An artificial intelligence-based first-aid resource hierarchical scheduling method and system

Abstract

The application provides an emergency resource grading and scheduling method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and obtains vital sign data and historical treatment data of a patient; the patient is graded according to the vital sign data and the historical treatment data, and the illness level of each patient is obtained; the emergency priority of the patient is determined according to the current number of patients, the patient waiting time and the illness level, and the emergency priority of only the critical patient is not the highest; medical resources are divided according to the illness level and the emergency priority, and the patient is assigned to corresponding medical staff for treatment, the main medical resources are inclined to the patient with higher emergency priority and heavier illness level, the treatment quality is improved, the critical patient is given priority treatment, the illness of the patient is prevented from being delayed, and the life safety of the patient is ensured.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to an artificial intelligence-based hierarchical scheduling method and system for emergency medical resources. Background Technology

[0002] The emergency department is a vital department in a hospital, responsible for the diagnosis and treatment of sudden illnesses, acute injuries, and other emergencies. Emergency room doctors and nurses need to be capable of handling a variety of emergency situations, including cardiac arrest, stroke, and severe trauma. During the emergency process, doctors and nurses must quickly make a diagnosis and provide emergency treatment to save patients' lives and health.

[0003] However, emergency departments often face a surge in patient numbers, especially during holidays, natural disasters, and other special periods. This can overwhelm medical staff, with some capable staff being assigned to treat mild cases, while severe cases go unattended, leading to an inefficient allocation of personnel, delayed patient care, and compromised quality of medical treatment. Summary of the Invention

[0004] The problem this invention aims to solve is that the allocation of emergency medical resources cannot be matched with the patient's symptoms in a timely manner, resulting in delayed treatment and affecting the quality of medical care.

[0005] To address the aforementioned problems, this invention provides, on the one hand, an artificial intelligence-based hierarchical scheduling method for emergency medical resources, comprising:

[0006] Obtain the patient's vital signs data and historical medical records;

[0007] The patients are classified according to the vital signs data and historical medical records to obtain the disease level of each patient.

[0008] The emergency priority of the patient is determined based on the current number of patients seeking medical attention, the patient's waiting time, and the severity of the illness.

[0009] Based on the severity of the illness and the priority of emergency care, medical resources are allocated, and the patients are assigned to the appropriate medical personnel for treatment.

[0010] The acquisition of the patient's vital signs data and historical medical records includes:

[0011] The patient is monitored by electrocardiogram, blood pressure, pulse oximetry and respiratory monitoring, and corresponding vital sign data of the patient are generated. The vital sign data includes real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation and real-time respiratory rate.

[0012] The patient's historical medical records are retrieved from the database based on the patient's personal information.

[0013] Optionally, the step of classifying the patients based on the vital sign data and historical medical data to obtain the disease level of each patient includes:

[0014] Analyze real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to determine the patient's abnormal vital signs data;

[0015] Analyze the historical medical records to identify any abnormal historical medical records of the patient;

[0016] The patient's disease level is determined based on the abnormal vital signs data, the abnormal historical medical records, and the weights corresponding to the abnormal data.

[0017] Optionally, the analysis of real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to determine the patient's abnormal vital signs data includes:

[0018] The real-time electrocardiogram (ECG) is compared with a preset ECG to obtain the ECG difference rate. When the ECG difference rate exceeds the preset difference rate, the real-time ECG is determined to be abnormal.

[0019] The real-time blood pressure is compared with a preset blood pressure range to obtain the blood pressure deviation value of the real-time blood pressure from the preset blood pressure range. When the blood pressure deviation value is greater than the preset blood pressure deviation value, the real-time blood pressure is determined to be abnormal.

[0020] The real-time blood oxygen saturation is compared with a preset blood oxygen saturation range to obtain the blood oxygen saturation deviation value of the real-time blood oxygen saturation from the preset blood oxygen saturation range. When the blood oxygen saturation deviation value is greater than the preset blood oxygen saturation deviation value, the real-time blood oxygen saturation is determined to be abnormal.

[0021] The real-time respiratory rate is compared with a preset respiratory rate range to obtain a respiratory rate deviation value where the real-time respiratory rate deviates from the preset respiratory rate range. When the respiratory rate deviation value is greater than the preset respiratory rate deviation value, the real-time respiratory rate is determined to be abnormal.

[0022] Optionally, determining the patient's disease level based on the abnormal vital sign data, the abnormal historical medical data, and the weights corresponding to the abnormal data includes:

[0023] The analysis value corresponding to the abnormal vital signs data is multiplied by the weight corresponding to the abnormal data, normalized, and then added together to obtain the real-time disease score. The analysis value includes the electrocardiogram difference rate, blood pressure deviation value, blood oxygen saturation deviation value, or respiratory rate deviation value.

[0024] The abnormal historical medical records are multiplied by the weights corresponding to the abnormal data, normalized, and then summed to obtain the historical data score.

[0025] The real-time symptom score and the historical data score are added together to obtain the comprehensive symptom score;

[0026] The disease level is obtained based on the comprehensive disease score and the preset level classification table. The preset level classification table includes multiple score ranges, and different score ranges correspond to different disease levels. The disease level includes mild, moderate and severe levels.

[0027] Optionally, determining the patient's emergency priority based on the current number of patients, patient waiting time, and the severity of the illness includes:

[0028] When a patient presents with a critical illness level, that critical illness level patient is designated as the first priority for emergency care.

[0029] When a patient presents with a moderate condition, the emergency priority of that patient is determined based on the patient waiting time for patients with moderate conditions and the number of patients with severe conditions.

[0030] When a patient seeking medical attention is classified as a mild case, the emergency priority of that patient is determined based on the patient waiting time for mild cases, the number of patients with severe cases, and the number of patients with moderate cases.

[0031] Optionally, when a patient seeking medical attention is classified as a moderate case, determining the emergency priority of the patient based on the patient waiting time for moderate cases and the number of severe cases includes:

[0032] When a patient presents with a moderate condition, determine whether the number of patients with a severe condition is zero.

[0033] If the number of patients with severe symptoms is zero, then the patients with moderate symptoms will be designated as the first priority for emergency care.

[0034] If the number of patients with severe symptoms is not zero, determine whether the waiting time for patients with moderate symptoms is greater than the first preset waiting time.

[0035] When the waiting time for a patient with moderate symptoms exceeds the first preset waiting time, the patient with moderate symptoms will be designated as the first emergency priority.

[0036] When the waiting time for a patient with moderate symptoms is less than or equal to the first preset waiting time, the patient with moderate symptoms is designated as the second emergency priority.

[0037] Optionally, when a patient seeking medical attention is classified as a mild case, determining the patient's emergency priority based on the patient waiting time for mild cases, the number of severe cases, and the number of moderate cases includes:

[0038] When a patient presents with a mild condition, determine whether the number of patients with a severe condition and the number of patients with a moderate condition are both zero.

[0039] If the number of patients with severe illness and the number of patients with moderate illness are both zero, then patients with mild illness are designated as the first priority for emergency care.

[0040] If the number of patients with severe illness or the number of patients with moderate illness is not zero, determine whether the waiting time for patients with mild illness is greater than the second preset waiting time.

[0041] When the waiting time for a patient with mild symptoms is less than or equal to the second preset waiting time, the patient with mild symptoms will be designated as the third emergency priority.

[0042] When the patient waiting time for a patient with mild symptoms exceeds a second preset waiting time, an emergency priority is assigned to the patient with mild symptoms. The emergency priority of the patient with mild symptoms is the same as the lowest emergency priority of the patient with moderate symptoms.

[0043] Optionally, the step of allocating medical resources and assigning the patient to appropriate medical personnel for treatment based on the severity of the illness and the priority of emergency care includes:

[0044] When the number of patients with the severe illness level exceeds the first preset number, a warning message is generated, wherein the warning message is used to remind medical staff that the current emergency department's capacity to receive patients has reached its limit;

[0045] When the number of patients with the severe illness level is less than or equal to the first preset number, a corresponding number of medical staff will be allocated to treat the patients according to the number of patients with the severe illness level.

[0046] The number of patients with moderate and mild symptoms at different emergency priorities was analyzed. Based on the proportion of patients with different emergency priorities to the total number of patients seeking medical treatment, the remaining medical staff were allocated to different emergency priorities for patient treatment.

[0047] On the other hand, this invention also proposes an artificial intelligence-based hierarchical dispatch system for emergency medical resources, comprising:

[0048] The data acquisition module is used to acquire patients' vital signs data and historical medical records.

[0049] The symptom grading module is used to grade the patients based on the vital signs data and historical medical records to obtain the symptom level for each patient.

[0050] The priority analysis module is used to determine the emergency priority of a patient based on the current number of patients, the patient's waiting time, and the severity of the illness.

[0051] The resource scheduling module is used to allocate medical resources according to the severity of the illness and the priority of emergency care, and to assign the patient to the corresponding medical staff for treatment.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This invention provides an artificial intelligence-based method and system for hierarchical scheduling of emergency medical resources. It acquires patients' vital sign data and historical medical records; classifies patients according to these data to determine their disease level; and determines their emergency priority based on the current number of patients, waiting time, and disease level. This prioritizes patients beyond just those with severe conditions; for example, if a patient with a moderate condition has a long waiting time, their priority is increased to prevent delays in treatment or negative emotional states. Based on the disease level and priority, medical resources are allocated, and patients are assigned to appropriate medical personnel for treatment. This prioritizes patients with higher priority and more severe conditions, improving treatment quality and ensuring that critically ill patients receive priority treatment, preventing delays in treatment and safeguarding patient safety. Attached Figure Description

[0054] Figure 1 A flowchart illustrating a hierarchical scheduling method for emergency medical resources based on artificial intelligence, as shown in an embodiment of the present invention, is presented.

[0055] Figure 2 The diagram illustrates the structure of an artificial intelligence-based hierarchical dispatch system for emergency medical resources according to an embodiment of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0058] In the description of this specification, references to terms such as "embodiment," "one embodiment," and "one implementation" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or implementation is included in at least one embodiment or illustrative implementation of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or implementation. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or implementations.

[0059] Figure 1 The diagram illustrates a flowchart of an AI-based hierarchical scheduling method for emergency medical resources according to an embodiment of the present invention. The AI-based hierarchical scheduling method for emergency medical resources includes:

[0060] Obtain the patient's vital signs data and historical medical records.

[0061] The patients are classified according to their vital signs data and historical medical records to obtain the disease level for each patient.

[0062] The emergency priority for each patient is determined based on the current number of patients seeking medical attention, the patient's waiting time, and the severity of the condition.

[0063] Based on the severity of the illness and the priority of emergency care, medical resources are allocated, and the patients are assigned to the appropriate medical personnel for treatment.

[0064] Specifically, vital sign data can be obtained by real-time monitoring of patients using devices such as electrocardiogram monitors, blood pressure monitors, or respiratory monitors, while historical medical data can be retrieved from medical databases or data platforms shared with other hospitals.

[0065] In this embodiment, the patient's vital signs data and historical medical records are acquired. The patient is then classified according to these data to determine the severity level of each patient. Based on the current number of patients, waiting time, and severity level, the emergency priority for each patient is determined. It's not only critically ill patients who have the highest emergency priority; for example, if a patient with moderate symptoms has a long waiting time, their emergency priority is also increased to prevent prolonged waiting, which could delay treatment or induce negative emotions. Medical resources are allocated according to the severity level and emergency priority, and patients are assigned to appropriate medical personnel for treatment. This prioritizes patients with higher emergency priority and more severe symptoms, improving treatment quality and ensuring that critically ill patients receive priority treatment, preventing delays in treatment, and ensuring patient safety.

[0066] In an optional embodiment of the present invention, obtaining the patient's vital signs data and historical medical records includes:

[0067] The patient is monitored by electrocardiogram, blood pressure, pulse oximetry and respiratory monitoring, and corresponding vital sign data of the patient are generated. The vital sign data includes real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation and real-time respiratory rate.

[0068] The patient's historical medical records are retrieved from the database based on the patient's personal information.

[0069] Specifically, multi-parameter monitors can integrate multiple vital sign monitoring functions, including electrocardiogram (ECG) monitoring, blood pressure monitoring, pulse oximetry monitoring, and respiratory monitoring, while simultaneously displaying the monitoring results of multiple vital signs. They can monitor the patient's ECG, recording the heart's electrical activity to obtain information such as heart rate and rhythm; measure the patient's blood pressure, including systolic and diastolic pressure, via cuffs and pressure sensors; measure the patient's pulse and blood oxygen saturation via sensors clipped to the patient's fingertips to obtain pulse and blood oxygen levels; monitor the patient's respiratory rate and breathing pattern via chest sensors or nasal cannulas; and measure the patient's body temperature using thermometers or temperature sensors to obtain body temperature information.

[0070] In an optional embodiment of the present invention, the step of classifying the patient based on the vital signs data and historical medical records to obtain the disease level of each patient includes:

[0071] Analyze real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to identify abnormal vital signs data of the patient.

[0072] Analyze the historical medical records to identify any abnormal historical medical records of the patient.

[0073] The patient's disease level is determined based on the abnormal vital signs data, the abnormal historical medical records, and the weights corresponding to the abnormal data.

[0074] Specifically, by analyzing real-time electrocardiograms, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate, and comparing the obtained data with pre-stored normal data, abnormalities in vital signs can be quickly identified. In addition, if the patient's historical medical records show that the patient has allergies, serious illnesses, or has undergone special surgeries, this noteworthy information needs to be extracted and then these abnormal data should be given special attention. Based on these abnormal data, the patient's current condition can be classified.

[0075] In an optional embodiment of the present invention, the analysis of real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to determine the patient's abnormal vital signs data includes:

[0076] The real-time electrocardiogram (ECG) is compared with a preset ECG to obtain the ECG difference rate. When the ECG difference rate exceeds the preset difference rate, the real-time ECG is determined to be abnormal.

[0077] The real-time blood pressure is compared with a preset blood pressure range to obtain the blood pressure deviation value of the real-time blood pressure from the preset blood pressure range. When the blood pressure deviation value is greater than the preset blood pressure deviation value, the real-time blood pressure is determined to be abnormal.

[0078] The real-time blood oxygen saturation is compared with a preset blood oxygen saturation range to obtain the blood oxygen saturation deviation value of the real-time blood oxygen saturation from the preset blood oxygen saturation range. When the blood oxygen saturation deviation value is greater than the preset blood oxygen saturation deviation value, the real-time blood oxygen saturation is determined to be abnormal.

[0079] The real-time respiratory rate is compared with a preset respiratory rate range to obtain a respiratory rate deviation value where the real-time respiratory rate deviates from the preset respiratory rate range. When the respiratory rate deviation value is greater than the preset respiratory rate deviation value, the real-time respiratory rate is determined to be abnormal.

[0080] For example, if the ECG deviation rate is 10% while the preset deviation rate is 5%, it indicates that the ECG has a large deviation and is not within the normal range. In this case, the real-time ECG can be judged as abnormal. If the preset blood pressure range is between 90 mmHg and 140 mmHg for systolic blood pressure and between 60 mmHg and 90 mmHg for diastolic blood pressure, and the real-time blood pressure is 145 / 99, then the blood pressure deviation values ​​are 5 and 9, respectively. Similarly, the deviation values ​​of blood oxygen saturation and respiratory rate can be calculated.

[0081] In an optional embodiment of the present invention, analyzing the historical medical records to determine the patient's abnormal historical medical records includes:

[0082] According to the preset abnormal data checklist, the historical medical records are checked to determine the patient's abnormal historical medical records.

[0083] Specifically, the abnormal data checklist lists some noteworthy medical records, such as allergy records, surgical records, and serious illness records. It can further list specific types of surgeries and serious illnesses to retrieve noteworthy data from a patient's historical medical records.

[0084] In an optional embodiment of the present invention, determining the patient's disease level based on the abnormal vital sign data, the abnormal historical medical data, and the weights corresponding to the abnormal data includes:

[0085] The real-time symptom score is obtained by multiplying the analytical value corresponding to the abnormal vital signs data with the weight corresponding to the abnormal data, normalizing the result, and then summing the results. The analytical value includes the electrocardiogram difference rate, blood pressure deviation, blood oxygen saturation deviation, or respiratory rate deviation.

[0086] The abnormal historical medical records are multiplied by their corresponding weights, normalized, and then summed to obtain the historical data score.

[0087] Specifically, for example, the weights of real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate are 0.3, 0.2, 0.2, and 0.3, respectively. Abnormal data are multiplied by their weights, and the product is normalized. The purpose of normalization is to process the magnitude of different data to the same order of magnitude, so that the influence of each data point is only affected by its weight, making the calculation more reasonable.

[0088] The real-time score and the historical data score of the disease are added together to obtain the comprehensive score of the disease.

[0089] The disease level is obtained based on the comprehensive disease score and the preset level classification table. The preset level classification table includes multiple score ranges, and different score ranges correspond to different disease levels. The disease level includes mild, moderate and severe levels.

[0090] For example, the scoring range for mild cases is 1-10, for moderate cases it is 10-30, and for severe cases it is over 30. When the comprehensive symptom score is 12, the patient's symptom level is determined to be moderate. By quantifying the patient's condition through analysis, and then using the quantified score, the patient can be quickly classified, thus determining the emergency priority more quickly, accelerating the emergency response, saving analysis time and costs, and optimizing the process.

[0091] In an optional embodiment of the present invention, determining the emergency priority of the patient based on the current number of patients, the patient waiting time, and the severity of the illness includes:

[0092] When a patient presents with a critical illness level, that critical illness level patient is designated as the first priority for emergency care.

[0093] When a patient presents with a moderate condition, the emergency priority of that patient is determined based on the patient waiting time for patients with moderate conditions and the number of patients with severe conditions.

[0094] When a patient seeking medical attention is classified as a mild case, the emergency priority of that patient is determined based on the patient waiting time for mild cases, the number of patients with severe cases, and the number of patients with moderate cases.

[0095] Specifically, once a patient is assessed as having a severe condition, their treatment becomes the top priority in emergency care. Severe cases require immediate attention and cannot be delayed; every second counts. Patients with moderate or mild conditions have lower urgency, allowing for a longer waiting period. This time difference allows more medical resources to be allocated to severe cases. The priority for moderate and mild cases is further determined based on the number of severe cases among those seeking treatment and patient waiting times.

[0096] In an optional embodiment of the present invention, when the patient seeking medical attention is a patient with moderate symptoms, determining the emergency priority of the patient based on the patient waiting time of the moderate symptoms and the number of patients with severe symptoms includes:

[0097] When a patient presents with a moderate condition, determine whether the number of patients with a severe condition is zero.

[0098] If the number of patients with severe illness is zero, it means that there are no severe patients at this time. At this time, patients with moderate illness are the most critical patients and should be designated as the first priority for emergency treatment.

[0099] If the number of patients with severe symptoms is not zero, it means that there are still some severe patients. At this time, it is necessary to further determine whether the waiting time for patients with moderate symptoms is greater than the first preset waiting time. The first preset waiting time is a preset value. The setting of this preset value should ensure that the patient is safe within this waiting time. For example, the first preset waiting time is 15 minutes.

[0100] When the waiting time for a patient with moderate symptoms exceeds the first preset waiting time, it indicates that the patient with moderate symptoms has been waiting for too long, and further waiting may lead to a worsening of the condition. In this case, such patients need to be treated immediately, and the patient with moderate symptoms can be designated as the first priority for emergency care.

[0101] When the waiting time for patients with moderate symptoms is less than or equal to the first preset waiting time, it means that patients with moderate symptoms can wait for a period of time. If there are remaining medical resources, these patients can be treated. If medical resources are scarce, critically ill patients can be treated first, while patients with moderate symptoms are designated as the second priority for emergency care, and treatment can be delayed.

[0102] In this embodiment, by prioritizing emergency care as described above, medical resources can be utilized to the maximum extent, enabling patients to receive timely treatment.

[0103] In an optional embodiment of the present invention, when the patient seeking medical attention is a patient with mild symptoms, determining the emergency priority of the patient based on the patient waiting time of the patients with mild symptoms, the number of patients with severe symptoms, and the number of patients with moderate symptoms includes:

[0104] When a patient presents with a mild condition, determine whether the number of patients with a severe condition and the number of patients with a moderate condition are both zero.

[0105] If the number of patients with severe illness and the number of patients with moderate illness are both zero, then patients with mild illness are designated as the first priority for emergency care.

[0106] If the number of patients with severe illness or the number of patients with moderate illness is not zero, determine whether the waiting time for patients with mild illness is greater than a second preset waiting time, wherein the second preset waiting time cannot exceed one hour.

[0107] When the waiting time for a patient with mild symptoms is less than or equal to the second preset waiting time, the patient with mild symptoms is designated as the third emergency priority.

[0108] When the patient waiting time for a patient with mild symptoms exceeds a second preset waiting time, an emergency priority is assigned to the patient with mild symptoms. The emergency priority of the patient with mild symptoms is the same as the lowest emergency priority of the patient with the previous symptom level.

[0109] In this embodiment, similar to the emergency priority assignment method for moderate-symptom patients, for mild-symptom patients, the number of severe-symptom patients and moderate-symptom patients needs to be considered. If patients in the two symptom levels are present, mild-symptom patients may not be assigned to the first emergency priority. Only when patients in the two symptom levels are absent will mild-symptom patients be assigned to the first emergency priority. This is to ensure that critically ill patients receive priority treatment. It is also important to note that when the waiting time for a mild-symptom patient is less than or equal to a second preset waiting time, the patient can be placed in the lowest emergency priority, i.e., the third emergency priority. Conversely, when the waiting time for a mild-symptom patient is longer than the second preset waiting time, indicating a longer waiting period, the patient's emergency priority can be increased. However, the increased emergency priority can only be the same as the lowest emergency priority of the patient in the previous symptom level. For example, if the lowest emergency priority for a moderate-symptom patient is the second emergency priority, then the increased emergency priority for a mild-symptom patient will be the second emergency priority.

[0110] In an optional embodiment of the present invention, the step of allocating medical resources according to the severity of the illness and the priority of emergency care, and assigning the patient to appropriate medical personnel for treatment, includes:

[0111] When the number of patients with severe conditions exceeds the first preset number, a warning message is generated. The warning message is used to remind medical staff that the emergency department's capacity has reached its limit. At this time, the emergency department can request support from other departments for medical staff and equipment, or report the situation to the emergency center and send subsequent patients to other hospitals to avoid the problem of too many patients not receiving timely treatment.

[0112] When the number of patients with severe illness is less than or equal to the first preset number, since patients with severe illness need to be treated immediately, the corresponding number of medical staff will be allocated to treat the patients first, and then the remaining medical staff and medical equipment will be redistributed.

[0113] The number of patients with moderate and mild symptoms at different emergency priorities was analyzed. Based on the proportion of patients with different emergency priorities to the total number of patients seeking medical treatment, the remaining medical staff were allocated to different emergency priorities for patient treatment.

[0114] Specifically, since patients with moderate and mild symptoms can have their treatment delayed for a certain period, and to account for patients' emotional state and prevent excessive waiting times, resources need to be allocated according to emergency priority. For example, if the total number of patients in these two symptom categories is 15, the number of patients in the first, second, and third emergency priority categories are 3, 7, and 5 respectively. Since the allocation weights for the first, second, and third emergency priority categories are different (e.g., 0.5, 0.3, and 0.2 respectively), the median values ​​for each priority category are first calculated: 3 / 15 * 0.5 = 0.1, 7 / 15 * 0.3 = 0.14, and 5 / 15 * 0.2 = 0.07. Then, the resource allocation ratio calculated based on these median values ​​is 0.1:0.14:0.07, approximately 3:5:2. The remaining medical resources are then redistributed according to this ratio.

[0115] Figure 2 The diagram illustrates the structure of an AI-based hierarchical dispatch system for emergency medical resources according to an embodiment of the present invention. The AI-based hierarchical dispatch system for emergency medical resources includes:

[0116] The data acquisition module 10 is used to acquire the patient's vital signs data and historical medical records.

[0117] The symptom grading module 20 is used to grade the patients based on the vital signs data and historical medical records to obtain the symptom level of each patient.

[0118] The priority analysis module 30 is used to determine the emergency priority of the patient based on the current number of patients, the patient waiting time, and the severity of the illness.

[0119] The resource scheduling module 40 is used to allocate medical resources according to the severity of the illness and the priority of emergency care, and to assign the patient to the corresponding medical staff for treatment.

[0120] The AI-based hierarchical dispatch system for emergency medical resources described in this invention has similar technical effects to the aforementioned AI-based hierarchical dispatch method for emergency medical resources, and will not be elaborated further here.

[0121] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A hierarchical scheduling method for emergency medical resources based on artificial intelligence, characterized in that, include: Obtain the patient's vital signs data and historical medical records; The patients are classified according to the vital signs data and historical medical records to obtain the disease level of each patient. The emergency priority of the patient is determined based on the current number of patients seeking medical attention, the patient's waiting time, and the severity of the illness. Based on the severity of the illness and the priority of emergency care, medical resources are allocated, and the patients are assigned to the appropriate medical personnel for treatment.

2. The method for hierarchical dispatching of emergency medical resources based on artificial intelligence according to claim 1, characterized in that, The acquisition of the patient's vital signs data and historical medical records includes: The patient is monitored by electrocardiogram, blood pressure, pulse oximetry and respiratory monitoring, and corresponding vital sign data of the patient are generated. The vital sign data includes real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation and real-time respiratory rate. The patient's historical medical records are retrieved from the database based on the patient's personal information.

3. The method for hierarchical dispatching of emergency medical resources based on artificial intelligence according to claim 2, characterized in that, The process of classifying patients based on vital sign data and historical medical records to obtain the disease level for each patient includes: Analyze real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to determine the patient's abnormal vital signs data; Analyze the historical medical records to identify any abnormal historical medical records of the patient; The patient's disease level is determined based on the abnormal vital signs data, the abnormal historical medical records, and the weights corresponding to the abnormal data.

4. The method for hierarchical dispatching of emergency medical resources based on artificial intelligence according to claim 3, characterized in that, The analysis of real-time electrocardiogram, real-time blood pressure, real-time blood oxygen saturation, and real-time respiratory rate to determine the patient's abnormal vital signs data includes: The real-time electrocardiogram (ECG) is compared with a preset ECG to obtain the ECG difference rate. When the ECG difference rate exceeds the preset difference rate, the real-time ECG is determined to be abnormal. The real-time blood pressure is compared with a preset blood pressure range to obtain the blood pressure deviation value of the real-time blood pressure from the preset blood pressure range. When the blood pressure deviation value is greater than the preset blood pressure deviation value, the real-time blood pressure is determined to be abnormal. The real-time blood oxygen saturation is compared with a preset blood oxygen saturation range to obtain the blood oxygen saturation deviation value of the real-time blood oxygen saturation from the preset blood oxygen saturation range. When the blood oxygen saturation deviation value is greater than the preset blood oxygen saturation deviation value, the real-time blood oxygen saturation is determined to be abnormal. The real-time respiratory rate is compared with a preset respiratory rate range to obtain a respiratory rate deviation value where the real-time respiratory rate deviates from the preset respiratory rate range. When the respiratory rate deviation value is greater than the preset respiratory rate deviation value, the real-time respiratory rate is determined to be abnormal.

5. The method for hierarchical scheduling of emergency medical resources based on artificial intelligence according to claim 3, characterized in that, The process of determining the patient's disease level based on the abnormal vital sign data, the abnormal historical medical data, and the weights corresponding to the abnormal data includes: The analysis value corresponding to the abnormal vital signs data is multiplied by the weight corresponding to the abnormal data, normalized, and then added together to obtain the real-time disease score. The analysis value includes the electrocardiogram difference rate, blood pressure deviation value, blood oxygen saturation deviation value, or respiratory rate deviation value. The abnormal historical medical records are multiplied by the weights corresponding to the abnormal data, normalized, and then summed to obtain the historical data score. The real-time symptom score and the historical data score are added together to obtain the comprehensive symptom score; The disease level is obtained based on the comprehensive disease score and the preset level classification table. The preset level classification table includes multiple score ranges, and different score ranges correspond to different disease levels. The disease level includes mild, moderate and severe levels.

6. The method for hierarchical scheduling of emergency medical resources based on artificial intelligence according to claim 5, characterized in that, The process of determining the emergency priority of a patient based on the current number of patients seeking medical attention, patient waiting time, and the severity of the illness includes: When a patient presents with a critical illness level, that critical illness level patient is designated as the first priority for emergency care. When a patient presents with a moderate condition, the emergency priority of that patient is determined based on the patient waiting time for patients with moderate conditions and the number of patients with severe conditions. When a patient seeking medical attention is classified as a mild case, the emergency priority of that patient is determined based on the patient waiting time for mild cases, the number of patients with severe cases, and the number of patients with moderate cases.

7. The method for hierarchical dispatching of emergency medical resources based on artificial intelligence according to claim 6, characterized in that, When a patient presents with moderate symptoms, the emergency priority for that patient is determined based on the patient waiting time for moderate symptoms and the number of severe symptoms. When a patient presents with a moderate condition, determine whether the number of patients with a severe condition is zero. If the number of patients with severe symptoms is zero, the patients with moderate symptoms will be designated as the first priority for emergency care. If the number of patients with severe symptoms is not zero, determine whether the waiting time for patients with moderate symptoms is greater than the first preset waiting time. When the waiting time for a patient with moderate symptoms exceeds the first preset waiting time, the patient with moderate symptoms will be designated as the first emergency priority. When the waiting time for a patient with moderate symptoms is less than or equal to the first preset waiting time, the patient with moderate symptoms is designated as the second emergency priority.

8. The method for hierarchical scheduling of emergency medical resources based on artificial intelligence according to claim 6, characterized in that, When a patient seeking medical attention is classified as having a mild condition, the emergency priority for that patient is determined based on the waiting time for patients with mild symptoms, the number of patients with severe symptoms, and the number of patients with moderate symptoms, including: When a patient presents with a mild condition, determine whether the number of patients with a severe condition and the number of patients with a moderate condition are both zero. If the number of patients with severe illness and the number of patients with moderate illness are both zero, then patients with mild illness are designated as the first priority for emergency care. If the number of patients with severe illness or the number of patients with moderate illness is not zero, determine whether the waiting time for patients with mild illness is greater than the second preset waiting time. When the waiting time for a patient with mild symptoms is less than or equal to the second preset waiting time, the patient with mild symptoms will be designated as the third emergency priority. When the patient waiting time for a patient with mild symptoms exceeds a second preset waiting time, an emergency priority is assigned to the patient with mild symptoms. The emergency priority of the patient with mild symptoms is the same as the lowest emergency priority of the patient with moderate symptoms.

9. The method for hierarchical dispatch of emergency medical resources based on artificial intelligence according to claim 8, characterized in that, The step of allocating medical resources and assigning patients to appropriate medical personnel for treatment based on the severity of the illness and the priority of emergency care includes: When the number of patients with the severe illness level exceeds the first preset number, a warning message is generated, wherein the warning message is used to remind medical staff that the current emergency department's capacity to receive patients has reached its limit; When the number of patients with the severe illness level is less than or equal to the first preset number, a corresponding number of medical staff will be allocated to treat the patients according to the number of patients with the severe illness level. The number of patients with moderate and mild symptoms at different emergency priorities was analyzed. Based on the proportion of patients with different emergency priorities to the total number of patients seeking medical treatment, the remaining medical staff were allocated to different emergency priorities for patient treatment.

10. An artificial intelligence-based hierarchical dispatch system for emergency medical resources, characterized in that, include: The data acquisition module is used to acquire patients' vital signs data and historical medical records. The symptom grading module is used to grade the patients based on the vital signs data and historical medical records to obtain the symptom level for each patient. The priority analysis module is used to determine the emergency priority of a patient based on the current number of patients, the patient's waiting time, and the severity of the illness. The resource scheduling module is used to allocate medical resources according to the severity of the illness and the priority of emergency care, and to assign the patient to the corresponding medical staff for treatment.

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