Information processing method for patient treatment appointment

Abstract

The invention discloses an information processing method for patient doctor-seeing appointment, and relates to the technical field of medical treatment, and the method comprises the steps: collecting doctor-seeing data and appointment data of each time period in real time, and carrying out the intelligent setting of the saturation appointment amount of each doctor-seeing time period through combining with a containing factor Rnyz, thereby reducing the phenomenon that resources are excessively crowded or idle, and achieving the intelligent setting of the saturation appointment amount of each doctor-seeing time period. By accurately analyzing the actual doctor seeing condition of each department in different time periods, the reservation amount of each time period can be dynamically adjusted, so that more efficient resource use is realized. According to the method, a priority evaluation score Yfs is generated through a patient data set constructed based on historical data in combination with factors such as health conditions, emergency situations and historical doctor seeing situations, so that high-priority patients can obtain corresponding medical services in a short time, and the medical service quality of the patients is improved by monitoring patient change data and resource condition data in real time. The system can continuously optimize the priority evaluation score Yfs according to real-time information and adjust resource allocation according to actual changes.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to an information processing method for patient appointment. Background Art

[0002] With the continuous development of the field of information and communication technology (ICT), intelligent and big data technologies have gradually penetrated into all walks of life, especially the medical industry. With the diversification and increase of medical needs, especially under the background of increasing public health pressure, the operation and management of hospitals are facing unprecedented challenges. As a core content of hospital management, patient appointment directly affects the effective use of medical resources, the patient's medical experience and the overall efficiency of medical services. The appointment system not only involves the management of patients, but also covers the allocation and optimization of medical resources.

[0003] At present, most hospitals' appointment booking systems rely on manual operations and regular scheduling to a certain extent, which has obvious limitations. However, this scheduling method has obvious problems. On the one hand, the differences in patients' health conditions and the urgency of their illnesses are often not accurately considered, resulting in the failure to ensure the priority of some high-risk or emergency patients in time. On the other hand, it is often difficult to achieve refined management of hospital resource allocation. There may be idle resources in certain periods, while in other periods, the needs of all patients cannot be met due to resource constraints. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an information processing method for patient appointment booking, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an information processing method for patient appointment, comprising the following steps:

[0006] S1. Based on the hospital's appointment system, the hospital's medical treatment and appointment status in each time period are collected in real time to obtain relevant medical treatment data and relevant medical appointment data, and after data preprocessing, the hospital's patient data set is constructed;

[0007] S2. Analyze the actual visit situation of each department in the hospital during the corresponding visit period based on historical data to calculate the capacity factor Rnyz. Based on the value of the capacity factor Rnyz, set the saturated appointment volume of the corresponding visit period in the visit appointment system;

[0008] S3. Analyze the health status, emergency situation and historical medical treatment of each patient in the corresponding medical treatment appointment period according to the relevant medical treatment appointment data in the in-hospital patient data set, and build a priority evaluation score Yfs based on the trained appointment evaluation model. Generate a preliminary patient details report for the corresponding medical treatment period in the hospital based on the priority evaluation score Yfs value;

[0009] S4. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, and optimize the priority evaluation score Yfs in real time based on the relevant patient change data;

[0010] S5. Based on S4, optimize the allocation of in-hospital resources to balance resource utilization and patient experience.

[0011] Preferably, the specific steps of S1 include:

[0012] S11. According to the medical appointment system built in the hospital, the medical treatment situation in each time period in the hospital is collected in real time to obtain relevant medical treatment data, which includes the actual number of patients who visited the corresponding department in the hospital during each medical treatment period. , the duration of each patient's visit Jsc, the historical frequency of each patient's visit in the corresponding department and the length of time in each consultation period within the corresponding department At the same time, the appointment information of each time period in the hospital is collected to obtain relevant appointment data, which includes the health characteristics Sbz of each patient in each appointment period in the corresponding department, whether each patient has abnormal respiratory rate , whether there is acute chest pain and whether there is abnormal blood pressure .

[0013] Preferably, the specific step S1 also includes:

[0014] S12. Use stream processing engine technology to perform real-time transmission and streaming data processing on the relevant medical data and relevant medical appointment data in S11, so as to capture the dynamic changes in the hospital in real time, and use a time series database to store and query real-time data with timestamps, and finally construct an in-hospital patient data set, which includes the relevant medical data and relevant medical appointment data processed and stored by S11 and S12.

[0015] Preferably, the specific steps of S2 include:

[0016] S21. Extract the in-hospital patient data set within the historical period from the medical appointment system, mark it as historical data, and analyze the actual medical treatment situation of each department in the hospital within the corresponding medical treatment period based on the historical data to calculate the accommodation factor Rnyz. The accommodation factor Rnyz is obtained by the following formula:

[0017]

[0018] In the formula, n represents the total duration of the visit in one day, i represents the number of each visit period in one day, represents the number of patients who actually visited the hospital during the i-th visit period, represents the average consultation time of each actual patient in the i-th consultation period, Represents the length of time within the i-th visit period.

[0019] Preferably, the specific step S2 also includes:

[0020] S22, the capacity factor Rnyz measures the maximum capacity of each consultation period, and according to the value of the capacity factor Rnyz, determines the saturated reservation volume of each department in the hospital in each consultation period in the consultation reservation system;

[0021] S23. According to the saturated reservation volume of each department in each consultation period in the consultation reservation system, the number of patients for reservation is limited, and the initial reservation number is initially arranged for each patient who needs to make a reservation based on the patient's reservation time requirements and the order of priority within the reservation time requirements.

[0022] Preferably, the specific steps of S3 include:

[0023] S31, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the health conditions of different patients in the corresponding department in each appointment period are analyzed to calculate the health status score Jpf, which is obtained by:

[0024]

[0025] In the formula, represents the health status score of the kth patient in the corresponding department, m represents the number of health status features in the relevant medical appointment data in the in-hospital patient dataset, j=1, 2, 3, ..., m, represents the actual value of the jth feature of the kth patient in the corresponding department, represents the overall mean of the jth feature, represents the standard deviation of the jth feature;

[0026] S32, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the emergency rescue situations of different patients in the corresponding departments during each appointment period are analyzed to calculate the emergency score Bjf of the condition, and the emergency score Bjf of the condition is obtained by the following formula:

[0027]

[0028] In the formula, represents the emergency score of the kth patient in the corresponding department, Indicates whether there is acute chest pain, Indicates whether there is abnormal breathing rate, Indicates whether there is abnormal blood pressure, represents the indicator function. When there is acute chest pain, =1, otherwise 0; when there is abnormal breathing rate, =1, otherwise 0; when there is abnormal blood pressure, =1, otherwise 0; , and Both represent the weights of the emergency features in the relevant medical appointment data in the in-hospital patient dataset.

[0029] Preferably, the specific step S3 also includes:

[0030] S33. Using deep learning technology and combining the in-hospital patient data set, an appointment evaluation model is built, and after dimensionless processing, an output priority evaluation score Yfs is fitted. The output priority evaluation score Yfs is obtained by the following formula:

[0031]

[0032] In the formula, represents the historical frequency of visits of each patient in the corresponding department, , and are weights, , and The specific value is set by the user according to the situation.

[0033] Preferably, the specific step S3 also includes:

[0034] S34. According to the method of obtaining the priority evaluation score Yfs in S33, the priority evaluation score Yfs of each patient in the corresponding department is obtained respectively, and based on the numerical value of the priority evaluation score Yfs, the initialized appointment numbers of each patient to be scheduled for treatment are re-prioritized according to the numerical value of the priority evaluation score Yfs of each patient in the corresponding department, so as to generate a preliminary patient details report for the corresponding treatment period in the hospital.

[0035] Preferably, the specific steps of S4 include:

[0036] S41. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, wherein the relevant patient change data includes the number of latecomers of the corresponding patients and the number of patients in the hospital who have not made an appointment for consultation offline; the relevant resource status data includes the resource utilization rate of each consultation period in the corresponding department and the load during each visit period ;

[0037] S42. According to the number of latecomers of the corresponding patients in the relevant patient change data and the number of patients without offline appointments in the hospital, the number of latecomers of the corresponding patients is deleted from the preliminary patient details report of the corresponding treatment period in the hospital, and the number of patients without offline appointments in the hospital is added to the preliminary patient details report of the corresponding treatment period in the hospital according to the saturated appointment volume of each department in the treatment appointment system in S22 in each treatment period, so as to optimize and update the priority evaluation score Yfs in real time, and mark it as the optimized priority evaluation score .

[0038] Preferably, the specific steps of S5 include:

[0039] S51, based on the optimized priority evaluation score obtained in S42 , and combined with the relevant resource status data obtained in S41, to analyze the resource allocation of the corresponding departments in the hospital, to construct a comprehensive evaluation index Zpzb, based on the comprehensive evaluation index Zpzb, to balance resource utilization and patient experience, the comprehensive evaluation index Zpzb is obtained by the following formula:

[0040]

[0041] In the formula, represents the optimized priority evaluation score of the kth patient in the corresponding department, represents the resource utilization rate of the ith consultation period, represents the load of the ith visit period, represents the weight of the load, n represents the total duration of consultation in one day, i represents the number of each consultation period in one day, K represents the number of patients scheduled in the corresponding department, and k represents the patient number in the corresponding department.

[0042] The present invention provides an information processing method for patient appointment, which has the following beneficial effects:

[0043] (1) This method collects the medical treatment data and appointment data of each time period in real time, and intelligently sets the saturated appointment volume for each medical treatment period in combination with the accommodation factor Rnyz, ensuring that hospital resources are reasonably allocated and reducing the phenomenon of overcrowding or idle resources. By accurately analyzing the actual medical treatment situation of each department in different time periods, the appointment volume of each time period can be dynamically adjusted, thereby achieving more efficient resource use. Optimize patient priority management: By combining the patient data set constructed based on historical data, the priority evaluation score Yfs is generated in combination with factors such as health status, emergency situation, and historical medical treatment situation, so that high-priority patients can receive corresponding medical services in a shorter time. This intelligent priority sorting can reduce the delay of emergency patients due to unreasonable appointment scheduling and improve patients' medical satisfaction. Dynamic adjustment and real-time optimization: By monitoring patient change data and resource status data in real time, the system can continuously optimize the priority evaluation score Yfs based on real-time information and adjust resource allocation according to actual changes. This flexible scheduling mode enables hospitals to respond and make adjustments in a timely manner when facing uncertain needs, effectively reducing resource waste and long waiting time for patients. Improve patient experience: Based on systematic data analysis and priority sorting, patients can see a doctor in time according to their health status and urgency, reducing unnecessary waiting in line and ensuring that emergency patients can receive necessary treatment in a shorter time. At the same time, the optimized resource allocation can avoid the impact of excessive crowding on the quality of medical treatment, improve the patient's medical experience and the service quality of the hospital. Enhance the level of intelligence in hospital management: This method improves the automation and intelligence of the hospital's appointment system through data-driven intelligent analysis and optimization, reduces the need for manual intervention, reduces operating costs, and improves the hospital's response speed to patient needs and the scientific nature of resource allocation. In summary, the method of the present invention achieves comprehensive optimization of the patient's appointment system through refined scheduling, dynamic optimization and intelligent management, and provides hospitals with an efficient, flexible and intelligent resource management solution, which further improves hospital operating efficiency and patient satisfaction while meeting patients' medical needs.

[0044] (2) Through step S31, the health status score Jpf is calculated based on the health characteristics of each patient in the in-hospital patient data set, which can accurately assess the health status of each patient. This scoring system can help doctors and hospital managers arrange appropriate medical treatment priorities for patients according to their health status, thereby achieving more personalized and accurate medical services and improving patients' medical experience. In step S32, by analyzing whether the patient has emergency situations such as acute chest pain, abnormal respiratory rate and abnormal blood pressure, the medical emergency score Bjf is calculated, and these emergency indicators are weighted by the indicator function. The medical emergency scoring system can accurately reflect the patient's emergency situation and provide timely emergency rescue assessment for hospital managers, which enables the hospital to give priority to patients with urgent conditions during the patient appointment process, improves the efficiency of hospital resource allocation, and ensures that patients in urgent need of treatment can be treated in a shorter time. The health status score Jpf and the medical emergency score Bjf are evaluated from the two dimensions of the patient's health status and emergency rescue needs, respectively, providing more comprehensive data support for the hospital. This multi-dimensional comprehensive scoring method enables the hospital to fully consider the patient's health status and emergency needs when making patient appointment arrangements, thereby formulating a more reasonable medical priority and improving the scientificity and rationality of the hospital's medical arrangements. The health status score and condition urgency score obtained in steps S31 and S32 can help the hospital to make more intelligent appointment number allocation in step S23. According to the health status and urgency of each patient, the system can dynamically adjust their appointment number to ensure that high-risk and seriously ill patients are given priority. This intelligent appointment number allocation system improves the hospital's ability to respond to patient flow management, resource allocation and emergency treatment, making hospital operations more efficient and flexible.

[0045] (3) Introducing deep learning technology through step S33, combining with the in-hospital patient data set, constructing and training an appointment evaluation model, this method can learn more complex rules from a large amount of historical data, thereby improving the accuracy of the priority evaluation score Yfs. By fitting the dimensionless processed data, the model can accurately capture the patient's historical visit frequency, health status and other characteristics, further improve the ability to predict patient priority, and provide more scientific data support for the allocation of medical resources. Dynamically adjust priority sorting and optimize resource allocation: In step S34, patients are re-sorted according to the priority evaluation score Yfs to arrange the patient's visit order more reasonably. Unlike the traditional static sorting method, the present invention can dynamically adjust the priority sorting according to the real-time situation of each patient to ensure that patients who need emergency treatment can be treated first. This dynamic adjustment mechanism enables the hospital to allocate resources more flexibly and avoids excessive reliance on fixed appointment number sorting, thereby further improving the efficiency and fairness of hospital resource allocation.

[0046] (4) In step S41, the system monitors the patient's medical treatment in real time. By obtaining the change data of relevant patients, the system can quickly adjust the patient details report in the hospital. This process avoids the waste of resources caused by static information, and ensures that the actual needs of patients can be accurately reflected by updating patient information in real time, thus providing a more accurate basis for subsequent resource allocation. In step S42, the patient's lateness and the number of patients without an appointment are updated in the preliminary patient details report in a timely manner, and the priority assessment score is re-optimized based on the new situation. This dynamic optimization process not only ensures that the patient's priority assessment score always reflects his or her actual situation, but also effectively handles sudden changes in patient needs. For example, timely adjustment of the priority score ensures that emergency patients and patients with high actual needs receive priority treatment. Dual balance between resource utilization and patient experience: In step S51, through the construction of the comprehensive evaluation index Zpzb, the system can find a relatively good entry point between balancing resource utilization and patient experience. This comprehensive evaluation index takes into account the optimized priority evaluation score of each patient, and combines the resource utilization and load of each consultation period to provide a comprehensive resource allocation plan. Through this plan, the hospital can improve resource utilization while avoiding patient dissatisfaction caused by overcrowding, thus ensuring the patient's medical experience. Improve the efficiency of hospital resource allocation and patient satisfaction: Through the comprehensive optimization of resource utilization and patient priority, the hospital's resource allocation has been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flowchart of an information processing method for patient appointment. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 The present invention provides an information processing method for patient appointment, comprising the following steps:

[0051] S1. Based on the hospital's appointment system, the hospital's medical treatment and appointment status in each time period are collected in real time to obtain relevant medical treatment data and relevant medical appointment data, and after data preprocessing, the hospital's patient data set is constructed;

[0052] S2. Analyze the actual visit situation of each department in the hospital during the corresponding visit period based on historical data to calculate the capacity factor Rnyz. Based on the value of the capacity factor Rnyz, set the saturated appointment volume of the corresponding visit period in the visit appointment system;

[0053] S3. Analyze the health status, emergency situation and historical medical treatment of each patient in the corresponding medical treatment appointment period according to the relevant medical treatment appointment data in the in-hospital patient data set, and build a priority evaluation score Yfs based on the trained appointment evaluation model. Generate a preliminary patient details report for the corresponding medical treatment period in the hospital based on the priority evaluation score Yfs value;

[0054] S4. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, and optimize the priority evaluation score Yfs in real time based on the relevant patient change data;

[0055] S5. Based on S4, optimize the allocation of in-hospital resources to balance resource utilization and patient experience.

[0056] In this embodiment, by collecting the hospital's medical treatment and appointment status in each time period in real time and calculating the capacity factor Rnyz based on historical data, the saturated appointment volume for each medical treatment period can be scientifically calculated, further avoiding resource waste or excessive congestion under the traditional scheduling method. This step effectively improves the efficiency of hospital resource utilization, ensures that resources in each time period are fully and reasonably allocated, and maximizes the utilization rate of medical resources. Intelligent priority assessment: During the patient's appointment process, combined with the patient's health status, urgency and historical medical treatment situation, the trained appointment assessment model can be used to construct an accurate priority assessment score Yfs. Through this assessment system, the hospital can automatically prioritize according to the patient's actual needs, ensuring that high-priority patients (such as emergency patients or seriously ill patients) are given priority, thereby improving patient satisfaction with the treatment and reducing the risks caused by delayed treatment. Dynamic optimization and real-time adjustment: This method can not only generate a patient detail report based on the preliminary priority evaluation score, but also obtain patient change data and resource status data through real-time information monitoring during the actual medical treatment process, and then dynamically optimize the priority. This real-time adjustment can more flexibly respond to changes in the number of patients and resource usage, ensure flexibility and response speed in hospital operations, further improve the efficiency of resource utilization, and reduce patient waiting time. Balance resource utilization and patient medical experience: Through the reasonable optimization and allocation of in-hospital resources, the present invention can ensure that resources are fully utilized while improving the patient's medical experience as much as possible. The optimized resource allocation scheme can further avoid excessive congestion during peak hours or idle resources during low hours, ensure that patients can be served smoothly and efficiently in each time period, and reduce patients' dissatisfaction caused by waiting too long. In short, this method not only improves the utilization efficiency of hospital resources, but also improves the patient's medical experience through accurate priority evaluation, real-time dynamic adjustment and intelligent resource allocation, further realizes the intelligent and refined management of patient appointments, and improves the efficiency of the overall operation of the hospital and the quality of medical services.

[0057] Example 2

[0058] Please refer to Figure 1 , specifically: S1 specific steps include:

[0059] S11. According to the medical appointment system built in the hospital, the medical treatment situation in each time period in the hospital is collected in real time to obtain relevant medical treatment data, which includes the actual number of patients who visited the corresponding department in the hospital during each medical treatment period. , the duration of each patient's visit Jsc, the historical frequency of each patient's visit in the corresponding department and the length of time in each consultation period within the corresponding department At the same time, the appointment information of each time period in the hospital is collected to obtain relevant appointment data, which includes the health characteristics Sbz of each patient in each appointment period in the corresponding department, whether each patient has abnormal respiratory rate , whether there is acute chest pain and whether there is abnormal blood pressure Among them, the health characteristics Sbz of each patient in each consultation period in the corresponding department include but are not limited to each patient's blood pressure, heart rate, respiratory rate, blood oxygen saturation (SpO2), white blood cell count, red blood cell count, platelet count and previous medical history;

[0060] The specific steps of S1 also include:

[0061] S12. Use stream processing engine technology to perform real-time transmission and streaming data processing on the relevant medical data and relevant medical appointment data in S11, so as to capture the dynamic changes in the hospital in real time, including but not limited to medical progress, patient queues, and resource allocation, etc., and use time series databases (such as InfluxDB, TimescaleDB) to store and query real-time data with timestamps (relevant medical data and relevant medical appointment data). The advantage is that it can support efficient time series data storage and retrieval, and can quickly respond to time period queries on medical conditions and appointment conditions, and finally construct an in-hospital patient data set, which includes the relevant medical data and relevant medical appointment data processed and stored by S11 and S12.

[0062] In this embodiment, real-time dynamic data collection and processing: by collecting the actual medical treatment and appointment status of each department in the hospital at different medical treatment time periods in real time in step S11, the resource utilization status and patient medical treatment needs in the hospital can be mastered in many aspects. These data include the patient's medical treatment duration, historical medical treatment frequency and health characteristics, etc., which can provide accurate basis for subsequent priority evaluation and resource optimization. The real-time collected appointment data is combined with the medical treatment data to ensure that the hospital's resource allocation and patient queuing can be reflected and adjusted in the first time. Accurate streaming data processing and storage: by using stream processing engine technology (such as Apache Kafka, Flink) and time series database (such as InfluxDB, TimescaleDB), efficient transmission, processing and storage of real-time data can be achieved. This processing method has the characteristics of high concurrency and high throughput, and can capture various dynamic changes in time during the peak period of medical treatment in the hospital, including medical treatment progress, patient queuing and resource allocation status. Using a time series database to store real-time data with timestamps enables hospitals to quickly respond to data query needs in specific time periods and make dynamic adjustments when necessary, thereby ensuring efficient utilization and optimized scheduling of hospital resources. Data-driven optimization decision support: Through real-time data processing and the support of time-series databases, the system can accurately track patients' medical treatment processes and appointments, promptly identify idle and crowded periods of resources, and make corresponding resource scheduling decisions. This data-driven decision-making method enables hospitals to make timely adjustments when faced with emergencies, avoiding problems such as long waiting times for patients and waste of medical resources due to improper resource allocation.

[0063] Example 3

[0064] Please refer to Figure 1 , specifically: S2 specific steps include:

[0065] S21. Extract the in-hospital patient data set within the historical period from the medical appointment system, mark it as historical data, and analyze the actual medical treatment situation of each department in the hospital within the corresponding medical treatment period based on the historical data to calculate the accommodation factor Rnyz. The accommodation factor Rnyz is obtained by the following formula:

[0066]

[0067] In the formula, n represents the total duration of the visit in one day, i represents the number of each visit period in one day, represents the number of patients who actually visited the hospital during the i-th visit period, represents the average consultation time of each actual patient in the i-th consultation period, Represents the length of time within the i-th visit period.

[0068] The actual number of patients who visited the hospital during each of the above visit periods Data can be obtained through the hospital's sign-in system. After the patient registers, the information will be recorded. Signing in indicates that the patient has visited the hospital during the corresponding period. The software system can count the actual number of patients in each period.

[0069] The average consultation time of each actual patient in each consultation period The queuing system at the door of the clinic can be used to count the time. The timing starts when the patient enters the clinic and ends when the patient leaves the clinic. The system calculates the average consultation time of the patient during that period.

[0070] Length of time in each consultation period It is directly obtained based on the consultation time periods set by the hospital, such as a fixed time period from 8 to 9 in the morning.

[0071] The specific steps of S2 also include:

[0072] S22, the capacity factor Rnyz measures the maximum capacity of each consultation period, and according to the value of the capacity factor Rnyz, determines the saturated reservation quantity of each department in the hospital in each consultation period in the consultation reservation system, that is, the maximum number of patients that can be booked in each department in the corresponding consultation period;

[0073] S23. According to the saturated reservation volume of each department in each consultation period in the consultation reservation system, the number of patients for reservation is limited, and the initial reservation number is initially arranged for each patient who needs to make a reservation based on the patient's reservation time requirements and the order of priority within the reservation time requirements.

[0074] In this embodiment, scientific calculation of the capacity factor improves the accuracy of resource allocation: through step S21, the actual treatment situation of each department in the hospital during different treatment periods is extracted from historical data, and the capacity factor Rnyz is calculated in combination with information such as the patient's treatment duration and the number of patients. The capacity factor can quantify the maximum capacity of each treatment period, ensuring that the reservation system can accurately set the maximum reservation quantity for each period according to the actual treatment situation, and further avoid excessive or insufficient reservations. Through scientific calculation of the capacity factor, the hospital can dynamically adjust the resource allocation of each department, thereby optimizing the patient's treatment arrangement and improving resource utilization. Accurately determine the saturated reservation quantity of the department's treatment period: in step S22, the capacity factor Rnyz is used to measure the maximum capacity of each period, and the maximum number of patients that each department can receive in each period is determined according to the value of the factor. In this way, the hospital can achieve accurate control of the reservation quantity of each treatment period, avoid excessive saturation of resources in certain periods and the inability to provide services, reduce the inconvenience caused by the inability of patients to make appointments, and avoid the problem of waste of resources in idle periods. In this way, the overall reservation system of the hospital is more efficient and adjustable. Dynamically adjust the allocation of appointment numbers to optimize the patient queuing experience: Through step S23, based on the saturated appointment volume of each time period in the department, the system can limit the number of appointments in each time period, and reasonably arrange the patient's appointment number according to the patient's appointment time requirements and the order of appointment time. This ensures that the number of appointments in each time period matches the actual resources of the hospital on the one hand, and avoids the patient's waiting time being too long on the other hand, improving the patient's medical experience. Through this intelligent allocation of appointment numbers, the hospital can better handle the problem of appointment congestion and achieve the smoothness of the patient's medical process. Optimize the patient's medical experience: Through intelligent scheduling and allocation of appointment numbers, patients can get medical treatment opportunities in a shorter time, avoiding long queuing time due to too many appointments and waste of resources due to too few appointments. At the same time, the optimized resource allocation strategy can ensure that patients enjoy a better experience during the medical treatment process and reduce the anxiety and dissatisfaction caused by excessive concentration or uneven allocation of resources. In summary, the present invention optimizes the appointment management of consultation time periods by scientifically calculating the capacity factor and dynamically adjusting the allocation of appointment numbers, improves the utilization rate of hospital resources, and enhances the patient's medical experience, solving the problems of excessive appointments, waste of resources, and long waiting times for patients in traditional appointment systems.

[0075] Example 4

[0076] Please refer to Figure 1 , specifically: S3 specific steps include:

[0077] S31, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the health conditions of different patients in the corresponding department in each appointment period are analyzed to calculate the health status score Jpf, which is obtained by:

[0078]

[0079] In the formula, represents the health status score of the kth patient in the corresponding department, m represents the number of health status features in the relevant medical appointment data in the in-hospital patient dataset, j=1, 2, 3, ..., m, represents the actual value of the jth feature of the kth patient in the corresponding department, represents the overall mean of the jth feature, represents the standard deviation of the jth feature;

[0080] S32, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the emergency rescue situations of different patients in the corresponding departments during each appointment period are analyzed to calculate the emergency score Bjf of the condition, and the emergency score Bjf of the condition is obtained by the following formula:

[0081]

[0082] In the formula, represents the emergency score of the kth patient in the corresponding department, Indicates whether there is acute chest pain, Indicates whether there is abnormal breathing rate, Indicates whether there is abnormal blood pressure, represents the indicator function. When there is acute chest pain, =1, otherwise 0; when there is abnormal breathing rate, =1, otherwise 0; when there is abnormal blood pressure, =1, otherwise 0; , and Both represent the weights of the emergency features in the relevant medical appointment data in the in-hospital patient dataset.

[0083] Is there any acute chest pain as mentioned above? , whether there is abnormal breathing rate , whether there is abnormal blood pressure Statistical analysis techniques are used to calculate the mean and standard deviation of the data. Data points that deviate from the mean by more than a certain multiple (such as 3 times) of the standard deviation are usually considered outliers.

[0084] In this embodiment, through step S31, the system combines the health characteristics in the in-hospital patient data set to calculate the health status score Jpf of each patient. The score comprehensively considers the actual situation of the patient in various health status characteristics (such as vital signs, medical history, etc.), and can reflect the patient's current health level by comparing with the overall mean and standard deviation. The calculation method of the health status score enables the hospital to evaluate the patient's physical condition more scientifically and accurately, thereby providing reliable data support for subsequent priority sorting. Timely identification of patient emergency situations: Through step S32, the system calculates the emergency score Bjf based on the patient's emergency rescue characteristics (such as whether there is acute chest pain, abnormal respiratory rate or abnormal blood pressure, etc.). This score takes into account whether the patient has acute symptoms or abnormal vital signs, and is weighted according to the weights of different emergency characteristics. It can accurately reflect the urgency of the patient. In this way, the hospital can timely identify high-risk patients and give priority to them, thereby effectively improving the efficiency and timeliness of emergency and critically ill patients. Dynamic adjustment of patient appointment management: Based on the health status and emergency treatment scores in S31 and S32, the system can adjust the patient's appointment number and medical arrangement in real time. By analyzing and updating the patient's health and emergency situations in real time, the system can dynamically optimize appointment arrangements to ensure that the patient's visit time, sequence, and resources of the visiting department are optimally allocated. For patients who are in urgent need of treatment, the system can automatically increase their priority for visiting, avoid waste of resources, and reduce the potential risks caused by patients waiting too long. Improve the overall operational efficiency of the hospital: By accurately calculating the health status score and the emergency score of the condition, the hospital can more reasonably allocate medical resources in multiple departments and multiple visiting periods, improve the efficiency of resource utilization, and reduce patients' waiting time and waste of medical resources. In addition, this system can effectively prevent medical accidents caused by patients not receiving timely medical treatment in emergency situations, thereby improving the hospital's service level and patients' sense of security.

[0085] Example 5

[0086] Please refer to Figure 1 , specifically: S3 specific steps also include:

[0087] S33. Using deep learning technology and combining the in-hospital patient data set, an appointment evaluation model is built, and after dimensionless processing, an output priority evaluation score Yfs is fitted. The output priority evaluation score Yfs is obtained by the following formula:

[0088]

[0089] In the formula, represents the historical frequency of visits of each patient in the corresponding department, , and are weights, where 0 < <1,0< <1,0< <1, , and The specific value is set by the user according to the situation.

[0090] The historical frequency of visits of each patient in the above corresponding departments It is mainly recorded by the hospital information system (HIS). Information such as the department and time of each patient's visit will be stored in the system. The historical frequency of visits can be obtained through data mining and analysis.

[0091] The specific steps of S3 also include:

[0092] S34. According to the method of obtaining the priority evaluation score Yfs in S33, the priority evaluation score Yfs of each patient in the corresponding department is obtained respectively, and based on the numerical value of the priority evaluation score Yfs, the initialized appointment numbers of each patient to be scheduled for treatment are re-prioritized according to the numerical value of the priority evaluation score Yfs of each patient in the corresponding department, so as to generate a preliminary patient details report for the corresponding treatment period in the hospital.

[0093] In this embodiment, through step S33, based on the in-hospital patient data set and combined with deep learning technology, the priority evaluation score Yfs is fitted and output. The evaluation model can comprehensively consider the patient's historical frequency of medical treatment and other characteristics, and accurately predict the priority of each patient's medical treatment. Dimensionless processing makes the data dimension more unified, avoids errors caused by different dimensional characteristics, and thus ensures the accuracy of the priority evaluation score. Through this model, the hospital can more intelligently identify and sort the patient's medical treatment priority, and improve the automation and intelligence of patient management. Optimize the patient's medical treatment sorting and improve the efficiency of medical treatment resource allocation: In step S34, based on the value of the priority evaluation score Yfs, the patient's appointment number is re-sorted, thereby effectively optimizing the allocation of medical treatment resources. This not only ensures that patients with poor health or more urgent conditions have priority in medical treatment, but also reduces the problem of overcrowding of resources caused by excessive patient flow. Priority sorting makes the medical treatment arrangement of each patient more reasonable, and improves the utilization efficiency of hospital resources as much as possible, especially in high-load departments and time periods, which can effectively reduce queuing waiting time and improve the operational efficiency of the hospital. Data-driven personalized medical services improve patients' medical experience: Through deep learning and data-driven appointment evaluation models, hospitals can intelligently assign priorities to patients based on their personal circumstances (such as historical frequency of visits, severity of illness, etc.). This personalized priority sorting can ensure that patients with urgent conditions or poor health receive timely medical treatment at appropriate times, further improving patients' medical experience and satisfaction. Especially in busy departments and peak hours, priority sorting can significantly alleviate the problem of long waiting times for patients. The output of the priority evaluation score Yfs enables hospitals to optimize patients' medical arrangements in real time and reduce long waiting times for patients due to uneven resource allocation. Through timely sorting and priority adjustment, hospitals can achieve efficient use of resources, reduce patients' anxiety caused by waiting in line for too long, and improve the smoothness of the overall medical process and patient satisfaction.

[0094] Example 6

[0095] Please refer to Figure 1 , specifically: S4 specific steps include:

[0096] S41. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, wherein the relevant patient change data includes the number of latecomers of the corresponding patients and the number of patients in the hospital who have not made an appointment for consultation offline; the relevant resource status data includes the resource utilization rate of each consultation period in the corresponding department and the load during each visit period ;

[0097] S42. According to the number of latecomers of the corresponding patients in the relevant patient change data and the number of patients without offline appointments in the hospital, the number of latecomers of the corresponding patients is deleted from the preliminary patient details report of the corresponding treatment period in the hospital, and the number of patients without offline appointments in the hospital is added to the preliminary patient details report of the corresponding treatment period in the hospital according to the saturated appointment volume of each department in the treatment appointment system in S22 in each treatment period, so as to optimize and update the priority evaluation score Yfs in real time, and mark it as the optimized priority evaluation score .

[0098] The specific steps of S5 include:

[0099] S51, based on the optimized priority evaluation score obtained in S42 , and combined with the relevant resource status data obtained in S41, to analyze the resource allocation of the corresponding departments in the hospital, to construct a comprehensive evaluation index Zpzb, based on the comprehensive evaluation index Zpzb, to balance resource utilization and patient experience, the comprehensive evaluation index Zpzb is obtained by the following formula:

[0100]

[0101] In the formula, represents the optimized priority evaluation score of the kth patient in the corresponding department, represents the resource utilization rate of the ith consultation period, represents the load of the i-th consultation period, n represents the total consultation time in a day, i represents the number of each consultation period in a day, K represents the number of patients scheduled in the corresponding department, k represents the patient number in the corresponding department, Represents the weight of the load, where 0 < <1.

[0102] The goal of the comprehensive evaluation index Zpzb is to optimize the allocation of hospital resources and further improve the efficiency of resource utilization while maintaining the patient's medical experience, so as to balance the contradictions between different goals and find an optimal solution so that the system can reach a relatively appropriate state in resource utilization and patient allocation.

[0103] Among them, the resource utilization rate of the i-th consultation period is It refers to the proportion of resources (such as doctors) actually used by a hospital or department in a certain period of time. It is usually used to measure the consumption of resources. It is obtained through the following formula:

[0104]

[0105] In the formula, It represents the consultation time occupied by the kth patient in the corresponding department under the condition of the i-th consultation period; represents the total duration of the ith consultation period, Indicates the number of doctors available;

[0106] The load of the i-th consultation period It refers to the workload carried by resources in a period of time;

[0107] In this embodiment, real-time dynamic monitoring and adjustment are performed to improve the efficiency of patient management: through the real-time monitoring of patient change data and resource status data in step S41, late patients and offline patients without appointments can be discovered in time. This dynamic monitoring mechanism enables the hospital to adjust appointment arrangements and resource allocation according to real-time conditions, optimize the preliminary patient details report, and ensure that the medical treatment process is smoother and more effective. This real-time feedback mechanism can significantly reduce the impact of patient changes and improve the response speed of hospital management. Accurately handle patient changes and improve resource allocation flexibility: Step S42 deletes late patients from the patient details report and adds offline patients without appointments in real time according to the saturated appointment volume of the department to ensure that the patient information of each medical treatment period is accurately updated. This process can respond to the dynamic changes of patients in the hospital in a timely manner and avoid waste of resources or unreasonable scheduling caused by patient changes. At the same time, the optimized priority assessment score can be adjusted in real time according to the patient's urgency and health status to ensure that the needs of each patient are better handled. The comprehensive evaluation index Zpzb achieves a balance between resources and patient experience: the comprehensive evaluation index Zpzb constructed by step S51, combined with the optimized priority evaluation score and real-time resource utilization and load data, can comprehensively analyze and balance the resource allocation of each department. This multi-dimensional analysis method effectively solves the contradiction between resource utilization and patient experience, ensuring that the hospital can improve the patient experience while ensuring efficient resource utilization, reduce patient waiting time and resource waste, and optimize the efficiency of medical treatment. Accurately optimize the medical arrangement and improve the quality of medical services: based on the results of the comprehensive evaluation index Zpzb, the hospital can reasonably arrange the order of resources and patients according to the resource status and patient needs of each medical period. Through this scientific scheduling method, the hospital can achieve efficient utilization of resources during busy periods, reduce the situation of long patient waiting time or uneven resource allocation, thereby improving the quality of medical services and ensuring that patients can get the required medical services in a more reasonable time. Improve the overall operation efficiency and patient satisfaction in the hospital: combined with the real-time priority evaluation score and dynamically optimized patient data, the optimization scheme of the present invention can reduce the problems caused by inaccurate appointments, patient changes and unreasonable resource allocation. Through these optimization measures, hospitals can provide services more efficiently with limited resources, thereby improving the overall operational efficiency of the hospital, enhancing patient satisfaction with the hospital, and promoting the healthy operation and development of the hospital.

[0108] In summary, the present invention realizes effective optimization of the medical appointment system by real-time dynamic monitoring of patient changes, optimizing resource allocation, and combining comprehensive evaluation indicators to balance resource utilization and patient experience, thereby improving the quality and efficiency of medical services and improving patients' medical experience.

[0109] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An information processing method for patient appointment, characterized in that: The following steps are included: S1. Based on the hospital's appointment system, the hospital's medical treatment and appointment status in each time period are collected in real time to obtain relevant medical treatment data and relevant medical appointment data, and after data preprocessing, the hospital's patient data set is constructed; S2. Analyze the actual visit situation of each department in the hospital during the corresponding visit period based on historical data to calculate the capacity factor Rnyz. Based on the value of the capacity factor Rnyz, set the saturated appointment volume of the corresponding visit period in the visit appointment system; S3. Analyze the health status, emergency situation and historical medical treatment of each patient in the corresponding medical treatment appointment period according to the relevant medical treatment appointment data in the in-hospital patient data set, and build a priority evaluation score Yfs based on the trained appointment evaluation model. Generate a preliminary patient details report for the corresponding medical treatment period in the hospital based on the priority evaluation score Yfs value; S4. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, and optimize the priority evaluation score Yfs in real time based on the relevant patient change data; S5. Based on S4, optimize the allocation of in-hospital resources to balance resource utilization and patient experience.

2. The information processing method for patient appointment according to claim 1, characterized in that: The specific steps of S1 include: S11. According to the medical appointment system built in the hospital, the medical treatment situation in each time period in the hospital is collected in real time to obtain relevant medical treatment data, which includes the actual number of patients who visited the corresponding department in the hospital during each medical treatment period. , the duration of each patient's visit Jsc, the historical frequency of each patient's visit in the corresponding department and the length of time in each consultation period within the corresponding department At the same time, the appointment information of each time period in the hospital is collected to obtain relevant appointment data, which includes the health characteristics Sbz of each patient in each appointment period in the corresponding department, whether each patient has abnormal respiratory rate , whether there is acute chest pain and whether there is abnormal blood pressure .

3. The information processing method for patient appointment according to claim 2, characterized in that: The specific steps of S1 also include: S12. Use stream processing engine technology to perform real-time transmission and streaming data processing on the relevant medical data and relevant medical appointment data in S11, so as to capture the dynamic changes in the hospital in real time, and use a time series database to store and query real-time data with timestamps, and finally construct an in-hospital patient data set, which includes the relevant medical data and relevant medical appointment data processed and stored by S11 and S12.

4. The information processing method for patient appointment according to claim 3, characterized in that: The specific steps of S2 include: S21. Extract the in-hospital patient data set within the historical period from the medical appointment system, mark it as historical data, and analyze the actual medical treatment situation of each department in the hospital within the corresponding medical treatment period based on the historical data to calculate the accommodation factor Rnyz. The accommodation factor Rnyz is obtained by the following formula: In the formula, n represents the total duration of the visit in one day, i represents the number of each visit period in one day, represents the number of patients who actually visited the hospital during the i-th visit period, represents the average consultation time of each actual patient in the i-th consultation period, Represents the length of time within the i-th visit period.

5. The information processing method for patient appointment according to claim 4, characterized in that: The specific steps of S2 also include: S22, the capacity factor Rnyz measures the maximum capacity of each consultation period, and according to the value of the capacity factor Rnyz, determines the saturated reservation volume of each department in the hospital in each consultation period in the consultation reservation system; S23. According to the saturated reservation volume of each department in each consultation period in the consultation reservation system, the number of patients for reservation is limited, and the initial reservation number is initially arranged for each patient who needs to make a reservation based on the patient's reservation time requirements and the order of priority within the reservation time requirements.

6. The information processing method for patient appointment according to claim 5, characterized in that: The specific steps of S3 include: S31, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the health conditions of different patients in the corresponding department in each appointment period are analyzed to calculate the health status score Jpf, which is obtained by: In the formula, represents the health status score of the kth patient in the corresponding department, m represents the number of health status features in the relevant medical appointment data in the in-hospital patient dataset, j=1, 2, 3, ..., m, represents the actual value of the jth feature of the kth patient in the corresponding department, represents the overall mean of the jth feature, represents the standard deviation of the jth feature; S32, based on the relevant appointment data in the in-hospital patient data set, and in combination with the appointment numbers initialized for each patient to be scheduled in S23, the emergency rescue situations of different patients in the corresponding departments during each appointment period are analyzed to calculate the emergency score Bjf of the condition, and the emergency score Bjf of the condition is obtained by the following formula: In the formula, represents the emergency score of the kth patient in the corresponding department, Indicates whether there is acute chest pain, Indicates whether there is abnormal breathing rate, Indicates whether there is abnormal blood pressure, represents the indicator function. When there is acute chest pain, =1, otherwise 0; when there is abnormal breathing rate, =1, otherwise 0; When blood pressure is abnormal, =1, otherwise 0; , and Both represent the weights of the emergency features in the relevant medical appointment data in the in-hospital patient dataset.

7. The information processing method for patient appointment according to claim 6, characterized in that: The specific steps of S3 also include: S33. Using deep learning technology and combining the in-hospital patient data set, an appointment evaluation model is built, and after dimensionless processing, an output priority evaluation score Yfs is fitted. The output priority evaluation score Yfs is obtained by the following formula: In the formula, represents the historical frequency of visits of each patient in the corresponding department, , and are weights, , and The specific value is set by the user according to the situation.

8. The information processing method for patient appointment according to claim 7, characterized in that: The specific steps of S3 also include: S34. According to the method of obtaining the priority evaluation score Yfs in S33, the priority evaluation score Yfs of each patient in the corresponding department is obtained respectively, and based on the numerical value of the priority evaluation score Yfs, the initialized appointment numbers of each patient to be scheduled for treatment are re-prioritized according to the numerical value of the priority evaluation score Yfs of each patient in the corresponding department, so as to generate a preliminary patient details report for the corresponding treatment period in the hospital.

9. The information processing method for patient appointment according to claim 1, characterized in that: The specific steps of S4 include: S41. According to the preliminary patient details report of the corresponding consultation period in the hospital obtained in S3, arrange the patients in the corresponding consultation period to go through the consultation procedures, and conduct real-time information monitoring in the corresponding consultation period to obtain relevant patient change data and relevant resource status data, wherein the relevant patient change data includes the number of latecomers of the corresponding patients and the number of patients in the hospital who have not made an appointment for consultation offline; the relevant resource status data includes the resource utilization rate of each consultation period in the corresponding department and the load during each visit period ; S42. According to the number of latecomers of the corresponding patients in the relevant patient change data and the number of patients without offline appointments in the hospital, the number of latecomers of the corresponding patients is deleted from the preliminary patient details report of the corresponding treatment period in the hospital, and the number of patients without offline appointments in the hospital is added to the preliminary patient details report of the corresponding treatment period in the hospital according to the saturated appointment volume of each department in the treatment appointment system in S22 in each treatment period, so as to optimize and update the priority evaluation score Yfs in real time, and mark it as the optimized priority evaluation score .

10. The information processing method for patient appointment according to claim 9, characterized in that: The specific steps of S5 include: S51, based on the optimized priority evaluation score obtained in S42 , and combined with the relevant resource status data obtained in S41, to analyze the resource allocation of the corresponding departments in the hospital, to construct a comprehensive evaluation index Zpzb, based on the comprehensive evaluation index Zpzb, to balance resource utilization and patient experience, the comprehensive evaluation index Zpzb is obtained by the following formula: In the formula, represents the optimized priority evaluation score of the kth patient in the corresponding department, represents the resource utilization rate of the ith consultation period, represents the load of the ith visit period, represents the weight of the load, n represents the total duration of consultation in one day, i represents the number of each consultation period in one day, K represents the number of patients scheduled in the corresponding department, and k represents the patient number in the corresponding department.

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