Surgical intelligent nursing system and method based on big data monitoring

Abstract

The invention discloses a surgical intelligent nursing system and method based on big data monitoring, and relates to the technical field of surgical nursing, and the system not only makes a first surgical nursing plan depending on the result of a prediction model, but also further optimizes the first surgical nursing plan through detailed data analysis, and forms a final surgical nursing plan. The dual verification mechanism enables the final nursing plan to reflect the quantitative result of the prediction model and to be combined with actual data analysis, so that the nursing plan is more flexible and accurate, and meanwhile, a dynamic adjustment mechanism is introduced in a weight coefficient design mode, so that the accuracy of the prediction model is improved. Therefore, the second surgical nursing plan is allowed to be updated in real time according to the actual recovery condition of the patient, the problem that an existing model is static is solved, the second surgical nursing plan can be dynamically adjusted according to the specific recovery condition of the patient, and the flexibility and adaptability of the final surgical nursing plan can be effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical nursing, and particularly to a surgical intelligent nursing system and method based on big data monitoring. Background Art

[0002] First, various sensors, wearable devices, and hospital information systems are used to collect patients' physiological parameters such as heart rate, blood pressure, etc., lifestyle information such as diet, exercise, etc., medical history and treatment records, etc. Then, the collected data is preprocessed to ensure the removal of duplicate or inaccurate data information and to ensure the quality and consistency of the data. Subsequently, machine learning algorithms are applied to construct a prediction model, and the constructed prediction model is trained using the preprocessed data to obtain a trained prediction model. At the same time, new patient data is input into the trained prediction model to obtain a prediction result. Finally, based on the prediction result, a surgical nursing plan is formulated.

[0003] Although the existing methods of big data monitoring and analysis can provide a scientific basis to support clinical decision-making and improve the accuracy of nursing plans, considering that the existing methods mainly rely on the results of prediction models to formulate nursing plans, these models are usually trained based on quantitative data such as physiological parameters, lifestyle habits, etc. However, once the prediction model is trained, its parameters are usually fixed, resulting in a static model, which cannot be dynamically adjusted according to the actual recovery situation of the patient. This means that if the patient's health condition changes or new situations occur, the model cannot adapt to these changes in a timely manner, resulting in the nursing plan being less flexible and accurate.

[0004] Therefore, the prior art urgently needs technical solutions for a surgical intelligent nursing system and method based on big data monitoring. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a surgical intelligent nursing system based on big data monitoring, which specifically includes the following modules: Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing; Prediction model acquisition module: connected to the data collection and preprocessing module, used to construct a prediction model, train the constructed prediction model using the preprocessed historical data to obtain a trained prediction model, input the patient data to be predicted into the trained prediction model, and obtain a prediction result; First surgical nursing plan formulation module: connected to the prediction model acquisition module, used to formulate a first surgical nursing plan based on the prediction result; Second surgical nursing plan acquisition module: connected to the prediction model acquisition module, used to analyze the patient data to be predicted and obtain a second surgical nursing plan based on the analysis result; Data classification unit: used to classify the patient data to be predicted according to data characteristics, and respectively obtain physiological parameter data, medical history information data and rehabilitation ability data according to the classification results; Standardization unit: used to perform standardization processing on physiological parameter data, medical history information data and rehabilitation ability data respectively; Surgical patient comprehensive health status index calculation unit: used to calculate the comprehensive health status index of surgical patients according to the standardized physiological parameter data, medical history information data and rehabilitation ability data; Surgical patient health index calculation subunit: used to calculate the health index of surgical patients according to the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index; First parameter acquisition interface: used to acquire the ideal value and fluctuation range of each physiological parameter data; Physiological parameter data adjustment factor calculation interface: used to calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Surgical patient health index calculation interface: used to calculate the health index of surgical patients by integrating the adjustment factors of each physiological parameter data; Surgical patient risk index calculation subunit: used to calculate the risk index of surgical patients according to the standardized medical history information data; the medical history information data includes white blood cell count, body temperature value and actual number of days for wound healing; Second parameter acquisition interface: used to acquire the critical value of white blood cell count, the ideal value and fluctuation range of body temperature value, and the expected number of days for actual number of days of wound healing; White blood cell count adjustment factor calculation interface: used to calculate the white blood cell count adjustment factor according to the white blood cell count and the critical value of white blood cell count; Body temperature adjustment factor calculation interface: used to calculate the body temperature value adjustment factor according to the body temperature value, the ideal value and fluctuation range of body temperature value; Wound healing adjustment factor calculation interface: used to calculate the wound healing adjustment factor according to the actual number of days of wound healing and the expected number of days of actual number of days of wound healing; Surgical patient risk index calculation interface: used to calculate the risk index of surgical patients by integrating the white blood cell count adjustment factor, the body temperature value adjustment factor and the wound healing adjustment factor; Surgical patient rehabilitation index calculation subunit: used to calculate the rehabilitation index of surgical patients according to the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance and actual energy level; Third parameter acquisition interface: used to acquire the threshold of pain score, the target distance of actual activity distance, and the target energy level of actual energy level; Pain score adjustment factor calculation interface: used to calculate the pain score adjustment factor according to the pain score and the threshold of pain score; Actual activity distance adjustment factor calculation interface: used to calculate the actual activity distance adjustment factor according to the actual activity distance and the target distance of actual activity distance; Actual energy level adjustment factor calculation interface: used to calculate the actual energy level adjustment factor according to the actual energy level and the target energy level of actual energy level; Surgical patient rehabilitation index calculation interface: used to comprehensively calculate the surgical patient rehabilitation index based on the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor; Weight coefficient calculation subunit: used to calculate the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index, and respectively calculate the ratios of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index, so as to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index; Surgical patient comprehensive health status index calculation subunit: used to comprehensively calculate the surgical patient comprehensive health status index based on the surgical patient health index, the risk index, the rehabilitation index, and the weight coefficients of the surgical patient health index, the risk index, and the rehabilitation index; Second surgical care plan formulation unit: used to formulate the second surgical care plan based on the surgical patient comprehensive health status index; Final surgical care plan acquisition module: connected to the first surgical care plan formulation module and the second surgical care plan acquisition module, used to integrate the second surgical care plan with the first surgical care plan to obtain the final surgical care plan and execute it.

[0006] The surgical intelligent nursing method based on big data monitoring, implementing the surgical intelligent nursing system based on big data monitoring as described above, includes the following steps: Step S1: Collect the historical data of surgical patients and perform preprocessing; Step S2: Construct a prediction model, and use the preprocessed historical data to train the constructed prediction model to obtain a trained prediction model, and input the patient data to be predicted into the trained prediction model to obtain a prediction result; Step S3: Based on the prediction result, formulate the first surgical care plan; Step S4: Analyze the patient data to be predicted, and obtain the second surgical care plan based on the analysis result; Step S4a: Classify the patient data to be predicted according to the data characteristics, and respectively obtain physiological parameter data, medical history information data, and rehabilitation ability data according to the classification results; Step S4b: Perform standardization processing on the physiological parameter data, medical history information data, and rehabilitation ability data respectively; Step S4c: Calculate the comprehensive health status index of surgical patients based on the standardized physiological parameter data, medical history information data, and rehabilitation ability data; Step S4c1: Calculate the health index of surgical patients based on the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation, and body mass index; Step S4c11: Obtain the ideal value and fluctuation range of each physiological parameter data; Step S4c12: Calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Step S4c13: Calculate the health index of surgical patients by integrating the adjustment factors of each physiological parameter data; Step S4c2: Calculate the risk index of surgical patients based on the standardized medical history information data; the medical history information data includes white blood cell count, body temperature value, and actual number of days for wound healing; Step S4c21: Obtain the critical value of white blood cell count, the ideal value and fluctuation range of body temperature value, and the expected number of days for actual wound healing; Step S4c22: Calculate the white blood cell count adjustment factor according to the white blood cell count and the critical value of white blood cell count; Step S4c23: Calculate the body temperature value adjustment factor according to the body temperature value, the ideal value and fluctuation range of body temperature value; Step S4c24: Calculate the wound healing adjustment factor according to the actual number of days for wound healing and the expected number of days for actual wound healing; Step S4c25: Calculate the risk index of surgical patients by integrating the white blood cell count adjustment factor, the body temperature value adjustment factor, and the wound healing adjustment factor; Step S4c3: Calculate the rehabilitation index of surgical patients based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance, and actual energy level; Step S4c31: Obtain the threshold of pain score, the target distance of actual activity distance, and the target energy level of actual energy level; Step S4c32: Calculate the pain score adjustment factor according to the pain score and the threshold of pain score; Step S4c33: Calculate the actual activity distance adjustment factor based on the actual activity distance and the target distance of the actual activity distance. Step S4c34: Calculate the actual energy level adjustment factor based on the actual energy level and the target energy level of the actual energy level. Step S4c35: Calculate the rehabilitation index of the surgical patient by integrating the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor. Step S4c4: Calculate the sum of the health index, risk index, and rehabilitation index of the surgical patient, and calculate the ratios of the health index, risk index, and rehabilitation index of the surgical patient to the sum of the health index, risk index, and rehabilitation index of the surgical patient respectively to obtain the weight coefficients of the health index, risk index, and rehabilitation index of the surgical patient. Step S4c5: Integrate the health index, risk index, and rehabilitation index of the surgical patient with the weight coefficients of the health index, risk index, and rehabilitation index of the surgical patient to obtain the comprehensive health status index of the surgical patient. Step S4d: Develop the second surgical care plan based on the comprehensive health status index of the surgical patient. Step S5: Integrate the second surgical care plan with the first surgical care plan to obtain the final surgical care plan and execute it.

[0007] The embodiments of the present invention have the following technical effects: The present invention not only relies on the results of the prediction model to develop the first surgical care plan, but also further optimizes the first surgical care plan through detailed data analysis to form the final surgical care plan. This dual verification mechanism enables the final care plan to not only reflect the quantitative results of the prediction model, but also combine actual data analysis, thus ensuring that the care plan is more flexible and accurate. At the same time, the present invention introduces a dynamic adjustment mechanism in the design method of weight coefficients, allowing the second surgical care plan to be updated in real time according to the actual recovery situation of the patient. This method overcomes the problem of the static nature of existing models, enabling the second surgical care plan to be dynamically adjusted according to the specific recovery situation of the patient, thereby effectively enhancing the flexibility and adaptability of the final surgical care plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 is a framework diagram of a surgical intelligent nursing system based on big data monitoring provided by an embodiment of the present invention; Figure 2 is a flowchart of a surgical intelligent nursing method based on big data monitoring provided by an embodiment of the present invention. Detailed implementation manners

[0010] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the scope protected by the present invention.

[0011] Embodiment 1: As Figure 1 shown, the present invention provides a surgical intelligent nursing system based on big data monitoring, including the following modules: Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing; It should be noted that for the means of data collection, sensors and wearable devices such as smart watches and electrocardiographs can be used. These devices can continuously collect data, provide high-resolution time series data, and obtain medical history and treatment records through hospital information systems. These systems store a large amount of structured and unstructured medical data, which is convenient for historical data analysis. Even methods such as questionnaires and interviews can be used to collect lifestyle information and partial rehabilitation ability data; the historical data collected includes physiological parameters: including blood pressure, heart rate, blood oxygen saturation, body mass index, etc. These data are key indicators for real-time monitoring and can reflect the patient's immediate health status; lifestyle information: such as eating habits, exercise frequency, sleep quality, etc. This type of data helps to understand the impact of the patient's lifestyle on health; medical history and treatment records: including past medical history, surgical records, drug allergy history, white blood cell count, body temperature value, wound healing condition, etc. These data provide background information on the patient's long-term health status; rehabilitation ability data: including pain score, actual activity distance, actual energy level, etc. These data reflect the patient's postoperative recovery ability; It is further worth noting that the applications of big data monitoring are mainly reflected in the fact that devices such as smartwatches and electrocardiographs can continuously collect patients' physiological parameters such as blood pressure, heart rate, blood oxygen saturation, body mass index, etc. The high-resolution time series data provided by these devices constitutes a part of big data. For example, heart rate data can be obtained once per minute through a smartwatch, and a huge dataset is formed after long-term accumulation; hospital information systems, which store a large amount of structured and unstructured medical data, including medical history and treatment records, such as past medical history, surgical records, drug allergy history, white blood cell count, body temperature value, wound healing condition, etc. These data are not only huge in quantity but also rich in content, providing a solid foundation for subsequent analysis, and thus are also one of the components of big data; through regular questionnaires and patient interviews, information on patients' living habits such as eating habits, exercise frequency, sleep quality, etc. and some rehabilitation ability data such as pain score, actual activity distance, actual energy level, etc. can be dynamically tracked. Although these data are relatively less, they are also an important part of big data; It is further worth noting that for the preprocessing operation, it mainly targets the data collected above, aiming to remove duplicate data, ensure the uniqueness of each record, avoid redundancy, and fill in missing values, such as using the mean value filling, interpolation method or other statistical methods to fill in missing values, etc.

[0012] Prediction model acquisition module: Connected to the data collection and preprocessing module, it is used to construct a prediction model, train the constructed prediction model using the preprocessed historical data, obtain the trained prediction model, and input the patient data to be predicted into the trained prediction model to obtain a prediction result; It should be noted that regarding the training process of the above prediction model, first, label definition is carried out, that is, the target labels for training the model are defined. Common labels can be "good recovery", "complication occurred", "need for additional care", etc. These labels are usually based on the professional judgment of doctors and the actual situation of patients; subsequently, a neural network model is selected, especially a deep learning model, because it performs excellently on large datasets and is therefore usually the most preferred; then the preprocessed data mentioned above is utilized and integrated into a dataset, and then the dataset is divided into a training set, a validation set, and a test set. Generally, the ratio is 70% for the training set, 15% for the validation set, and 15% for the test set. Next, the selected model is trained using 70% of the training set. During the training process, the loss function needs to be calculated and the model parameters are updated with the goal of minimizing the loss function value. Next, 15% of the validation set is used for hyperparameter optimization. Common methods include grid search, random search, and Bayesian optimization. By adjusting the hyperparameters of the model, such as the learning rate, regularization coefficient, etc., until the optimal model configuration is found, that is, the trained prediction model is obtained; among them, the commonly selected metrics for model evaluation are accuracy, precision, recall, and F1-score, etc. Finally, 15% of the test set is used to verify the trained model to ensure that the model has strong generalization ability and does not overfit the training data. Finally, the trained model is deployed to the actual application scenario to receive the data of new patients in real time and make predictions.

[0013] The first surgical care plan formulation module: connected to the prediction model acquisition module, used to formulate the first surgical care plan based on the prediction results; It should be noted that based on the above prediction results including the risk of postoperative complications, the estimated recovery time, the special care measures required, etc., these results provide important reference bases for formulating the care plan; according to the prediction results, a preliminary care plan is formulated, such as whether intensive care needs to be strengthened, the medication plan needs to be adjusted, physical therapy needs to be arranged, etc. For example, if the prediction results show that the patient has a high risk of infection, the frequency of using antibiotics can be increased in the care plan.

[0014] The second surgical care plan acquisition module: connected to the prediction model acquisition module, used to analyze the data of the patient to be predicted and obtain the second surgical care plan based on the analysis results; The data classification unit: used to classify the data of the patient to be predicted according to the data characteristics, and respectively obtain physiological parameter data, medical history information data, and rehabilitation ability data based on the classification results; It should be noted that among them, the data characteristics of the physiological parameter data are continuous, periodic, etc. The medical history information data is mostly discrete data, and the rehabilitation ability data has both quantitative and qualitative components. Therefore, based on these data characteristics, the data of the patient to be predicted can be reasonably classified quickly; It is further worth noting that through classification, more precise nursing strategies can be formulated for different types of data, improving the nursing effect. For example, physiological parameter data can be used to monitor the patient's immediate health status, while medical history information data can be used to assess long-term health risks; moreover, the classified data is easier to analyze specifically, reducing unnecessary computational complexity. For example, when calculating the health index, only the relevant physiological parameter data needs to be considered, rather than processing all the original data, that is, all the patient data to be predicted.

[0015] Standardization unit: used to perform standardization processing on physiological parameter data, medical history information data, and rehabilitation ability data respectively; It is worth noting that the reason for performing standardization processing on physiological parameter data, medical history information data, and rehabilitation ability data respectively is that different types of physiological parameters, medical history information, and rehabilitation ability data often have different dimensions. After standardization processing, they can be compared and analyzed on the same scale. For example, the units of blood pressure and body mass index are different, and after standardization processing, it is convenient to calculate and avoid calculation errors at the same time.

[0016] Surgical patient comprehensive health status index calculation unit: used to calculate the comprehensive health status index of surgical patients based on the standardized physiological parameter data, medical history information data, and rehabilitation ability data; It is worth noting that through the comprehensive health status index, the health status of patients can be comprehensively evaluated from multiple dimensions, providing a more scientific basis for decision-making. For example, the comprehensive health status index can simultaneously reflect the physiological health, risk level, and rehabilitation ability of patients, helping medical staff formulate a comprehensive nursing plan.

[0017] Surgical patient health index calculation subunit: used to calculate the health index of surgical patients based on the standardized physiological parameter data; the physiological parameter data includes blood pressure 、heart rate 、blood oxygen saturation and body mass index ; It is worth noting that by calculating the health index, the physiological state of patients can be refined and managed, and potential problems can be detected in a timely manner. For example, the health index can be used as a reference indicator for daily health monitoring, helping medical staff adjust the treatment plan in a timely manner; at the same time, based on the health index, personalized health management suggestions can be provided for each patient. For example, for patients with a lower health index, it can be recommended to increase rest time or adjust the drug dosage; for patients with a higher health index, appropriate increase in activity can be encouraged.

[0018] First parameter acquisition interface: used to acquire the ideal value and fluctuation range of each physiological parameter data; Physiological parameter data adjustment factor calculation interface: used to calculate the adjustment factor for each physiological parameter data based on the ideal value and fluctuation range of each physiological parameter data; Among them, the calculation formula group for the adjustment factor of each physiological parameter data is:

[0019]

[0020]

[0021]

[0022] In the formula, , , and respectively represent the adjustment factors of blood pressure , heart rate , blood oxygen saturation and body mass index ; , , and respectively represent the ideal values of blood pressure , heart rate , blood oxygen saturation and body mass index ; , , and respectively represent the fluctuation ranges of blood pressure , heart rate , blood oxygen saturation and body mass index ; It should be noted that the , , and calculated separately above are mainly used to reflect the gap between the actual values and the ideal values of each parameter such as blood pressure , heart rate , blood oxygen saturation and body mass index , and consider their fluctuation ranges. And when the adjustment factor is closer to 1, it means that the parameter is closer to the ideal state; the closer it is to 0, the farther the parameter deviates from the ideal state. At the same time, in the above, each parameter such as blood pressure , heart rate , blood oxygen saturation and body mass index It is included in the calculation after being standardized. Similarly, the ideal values and fluctuation ranges of each parameter also need to be standardized before being included in the calculation. Therefore, the calculated , , and is a dimensionless proportionality coefficient, representing the degree to which the actual value deviates from the ideal value relative to its allowable fluctuation range.

[0023] Surgical patient health index calculation interface: used to calculate the surgical patient health index by integrating the adjustment factors of each physiological parameter data; Among them, the calculation formula for the surgical patient health index is:

[0024] In the formula, represents the surgical patient health index; , , and represent the adjustment factors of blood pressure , heart rate , blood oxygen saturation and body mass index respectively; The above calculation formula for the surgical patient health index is mainly used to comprehensively reflect the overall health status of the patient. The higher the health index, the better the overall health condition of the patient. And since the calculated , , and is a dimensionless proportionality coefficient, so is also a dimensionless value, usually ranging from 0 to 1, and the larger the value, the better the health condition.

[0025] Surgical patient risk index calculation subunit: used to calculate the surgical patient risk index based on the standardized medical history information data; the medical history information data includes white blood cell count WBC, body temperature value Temp, and the actual number of days for wound healing Healing; It should be noted that by calculating the risk index, high-risk patients can be identified in advance and preventive measures can be taken. For example, the risk index can be an important indicator for preoperative assessment to help doctors decide whether additional preoperative preparations are needed; at the same time, medical resources can be reasonably allocated according to the risk index to improve the overall nursing efficiency. For example, for patients with a higher risk index, more monitoring personnel and equipment can be arranged to ensure safety.

[0026] Second parameter acquisition interface: used to acquire the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days for the actual number of days of wound healing; White blood cell count adjustment factor calculation interface: used to calculate the white blood cell count adjustment factor according to the white blood cell count and the critical value of white blood cell count; Among them, the calculation formula of the white blood cell count adjustment factor is:

[0027] In the formula, represents the white blood cell count adjustment factor; represents the white blood cell count; represents the critical value of white blood cell count; It should be noted that the calculation formula of the white blood cell count adjustment factor mainly reflects the gap between the actual value and the critical value of the white blood cell count; the smaller the adjustment factor, the closer the white blood cell count is to the critical value, indicating that there may be an infection or other inflammatory reactions.

[0028] Body temperature adjustment factor calculation interface: used to calculate the body temperature adjustment factor according to the body temperature value, the ideal value and fluctuation range of body temperature;

[0029] Among them, the calculation formula of the body temperature adjustment factor is:

[0030]

[0031] In the formula, represents the body temperature adjustment factor; represents the ideal value of body temperature; represents the fluctuation range of body temperature; It should be noted that the calculation formula of the body temperature adjustment factor reflects the gap between the actual value and the ideal value of body temperature and takes into account its fluctuation range. When the adjustment factor is smaller, it indicates that the body temperature is closer to the ideal value, suggesting that the patient's body temperature is normal or close to normal; among them, the body temperature value represents the actual value of the body temperature of surgical patients; Wound healing adjustment factor calculation interface: used to calculate the wound healing adjustment factor according to the actual number of days of wound healing and the expected number of days for the actual number of days of wound healing; Among them, the calculation formula of the wound healing adjustment factor is:

[0032] In the formula, represents the wound healing adjustment factor; represents the actual number of days of wound healing; represents the expected number of days for the actual number of days of wound healing; It should be noted that the calculation formula of the wound healing adjustment factor mainly reflects the gap between the actual healing days and the expected healing days. When the adjustment factor is smaller, it indicates that the wound healing time is closer to the expectation, suggesting good healing conditions. Meanwhile, in the above, since the white blood cell count WBC, body temperature value Temp, and the actual number of days of wound healing Healing are included in the calculation after being standardized, and regarding these parameters such as , , and also need to be standardized before they can be included in the calculation and used. Therefore, regarding the , and obtained from the above calculation are all dimensionless proportionality coefficients.

[0033] Surgical patient risk index calculation interface: used to comprehensively calculate the surgical patient risk index by combining the white blood cell count adjustment factor, body temperature value adjustment factor, and wound healing adjustment factor; Among them, the calculation formula of the surgical patient risk index is:

[0034] In the formula, represents the surgical patient risk index; represents the white blood cell count adjustment factor; represents the body temperature value adjustment factor; represents the wound healing adjustment factor; It should be noted that the calculation formula of the surgical patient risk index is mainly used to comprehensively reflect the postoperative risk level of the patient. When the risk index is higher, it indicates that the patient faces greater risks. At the same time, due to the various adjustment factors involved in it, such as , and are all dimensionless proportionality coefficients, so the calculated is also a dimensionless value, usually between 0 and 1, and the larger the value, the higher the risk.

[0035] Surgical patient rehabilitation index calculation subunit: used to calculate the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score Pain, actual activity distance Mobility, and actual energy level Energy; It should be noted that by calculating the rehabilitation index, a targeted rehabilitation plan can be formulated to accelerate the patient's recovery process. For example, the rehabilitation index can be used as a basis for adjusting the intensity of rehabilitation training to help the patient gradually recover to the best state.

[0036] Third parameter acquisition interface: used to obtain the threshold of pain score, the target distance of actual activity distance, and the target energy level of actual energy level; Pain score adjustment factor calculation interface: used to calculate the pain score adjustment factor according to the pain score and the threshold of the pain score; Among them, the calculation formula of the pain score adjustment factor is:

[0037]

[0038] In the formula, represents the pain score adjustment factor; represents the threshold of the pain score; It should be noted that the calculation formula of the pain score adjustment factor reflects the gap between the actual value and the threshold of the pain score. When the adjustment factor is smaller, it means that the pain score is closer to the threshold, indicating a lower degree of pain; Actual activity distance adjustment factor calculation interface: used to calculate the actual activity distance adjustment factor according to the actual activity distance and the target distance of the actual activity distance; Among them, the calculation formula of the actual activity distance adjustment factor is:

[0039] In the formula, represents the actual activity distance adjustment factor; represents the target distance of the actual activity distance; It should be noted that the calculation formula of the actual activity distance adjustment factor reflects the gap between the actual activity distance and the target distance. When the adjustment factor is smaller, it means that the actual activity distance is closer to the target distance, indicating that the patient's rehabilitation progress is good; Actual energy level adjustment factor calculation interface: used to calculate the actual energy level adjustment factor according to the actual energy level and the target energy level of the actual energy level;

[0040] Among them, the calculation formula of the actual energy level adjustment factor is:

[0041] In the formula, represents the actual energy level adjustment factor; represents the target energy level of the actual energy level; It should be noted that the calculation formula of the actual energy level adjustment factor reflects the gap between the actual energy level and the target energy level. When the adjustment factor is smaller, it means that the actual energy level is closer to the target energy level, indicating that the patient's energy recovery is good; Meanwhile, the pain score Pain, the actual activity distance Mobility, and the actual energy level Energy mentioned above are included in the calculation after being standardized. At the same time, for these parameters such as , , they also need to be standardized before they can be included in the calculation and used. Therefore, the , and obtained from the above calculations are all dimensionless proportionality coefficients.

[0042] Surgical patient rehabilitation index calculation interface: used to comprehensively calculate the surgical patient rehabilitation index by combining the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor; Among them, the calculation formula for the surgical patient rehabilitation index is:

[0043] In the formula, represents the surgical patient rehabilitation index; represents the pain score adjustment factor; represents the actual activity distance adjustment factor; represents the actual energy level adjustment factor; It should be noted that the calculation formula for the surgical patient rehabilitation index is mainly used to comprehensively reflect the patient's postoperative recovery ability. The higher the rehabilitation index, the stronger the patient's recovery ability. At the same time, because the , and in it are all dimensionless proportionality coefficients, therefore, is also a dimensionless value, usually between 0 and 1, and the larger the value, the stronger the rehabilitation ability.

[0044] Weight coefficient calculation sub-unit: used to calculate the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index, and respectively calculate the ratios of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index; Among them, the calculation formula group for the weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index is:

[0045]

[0046]

[0047] In the formula, represents the weight coefficient of the health index of surgical patients; represents the weight coefficient of the risk index of surgical patients; represents the weight coefficient of the rehabilitation index of surgical patients; , and respectively represent the health index of surgical patients, the risk index of surgical patients, and the rehabilitation index of surgical patients; Regarding the calculation formula group of the above weight coefficients, it is mainly used to calculate the weight coefficients of the health index, risk index, and rehabilitation index, so as to reasonably and scientifically allocate their importance in the comprehensive health status index, and the weight coefficients reflect the relative importance of each index in the overall evaluation; at the same time, because are all dimensionless values, the calculated weight coefficients are also dimensionless proportional coefficients.

[0048] It should be noted that by dynamically adjusting the weight coefficients, it is ensured that the nursing plan can be flexibly adjusted according to the actual health status of the patients, improving the accuracy and effectiveness of nursing. For example, when a certain physiological parameter of a patient changes significantly, the system will automatically adjust the weight of this parameter in the comprehensive health status index, thereby updating the nursing plan. At the same time, the comprehensive health status index based on the weight coefficients provides a scientific decision-making basis to help the subsequent medical staff formulate the optimal nursing plan. For example, the comprehensive health status index can be used as a standard for nursing priority ranking to ensure that high-risk patients receive timely attention.

[0049] Comprehensive health status index calculation subunit for surgical patients: used to combine the health index of surgical patients, the risk index of surgical patients, and the rehabilitation index of surgical patients with the weight coefficients of the health index of surgical patients, the risk index of surgical patients, and the rehabilitation index of surgical patients to obtain the comprehensive health status index of surgical patients;

[0050] Among them, the calculation formula of the comprehensive health status index of surgical patients is:

[0051] In the formula, represents the comprehensive health status index of surgical patients; represents the weight coefficient of the health index of surgical patients; represents the weight coefficient of the risk index of surgical patients; represents the weight coefficient of the rehabilitation index of surgical patients; , and respectively represent the health index of surgical patients, the risk index of surgical patients, and the rehabilitation index of surgical patients; It should be noted that the comprehensive health status index can comprehensively reflect the health status of patients and provide a reliable quantitative basis for formulating the final care plan. For example, the comprehensive health status index can simultaneously reflect the physiological health, risk level, and rehabilitation ability of patients, helping the subsequent medical staff to formulate a comprehensive care plan. At the same time, according to the comprehensive health status index of different patients, a personalized care plan can be formulated to improve the care effect. For example, for patients with a lower comprehensive health status index, it is recommended to increase the monitoring frequency and adjust the drug dosage; for patients with strong rehabilitation ability, the care intervention can be appropriately reduced and independent rehabilitation can be encouraged; At the same time, it is worth further explaining that the calculation formula for the comprehensive health status index of surgical patients is mainly used to calculate the comprehensive health status index of surgical patients. It is calculated based on the health index, risk index, and rehabilitation index and their weight coefficients, comprehensively reflecting the overall health status of patients. The higher the comprehensive health status index, the better the overall health status of the patient; at the same time, since are all dimensionless values, and each weight coefficient is also a dimensionless proportional coefficient, therefore, is also a dimensionless value, usually ranging from 0 to 1, and the larger the value, the better the comprehensive health status.

[0052] The second surgical care plan formulation unit: used to formulate the second surgical care plan based on the comprehensive health status index of surgical patients; It should be noted that regarding how to formulate the second surgical care plan, specifically, based on the comprehensive health status index, analyze the patient's current health status, risk level, and rehabilitation ability. For example, by analyzing the change trends of the health index, risk index, and rehabilitation index, to understand the overall health status of the patient, and then according to the analysis results, formulate specific care measures, such as adjusting the medication plan, increasing the intensity of rehabilitation training, etc. For example, for patients with a lower comprehensive health status index, it may be recommended to increase the monitoring frequency and adjust the drug dosage; for patients with strong rehabilitation ability, the care intervention can be appropriately reduced and independent rehabilitation can be encouraged; At the same time, it is worth further explaining that the second surgical care plan is a comprehensive, scientific, and personalized care plan, covering multiple aspects such as short-term risk control, medium-term health management, and long-term rehabilitation guidance, ensuring the effectiveness and sustainability of care. For example, for patients with a lower comprehensive health status index, it may be recommended to increase the monitoring frequency and adjust the drug dosage; for patients with strong rehabilitation ability, the care intervention can be appropriately reduced and independent rehabilitation can be encouraged.

[0053] The final surgical care plan acquisition module: connected to the first surgical care plan formulation module and the second surgical care plan acquisition module, used to integrate the second surgical care plan with the first surgical care plan to obtain the final surgical care plan and execute it; It should be noted that regarding how to integrate the second surgical care plan with the first surgical care plan to obtain the final surgical care plan, the specific integration process is as follows: First, compare the first and second surgical care plans to identify the differences and commonalities. For example, if the first plan focuses on short-term risk control while the second plan emphasizes long-term rehabilitation, then find the balance point through comparison. Subsequently, combine the advantages of the two plans to formulate the final care plan. For example, if the first plan focuses on short-term risk control and the second plan emphasizes long-term rehabilitation, then include the key measures of both in the final plan.

[0054] Embodiment 2: As Figure 2 shown, the present invention also proposes a surgical intelligent care method based on big data monitoring. Implement the surgical intelligent care system based on big data monitoring as described above, including the following steps: Step S1: Collect the historical data of surgical patients and perform preprocessing; Step S2: Construct a prediction model and use the preprocessed data to train the constructed prediction model to obtain a trained prediction model. Input the patient data to be predicted into the trained prediction model to obtain a prediction result; Step S3: Based on the prediction result, formulate the first surgical care plan; Step S4: Analyze the patient data to be predicted and obtain the second surgical care plan based on the analysis result; Step S4a: Classify the patient data to be predicted according to the data characteristics, and respectively obtain physiological parameter data, medical history information data, and rehabilitation ability data according to the classification results; Step S4b: Perform standardization processing on the physiological parameter data, medical history information data, and rehabilitation ability data respectively; Step S4c: Calculate the comprehensive health status index of the surgical patient according to the standardized physiological parameter data, medical history information data, and rehabilitation ability data; Step S4c1: Calculate the health index of the surgical patient according to the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation, and body mass index; Step S4c11: Obtain the ideal value and fluctuation range of each physiological parameter data; Step S4c12: Calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Step S4c13: Calculate the health index of the surgical patient by integrating the adjustment factors of each physiological parameter data; Step S4c2: Calculate the surgical patient risk index based on the standardized medical history information data. The medical history information data includes white blood cell count, body temperature value, and actual number of days for wound healing. Step S4c21: Obtain the critical value of white blood cell count, the ideal value and fluctuation range of body temperature value, and the expected number of days for actual wound healing. Step S4c22: Calculate the white blood cell count adjustment factor according to the white blood cell count and the critical value of white blood cell count. Step S4c23: Calculate the body temperature value adjustment factor according to the body temperature value, the ideal value and fluctuation range of body temperature value. Step S4c24: Calculate the wound healing adjustment factor according to the actual number of days for wound healing and the expected number of days for actual wound healing. Step S4c25: Calculate the surgical patient risk index by integrating the white blood cell count adjustment factor, the body temperature value adjustment factor, and the wound healing adjustment factor. Step S4c3: Calculate the surgical patient rehabilitation index based on the standardized rehabilitation ability data. The rehabilitation ability data includes pain score, actual activity distance, and actual energy level. Step S4c31: Obtain the threshold of pain score, the target distance of actual activity distance, and the target energy level of actual energy level. Step S4c32: Calculate the pain score adjustment factor according to the pain score and the threshold of pain score. Step S4c33: Calculate the actual activity distance adjustment factor according to the actual activity distance and the target distance of actual activity distance. Step S4c34: Calculate the actual energy level adjustment factor according to the actual energy level and the target energy level of actual energy level. Step S4c35: Calculate the surgical patient rehabilitation index by integrating the pain score adjustment factor, the actual activity distance adjustment factor, and the actual energy level adjustment factor. Step S4c4: Calculate the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index, and calculate the ratios of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index respectively to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index. Step S4c5: Integrate the surgical patient health index, the surgical patient risk index, the surgical patient rehabilitation index, and the weight coefficients of the surgical patient health index, the surgical patient risk index, and the surgical patient rehabilitation index to obtain the surgical patient comprehensive health status index. Step S4d: Develop a second surgical care plan based on the comprehensive health status index of the surgical patient; Step S5: Integrate the second surgical care plan with the first surgical care plan to obtain the final surgical care plan and implement it.

[0055] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates otherwise, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method or device including the said element.

[0056] It should also be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. Unless otherwise clearly specified and defined, terms such as "installed", "connected", "connected to" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific cases.

[0057] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. The surgical intelligent nursing system based on big data monitoring is characterized by: Includes the following modules: Data collection and preprocessing module: used to collect historical data of surgical patients and perform preprocessing; Prediction model acquisition module: connected to the data collection and preprocessing module, used to build a prediction model, and use the preprocessed historical data to train the constructed prediction model to obtain a trained prediction model, and input the patient data to be predicted into the trained prediction model to obtain the prediction result; The first surgical nursing plan formulation module is connected to the prediction model acquisition module and is used to formulate the first surgical nursing plan based on the prediction results; Second surgical nursing plan acquisition module: connected to the prediction model acquisition module, used to analyze the patient data to be predicted and obtain the second surgical nursing plan based on the analysis results; Final surgical care plan acquisition module: connected to the first surgical care plan formulation module and the second surgical care plan acquisition module, used to integrate the second surgical care plan with the first surgical care plan, obtain the final surgical care plan and execute it.

2. The surgical intelligent nursing system based on big data monitoring according to claim 1 is characterized in that: The step of analyzing the patient data to be predicted and obtaining a second surgical care plan based on the analysis result includes: Data classification unit: used to classify the patient data to be predicted according to data characteristics, and obtain physiological parameter data, medical history information data and rehabilitation ability data according to the classification results; Standardization unit: used to standardize physiological parameter data, medical history information data and rehabilitation ability data respectively; Surgical patient comprehensive health status index calculation unit: used to calculate the surgical patient comprehensive health status index based on the standardized physiological parameter data, medical history information data and rehabilitation ability data; Second surgical nursing plan formulation unit: used to formulate the second surgical nursing plan based on the comprehensive health status index of surgical patients.

3. The surgical intelligent nursing system based on big data monitoring according to claim 2 is characterized in that: The comprehensive health status index of surgical patients is calculated based on the standardized physiological parameter data, medical history information data and rehabilitation ability data, including: The surgical patient health index calculation subunit is used to calculate the surgical patient health index based on the standardized physiological parameter data; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index; Surgical patient risk index calculation subunit: used to calculate the surgical patient risk index based on the standardized medical history information data; the medical history information data includes white blood cell count, body temperature and actual number of days for wound healing; Surgical patient rehabilitation index calculation subunit: used to calculate the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance and actual energy level; The weight coefficient calculation subunit is used to calculate the sum of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index, and respectively calculate the ratio of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index, so as to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index; The surgical patient comprehensive health status index calculation subunit is used to comprehensively calculate the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index with the weight coefficient of the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index to obtain the surgical patient comprehensive health status index.

4. The surgical intelligent nursing system based on big data monitoring according to claim 3 is characterized in that: The health index of surgical patients is calculated based on the physiological parameter data after the standardization process; The physiological parameter data include blood pressure, heart rate, blood oxygen saturation and body mass index, including: The first parameter acquisition interface is used to obtain the ideal value and fluctuation range of each physiological parameter data; Physiological parameter data adjustment factor calculation interface: used to calculate the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Surgical patient health index calculation interface: used to integrate the adjustment factors of each physiological parameter data to calculate the surgical patient health index.

5. The surgical intelligent nursing system based on big data monitoring according to claim 3 is characterized in that: The surgical patient risk index is calculated based on the standardized medical history information data; The medical history information data includes white blood cell count, body temperature and actual number of days for wound healing; including: The second parameter acquisition interface: used to obtain the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days for actual wound healing; White blood cell count adjustment factor calculation interface: used to calculate the white blood cell count adjustment factor based on the white blood cell count and the critical value of the white blood cell count; Temperature adjustment factor calculation interface: used to calculate the temperature adjustment factor based on the temperature value, the ideal value of the temperature value and the fluctuation range; Wound healing adjustment factor calculation interface: used to calculate the wound healing adjustment factor according to the actual number of days for wound healing and the expected number of days for wound healing; Surgical patient risk index calculation interface: used to integrate the white blood cell count adjustment factor, body temperature adjustment factor and wound healing adjustment factor to calculate the surgical patient risk index.

6. The surgical intelligent nursing system based on big data monitoring according to claim 3 is characterized in that: The rehabilitation index of surgical patients is calculated based on the standardized rehabilitation ability data; The rehabilitation ability data includes pain score, actual activity distance and actual energy level, including: The third parameter acquisition interface is used to obtain the threshold of the pain score, the target distance of the actual activity distance, and the target energy level of the actual energy level; Pain score adjustment factor calculation interface: used to calculate the pain score adjustment factor according to the pain score and the pain score threshold; Actual activity distance adjustment factor calculation interface: used to calculate the actual activity distance adjustment factor based on the actual activity distance and the target distance of the actual activity distance; Actual energy level adjustment factor calculation interface: used to calculate the actual energy level adjustment factor according to the actual energy level and the target energy level of the actual energy level; Surgical patient rehabilitation index calculation interface: used to calculate the surgical patient rehabilitation index by integrating the pain score adjustment factor, actual activity distance adjustment factor and actual energy level adjustment factor.

7. A surgical intelligent nursing method based on big data monitoring, applied to implement a surgical intelligent nursing system based on big data monitoring as claimed in any one of claims 1 to 6, characterized in that: The method comprises the following steps: Step S1, collecting historical data of surgical patients and performing preprocessing; Step S2: construct a prediction model, and use the preprocessed historical data to train the constructed prediction model to obtain a trained prediction model, and input the patient data to be predicted into the trained prediction model to obtain a prediction result; Step S3: formulating a first surgical nursing plan based on the prediction result; Step S4, analyzing the patient data to be predicted, and obtaining a second surgical care plan based on the analysis results; Step S4a, classifying the patient data to be predicted according to data characteristics, and obtaining physiological parameter data, medical history information data and rehabilitation ability data according to the classification results; Step S4b, standardizing the physiological parameter data, medical history information data and rehabilitation ability data respectively; Step S4c, calculating the comprehensive health status index of the surgical patient according to the standardized physiological parameter data, medical history information data and rehabilitation ability data; Step S4c1, calculating the health index of the surgical patient based on the physiological parameter data after the normalization process; the physiological parameter data includes blood pressure, heart rate, blood oxygen saturation and body mass index; Step S4c11, obtaining the ideal value and fluctuation range of each physiological parameter data; Step S4c12, calculating the adjustment factor of each physiological parameter data according to each physiological parameter data and the ideal value and fluctuation range of each physiological parameter data; Step S4c13, combining the adjustment factors of each physiological parameter data to calculate the surgical patient health index; Step S4c2, calculating the surgical patient risk index based on the standardized medical history information data; the medical history information data includes white blood cell count, body temperature and actual number of days for wound healing; Step S4c21, obtaining the critical value of white blood cell count, the ideal value and fluctuation range of body temperature, and the expected number of days of actual wound healing; Step S4c22, calculating a white blood cell count adjustment factor according to the white blood cell count and the critical value of the white blood cell count; Step S4c23, calculating a body temperature adjustment factor according to the body temperature value, the ideal value of the body temperature value and the fluctuation range; Step S4c24, calculating a wound healing adjustment factor according to the actual number of days for wound healing and the expected number of days for wound healing; Step S4c25, calculating the surgical patient risk index by combining the white blood cell count adjustment factor, the body temperature adjustment factor and the wound healing adjustment factor; Step S4c3, calculating the surgical patient rehabilitation index based on the standardized rehabilitation ability data; the rehabilitation ability data includes pain score, actual activity distance and actual energy level; Step S4c31, obtaining a threshold value of a pain score, a target distance of an actual activity distance, and a target energy level of an actual energy level; Step S4c32, calculating a pain score adjustment factor according to the pain score and the pain score threshold; Step S4c33, calculating an actual activity distance adjustment factor according to the actual activity distance and the target distance of the actual activity distance; Step S4c34, calculating an actual energy level adjustment factor according to the actual energy level and the target energy level of the actual energy level; Step S4c35, calculating the surgical patient rehabilitation index by combining the pain score adjustment factor, the actual activity distance adjustment factor and the actual energy level adjustment factor; Step S4c4, calculating the sum of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index, and respectively calculating the ratio of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index to the sum of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index, to obtain the weight coefficients of the surgical patient health index, the surgical patient risk index and the surgical patient rehabilitation index; Step S4c5, synthesizing the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index with the weight coefficient of the surgical patient health index, surgical patient risk index and surgical patient rehabilitation index to obtain a comprehensive health status index of the surgical patient; Step S4d, formulating a second surgical nursing plan based on the surgical patient's comprehensive health status index; Step S5: Integrate the second surgical care plan with the first surgical care plan to obtain a final surgical care plan and execute it.

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