A medical care integrated management method and system

Abstract

The present application belongs to the field of medical care task management, in particular to a medical care comprehensive management method and system, which obtains a medical care task set and real-time vital sign data of associated patients, the task set including emergency degree, importance level, dependency relationship and resource demand; calculates a physiological state fluctuation risk index of the patient; combines the task emergency degree, importance level and risk index to calculate a basic priority score; calculates a time weight factor considering waiting time length and adjusted by the risk index; identifies medical care resources required by the task and calculates a resource competition index; based on the task dependency relationship, assigns a gain coefficient positively correlated with the resource competition index or a default gain coefficient to the pre-task of the high-priority task; corrects the basic priority score by using the time weight factor and the gain coefficient, generates a task priority and sorts it, and forms a medical care work execution sequence.

Description

Technical Field

[0001] This application belongs to the field of medical and nursing task management, and in particular relates to a comprehensive medical and nursing management method and system. Background Technology

[0002] In modern medical institutions, especially in hospital wards and intensive care units, medical staff need to complete a large number of diverse medical and nursing tasks, such as medication administration, examination, treatment, and nursing, within limited time and resources. Currently, the scheduling of medical and nursing tasks relies heavily on the experience of head nurses or senior medical staff for manual judgment and allocation. Traditional methods typically prioritize tasks based on their type and pre-set urgency, often using a first-come, first-served principle for tasks of the same level. However, this approach struggles to respond in real time to changes in patients' conditions and cannot reliably address the strain on medical resources during peak periods, easily leading to uneven resource allocation or delays in critical tasks. Furthermore, the assessment of patient urgency often relies on whether single or instantaneous vital sign data exceeds warning thresholds, neglecting the fluctuations and dispersion trends of vital signs over a period of time, which are often better indicators of the stability and potential risks of a patient's physiological state. Existing scheduling logic has a rather crude consideration of task waiting time, failing to integrate the impact of waiting time with the individual risk status of patients, resulting in insufficient sensitivity to time delays for critically ill patients. Moreover, the dependencies between tasks and resource competition issues have not been adequately addressed. When multiple tasks compete for the same scarce resource, scheduling adjustments cannot be made based on the degree of resource congestion. Especially in a complex treatment process, if a prerequisite task for a critical follow-up task is delayed due to resource bottlenecks, the efficiency of the entire task chain will be significantly reduced. Current technology lacks an intelligent strategy to prioritize such bottlenecked prerequisite tasks, thus affecting the smoothness and timeliness of the overall medical care process. Summary of the Invention

[0003] To address the aforementioned problems, in the first aspect, this invention proposes a comprehensive medical and nursing management method, comprising the following steps:

[0004] Acquire a set of medical and nursing tasks to be processed and real-time vital sign data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resources of each task.

[0005] For each patient associated with a task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period of the patient; for each medical task, a basic priority score is calculated by combining the task urgency, importance level, and the physiological state fluctuation risk index.

[0006] Calculate the time weighting factor for each task; identify the medical resources required to execute each medical task, and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index;

[0007] Based on the task dependencies, the task chain gain coefficient of each task is calculated. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

[0008] Optionally, for each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period for that patient, including:

[0009] Within the preset time period, the patient's heart rate, respiratory rate, and blood oxygen saturation data points were collected at 5-minute intervals to obtain three independent data sequences;

[0010] Calculate the standard deviation of the three data series: heart rate, respiratory rate, and blood oxygen saturation.

[0011] After normalizing the three standard deviations obtained from the calculation, the physiological state fluctuation risk index is obtained by weighting and summing them according to the weighting coefficients.

[0012] Optionally, for each medical task, a basic priority score is calculated based on the task's urgency, importance level, and the physiological state fluctuation risk index, including:

[0013] The urgency and importance levels of the tasks are represented by integer scores from 1 to 10;

[0014] The basic priority score is calculated using linear weighting.

[0015] Optionally, the calculation of the time weighting factor for each task includes:

[0016] The time elapsed from the time a task is generated to the current time of scheduling is measured as the task waiting time, in minutes.

[0017] The formula for calculating the time weighting factor is as follows: ;

[0018] in, Waiting time for the task This is a risk index for fluctuations in physiological state. This is a preset baseline waiting time constant.

[0019] Optionally, the calculation of the ratio of the number of tasks requesting the same medical resources to the total available resources within the scheduling period, to obtain the resource competition index, includes:

[0020] Medical resources are divided into human resources and equipment resources;

[0021] For the human resources of a specific doctor or nurse, the total available resources are the number of working time units of the personnel within the scheduling cycle, and the number of requests is the total number of time units required for all tasks assigned to the personnel.

[0022] For equipment resources, the total available resources are the total number of online available units of the same type of equipment, and the number of requests is the total number of tasks that explicitly request to use the equipment within the current scheduling period.

[0023] Optionally, based on the task dependencies, the task chain gain coefficient for each task is calculated. For a preceding task that is a high-priority task, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned, including:

[0024] Tasks that rank in the top 25% of all tasks based on their base priority score are defined as high base priority score tasks, and tasks that are prerequisites for other tasks are identified as key prerequisite tasks.

[0025] Determine the maximum value of the resource competition index among one or more medical resources required for each key prerequisite task. Is it greater than the resource congestion threshold? ;

[0026] If the above judgment result is yes, then the task chain gain coefficient According to the formula Perform calculations, where This is the gain adjustment parameter;

[0027] If not, or if the task is not a critical prerequisite task, the task chain gain coefficient is the default value of 1.0.

[0028] Optionally, the step of weighting and correcting the basic priority score using the time weighting factor and the task chain gain coefficient to obtain the task priority includes:

[0029] The calculation formula is: ;

[0030] in, Based on priority scores, As a time-weighted factor, This is the task chain gain coefficient.

[0031] In a second aspect, the present invention provides a comprehensive medical and nursing management system, comprising the following modules:

[0032] The acquisition module is used to acquire a set of medical and nursing tasks to be processed and real-time vital sign data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resource information of each task.

[0033] The first calculation module is used to calculate the physiological state fluctuation risk index for each patient associated with a task, based on the dispersion of one or more vital sign data within a preset time period of the patient; and to calculate a basic priority score for each medical task, combining the task urgency, importance level and the physiological state fluctuation risk index.

[0034] The second calculation module is used to calculate the time weight factor of each task; identify the medical resources required to execute each medical task; and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index.

[0035] The generation module is used to calculate the task chain gain coefficient of each task based on the task dependency relationship. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

[0036] Preferably, for each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period of the patient, including:

[0037] Within the preset time period, the patient's heart rate, respiratory rate, and blood oxygen saturation data points were collected at 5-minute intervals to obtain three independent data sequences;

[0038] Calculate the standard deviation of the three data series: heart rate, respiratory rate, and blood oxygen saturation.

[0039] After normalizing the three standard deviations obtained from the calculation, the physiological state fluctuation risk index is obtained by weighting and summing them according to the weighting coefficients.

[0040] Preferably, for each medical task, a basic priority score is calculated by combining the task's urgency, importance level, and the physiological state fluctuation risk index, including:

[0041] The urgency and importance levels of the tasks are represented by integer scores from 1 to 10;

[0042] The basic priority score is calculated using linear weighting.

[0043] Preferably, the calculation of the time weighting factor for each task includes:

[0044] The time elapsed from the time a task is generated to the current time of scheduling is measured as the task waiting time, in minutes.

[0045] The formula for calculating the time weighting factor is as follows: ;

[0046] in, Waiting time for the task This is a risk index for fluctuations in physiological state. This is a preset baseline waiting time constant.

[0047] Preferably, the calculation of the ratio of the number of tasks requesting the same medical resources to the total available resources within the scheduling cycle to obtain the resource competition index includes:

[0048] Medical resources are divided into human resources and equipment resources;

[0049] For the human resources of a specific doctor or nurse, the total available resources are the number of working time units of the personnel within the scheduling cycle, and the number of requests is the total number of time units required for all tasks assigned to the personnel.

[0050] For equipment resources, the total available resources are the total number of online available units of the same type of equipment, and the number of requests is the total number of tasks that explicitly request to use the equipment within the current scheduling period.

[0051] Preferably, based on the task dependency relationship, the task chain gain coefficient of each task is calculated. Specifically, for a prerequisite task that is a high-priority task, if the resource competition index of the medical resources required by the prerequisite task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned, including:

[0052] Tasks that rank in the top 25% of all tasks based on their base priority score are defined as high base priority score tasks, and tasks that are prerequisites for other tasks are identified as key prerequisite tasks.

[0053] Determine the maximum value of the resource competition index among one or more medical resources required for each key prerequisite task. Is it greater than the resource congestion threshold? ;

[0054] If the above judgment result is yes, then the task chain gain coefficient According to the formula Perform calculations, where This is the gain adjustment parameter; if not, or if the task is a non-critical prerequisite task, the task chain gain coefficient is the default value of 1.0.

[0055] Preferably, the step of weighting and correcting the basic priority score using the time weighting factor and the task chain gain coefficient to obtain the task priority includes:

[0056] The calculation formula is: ;

[0057] in, Based on priority scores, As a time-weighted factor, This is the task chain gain coefficient.

[0058] This invention proposes a medical task priority scheduling method. By calculating the dispersion of a patient's vital signs data over a certain period, it represents the risk of fluctuations in the patient's physiological state, thereby identifying patients with potentially unstable conditions and achieving a refined assessment of patient urgency. The method correlates task waiting time with the patient's risk index and utilizes a resource competition index and task chain gain mechanism, ensuring that scheduling decisions not only consider time urgency but also address the impact of resource bottlenecks on critical task chains. By prioritizing preceding tasks that may block important subsequent tasks, delays in the overall medical process due to resource contention are avoided. The generated task sequence integrates multiple factors such as patient condition trends, time sensitivity, resource availability, and logical relationships between tasks, improving the rationality of medical resource allocation and the accuracy of scheduling results, thus contributing to ensuring medical safety and improving work efficiency. Attached Figure Description

[0059] Figure 1 A flowchart of the first embodiment;

[0060] Figure 2 A schematic diagram illustrating the calculation of the physiological state fluctuation risk index;

[0061] Figure 3 A diagram illustrating the calculation of basic priority scores;

[0062] Figure 4 This diagram illustrates how the time weighting factor changes with waiting time and risk index.

[0063] Figure 5 A schematic diagram of the framework for a priority scheduling method for medical and nursing tasks;

[0064] Figure 6 A schematic diagram of the logic for calculating the task chain gain coefficient. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] In the first embodiment, the present invention proposes a comprehensive medical and nursing management method, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0067] S1, Obtain the set of medical and nursing tasks to be processed and the real-time vital signs data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resources of each task.

[0068] The system periodically retrieves medical and nursing task instructions issued by doctors or nurses from the Hospital Information System (HIS) or Electronic Medical Record System (EMR) via an interface, forming a list of tasks to be processed. The task information fields include task ID, patient ID, urgency level (e.g., urgent, priority, routine), importance level (e.g., high, medium, low), preceding task ID to indicate task dependencies, and required resource tags (e.g., senior nurse, infusion pump, etc.). Simultaneously, the system connects to the monitoring devices of each patient bed in real time through an IoT gateway, continuously collecting vital sign data streams such as patient heart rate, blood pressure, blood oxygen saturation, and respiratory rate, and storing them indexed by patient ID.

[0069] S2, For each patient associated with a task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period of the patient; For each medical task, a basic priority score is calculated by combining the task urgency, importance level, and the physiological state fluctuation risk index.

[0070] Specifically, a time window is set, such as the past 30 minutes. All measurement data points of a key vital sign of the patient, such as heart rate, are extracted within this time period. The coefficient of variation (COP) of these data points, i.e., standard deviation divided by the mean, is calculated to obtain the fluctuation value of this vital sign. This operation is repeated for multiple vital signs such as heart rate, blood pressure, and blood oxygen saturation to obtain their respective fluctuation values. Preset clinical weights are assigned to each fluctuation value, for example, heart rate weight 0.4, blood pressure weight 0.4, and blood oxygen weight 0.2. Each fluctuation value is multiplied by its weight, summed, and then normalized to obtain a physiological state fluctuation risk index between 0 and 1. The higher the index, the more unstable the patient's physiological state. The normalization uses a max-min normalization method, with the maximum and minimum values ​​set based on medical common sense, such as the normal and extreme ranges of the heart rate standard deviation.

[0071] The urgency and importance levels are represented, for example, urgency is divided into 3 points for urgent, 2 points for priority, and 1 point for routine; importance is divided into 3 points for high, 2 points for medium, and 1 point for low. A weighted summation method is used to calculate the basic priority score. In some embodiments, the formula is: Basic Priority Score = Weight A × Urgency Score + Weight B × Importance Score + Weight C × Physiological State Fluctuation Risk Index. The weights A, B, and C are preset according to the hospital management strategy. For example, weight A is 0.5, weight B is 0.2, and weight C is 0.3, highlighting the focus on the patient's real-time risk. Alternatively, they can be preset according to departmental characteristics. For example, in the respiratory department, the weights for fluctuations in blood oxygen and respiratory rate should be set higher, such as 0.4, 0.4, and 0.2; while in the cardiology department, the weights for heart rate fluctuations should be set higher, such as 0.6, 0.2, and 0.2.

[0072] In an optional embodiment, for each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period for the patient, including:

[0073] Within the preset time period, the patient's heart rate, respiratory rate, and blood oxygen saturation data points were collected at 5-minute intervals to obtain three independent data sequences;

[0074] Calculate the standard deviation of the three data series: heart rate, respiratory rate, and blood oxygen saturation.

[0075] After normalizing the three standard deviations obtained from the calculation, the physiological state fluctuation risk index is obtained by weighting and summing them according to the weighting coefficients.

[0076] The preset time period is set to the past 60 minutes. Taking a patient as an example, vital signs are collected at 5-minute intervals within the past 60 minutes, obtaining 12 heart rate data points, 12 respiratory rate data points, and 12 blood oxygen saturation data points. For example, the heart rate data sequence is 78, 80, 82, 79, 81, 85, 83, 80, 79, 81, 82, 84 beats per minute. The standard deviations of the three data sequences are calculated to represent the degree of fluctuation of each vital sign. Assume that the calculated standard deviation of heart rate is 2.1, the standard deviation of respiratory rate is 1.2, and the standard deviation of blood oxygen saturation is 0.8. The risk index model can be selected as a linear weighted summation model to comprehensively assess the overall fluctuation risk of multiple vital signs. Before weighted summation, each standard deviation value needs to be normalized, for example, by using the minimum-maximum method to map the standard deviation to the interval between 0 and 1, eliminating the influence of different dimensions of the vital sign data. Assuming the standard deviations of heart rate, respiratory rate, and blood oxygen saturation after normalization are 0.25, 0.30, and 0.20, respectively, a weighted summation is performed according to preset weighting coefficients: heart rate 0.4, respiratory rate 0.3, and blood oxygen saturation 0.3. The patient's physiological state fluctuation risk index is calculated to be 0.25. A higher index indicates a more unstable physiological state, such as... Figure 2 .

[0077] In an optional embodiment, for each medical task, a basic priority score is calculated by combining the task's urgency, importance level, and the physiological state fluctuation risk index, including:

[0078] The urgency and importance levels of the tasks are represented by integer scores from 1 to 10;

[0079] The basic priority score is calculated using a linear weighted average, and the formula is as follows: ;

[0080] in, Based on priority scores, The score represents the urgency of the task. As an importance rating score, This is a risk index for fluctuations in physiological state.

[0081] One of the nursing tasks to be performed for Patient A is to administer intravenous antibiotics at scheduled times. Based on the doctor's orders and the patient's condition, the nursing station has determined the urgency of this task. A score of 8 indicates that implementation must be completed as soon as possible within a specific timeframe; this will be considered an important level. A score of 9 indicates that it is crucial to the patient's recovery. Simultaneously, a risk index for physiological state fluctuations associated with patient A was obtained. Assuming the value calculated using the aforementioned method is 0.25, a linear weighted model is applied to calculate the baseline priority score. This model is a multi-factor weighted summation that integrates the urgency and importance of the task itself, as well as the risk associated with the patient's condition. By assigning weights to different factors, the emphasis of the scheduling strategy is reflected. For example, the weight of task urgency... The weight of importance level is 0.5. The weight of the physiological state fluctuation risk index is 0.3. The value is 0.2. Substituting the expressed score and index into the formula, the basic priority score is calculated as follows: =6.75. This score serves as the initial benchmark for task scheduling, such as... Figure 3 .

[0082] S3, calculate the time weighting factor for each task; identify the medical resources required to execute each medical task, and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index;

[0083] The task waiting time equals the current system time minus the task generation time. The calculation formula for the time weight factor is, for example: Time weight factor = 1 + coefficient K × waiting time raised to the power of (1 + physiological state fluctuation risk index). Here, coefficient K is an adjustable parameter used to control the overall impact of the waiting time. By placing the risk index in the exponential part of the waiting time, when the patient's risk index is high, even if the waiting time is short, the exponent will increase, causing a sharp increase in the time weight factor. Conversely, for patients with low risk indices, the time weight factor increases more gradually with the waiting time. The system iterates through the set of tasks to be processed, classifying and statistically analyzing them according to the resource tags required by each task. For example, it identifies 5 tasks requiring the use of a portable ultrasound machine within the next hour. It then queries the resource management module to obtain the number of currently available portable ultrasound machines, for example, 2. The resource competition index is the number of requests divided by the number of available machines, which is 2.5. If the number of requests is 3 and the number of available machines is 4, the index is 0.75.

[0084] In an optional embodiment, calculating the time weighting factor for each task includes:

[0085] The time elapsed from the time a task is generated to the current time of scheduling is measured as the task waiting time, in minutes.

[0086] The formula for calculating the time weighting factor is as follows: ;

[0087] in, Waiting time for the task This is a risk index for fluctuations in physiological state. This is a preset baseline waiting time constant.

[0088] For a task to be scheduled, record the task generation time and obtain the current scheduling time; the difference between the two is the task waiting time. For example, if a blood glucose measurement task is generated at 9:10 AM, and the current scheduling time is 9:40 AM, then the waiting time is... The time limit is 30 minutes. Obtain the risk index of physiological state fluctuations associated with this task. Assuming a value of 0.25. Baseline waiting time. This is a preset system parameter, set here to 60 minutes, used to adjust the impact of waiting time. For patients with higher risk, task priority increases more rapidly over time. Substituting the example data into the formula, the time weighting factor for this task is calculated as follows: =1.625. If the patient risk index associated with another task is 0.8, even with the same waiting time, the time weighting factor will be 1.9, resulting in a greater increase in priority. Figure 4 .

[0089] In an optional embodiment, the calculation of the ratio of the number of tasks requesting the same medical resources to the total available resources within a scheduling period to obtain the resource competition index includes:

[0090] Medical resources are divided into human resources and equipment resources;

[0091] For the human resources of a specific doctor or nurse, the total available resources are the number of working time units of the personnel within the scheduling cycle, and the number of requests is the total number of time units required for all tasks assigned to the personnel.

[0092] For equipment resources, the total available resources are the total number of online available units of the same type of equipment, and the number of requests is the total number of tasks that explicitly request to use the equipment within the current scheduling period.

[0093] Taking Nurse A as an example, within a 1-hour scheduling cycle, the work time is divided into 12 5-minute time units, so the total available resources are 12. Within this cycle, three tasks are pre-assigned to Nurse A: Task 1 is expected to take 15 minutes (3 time units), Task 2 is expected to take 10 minutes (2 time units), and Task 3 is expected to take 20 minutes (4 time units). Therefore, the total number of time units requesting Nurse A is 9. Thus, Nurse A's resource contention index is the number of requests divided by the total available resources, which is 0.75.

[0094] Taking a mobile ultrasound machine in the ward as an example, there are 3 such devices currently online and available, so the total available resources are 3. Within the current scheduling cycle, there are 2 different medical and nursing tasks that explicitly request the use of the mobile ultrasound machine. Therefore, the total number of tasks requesting this device is 2. Thus, the resource competition index for the mobile ultrasound machine is the total number of requests divided by the total number of available units, which is approximately 0.67. These two indices reflect the busyness and scarcity of specific human and equipment resources, respectively; the closer the value is to 1, the more intense the resource competition.

[0095] S4. Based on the task dependency relationship, calculate the task chain gain coefficient for each task. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

[0096] Specifically, the congestion threshold is set to 1.2. It checks whether task A is a prerequisite task for another task B, and whether task B's base priority score ranks in the top 20% of all tasks. If so, it further checks the resources required to execute task A. For example, if it's a senior nurse, the resource competition index for senior nurses is found to be 1.8, which exceeds the congestion threshold of 1.2. In this case, the task chain gain coefficient for task A is calculated as: 1 + adjustment coefficient M × (resource competition index - congestion threshold). If the resource competition index does not exceed the threshold, or task A is not a prerequisite task on a critical task chain, the task chain gain coefficient is assigned a default value of 1.

[0097] For each healthcare task, the task priority is calculated using the formula: Task Priority = Base Priority Score × Time Weighting Factor × Task Chain Gain Coefficient. A task priority value is calculated for each task in the set of tasks to be processed. After all calculations are completed, the entire task set is sorted in descending order of task priority scores. The resulting task order is the generated work sequence recommended for healthcare workers to execute, with the task at the top being the most urgent task to be processed. Figure 5 .

[0098] In an optional embodiment, based on the task dependencies, the task chain gain coefficient of each task is calculated. For a preceding task that is a high-priority task, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned, including:

[0099] Tasks that rank in the top 25% of all tasks based on their base priority score are defined as high base priority score tasks, and tasks that are prerequisites for other tasks are identified as key prerequisite tasks.

[0100] Determine the maximum value of the resource competition index among one or more medical resources required for each key prerequisite task. Is it greater than the resource congestion threshold? ;

[0101] If the above judgment result is yes, then the task chain gain coefficient According to the formula Perform calculations, where This is the gain adjustment parameter;

[0102] If not, or if the task is not a critical prerequisite task, the task chain gain coefficient is the default value of 1.0.

[0103] Assume there are 40 tasks in the current scheduling pool, sorted according to their base priority scores. The top 10 tasks, or the top 25%, are defined as high base priority score tasks. One of these high-scoring tasks is an emergency consultation for a critically ill patient, with a base priority score of 9.2. This task has a prerequisite task: performing a bedside X-ray for the patient. Therefore, the X-ray task is identified as a critical prerequisite task.

[0104] An X-ray examination requires two resources: a portable X-ray machine and a radiology technician. Currently, the resource competition index for the portable X-ray machine is 0.85, while the resource competition index for the radiology technician is 0.96. The maximum of these two indices is required. It is 0.96. Compare this value with the preset resource congestion threshold. The comparison is made with 0.9. Since 0.96 > 0.9, the judgment result is "yes," indicating that this critical preliminary task faces a resource bottleneck. Because the judgment result is "yes," a gain coefficient greater than 1 will be calculated for this X-ray examination task to increase its priority and ensure that subsequent high-priority tasks can proceed smoothly. This is done according to the formula, and gain adjustment parameters are set. The value is 1.5, and the task chain gain coefficient is calculated as follows: For other non-critical prerequisite tasks, or critical prerequisite tasks with low resource contention, the gain coefficient remains at the default value of 1.0, such as... Figure 6 .

[0105] In an optional embodiment, the step of weighting and correcting the base priority score using the time weighting factor and the task chain gain coefficient to obtain the task priority includes:

[0106] The calculation formula is: ;

[0107] in, As a task priority, Based on priority scores, As a time-weighted factor, This is the task chain gain coefficient.

[0108] Taking the aforementioned bedside X-ray examination task as an example, its various calculation parameters were obtained. Its basic priority score... It is calculated based on its own attributes, let's assume it's 5.5. Its time weighting factor... This value, calculated based on waiting time and patient risk, is assumed to be 1.2. Its task chain gain coefficient... Because it is a critical prerequisite task facing resource bottlenecks, its priority is calculated to be 1.09. The multiplicative model achieves a comprehensive adjustment of priority by multiplying the base score by two correction factors. The time weight factor reflects the task's waiting cost, while the task chain gain coefficient addresses potential bottlenecks on the critical path. Substituting all collected factors into the formula, the task priority of this X-ray examination task is calculated as follows: The priority score integrates the inherent importance of a task, time pressure, and strategic value within the task network. All tasks are ranked based on this score to generate an execution scheduling sequence.

[0109] In a second embodiment, the present invention also provides a comprehensive medical and nursing management system, comprising the following modules:

[0110] The acquisition module is used to acquire a set of medical and nursing tasks to be processed and real-time vital sign data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resource information of each task.

[0111] The first calculation module is used to calculate the physiological state fluctuation risk index for each patient associated with a task, based on the dispersion of one or more vital sign data within a preset time period of the patient; and to calculate a basic priority score for each medical task, combining the task urgency, importance level and the physiological state fluctuation risk index.

[0112] The second calculation module is used to calculate the time weight factor of each task; identify the medical resources required to execute each medical task; and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index.

[0113] The generation module is used to calculate the task chain gain coefficient of each task based on the task dependency relationship. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

[0114] In this specification, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise limited, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the associated listed items.

[0115] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0116] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A medical care integrated management method characterized by comprising: Includes the following steps: Acquire a set of medical and nursing tasks to be processed and real-time vital sign data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resources of each task. For each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within the preset time period of the patient. For each medical task, a basic priority score is calculated by combining the task's urgency, importance level, and the aforementioned physiological state fluctuation risk index. Calculate the time weighting factor for each task; Identify the medical resources required to perform each medical task, and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index; The calculation of the time weighting factor for each task includes: The time elapsed from the time a task is generated to the current time of scheduling is measured as the task waiting time, in minutes. The calculation formula of the time weight factor is: ; wherein, is a task waiting duration, is a physiological state fluctuation risk index, is a preset reference waiting duration constant; Based on the task dependencies, the task chain gain coefficient of each task is calculated. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

2. The method according to claim 1, characterized in that, For each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period, including: Within the preset time period, the patient's heart rate, respiratory rate, and blood oxygen saturation data points were collected at 5-minute intervals to obtain three independent data sequences; Calculate the standard deviation of the three data series: heart rate, respiratory rate, and blood oxygen saturation. After normalizing the three standard deviations obtained from the calculation, the physiological state fluctuation risk index is obtained by weighting and summing them according to the weighting coefficients.

3. The method according to claim 1, characterized in that, For each medical task, a basic priority score is calculated based on the task's urgency, importance level, and the risk index of physiological state fluctuations, including: Task urgency and importance levels are represented as integer scores from 1 to 10; a linear weighted average is used to calculate the base priority score.

4. The method according to claim 1, characterized in that, The calculation of the ratio of the number of tasks requesting the same medical resources to the total available resources within the scheduling period yields the resource competition index, which includes: Medical resources are divided into human resources and equipment resources; For the human resources of a specific doctor or nurse, the total available resources are the number of working time units of the personnel within the scheduling cycle, and the number of requests is the total number of time units required for all tasks assigned to the personnel. For equipment resources, the total available resources are the total number of online available units of the same type of equipment, and the number of requests is the total number of tasks that explicitly request to use the equipment within the current scheduling period.

5. The method according to claim 1, characterized in that, Based on the task dependencies, the task chain gain coefficient for each task is calculated. For a preceding task that is a high-priority task, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned, including: Tasks that rank in the top 25% of all tasks based on their base priority score are defined as high base priority score tasks, and tasks that are prerequisites for other tasks are identified as key prerequisite tasks. Determine the maximum value of the resource competition index among one or more medical resources required for each key prerequisite task. Is it greater than the resource congestion threshold? ; If the above judgment result is yes, then the task chain gain coefficient According to the formula Perform calculations, where This is the gain adjustment parameter; If not, or if the task is not a critical prerequisite task, the task chain gain coefficient is the default value of 1.

0.

6. The method according to claim 1, characterized in that, The step of weighting and correcting the base priority score using the time weighting factor and the task chain gain coefficient to obtain the task priority includes: The calculation formula is: ; in, Based on priority scores, As a time-weighted factor, This is the task chain gain coefficient.

7. A comprehensive medical and nursing management system, characterized in that, Includes the following modules: The acquisition module is used to acquire a set of medical and nursing tasks to be processed and real-time vital sign data of patients associated with the tasks. The set of medical and nursing tasks includes the urgency, importance level, task dependency relationship and required medical and nursing resource information of each task. The first calculation module is used to calculate the physiological state fluctuation risk index for each patient associated with the task, based on the degree of dispersion of one or more vital sign data within a preset time period of the patient. For each medical task, a basic priority score is calculated by combining the task's urgency, importance level, and the aforementioned physiological state fluctuation risk index. The second calculation module is used to calculate the time weighting factor for each task. Identify the medical resources required to perform each medical task, and calculate the ratio of the number of tasks requesting the same medical resource to the total available resources within the scheduling cycle to obtain the resource competition index; The calculation of the time weight factor for each task includes: measuring the time elapsed from the task's generation time to the current scheduling time as the task waiting time, in minutes; the calculation formula for the time weight factor is: ;in, Waiting time for the task This is a risk index for fluctuations in physiological state. This is a preset baseline waiting time constant; The generation module is used to calculate the task chain gain coefficient of each task based on the task dependency relationship. For a preceding task that is a task with a high basic priority score, if the resource competition index of the medical resources required by the preceding task exceeds a preset congestion threshold, a gain coefficient positively correlated with the resource competition index is assigned; otherwise, a preset default gain coefficient is assigned. The basic priority score is weighted and corrected using the time weight factor and the task chain gain coefficient to obtain the task priority. The medical task set is then sorted according to the task priority to generate a medical work execution sequence.

8. The system according to claim 7, characterized in that, For each patient associated with the task, a physiological state fluctuation risk index is calculated based on the dispersion of one or more vital sign data within a preset time period, including: Within the preset time period, the patient's heart rate, respiratory rate, and blood oxygen saturation data points were collected at 5-minute intervals to obtain three independent data sequences; Calculate the standard deviation of the three data series: heart rate, respiratory rate, and blood oxygen saturation. After normalizing the three standard deviations obtained from the calculation, the physiological state fluctuation risk index is obtained by weighting and summing them according to the weighting coefficients.

9. The system according to claim 7, characterized in that, For each medical task, a basic priority score is calculated based on the task's urgency, importance level, and the risk index of physiological state fluctuations, including: The urgency and importance levels of the tasks are represented by integer scores from 1 to 10; The basic priority score is calculated using linear weighting.

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