A neurosurgery nursing management system based on cloud computing

Abstract

The present invention relates to the field of nursing management technology, specifically a neurosurgery nursing management system based on cloud computing, the system includes: a nursing path identification module extracts the sequence information of the operation type, postoperative recovery monitoring index, neurosurgery nursing intervention measures, and nervous system medication based on the nursing process data of neurosurgery patients, and performs serial processing of nursing behavior. In the present invention, multiple features are extracted from the nursing process data of neurosurgery patients through serial processing of nursing behavior, the nursing behavior is processed by time node refinement, a nursing behavior framework diagram is constructed, and the characteristic relationship of nursing behavior is presented. Then, time series simulation and mapping predict future nursing nodes and resource requirements, so that the key nodes and intervention measures of the nursing path can be flexibly adjusted, performance modeling associates nursing path goals with rehabilitation indicators, data parameterization modeling identifies resource allocation differences, monitors nursing resources and execution data, and tracks the indicator relationship of resource allocation.

Description

Technical Field

[0001] The present invention relates to the field of nursing management technology, and in particular to a neurosurgery nursing management system based on cloud computing. Background Art

[0002] The field of nursing management technology mainly involves the use of information technology to optimize medical care processes and improve the quality of patient care. This technology includes the development and application of software tools and systems to support the daily work of nursing staff, improve patient data recording, monitoring and analysis, and the effective allocation of medical resources. Through these systems, hospitals and other medical institutions can more effectively manage patient records, drug delivery, treatment plans and task allocation of nursing teams. In addition, these technologies also support remote care services, allowing nursing staff to remotely monitor the health status of patients and provide necessary guidance and intervention.

[0003] Among them, the neurosurgery nursing management system is an information management system specially designed for the field of neurosurgery nursing. Such a system aims to provide a comprehensive platform for managing all relevant information of neurosurgery patients, including surgical records, condition monitoring, rehabilitation progress and drug management. The main purpose of the system is to ensure the smoothness and efficiency of nursing work, while reducing human errors and improving the overall quality of patient care. By integrating patient data and nursing resources, the neurosurgery nursing management system helps nurses and doctors collaborate better and develop and adjust nursing plans to suit the specific needs of patients.

[0004] Although existing technologies support nursing processes and monitoring through information technology, they are mostly presented in a static manner and cannot provide serialized nursing path analysis. It is difficult to identify the key nodes and resource requirements of nursing behaviors, resulting in a lack of flexible adjustment capabilities for nursing plans. Due to the lack of dynamic performance modeling and data tracking, it is difficult to effectively associate resource allocation with differences in nursing quality and provide accurate monitoring feedback, which increases the uncertainty of postoperative care for patients. The dispersion and independence of nursing data make it difficult for doctors and nursing teams to fully grasp the patient's recovery status and resource usage when adjusting nursing plans, which may lead to problems such as improper resource allocation and uneven distribution of nursing tasks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a neurosurgery nursing management system based on cloud computing.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: A neurosurgery nursing management system based on cloud computing includes:

[0007] The nursing pathway identification module extracts the sequence information of surgery type, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication based on the nursing process data of neurosurgery patients, performs serial processing of nursing behaviors, establishes the time nodes and feature relationships of multiple nursing behaviors, and generates a nursing behavior framework diagram; the nursing pathway prediction and optimization module integrates the postoperative recovery indicators of neurosurgery patients based on the nursing behavior framework diagram, performs time series simulation of surgery types and intervention measures, maps recovery status and nursing intervention nodes, predicts future nursing nodes and resource requirements, and adjusts and generates optimized nursing pathways;

[0008] The performance modeling and evaluation module is based on the optimized nursing pathway, uses neurosurgery nursing records and rehabilitation indicator data, associates nursing performance goals with patient recovery indicators, and parameterizes and sequentially models performance data by comparing resource allocation and indicator differences to generate performance analysis results;

[0009] Based on the performance analysis results, the nursing execution monitoring module monitors neurosurgery nursing resources and execution data in real time, compares nursing plans with recovery status, tracks the mapping relationship between resource allocation and indicators, integrates nursing pathways with rehabilitation data, and generates nursing execution monitoring results.

[0010] As a further solution of the present invention, the steps of serializing the nursing behavior are specifically as follows:

[0011] The nursing process data of neurosurgery patients were collected, and a nursing data set was established. The types of surgery, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication information were extracted from the data to construct a surgery and nursing information matrix. The reference formula is as follows:

[0012]

[0013] in, Indicates The weighted sum of class information, is the first Class information data points, It is a data point The weight coefficient of

[0014] The operation and nursing information matrix is ​​serialized to determine the time nodes and feature relationships between the information. The reference formula is as follows:

[0015]

[0016] in, As a result of serializing the relational model, is the coefficient, indicating the The strength of the effect of information category, is the extracted information value, It is The time node of the information category.

[0017] As a further solution of the present invention, the steps for obtaining the nursing behavior framework diagram are specifically as follows:

[0018] Using the surgery and nursing information matrix obtained by the serialization process, determining the relationship between key time nodes and nursing behavior characteristics, and generating a mapping table of time node and characteristic relationship;

[0019] From the time node and feature relationship mapping table, extract the data of each key nursing behavior and the corresponding time node, refer to the following formula:

[0020]

[0021] in, is a function of time, Indicates The impact strength of each nursing behavior is the time decay factor, is the adjustment parameter, is the time offset parameter, It is a time node;

[0022] Combined with the types of nursing behaviors, a nursing behavior framework diagram is constructed using a composite relationship, and the reference formula is as follows:

[0023]

[0024] in, is the composite graph generating function, It is The time function result of nursing behavior is is the visualization weight, is the normalization factor; It’s a time node.

[0025] As a further solution of the present invention, the steps for obtaining the optimized nursing path are specifically as follows:

[0026] The nursing behavior framework diagram is used to integrate the postoperative recovery indicators to obtain a recovery indicator summary table, and the reference formula is as follows:

[0027]

[0028] in, is the integrated recovery index result, is the indicator weight, It is Recovery indicators, is the coefficient of variation of the indicator, is the average value;

[0029] The recovery index summary table is associated with the surgery type data, and a time series simulation is performed to generate a time series relationship diagram, with the reference formula being as follows:

[0030]

[0031] in, is the output of the time series simulation, is the time response coefficient; is the value of the recovery index, It's the time point. is the time response coefficient;

[0032] Based on the time series relationship diagram, the recovery status and nursing intervention nodes are mapped, the nursing nodes and resource requirements in the future time period are predicted, and the optimized nursing path is constructed. The reference formula is as follows:

[0033]

[0034] in, It is an optimized care pathway. is the weight of the nursing intervention, is the time series simulation value, is the adjustment parameter, It’s a time node.

[0035] As a further solution of the present invention, the step of associating the nursing performance goal with the patient recovery index is specifically as follows:

[0036] Integrate neurosurgery nursing records and rehabilitation indicator data to form a preliminary data overview, with the following reference formula:

[0037]

[0038] in, Represents an overview of the data. is the adjustment factor, is extracted from nursing records or rehabilitation data data points, and Represents data points The upper and lower impact factors of

[0039] The preliminary data overview is deeply analyzed and mapped to the optimized nursing pathway to obtain a mapping result, with reference to the following formula:

[0040]

[0041] in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point in the care pathway, is the adjustment parameter;

[0042] The mapping results are used to perform correlation analysis between nursing performance and recovery indicators, and the reference formula is as follows:

[0043]

[0044] in, Indicates the result of association. is the weight parameter, is the mapping data point, is the coefficient of variation of the data points.

[0045] As a further solution of the present invention, the steps of obtaining the performance analysis results are specifically as follows:

[0046] Collect neurosurgery nursing records and rehabilitation indicator data, combine with the analysis of the nursing pathway to generate a comprehensive data view;

[0047] To evaluate the difference, refer to the following formula:

[0048]

[0049] Calculate the difference and generate the difference evaluation result;

[0050] in, represents the difference result after evaluation, is the actual recovery indicator, is the expected recovery indicator, is the resource allocation sensitivity coefficient;

[0051] The data in the difference evaluation results are used for parameterization of performance data, with reference to the following formula:

[0052]

[0053] in, represents the parameterized performance data, is the weight factor, is the difference data, is the adjustment factor, is the normalization factor;

[0054] The parameterized performance data is used to construct a sequence model and generate performance analysis results. The reference formula is as follows:

[0055]

[0056] in, is the sequence model output, are the model coefficients, is the periodic adjustment parameter, are parameterized performance data points.

[0057] As a further solution of the present invention, the steps of obtaining the nursing execution monitoring results are specifically as follows:

[0058] Extract key data from the performance analysis results, integrate neurosurgery nursing resources and execution data, and form an updated data view. The reference formula is as follows:

[0059]

[0060] in, represents an updated view of the data. is the adjustment factor for each data point, is the original data value, and are the response rate and stability parameter of the data points, respectively;

[0061] The updated data view Mapped onto the nursing pathway, the resource utilization efficiency and real-time changes in execution data are evaluated, with reference to the following formula:

[0062]

[0063] in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point on the path, is the adjustment parameter;

[0064] Using the mapping results , track and update the comparison between the nursing plan and the actual implementation, and generate the nursing execution monitoring results. The reference formula is as follows:

[0065]

[0066] in, It is the result of nursing execution monitoring. is the efficiency parameter, is the mapping data point, is the update frequency parameter of the data points.

[0067] Compared with the prior art, the advantages and positive effects of the present invention are:

[0068] In the present invention, the type of surgery, recovery monitoring indicators, nursing intervention measures and medication information are extracted from the nursing process data of neurosurgery patients through serialization processing of nursing behaviors, the nursing behaviors are refined by time nodes, a nursing behavior framework diagram is constructed, and the characteristic relationships of nursing behaviors are clearly presented. Based on this framework, time series simulation and mapping predict future nursing nodes and resource requirements, so that the key nodes and intervention measures of the nursing path can be flexibly adjusted. Performance modeling associates nursing path goals with rehabilitation indicators, data parameterized modeling identifies resource allocation differences, monitors nursing resources and execution data, and tracks the indicator relationship of resource allocation. Through the identification and optimization of nursing pathways, combined with performance modeling and monitoring, nursing plans are dynamically adjusted, resource allocation is optimized, nursing quality is improved, errors and accidents are reduced, and a decision-making basis for data analysis is provided to formulate accurate nursing plans for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a system flow chart of the present invention;

[0070] Figure 2 A flowchart of the steps of serialization processing of nursing behavior of the present invention;

[0071] Figure 3 A flowchart of the steps for obtaining the nursing behavior framework diagram of the present invention;

[0072] Figure 4 A flowchart of the steps for obtaining the optimized nursing pathway of the present invention;

[0073] Figure 5 A flowchart of the steps of associating nursing performance goals with patient recovery indicators of the present invention;

[0074] Figure 6 A flowchart of the steps for obtaining the performance analysis results of the present invention;

[0075] Figure 7 A flow chart of the steps for obtaining the nursing execution monitoring results of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0077] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0078] Example

[0079] See also Figure 1 , a neurosurgery care management system based on cloud computing includes:

[0080] The nursing pathway identification module extracts the sequence information of surgery type, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication based on the nursing process data of neurosurgery patients, performs serial processing of nursing behaviors, establishes the time nodes and feature relationships of multiple nursing behaviors, and generates a nursing behavior framework diagram;

[0081] The nursing pathway prediction and optimization module is based on the nursing behavior framework diagram, integrates the postoperative recovery indicators of neurosurgery patients, simulates the time series of surgical types and intervention measures, maps the recovery status and nursing intervention nodes, predicts future nursing nodes and resource requirements, and adjusts and generates optimized nursing pathways;

[0082] The performance modeling and evaluation module is based on the optimized nursing pathway and uses neurosurgery nursing records and rehabilitation indicator data to associate nursing performance goals with patient recovery indicators. By comparing resource allocation and indicator differences, the performance data is parameterized and sequence modeled to generate performance analysis results.

[0083] Based on the performance analysis results, the nursing execution monitoring module monitors neurosurgery nursing resources and execution data in real time, compares nursing plans with recovery status, tracks the mapping relationship between resource allocation and indicators, integrates nursing pathways with rehabilitation data, and generates nursing execution monitoring results.

[0084] The nursing behavior framework diagram includes the nursing stage sequence, time node division, and behavior characteristic categories. The optimized nursing pathway includes the adjusted nursing stage sequence, key nursing nodes, and resource allocation plan. The performance analysis results include nursing performance goals, recovery indicator differences, and resource allocation. The nursing execution monitoring results specifically include resource allocation data, recovery status tracking, and nursing plan adjustments.

[0085] See also Figure 2,The specific steps of serialization processing of nursing behavior are:

[0086] The nursing process data of neurosurgery patients were collected, and a nursing data set was established. The types of surgery, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication information were extracted from the data to construct a surgery and nursing information matrix. The reference formula is as follows:

[0087]

[0088] in, Indicates The weighted sum of class information, is the first Class information data points, It is a data point The weight coefficient of

[0089] The operation and nursing information matrix is ​​serialized to determine the time nodes and feature relationships between the information. The reference formula is as follows:

[0090]

[0091] in, As a result of serializing the relational model, is the coefficient, indicating the The strength of the effect of information category, is the extracted information value, It is The time node of the information category.

[0092] Detailed explanation and derivation of formula 1

[0093] formula:

[0094] Parameter definition:

[0095] : No. Weighted sum of class information;

[0096] : Data Points Weight coefficients, which are assigned according to the importance or reliability of data points;

[0097] : The bth type of information in the data matrix data points.

[0098] Calculation process and examples:

[0099] Assume there are 3 data points , , are the surgical type information and the corresponding weights , , The distribution is 0.5, 0.3, and 0.2; if the surgery type data are 1 (routine surgery), 2 (emergency surgery), and 1 (minor surgery), the calculation is as follows:

[0100] Multiply each data point by its weight:

[0101]

[0102]

[0103]

[0104] Add all the results together to get :

[0105]

[0106] This gives the weighted sum , represents the comprehensive score of the information on the type of surgery.

[0107] Detailed explanation and derivation of formula 2

[0108] formula:

[0109]

[0110] Parameter definition:

[0111] : Calculation results of serialized relational model;

[0112] : Coefficient, indicating the the strength of the effect of the information category;

[0113] : The extracted information value, i.e., various types of information obtained from the surgery and nursing information matrix;

[0114] : Time node, corresponding to the time stamp of each information value.

[0115] Calculation process and examples:

[0116] Suppose there are two kinds of information , , respectively, the type of surgery and postoperative monitoring index, the coefficient 2 and 3, time nodes are 1 and 2 respectively. If (result of the previous example) and (Assumed postoperative recovery index value), the calculation is as follows:

[0117] Each type of information is processed according to the formula:

[0118]

[0119]

[0120] Add all the results together to get :

[0121]

[0122] This gives the overall score of the serialized model , represents the time series relationship between surgery type and postoperative recovery indicators.

[0123] See also Figure 3 ,The specific steps for obtaining the nursing behavior framework diagram are:

[0124] Using the surgery and nursing information matrix obtained through serialization processing, determine the relationship between key time nodes and nursing behavior characteristics, and generate a mapping table of time node and characteristic relationship;

[0125] From the time node and feature relationship mapping table, extract the data of each key nursing behavior and the corresponding time node, refer to the formula as follows:

[0126]

[0127] in, is a function of time, Indicates The impact strength of each nursing behavior is the time decay factor, is the adjustment parameter, is the time offset parameter, It is a time node;

[0128] Combined with the types of nursing behaviors, a nursing behavior framework diagram is constructed using a composite relationship, and the reference formula is as follows:

[0129]

[0130] in, is the composite graph generating function, It is The time function result of nursing behavior is is the visualization weight, is the normalization factor; It’s a time node.

[0131] Detailed explanation of the formula and derivation process

[0132] Time function formula:

[0133]

[0134] Parameter explanation:

[0135] : No. The impact strength of a nursing behavior determines the dominance of the behavior in the model;

[0136] : Time decay factor, which controls the speed at which the behavior decays over time;

[0137] : Adjustment parameters are used to fine-tune the model to adapt to specific data;

[0138] : Time offset parameter, adjusts the time offset of the start of the behavior;

[0139] : Time variable, indicating the time elapsed since the start of care;

[0140] Assumptions:

[0141] (Indicates there are two nursing behaviors)

[0142]

[0143] (Assuming the time point is 5 days later)

[0144] Calculation process:

[0145]

[0146] Graphics generation functions formula:

[0147]

[0148] Parameter explanation:

[0149] : No. The output of a time function;

[0150] : Visualization weights, used to adjust the visual importance of different nursing behaviors;

[0151] : Normalization factor to ensure that the data are compared on the same scale;

[0152] : time node;

[0153] Assumptions:

[0154] (An act of care)

[0155]

[0156] Calculation process:

[0157]

[0158] Summary of calculation process

[0159] Calculate the time function of each nursing behavior based on known parameters ;

[0160] use The result and other parameters of the calculation graph generation function ,result Indicates at time The comprehensive score of the nursing behavior framework diagram can be used to intuitively display the time series dynamics of nursing behavior.

[0161] See also Figure 4 , the steps to obtain the optimized nursing path are as follows:

[0162] The nursing behavior framework diagram was used to integrate the postoperative recovery indicators to obtain a recovery indicator summary table. The reference formula is as follows:

[0163]

[0164] in, is the integrated recovery index result, is the indicator weight, It is Recovery indicators, is the coefficient of variation of the indicator, is the average value;

[0165] Associate the recovery index summary table with the surgery type data, perform time series simulation, and generate a time series relationship graph. The reference formula is as follows:

[0166]

[0167] in, is the output of the time series simulation, is the time response coefficient; is the value of the recovery index, It's the time point. is the time response coefficient;

[0168] Based on the time series relationship diagram, the recovery status and nursing intervention nodes are mapped, the nursing nodes and resource requirements in the future time period are predicted, and the optimized nursing path is constructed. The reference formula is as follows:

[0169]

[0170] in, It is an optimized care pathway. is the weight of the nursing intervention, is the time series simulation value, is the adjustment parameter, It’s a time node.

[0171] Recovery Index Integration Formula

[0172] formula:

[0173]

[0174] Parameter explanation:

[0175] : The integrated recovery index results;

[0176] : No. The weight coefficient of each recovery indicator;

[0177] : Extracted from the nursing behavior framework diagram Recovery indicators;

[0178] : No. The standard deviation of the recovery indicator;

[0179] : No. The average value of the recovery indicators;

[0180] Calculation process and examples:

[0181] Assume there are three recovery indicators , the corresponding weight is , and the standard deviations are The average values ​​are .

[0182] calculate The steps are as follows:

[0183] Calculate the weighted value and normalized term for each indicator:

[0184]

[0185] Sum to get :

[0186]

[0187] Time Series Simulation Formula

[0188] formula:

[0189]

[0190] Parameter explanation:

[0191] : Output of time series simulation;

[0192] : coefficient corresponding to the type of surgery;

[0193] : The value of the recovery indicator;

[0194] : time point;

[0195] : Time response coefficient, affecting the change of indicators over time;

[0196] Calculation process and examples:

[0197] Assume there are two types of surgery, coefficient , recovery index value , time point , time response coefficient .

[0198] calculate The steps are as follows:

[0199] The time-adjusted recovery index values ​​were calculated for each surgery type:

[0200]

[0201] Compute time series simulation output :

[0202]

[0203] Path Optimization Formula

[0204] formula:

[0205]

[0206] Parameter explanation:

[0207] : Optimized care pathway;

[0208] : Weight of nursing intervention;

[0209] : time series simulation value;

[0210] : Adjustment parameters;

[0211] : time node;

[0212] Calculation process and examples:

[0213] Assumptions , analog value , adjustment parameters , time node .

[0214] calculate The steps are as follows:

[0215] Calculate the care pathway value for each time node:

[0216]

[0217] Compute path optimization output :

[0218] .

[0219] See also Figure 5 , the steps to link nursing performance goals with patient recovery indicators are as follows:

[0220] Integrate neurosurgery nursing records and rehabilitation indicator data to form a preliminary data overview, with the following reference formula:

[0221]

[0222] in, Represents an overview of the data. is the adjustment factor, is extracted from nursing records or rehabilitation data data points, and Represents data points The upper and lower impact factors of

[0223] Conduct an in-depth analysis of the preliminary data overview and map it to the optimized care pathway to obtain the mapping results. The reference formula is as follows:

[0224]

[0225] in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point in the care pathway, is the adjustment parameter;

[0226] The mapping results were used to conduct correlation analysis between nursing performance and recovery indicators, and the reference formula was as follows:

[0227]

[0228] in, Indicates the result of association. is the weight parameter, is the mapping data point, is the coefficient of variation of the data points.

[0229] formula:

[0230]

[0231] Parameter explanation:

[0232] : Data overview, is the result of integration;

[0233] : Adjustment factor used to control each data point influence;

[0234] : Data points extracted from nursing records or rehabilitation data;

[0235] and : Data Points Upper and lower impact factors for calculating additional adjustments;

[0236] Derivation process and examples:

[0237] Suppose there are three data points, and the weight of each point is They are ; Data points The values ​​of are 10, 20, and 30 respectively; the upper limit of the data point and lower limit 100,400 and 900,400 respectively;

[0238] .

[0239] formula:

[0240]

[0241] Parameter explanation:

[0242] : Mapping result;

[0243] : Mapping coefficient, used to adjust the data points Contribution

[0244] : Data points obtained from the first step;

[0245] : The location of the data point in the care pathway;

[0246] : Parameters for adjusting the sensitivity of the mapping function;

[0247] Derivation process and examples:

[0248] Assumptions They are 2, 3, and 4 respectively. 10, 20, 30, 1, 2, 3, ;

[0249] .

[0250] formula:

[0251]

[0252] Parameter explanation:

[0253] : association results;

[0254] : Weight parameter, affecting the association results;

[0255] : Mapped data points obtained from the second step;

[0256] : coefficient of variation of the data points;

[0257] Derivation process and examples:

[0258] Assumptions They are 1, 2, and 3 respectively. 11, 39, 89, 2, 5, 9;

[0259] .

[0260] See also Figure 6, the specific steps for obtaining performance analysis results are:

[0261] Collect neurosurgery nursing records and rehabilitation indicator data, combine with analysis of nursing pathways to generate a comprehensive data view;

[0262] To evaluate the difference, refer to the following formula:

[0263]

[0264] Calculate the difference and generate the difference evaluation result;

[0265] in, represents the difference result after evaluation, is the actual recovery indicator, is the expected recovery indicator, is the resource allocation sensitivity coefficient;

[0266] The data in the difference evaluation results are used for parameterized processing of performance data, with reference to the following formula:

[0267]

[0268] in, represents the parameterized performance data, is the weight factor, is the difference data, is the adjustment factor, is the normalization factor;

[0269] The parameterized performance data is used to construct a sequence model and generate performance analysis results. The reference formula is as follows:

[0270]

[0271] in, is the sequence model output, are the model coefficients, is the periodic adjustment parameter, are parameterized performance data points.

[0272] formula:

[0273]

[0274] Parameter details:

[0275] : Actual recovery index;

[0276] : Expected recovery index;

[0277] resource allocation sensitivity coefficient, which may be based on historical data analysis and characterizes the degree of impact of resource allocation on recovery indicators;

[0278] Calculation process and examples:

[0279] Assume there are three recovery indicators, the actual value of each indicator , Expected value and sensitivity coefficient as follows:

[0280]

[0281] Calculate the variance contribution of each metric:

[0282]

[0283] Total Difference :

[0284] .

[0285] formula:

[0286]

[0287] Parameter details:

[0288] : Weight factor, reflecting the importance of different performance parameters;

[0289] : Difference data obtained from the difference assessment report;

[0290] : Adjustment coefficient, used to ensure numerical stability, usually takes a small value such as 1;

[0291] : Normalization factor, used to ensure the consistency of the results, usually the value may be 10 depending on the specific scenario;

[0292] Calculation process and examples:

[0293] Continue using the above Value, assuming:

[0294]

[0295] Calculate parameterized performance data:

[0296] .

[0297] formula:

[0298]

[0299] Parameter details:

[0300] : Model coefficients, which determine the contribution of different parameters;

[0301] : Periodicity parameter, adjusts the intensity of data periodicity;

[0302] : Values ​​obtained from parameterized performance data;

[0303] Calculation process and examples:

[0304] Assumptions:

[0305]

[0306] Compute the performance sequence model:

[0307] .

[0308] See also Figure 7 , the specific steps for obtaining nursing execution monitoring results are:

[0309] Extract key data from performance analysis results, integrate neurosurgery nursing resources and execution data, and form an updated data view. The reference formula is as follows:

[0310]

[0311] in, represents an updated view of the data. is the adjustment factor for each data point, is the original data value, and are the response rate and stability parameter of the data points, respectively;

[0312] The data view that will be updated Mapped onto the nursing pathway, the resource utilization efficiency and real-time changes in execution data are evaluated, with reference to the following formula:

[0313]

[0314] in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point on the path, is the adjustment parameter;

[0315] Using the mapping results , track and update the comparison between the nursing plan and the actual implementation, and generate the nursing execution monitoring results. The reference formula is as follows:

[0316]

[0317] in, It is the result of nursing execution monitoring. is the efficiency parameter, is the mapping data point, is the update frequency parameter of the data points.

[0318] Formula 1: Data Integration Formula

[0319]

[0320] Parameter details:

[0321] : The adjustment factor for each data point, reflecting the importance of the data point in the overall integration;

[0322] : Raw data values, extracted from real-time nursing resources and execution data;

[0323] : The response rate of a data point refers to how quickly a data point responds to real-time changes;

[0324] : Stability parameter of the data point, which affects the adjustment degree of the data point in the integration;

[0325] Derivation process:

[0326] Assume there are three data points, the specific values ​​and parameters are as follows:

[0327]

[0328] calculate Values:

[0329] .

[0330] Formula 2: Data mapping formula

[0331]

[0332] Parameter details:

[0333] : mapping coefficient, which defines the weight of the data point in mapping to the care pathway;

[0334] : data points in the updated data view;

[0335] : The location of the data point in the care pathway;

[0336] : Adjust the sensitivity parameter of the mapping function;

[0337] Derivation process:

[0338] Assume that three data points are mapped, and the specific values ​​and parameters are as follows:

[0339]

[0340] calculate :

[0341] .

[0342] Formula 3: Performance monitoring update formula

[0343]

[0344] Parameter details:

[0345] : efficiency parameter, reflecting the weight of the data point in performance monitoring;

[0346] : Data points in the mapping results;

[0347] : Update frequency parameter of data points;

[0348] Derivation process:

[0349] Assume that three data points are monitored, with the following specific values ​​and parameters:

[0350]

[0351] calculate :

[0352] .

[0353] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A neurosurgery nursing management system based on cloud computing, characterized in that: The system comprises: The nursing pathway identification module extracts the sequence information of surgery type, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication based on the nursing process data of neurosurgery patients, performs serial processing of nursing behaviors, establishes the time nodes and feature relationships of multiple nursing behaviors, and generates a nursing behavior framework diagram; The steps for obtaining the nursing behavior framework diagram are specifically as follows: Using the surgery and nursing information matrix obtained by the serialization process, determining the relationship between key time nodes and nursing behavior characteristics, and generating a mapping table of time node and characteristic relationship; From the time node and feature relationship mapping table, extract the data of each key nursing behavior and the corresponding time node, refer to the following formula: in, is a function of time, Indicates The impact strength of each nursing behavior is the time decay factor, is the adjustment parameter, is the time offset parameter, It is a time node; Combined with the types of nursing behaviors, a nursing behavior framework diagram is constructed using a composite relationship, and the reference formula is as follows: in, is the composite graph generating function, It is The time function result of nursing behavior is is the visualization weight, is the normalization factor; It is a time node; The nursing pathway prediction and optimization module integrates the postoperative recovery indicators of neurosurgery patients based on the nursing behavior framework diagram, performs time series simulation on the types of surgery and intervention measures, maps the recovery status and nursing intervention nodes, predicts future nursing nodes and resource requirements, and adjusts and generates the optimized nursing pathway; The steps for obtaining the optimized nursing path are specifically as follows: The nursing behavior framework diagram is used to integrate the postoperative recovery indicators to obtain a recovery indicator summary table, and the reference formula is as follows: in, is the integrated recovery index result, is the indicator weight, It is Recovery indicators, is the coefficient of variation of the indicator, is the average value; The recovery index summary table is associated with the surgery type data, and a time series simulation is performed to generate a time series relationship diagram, with the reference formula being as follows: in, is the output of the time series simulation, is the coefficient corresponding to the type of surgery, is the value of the recovery index, It's the time point. is the time response coefficient; Based on the time series relationship diagram, the recovery status and nursing intervention nodes are mapped, the nursing nodes and resource requirements in the future time period are predicted, and the optimized nursing path is constructed. The reference formula is as follows: in, It is an optimized care pathway. is the weight of the nursing intervention, is the time series simulation value, is the adjustment parameter, It is a time node; The performance modeling and evaluation module is based on the optimized nursing pathway, uses neurosurgery nursing records and rehabilitation indicator data, associates nursing performance goals with patient recovery indicators, and parameterizes and sequentially models performance data by comparing resource allocation and indicator differences to generate performance analysis results; Based on the performance analysis results, the nursing execution monitoring module monitors neurosurgery nursing resources and execution data in real time, compares nursing plans with recovery status, tracks the mapping relationship between resource allocation and indicators, integrates nursing pathways with rehabilitation data, and generates nursing execution monitoring results.

2. The neurosurgery nursing management system based on cloud computing according to claim 1 is characterized in that: The steps of serialization processing of the nursing behavior are specifically as follows: The nursing process data of neurosurgery patients were collected, and a nursing data set was established. The types of surgery, postoperative recovery monitoring indicators, neurosurgery nursing intervention measures, and nervous system medication information were extracted from the data to construct a surgery and nursing information matrix. The reference formula is as follows: in, Indicates The weighted sum of class information, is the first Class information data points, It is a data point The weight coefficient of The operation and nursing information matrix is ​​serialized to determine the time nodes and feature relationships between the information. The reference formula is as follows: in, As a result of serializing the relational model, is the coefficient, indicating the The strength of the effect of information category, is the extracted information value, It is The time node of the information category.

3. The neurosurgery nursing management system based on cloud computing according to claim 1 is characterized in that: The steps of associating nursing performance goals with patient recovery indicators are specifically as follows: Integrate neurosurgery nursing records and rehabilitation indicator data to form a preliminary data overview, with the following reference formula: in, Represents an overview of the data. is the adjustment factor, is extracted from nursing records or rehabilitation data data points, and Represents data points The upper and lower impact factors of The preliminary data overview is deeply analyzed and mapped to the optimized nursing pathway to obtain a mapping result, which is shown in the following formula: in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point in the care pathway, is the adjustment parameter; The mapping results are used to perform correlation analysis between nursing performance and recovery indicators, and the reference formula is as follows: in, Indicates the result of association. is the weight parameter, is the mapping data point, is the coefficient of variation of the data points.

4. The neurosurgery nursing management system based on cloud computing according to claim 1, characterized in that: The steps for obtaining the performance analysis results are specifically as follows: Collect neurosurgery nursing records and rehabilitation indicator data, combine with the analysis of the nursing pathway to generate a comprehensive data view; To evaluate the difference, refer to the following formula: Calculate the difference and generate the difference evaluation result; in, represents the difference result after evaluation, is the actual recovery indicator, is the expected recovery indicator, is the resource allocation sensitivity coefficient; The data in the difference evaluation results are used for parameterization of performance data, with reference to the following formula: in, represents the parameterized performance data, is the weight factor, is the difference data, is the adjustment factor, is the normalization factor; The parameterized performance data is used to construct a sequence model and generate performance analysis results. The reference formula is as follows: in, is the sequence model output, are the model coefficients, is the periodic adjustment parameter, are parameterized performance data points.

5. The neurosurgery nursing management system based on cloud computing according to claim 1, characterized in that: The steps for obtaining the nursing execution monitoring results are specifically as follows: Extract key data from the performance analysis results, integrate neurosurgery nursing resources and execution data, and form an updated data view. The reference formula is as follows: in, represents an updated view of the data. is the adjustment factor for each data point, is the original data value, and are the response rate and stability parameter of the data points, respectively; The updated data view Mapped onto the nursing pathway, the resource utilization efficiency and real-time changes in execution data are evaluated, with reference to the following formula: in, is the mapping result, is the mapping coefficient, is a data point, is the position of the data point on the path, is the adjustment parameter; Using the mapping results , track and update the comparison between the nursing plan and the actual implementation, and generate the nursing execution monitoring results. The reference formula is as follows: in, It is the result of nursing execution monitoring. is the efficiency parameter, is the mapping data point, is the update frequency parameter of the data points.

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