensive reflection of experience, knowledge and practice. Information integration ability: The amount of information obtained from the assessment may be very large. Nurses need to effectively integrate this information and extract data that is critical to nursing diagnosis. In the application of clinical nursing diagnosis, the existing PES theory cannot be directly used as an application of nursing diagnosis informationization.

[0043] The embodiments of the present application provide a clinical nursing assessment risk control method, storage medium and electronic device for improving the efficiency and accuracy of nursing assessment and diagnosis.

[0044] The following will be combined with the attached examples of the present application Figure 1 To Attachment Fig.16 , the technical scheme in the embodiment of the present application is described in detail, so that those skilled in the art can understand and implement the clinical nursing assessment risk control method, storage medium and electronic device of the embodiment without creative work.

[0045] Figure 1 Shown is a flow chart of the clinical nursing assessment risk control method in the embodiment of the present application. Figure 1 As shown, the clinical nursing assessment risk control method provided in the embodiment of the present application includes the following steps S110 to S150.

[0046] Step S110, obtaining multiple categories of clinical evaluation data of patients, and storing the multiple categories of clinical evaluation data of patients in a preset patient evaluation database;

[0047] Step S120, matching risk types and risk applicable conditions based on the clinical assessment data of each category, and combining the risk types and risk applicable conditions respectively to form a plurality of combined screening items;

[0048] Step S130, matching a corresponding risk scale template for each of the combined screening items from a pre-configured risk scale template library;

[0049] Step S140, controlling the risk scale template to enter an editing mode, wherein the risk scale template provides a plurality of assessment risk factors for editing and entry in the editing mode, matching the assessment risk factors in the risk scale template based on the clinical assessment data, and determining a risk score and a risk assessment level based on each matched assessment risk factor to form an initial risk scale;

[0050] Step S150, in response to receiving a confirmation instruction after confirming the adjustment of each of the assessed risk factors, generating a final risk scale.

[0051] The above steps S110 to S150 of the clinical nursing assessment risk control method in this embodiment are described in detail below.

[0052] Step S110, obtaining multiple categories of patient clinical assessment data, and storing the multiple categories of patient clinical assessment data in a preset patient assessment database.

[0053] Figure 2 Shown is a flow chart of forming patient clinical assessment data in a clinical nursing assessment risk control method according to an embodiment of the present application. Figure 2 As shown, in an implementation of this embodiment, the acquisition of multiple categories of clinical assessment data of patients includes the following steps S111 and S112.

[0054] Step S111, collecting patient medical data from a data source, and matching corresponding CCC classification standard codes based on the type of the patient medical data;

[0055] Step S112: evaluating and processing the patient medical data based on the CCC classification standard code to form the patient clinical evaluation data.

[0056] In one implementation of the present embodiment, the patient's clinical assessment data includes test report assessment data generated by evaluating test report data, physician order execution and medication assessment data generated by evaluating physician order execution and medication data, physical examination and physical sign assessment data generated by evaluating physical examination and physical sign data, bedside instrument monitoring assessment data generated by evaluating bedside instrument monitoring data, and nursing assessment data generated by evaluating nurses' clinical nursing work system data.

[0057] In the prior art, most of the nursing assessment content is hindered by the dynamic state of patient information and the diversity of sources, resulting in incomplete and untimely acquisition of assessment content. In this embodiment, patient medical data is dynamically collected from multiple data sources, including but not limited to the hospital test report system, doctor's order and medication execution system, nurse's patient examination and assessment operation system, bedside monitoring equipment, clinical nursing work system, etc., to dynamically, timely and comprehensively collect patient clinical information. Specifically, in this embodiment, the patient medical data includes: test report data, doctor's order and medication execution data, physical examination and physical sign assessment data, bedside instrument monitoring data and nurse clinical nursing work system data. The specific process based on obtaining patient clinical assessment data is as follows:

[0058] a) Test report data: After collecting the patient's test report data, import the CCC classification standard code, obtain the normal value range of the standard code data, identify whether the test report result is positive, and if positive, assign the CCC classification standard code positive evaluation item to generate the test report evaluation data.

[0059] b) Doctor’s order execution and medication data: Collect the data collected by nurses by scanning the code with PDA or mobile device to obtain the patient’s recent medication data. Import the CCC classification standard code to obtain the CCC classification standard code of drug categories, such as antihypertensive drugs, analgesics, sleep aids, anticoagulants, etc., and generate doctor’s order execution and medication evaluation data.

[0060] c) Physical examination and sign data: Use the clinical care assessment template and CCC classification as a specialist assessment template, such as life history, past medical history, cardiovascular system, respiratory system, nervous system, genitourinary system, blood system, psychology, etc. After the assessment, the CCC classification standard coding positive assessment items are directly generated to generate physical examination and sign assessment data.

[0061] d) Bedside instrument monitoring data: After collecting the patient's bedside instrument monitoring data, import the CCC classification standard code, obtain the normal value range of the standard code data, and identify whether the clinical instrument monitoring data is positive. If positive, assign the CCC classification standard code positive evaluation item to generate bedside instrument monitoring evaluation data.

[0062] e) Nurses' clinical nursing work system data: During clinical nursing, nurses obtain relevant information data such as the patient's latest external equipment during nursing operations such as catheter placement and removal, oxygen therapy, etc., import CCC classification standard codes, obtain catheter types or oxygen therapy methods, and detailed CCC classification standard codes for ventilators, and assign CCC classification standard coded equipment use codes to generate nursing assessment data.

[0063] In an implementation of this embodiment, the method further includes: constructing the patient assessment database: Figure 3 The flowchart of constructing a patient assessment database in the clinical nursing assessment risk control method according to one embodiment of the present application is shown. Figure 3 As shown, constructing the patient assessment database includes the following steps S101 and S103.

[0064] Step S101, collecting patient medical data from multiple data sources, and obtaining patient clinical evaluation data based on the patient medical data and the CCC classification standard code corresponding to the patient medical data; wherein, the method of obtaining the patient clinical evaluation data has been described above and will not be repeated here.

[0065] Step S102: storing the patient clinical evaluation data based on the data association and mutually exclusive logical attributes between the patient clinical evaluation data to form a patient evaluation database.

[0066] The clinical evaluation data of each patient formed by different data sources are correlated with each other. These evaluation details are interdependent with clinical positive signs, such as cataracts associated with visual impairment, hearing loss associated with hearing impairment, etc. This embodiment can ensure that the positive signs of the patient can be more completely grasped during the evaluation process through the data correlation between the clinical evaluation data of each patient.

[0067] On the contrary, some newly added assessment items in the patient's clinical assessment data have mutually exclusive associations with the original assessments, such as catheter removal, which is mutually exclusive with the assessment item of the original catheter placement mark. If the clinical nursing operation removes the catheter, this embodiment automatically removes the catheter placement mark on the patient through the mutually exclusive association logic of each patient's clinical assessment data.

[0068] Step S103: updating the patient evaluation database based on the patient clinical evaluation data acquired in real time and the timeliness of the data.

[0069] Figure 4 The schematic diagram of the principle structure of constructing a patient assessment database in the clinical nursing assessment risk control method according to one embodiment of the present application is shown. Figure 4 As shown, this embodiment establishes a patient evaluation summary database (CDR) based on the evaluation data obtained from multiple data sources to maintain the latest clinical information data of the patient. The CCC classification standard coding content automatically generated through dynamic data collection is continuously updated to update the patient's clinical information data and ensure that the content has mutual logical consistency. In addition, the data content of the clinical information data that has exceeded the validity period is automatically cleared. For example, clinical vital signs data such as body temperature, pulse rate, and respiratory rate are only retained for 24 hours, and expired data will be automatically cleared to ensure that the patient's clinical data information database contains valid information content.

[0070] This embodiment can ensure that nurses obtain the latest comprehensive, complete, and effective overall clinical assessment data content of patients during the clinical care process, so as to use the clinical assessment data to perform subsequent nursing diagnosis and risk assessment management for patients.

[0071] Step S120, matching risk types and risk applicable conditions based on the clinical assessment data of each category, and combining the risk types and the risk applicable conditions respectively to form a plurality of combined screening items.

[0072] Figure 5 Shown is a schematic diagram of the applicable dimensions of the risk scale screening in the clinical nursing assessment risk control method according to an embodiment of the present application. Figure 5 As shown, in this embodiment, two dimensions are distinguished according to the risk screening tool type and applicable conditions: risk type and risk applicable conditions.

[0073] In this embodiment, multiple risk types and multiple risk applicable conditions are preset, and a mapping relationship between clinical assessment data and risk types and risk applicable conditions is preset. In this embodiment, the corresponding risk type and risk applicable condition can be automatically matched and obtained according to the clinical assessment data.

[0074] Among them, in this embodiment, the risk types include: pressure injury risk, fall risk, self-care ability classification, malnutrition risk, early warning, bleeding risk, VTE risk, unplanned extubation risk, infection risk, etc.

[0075] Among them, in this embodiment, the risk applicable conditions include but are not limited to: a) age; b) childbirth; c) surgery; d) catheter.

[0076] Furthermore, each of the risk applicable conditions has at least one sub-applicable condition. In this embodiment, the sub-applicable conditions of age include but are not limited to: a) adults; b) children; the sub-applicable conditions of delivery include but are not limited to: a) during delivery; b) after delivery; the sub-applicable conditions of surgery include but are not limited to: a) surgery; b) non-surgery; the sub-applicable conditions of catheter include but are not limited to: a) catheter placement; b) endotracheal intubation; c) indwelling catheterization.

[0077] The risk type and the risk applicable condition are respectively combined to form a plurality of combined screening items, specifically: a risk type and a sub-applicability condition of the risk applicable condition form a combined screening item, and the corresponding risk scale is screened and matched through the combined screening item.

[0078] Step S130: Match corresponding risk scale templates for each of the combined screening items from a pre-configured risk scale template library.

[0079] Specifically, in this embodiment, the risk scale templates corresponding to the matched combination screening items are a set of risk scale templates, and accordingly, the initial risk scale subsequently formed according to the set of risk scale templates is also a set of initial risk scales.

[0080] Exemplarily, for example, the risk scales of each screening type corresponding to each screening type are screened and matched based on the patient age dimension and the screening type dimension, and the content is shown in Table 1.

[0081] Table 1

[0082]

[0083] For example, for example, the risk scales of the corresponding screening types are screened and matched based on the patient's delivery, surgery, catheterization dimensions and screening type dimensions, as shown in Table 2.

[0084] Table 2

[0085]

[0086] The above dimensions in Table 1 and Table 2 are combined to obtain and generate risk screening content that meets the patient's conditions as shown in Table 3.

[0087] Table 3

[0088]

[0089] Step S140, controlling the risk scale template to enter an editing mode, wherein the risk scale template provides a plurality of assessment risk factors for editing and entry in the editing mode, matches the assessment risk factors in the risk scale template based on the clinical assessment data, and determines a risk score and a risk assessment level based on each matched assessment risk factor to form an initial risk scale.

[0090] Figure 6 The flowchart is shown as a matching process between the risk factors in the risk scale template and the clinical assessment data in the clinical nursing assessment risk control method according to an embodiment of the present application. Figure 6 As shown, in an implementation of this embodiment, the matching of the assessment risk factors in the risk scale template based on the clinical assessment data includes the following steps S141 and S142.

[0091] Step S141: decompose the assessment risk factors in the risk scale template into a structured data set containing at least one minimum structured data factor.

[0092] In this embodiment, the risk scale template includes risk dimensions and risk assessment factors. Since various types of risk scale templates basically have standardized assessment annotations, the risk assessment factors in the risk scale template are also corresponding standardized expressions, which results in the risk assessment factors in the risk scale template being unable to directly correspond to the clinical assessment data. Table 4 shows a comparison of the risk assessment factors in the risk scale template and the clinical assessment data.

[0093] Table 4

[0094]

[0095]

[0096]

[0097] In this embodiment, the assessment risk factors in the risk scale template are structurally decomposed to facilitate subsequent data matching and score calculation.

[0098] Figure 7The flowchart of decomposing the assessment risk factor into the minimum structured data factor in the clinical nursing assessment risk control method according to one embodiment of the present application is shown. Figure 7 As shown, in an implementation of this embodiment, the step of decomposing the assessment risk factors in the risk scale template into a structured data set containing at least one minimum structured data factor includes the following steps S1411 and S1412.

[0099] Step S1411, performing semantic analysis on the risk assessment factor to extract multiple factor indicators.

[0100] Step S1412, based on any one or more combinations of preset calculation formulas, reference tables, and factor association relationships, the factor indicators are calculated and processed in sequence to generate a structured data set containing at least one minimum structured data factor.

[0101] Step S142, matching each of the minimum structured data factors in the structured data set with the clinical evaluation data one by one.

[0102] In this embodiment, according to the risk assessment factors in the risk scale template, a plurality of corresponding calculation formulas, reference tables and factor association relationships are preset. In this way, after the factor indicators are extracted, corresponding calculations can be performed to generate the required minimum structured data factors.

[0103] This embodiment is described by taking the risk scale template as a nutrition risk scale template as an example. Table 5 is an example table of a structured data set containing minimum structured data factors formed by decomposing risk factors in the nutrition risk scale template.

[0104] Table 5

[0105]

[0106] In this embodiment, the patient's weight change rate and food intake reduction rate can be automatically calculated using a preset formula. The specific algorithm is as follows:

[0107] For the risk factor: 5% weight loss in the past three months, the process of disassembling to form a structured data set containing the minimum structured data factor is as follows:

[0108] 1) Obtain the weight loss rate in the past 1-3 months: Calculate the average monthly weight loss rate within three months.

[0109] In order to more accurately assess the nutritional risk of patients, this embodiment designs an automated weight loss rate calculation method, especially for the weight change trend within three months. This embodiment determines the risk level by calculating the weight loss slope and the average monthly weight loss rate. The weight loss slope reflects the weight change trend of the patient in a specified time period (such as within three months). The calculation formula of the weight loss slope is:

[0110]

[0111] Where Sw is the weight loss slope (unit: kg / month), W start is the weight measured previously (unit: kg), W current is the current weight (unit: kg), T is the time interval in days (unit: day), and the slope represents the patient's monthly weight loss. If the slope is positive, it means that the weight is decreasing, and this embodiment will further evaluate the specific weight loss rate. After obtaining the weight loss slope, this embodiment calculates the average monthly weight loss rate of the patient based on his initial weight. The specific formula is as follows:

[0112]

[0113] Where Rloss_avg is the average monthly weight loss rate (unit: %), Sw is the weight loss slope (unit: kg / month), W start is the weight three months ago (unit: kg). Through this formula, this embodiment calculates the patient's weight loss rate each month and classifies it into different risk levels.

[0114] According to the weight loss rate R loss_avg The result is determined by the segment where the final minimum structured data factor is located:

[0115] 1.67% ≤ R loss_avg ≤2.5%

[0116] 2.6%≤R loss_avg ≤5%

[0117] R loss_avg >5%

[0118] Food intake reduction rate in the past week: Food intake reduction rate in the past week = ((food intake last week - food intake this week) / food intake last week) x 100;

[0119]

[0120] Among them, R f : Food intake reduction rate in the past week, E w-1 : Average food intake last week (unit: grams or milliliters), E w: Average food intake this week (unit: grams or milliliters). Through the above formula, this embodiment automatically extracts food intake data from the patient's daily food intake record and calculates the food intake reduction rate. f The results will automatically select risk options and assign weights based on the grade range.

[0121] For example, in order to evaluate the growth and development of children, this embodiment will combine the patient's height, weight, gender and corresponding age group to calculate the growth and development Z value and determine whether the value is within the normal range. The calculation method is as follows:

[0122]

[0123] Where: X is the Z value deviation; D is the actual height or weight of the child; M is the standard mean of the height or weight of children of the same age and gender; S is the standard deviation of the height or weight of children of the same age and gender.

[0124] In this embodiment, standard growth data tables for children of different age groups and genders are pre-stored, and the data table format is as shown in Table 6. The data content is based on the national health industry standard WS / T423-2022 "Growth Standards for Children Under 7 Years Old" and WS / T612-2018 "Evaluation of Height Development Levels for Children and Adolescents Aged 7-18". The data content evaluates the growth level and nutritional status of weight, height, and body mass index (BMI) of male and female children of different ages, and the evaluation method includes the percentile method and the standard deviation method. In conjunction with the Children's Nutrition Risk Screening Table (STAMP), the standard deviation is used to distinguish the evaluation level, and the data content only organizes and imports critical data of three standard deviations each, positive and negative.

[0125] Table 6

[0126]

[0127] After the nurse evaluates the child and collects the height and weight data, this embodiment automatically searches for the gender standard mean and standard deviation of the child's corresponding age, calculates the deviation Z value, and then uses the data type (weight, height, BMI, head circumference), gender, and converted age in months as indexes to search for the corresponding segment data in the data table. It then determines which standard deviation segment the child's deviation value Z belongs to, and the risk options are checked and weighted points are assigned according to the grade segment.

[0128] In view of the disease severity scores in the risk table, this embodiment classifies and processes the complex disease information. This is mainly because the scoring standards for different disease severities are not unified, and subjective factors affect the accuracy of the evaluation results. In this embodiment, the requirements for the degree of nutritional risk of common diseases in various specialties are sorted out by the clinical evidence-based method (EBM), and a reference correspondence table is established after comparing the detailed items with reference to the source risk table for data comparison. Based on the type of disease and the patient's condition, this embodiment automatically generates a recommended score for disease severity and simplifies the scoring process to ensure the consistency and accuracy of the evaluation.

[0129] Through automatic analysis of historical data and condition, this embodiment can provide standardized scoring options to reduce the impact of nurses' subjective judgment on the results. The exemplary content of the evidence-based and organized table is shown in Table 7 below.

[0130] Table 7

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Step S150, in response to receiving a confirmation instruction after confirming the adjustment of each of the assessed risk factors, generating a final risk scale.

[0138] In this embodiment, the initial risk scale is in editable mode. In editable mode, each of the assessment risk factors can be manually viewed and adjusted. Before generating the final risk scale, in order to ensure that the nurse understands the patient's risk situation, the nurse is required to view each of the assessment risk factors in the initial risk scale and then make adjustments and confirmations based on actual conditions.

[0139] This embodiment seamlessly connects with hospital information systems (HIS) and patient databases (such as electronic health records EHR) to achieve automated data entry and updating.

[0140] In generating the final risk scale, this embodiment associates the patient assessment data with the patient's medical history, eating habits, laboratory data, vital signs (height and weight) and other information to form a comprehensive nutritional risk report. Through this association, nurses can have a more comprehensive understanding of the patient's overall health status and assist in making nursing and treatment decisions. After matching the associated data content with the clinical assessment content, a comparison table of risk factors and assessment content is prepared (as shown in Table 4), and an associated relationship is established in the database. After the nurse completes the clinical assessment, the relevant information can be automatically checked in combination with the risk factors. The main goals to be achieved are:

[0141] 1) Comprehensive assessment of individual patients: This embodiment automatically combines patient medical records and nutritional risk assessment data to generate a comprehensive nutritional risk analysis report, which is updated in real time to help nurses and doctors make decisions.

[0142] 2) Cross-system data integration: This embodiment can integrate nutritional assessment data with other clinical data (such as laboratory test results, doctor's dietary regimen instructions, etc.) to provide a more comprehensive analysis of the patient's health status.

[0143] 3) Visualization presentation and decision support module:

[0144] This embodiment directly uses a visual chart to see the risk assessment results, automatically calculates the weight score of each risk factor and the total score, and automatically displays the actual corresponding position of the cursor at the risk level according to the risk level, and also enables nurses to intuitively understand the actual situation of the patient's nutritional risk.

[0145] In one implementation of this embodiment, Fig.13 As shown, a risk management page is generated based on the final risk scale; the risk management page includes risk management sub-interface areas for multiple patients, each of the risk management sub-interface areas includes at least one corresponding matching display of the risk assessment level and risk type, and different risk assessment levels are displayed in different colors.

[0146] In an implementation of this embodiment, it also includes: matching a nursing plan including bundled nursing measures based on the final risk scale, and generating a nursing execution interface including bundled nursing measures based on the nursing plan.

[0147] In an implementation of this embodiment, it also includes: based on the patient clinical assessment data collected in real time, updating the final risk scale in real time, and updating the risk management page in real time.

[0148] In this embodiment, for each risky nursing problem, risk prevention measures for each relevant risk factor are screened and an association is established so that when the nurse implements the nursing plan, the nurse can push out appropriate preventive measures based on the relevant positive signs and medical history or life history and other risk factors collected during the assessment.

[0149] In this embodiment, for each existing nursing problem, the relevant causal treatment measures that lead to the problem are screened and an association is established, so that when the nurse implements the nursing plan, he or she can push out appropriate intervention instructions based on the relevant positive signs and medical history or life history collected during the assessment.

[0150] like Figure 8 As shown, in the clinical nursing assessment risk control method provided in the embodiment of the present application, a risk cycle closed-loop management of risk screening, preventive intervention, risk management, clustered care, and emergency event reporting is also constructed to form a fully intelligent big data risk assessment record; the clinical nursing assessment risk control method provided in the embodiment of the present application has a cyclic process from evaluation to diagnosis: primary assessment (forming an initial risk scale) - possible diagnosis (possible nursing diagnosis) - in-depth assessment (forming a final risk scale) - nursing diagnosis (generating a risk management page).

[0151] This embodiment can automatically generate nursing diagnoses based on the patient's assessment information and medical record data to reduce the burden on nurses and improve the accuracy of diagnosis; it can provide personalized nursing questions and nursing plans based on the content of the nursing diagnosis to help nurses better formulate and implement nursing measures; it can automatically generate the content of nursing documents to reduce the clerical work burden of nurses and improve the accuracy and standardization of records.

[0152] In one implementation of this embodiment, for the primary assessment: based on different types of patients admitted to the hospital, risk screening is performed, and the entire process is recorded, and a primary assessment is performed, and patient-related information and materials are collected and organized to form a primary risk assessment screening; the risk level of the primary risk assessment is estimated, and different levels of possible nursing diagnoses are performed and graded, and relevant nursing scales are automatically generated to screen out high-risk content, assessment time, and status confirmation; for the in-depth assessment: a risk screening form containing assessment criteria, scores, and assessment content options is constructed for assessment.

[0153] Risk screening such as Fig. 9 As shown, overall nursing assessment, data collection and organization. Based on different types of patients admitted to the hospital, risk screening is carried out, and the whole process is recorded. Primary assessment is carried out, and relevant information of patients is collected and organized to form a primary risk assessment screening.

[0154] like Fig.10 and Fig.11 As shown, possible diagnosis judgment and list (including risk assessment table + possible diagnosis) are used to estimate the risk level of the first level assessment, and then make different levels of possible nursing diagnosis. The level is divided on the page, and the relevant nursing scale is automatically generated to screen out high-risk content, assessment time, and status confirmation.

[0155] like Fig.12 As shown, this embodiment provides analysis, re-analysis and elimination—precise evaluation and analysis, and multi-level confirmation.

[0156] According to the above evaluation and analysis, the more registration and analysis is performed, and the more patient information is used in the evaluation and diagnosis process, the more accurate the nursing diagnosis information obtained will be, and the personalized nursing diagnosis that can be provided will be more targeted, thereby providing better care for patients and reducing nursing costs.

[0157] In an implementation of this embodiment, it also includes confirmation of risk information assessment and sorting according to the level of relevant content.

[0158] like Fig.13 As shown, the last step is to confirm the risk information assessment and display it according to the level of relevant content.

[0159] This embodiment uses clinical data integration (CDR) technology and nursing clinical evidence-based knowledge base (NEBM) to complete the active guidance information of the nursing process (Nursing Process) full intelligent process, and uses clinical data integration (CDR) technology to complete the clinical nursing risk closed-loop management information.

[0160] like Fig.14 As shown, it is a risk closed-loop management model of this embodiment. Risk screening is an important basis for preventive intervention, risk management, cluster care, emergency event reporting, and later etiology factor analysis. The above-mentioned risk closed-loop management forms a cycle. During the evaluation process, hierarchical progressive intervention is carried out for different contents to form a closed-loop cycle.

[0161] (I) Principles of risk assessment and organization: First, use clinical data integration (CDR) technology and nursing clinical evidence-based knowledge base (NEBM) to complete the active guidance information of the nursing process (Nursing Process) full intelligent process; Second, use clinical data integration (CDR) technology to complete the closed-loop management information of clinical nursing risks.

[0162] Take the information management of pressure injury management nursing as an example.

[0163] Risk identification: Automatically screen patients for high-risk scores through holistic assessment

[0164] Risk intervention: Automatically import clustered nursing instructions based on the risk level and include them in the nursing work schedule

[0165] Risk record: automatically generate risk scores and implement measures results record (PDCA)

[0166] Risk monitoring: Automatically collect statistics on the latest risk score distribution of each ward and the entire hospital

[0167] Risk indicators: Automatically extract clinical data as indicator statistics and report

[0168] Adverse event reporting: Automatically extract patient clinical data when adverse events occur

[0169] Risk analysis: Automatically collect process nurse intervention data based on the results

[0170] The specific cases are as follows:

[0171] Ms. Zhao, female, 78 years old, was admitted to the hospital for treatment of mid-thoracic esophageal squamous cell carcinoma

[0172] During the admission assessment, the patient used a walker to walk, was assessed to have a history of depression and dementia, had a pain assessment score of 2 (PAINAD), and was admitted from another hospital with a nasogastric tube.

[0173] During the overall evaluation during hospitalization, clinical manifestations of agitation were found.

[0174] PICC catheter was placed after hospitalization and removed before discharge.

[0175] Catheterization information: PICC catheter is placed in the median vein of the left antecubital fossa and fixed with a fixator.

[0176] The doctor ordered an intravenous catheter and the nurse inserted it.

[0177] After catheterization, PICC bundle care will be automatically included in the care plan.

[0178] Nurses schedule time for catheter bundle care based on catheter risk level.

[0179] When different catheters are maintained, the contents of each catheter bundle care template are displayed separately.

[0180] Catheter care content automatically generates catheter care record

[0181] When removing or replacing the tube, the reason for the removal or replacement must be selected, and relevant sensitive indicators and adverse event reports will be automatically counted, and all related cluster care will be automatically stopped.

[0182] The nursing department and each ward can check the risk distribution status of all patients in a timely manner.

[0183] Constructing a fully intelligent big data risk assessment method is the basis for recording follow-up nursing. Patient data information is an important basis for follow-up work. Nursing diagnosis for patients is an important part of implementing follow-up nursing work, indicating the important direction of nursing work and providing accurate data guidance.

[0184] like Fig.15 As shown, in this embodiment, assessment is the beginning of diagnosis. Without assessment information, understanding diagnosis and subsequent planning, execution and evaluation are out of the question. Using evidence-based knowledge base to conduct patient assessment is not only the first step in clinical medical diagnosis, but also an important means to accurately grasp patient information, and to conduct risk assessment and big data information collation based on patient assessment information.

[0185] This embodiment can fully control patient information, conduct risk assessment on patients, and make clinical analysis more accurate based on data analysis. Similarly, based on information screening and risk assessment, different levels of clinical care can be carried out for high-risk, medium-risk, and low-risk, which can relieve nursing pressure and improve nursing accuracy and implementation effect. Patient information collection, as the patient goes to the hospital for treatment and care, the more complete the data collection is, the more planning guidance can be provided for nursing and clinical diagnosis. Data guidance can prevent subjective misleading.

[0186] The protection scope of the clinical nursing assessment risk control method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0187] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the clinical nursing assessment risk control method provided in any embodiment of the present application is implemented.

[0188] In the embodiment of the present application, any combination of one or more storage media can be used. The storage medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - electrical, magnetic, optical, electromagnetic, infrared, or semiconductor, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, RAM, ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution, a device or a device or used in combination with it.

[0189] An embodiment of the present application also provides an electronic device. Fig.16 Shown is a schematic diagram of the structure of the electronic device 100 provided in the embodiment of the present application. In some embodiments, the electronic device can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other terminal devices. In addition, the clinical nursing assessment risk control method provided in the present application can also be applied to databases, servers and service responses based on terminal artificial intelligence. The embodiment of the present application does not impose any restrictions on the specific application scenarios of the clinical nursing assessment risk control method.

[0190] like Fig.16 As shown, the electronic device 100 provided in the embodiment of the present application includes a memory 101 and a processor 102 .

[0191] The memory 101 is used to store computer programs; preferably, the memory 101 includes: ROM, RAM, disk, USB flash drive, memory card or CD-ROM and other media that can store program codes.

[0192] Specifically, the memory 101 may include a computer-readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 100 may further include other removable / non-removable, volatile / non-volatile computer storage media. The memory 101 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0193] The processor 102 is connected to the memory 101 and is used to execute the computer program stored in the memory 101 so that the electronic device 100 executes the clinical nursing assessment risk control method provided in any embodiment of the present application.

[0194] Optionally, the processor 102 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.

[0195] Optionally, the electronic device 100 in this embodiment may further include a display 103. The display 103 is communicatively connected with the memory 101 and the processor 102, and is used to display a GUI interaction interface related to the clinical nursing assessment risk control method.

[0196] In summary, the present application can automatically generate risk scales for various assessments based on assessment data, automatically calculate risk levels, and effectively obtain the latest comprehensive, complete, and effective overall clinical assessment data content of patients, ensuring the integrity of risk assessment data, so the present application can effectively improve the accuracy and completeness of risk management. Therefore, the present application effectively overcomes various shortcomings in the prior art and has a high industrial utilization value.

[0197] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A clinical nursing assessment risk control method, characterized in that: include: Acquire multiple categories of patient clinical assessment data, and store the multiple categories of patient clinical assessment data in a preset patient assessment database; Matching risk types and risk applicable conditions based on the clinical assessment data of each category, and combining the risk types and the risk applicable conditions respectively to form a plurality of combined screening items; Matching a corresponding risk scale template to each of the combined screening items from a pre-configured risk scale template library; Controlling the risk scale to enter an editing mode, wherein the risk scale provides a plurality of assessment risk factors for editing in the editing mode, matching the assessment risk factors in the risk scale template based on the clinical assessment data, and determining a risk score and a risk assessment level based on each matched assessment risk factor to form an initial risk scale; In response to receiving a confirmation instruction after confirming the adjustment of each of the assessed risk factors, a final risk scale is generated.

2. The clinical nursing assessment risk control method according to claim 1, characterized in that: The obtaining of multiple categories of clinical assessment data of patients comprises: Collecting patient medical data from a data source, and matching corresponding CCC classification standard codes based on the type of the patient medical data; The patient medical data is evaluated and processed based on the CCC classification standard code to form the patient clinical evaluation data.

3. The clinical nursing assessment risk control method according to claim 1, characterized in that: Also includes: Constructing the patient assessment database: Collecting patient medical data from multiple data sources, and obtaining patient clinical assessment data based on the patient medical data and a CCC classification standard code corresponding to the patient medical data; Based on the data association and mutually exclusive logical attributes between the clinical evaluation data of each patient, the clinical evaluation data of the patient are stored to form a patient evaluation database; The patient assessment database is updated based on the patient clinical assessment data acquired in real time and the timeliness of the data.

4. The clinical nursing assessment risk control method according to claim 1, characterized in that: The matching of the assessment risk factors in the risk scale template based on the clinical assessment data comprises: Decomposing the assessment risk factors in the risk scale template into a structured data set including at least one minimum structured data factor; Each of the minimum structured data factors in the structured data set is matched with the clinical assessment data one by one.

5. The clinical nursing assessment risk control method according to claim 1, characterized in that: The step of decomposing the assessment risk factor in the risk scale template into a structured data set containing at least one minimum structured data factor comprises: Performing semantic analysis on the risk assessment factors to extract multiple factor indicators; Based on any one or more combinations of preset calculation formulas, reference tables, and factor association relationships, the factor indicators are calculated and processed in sequence to generate a structured data set containing at least one minimum structured data factor.

6. The clinical nursing assessment risk control method according to claim 1, characterized in that: Also includes: generating a risk management page based on the final risk scale; The risk management page includes risk management sub-interface areas for multiple patients, each of which includes a corresponding matching display of at least one risk assessment level and risk type, and different risk assessment levels are displayed in different colors.

7. The clinical nursing assessment risk control method according to claim 1, characterized in that: Also includes: A nursing plan including bundled nursing measures is matched based on the final risk scale, and a nursing execution interface including bundled nursing measures is generated based on the nursing plan.

8. The clinical nursing assessment risk control method according to claim 1, characterized in that: Also includes: The final risk scale is updated in real time based on the patient clinical assessment data collected in real time.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the clinical nursing assessment risk control method described in any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and executes the clinical nursing assessment risk control method according to any one of claims 1 to 8 when calling the computer program.

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