Doctor scheduling method, system, equipment and medium

Abstract

The invention provides a doctor scheduling method, system and device and a medium. The doctor scheduling method comprises the steps that doctor information corresponding to doctors in a set area is stored in advance; wherein the doctor information comprises basic personal information of doctors, first-aid treatment levels of the doctors and actual treatment scores corresponding to the first-aid treatment levels; acquiring an actual disease diagnosis result of the patient; determining a disease emergency level of the patient based on the actual disease diagnosis result; and based on the disease first-aid level and the doctor information, doctors in the set area are scheduled to form a target medical group for performing first-aid treatment on the patient. According to the method and the device, the doctors in the set area can be flexibly scheduled according to the disease first-aid level of the patient while the medical requirements of the patient are met.

Description

Technical Field

[0001] The present disclosure relates to the technical field of doctor scheduling, and in particular to a doctor scheduling method, system, device and medium. Background Art

[0002] In modern medical methods, diagnosis and treatment based on the patient's diagnostic data is an important means to determine the patient's disease. However, in the prior art, when determining the patient's disease, medical personnel often need to conduct a large number of medical examinations on the patient without a lot of diagnosis and treatment experience. Only after obtaining sufficient diagnosis and treatment data can the patient's disease be accurately identified. The diagnosis and treatment data usually include the patient's vital signs examination values ​​(measurement values), including biochemical tests, blood tests and other sample tests or electroencephalograms and other physiological tests. These examination methods increase the patient's pain on the one hand and increase the patient's diagnosis costs on the other. After the patient is diagnosed with the disease, the emergency level is determined according to the severity of the disease. Different types of diseases require different numbers of doctors for different emergency levels, and even emergency surgery is required. The existing medical system cannot flexibly dispatch doctors according to demand. Summary of the invention

[0003] The technical problem to be solved by the present disclosure is to overcome the defect in the prior art that doctors cannot be flexibly dispatched according to the emergency level of the patient, and to provide a doctor dispatching method, system, device and medium.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] In a first aspect, a doctor scheduling method is provided, the doctor scheduling method comprising:

[0006] Pre-store the doctor information corresponding to each doctor in the set area;

[0007] The doctor information includes the doctor's basic personal information, the doctor's emergency treatment level, and the actual treatment score corresponding to the emergency treatment level;

[0008] Obtain the patient’s actual disease diagnosis results;

[0009] Determine the patient's disease emergency level based on the actual disease diagnosis result;

[0010] Based on the disease emergency level and the doctor information, doctors in the set area are dispatched to form a target medical team for providing emergency treatment to the patient.

[0011] Preferably, the step of determining the patient's disease emergency level based on the actual disease diagnosis result comprises:

[0012] Obtaining the actual disease type corresponding to the actual disease diagnosis result;

[0013] The disease emergency level of the patient is determined based on the risk level of the actual disease type.

[0014] Preferably, the step of dispatching doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient includes:

[0015] Obtain the target hospital where the patient is located;

[0016] Wherein, the target hospital is located in the set area;

[0017] In the target hospital, a doctor with the same emergency treatment level as the emergency level of the disease is selected as the first target emergency doctor;

[0018] Obtain the actual treatment score corresponding to each first target emergency doctor;

[0019] Determining whether the actual treatment score is greater than or equal to a preset treatment score;

[0020] If yes, outputting the doctor information of the first target emergency doctor to form the target medical team;

[0021] Among them, the preset treatment score is determined based on the emergency level of the disease.

[0022] Preferably, the step of outputting the doctor information of the first target emergency doctor to form the target medical team includes:

[0023] Obtaining the emergency surgery schedule of the patient;

[0024] Eliminate doctors who are busy during the emergency surgery schedule from the first target emergency doctors to obtain first remaining doctors;

[0025] Obtain the preset number of doctors corresponding to the emergency level of the disease;

[0026] Determining whether the actual number of doctors of the first remaining doctors is greater than or equal to the preset number of doctors;

[0027] If yes, output the doctor information of the first remaining doctors to form the target medical group;

[0028] Among them, the preset number of doctors is determined based on the difficulty of the emergency surgery corresponding to the actual disease diagnosis result.

[0029] Preferably, if the actual number of the first remaining doctors is less than the preset number of doctors, the doctor scheduling method further includes:

[0030] In the remaining hospitals except the target hospital, select doctors with the same emergency treatment level as the emergency level of the disease as the second target emergency doctors;

[0031] Wherein, the remaining hospitals are located within the set area;

[0032] Eliminate doctors who are busy during the emergency surgery schedule from the second target emergency doctors to obtain second remaining doctors;

[0033] Obtaining the actual treatment score of each second remaining doctor, and selecting doctors whose actual treatment score is greater than or equal to the preset treatment score from the second remaining doctors to obtain third remaining doctors;

[0034] Obtaining the difference between the preset number of doctors and the actual number of doctors, and selecting doctors meeting the difference in number from the third remaining doctors to form a fourth remaining doctor;

[0035] The doctor information of the first remaining doctors and the fourth remaining doctors is output to form the target medical group.

[0036] Preferably, the step of obtaining the actual disease diagnosis result of the patient includes:

[0037] Based on the diagnostic feature extraction model, the corresponding relationship between historical diagnostic feature data and historical diagnostic results of several historical disease diagnostic data is pre-constructed;

[0038] Wherein, the diagnostic feature extraction model is used to extract actual diagnostic feature data corresponding to the input actual diagnostic data;

[0039] Acquiring actual diagnosis data of the patient, and inputting the actual diagnosis data into the diagnosis feature extraction model to obtain the actual diagnosis feature data of the patient;

[0040] Based on the actual diagnosis feature data and the corresponding relationship, the actual disease diagnosis result of the patient is obtained.

[0041] Preferably, the step of obtaining the actual disease diagnosis result of the patient based on the actual diagnosis feature data and the corresponding relationship includes:

[0042] Calculating the similarity between the actual diagnostic feature data and each of the historical diagnostic feature data to obtain a number of actual similarity values;

[0043] Selecting historical diagnostic feature data corresponding to an actual similarity value that meets a preset similarity threshold as target diagnostic feature data;

[0044] Based on the corresponding relationship, the historical diagnosis result corresponding to the target diagnosis feature data is used as the actual disease diagnosis result of the patient.

[0045] In a second aspect, a doctor scheduling system is provided, the doctor scheduling system comprising:

[0046] An information storage module, used to pre-store doctor information corresponding to each doctor in a set area;

[0047] The doctor information includes the doctor's basic personal information, the doctor's emergency treatment level, and the actual treatment score corresponding to the emergency treatment level;

[0048] A diagnosis result acquisition module is used to obtain the patient's actual disease diagnosis result;

[0049] An emergency level determination module, used to determine the patient's disease emergency level based on the actual disease diagnosis result;

[0050] The doctor scheduling module is used to schedule doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient.

[0051] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor implements the above-mentioned doctor scheduling method when executing the computer program.

[0052] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned doctor scheduling method is implemented.

[0053] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0054] The positive and progressive effects of this disclosure are:

[0055] The doctor scheduling method, system, device and medium disclosed in the present invention obtain the actual disease diagnosis result of the patient by pre-storing the doctor information corresponding to each doctor in a set area; determine the patient's disease emergency level based on the actual disease diagnosis result; and schedule the doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient; while meeting the medical needs of the patient, the present invention can flexibly schedule the doctors in the set area according to the patient's disease emergency level. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A first flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0057] Figure 2 A second flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0058] Figure 3 A third flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0059] Figure 4 A fourth flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0060] Figure 5 A fifth flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0061] Figure 6 A sixth flow chart of the doctor scheduling method provided in Embodiment 1 of the present disclosure;

[0062] Figure 7 A schematic diagram of the structure of a doctor scheduling system provided in Embodiment 2 of the present disclosure;

[0063] Figure 8 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0064] The present disclosure is further described below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0065] Prefixes such as "first" and "second" are used in the embodiments of the present disclosure only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present disclosure does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the embodiments, and no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0066] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0067] Example 1

[0068] This embodiment provides a doctor scheduling method, such as Figure 1 As shown, the doctor scheduling method includes:

[0069] S1. Pre-store the doctor information corresponding to each doctor in the set area.

[0070] S2. Obtain the patient's actual disease diagnosis result.

[0071] S3. Determine the patient's disease emergency level based on the actual disease diagnosis results.

[0072] S4. Based on the disease emergency level and doctor information, doctors in the set area are dispatched to form a target medical team to provide emergency treatment to patients.

[0073] Among them, the doctor information includes the doctor’s basic personal information, the doctor’s emergency treatment level, and the actual treatment score corresponding to the emergency treatment level.

[0074] The doctor's basic personal information includes but is not limited to the doctor's name, number, hospital, department, contact number, etc. The doctor's emergency treatment level can be divided into level one, level two, level three, etc. Different disease emergency treatment levels correspond to different urgency levels and require different numbers of emergency doctors. The doctor's emergency treatment level corresponds to the patient's disease emergency treatment level. For example, the patient's disease emergency treatment level is level one, level two, level three, etc.

[0075] Specifically, the set area can be a hospital, district, city, province, etc.

[0076] The doctor scheduling method disclosed in the present invention obtains the patient's actual disease diagnosis result by pre-storing the doctor information corresponding to each doctor in a set area, determines the patient's disease emergency level based on the actual disease diagnosis result, and schedules the doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient; while meeting the patient's medical needs, the doctors in the set area can be flexibly scheduled according to the patient's disease emergency level.

[0077] In an optional embodiment, if Figure 2 As shown, the above step S3 includes:

[0078] S31. Obtain the actual disease type corresponding to the actual disease diagnosis result.

[0079] S32. Determine the patient's disease emergency level based on the risk level of the actual disease type.

[0080] Each actual disease diagnosis result will correspond to an actual disease type, and different actual disease types will correspond to different risk levels. Therefore, the patient's disease emergency level can be determined based on the risk level of the actual disease type.

[0081] In an optional embodiment, if Figure 3 As shown, the above step S4 includes:

[0082] S41. Obtain the target hospital where the patient is located.

[0083] Among them, the target hospital is located within the set area.

[0084] S42. In the target hospital, select a doctor with the same emergency treatment level as the disease emergency level as the first target emergency doctor.

[0085] S43: Obtain the actual treatment score corresponding to each first target emergency doctor.

[0086] S44: Determine whether the actual treatment score is greater than or equal to the preset treatment score.

[0087] If yes, execute step S45;

[0088] S45. Output the doctor information of the first target emergency doctor to form a target medical team.

[0089] Among them, the preset treatment score is determined based on the emergency level of the disease.

[0090] The set area in this embodiment is the target hospital, and the hospital where the patient is located is called the target hospital. In the target hospital, a doctor with the same emergency treatment level as the emergency level of the disease is selected as the first target emergency doctor, that is, the first target emergency doctor can treat the disease corresponding to the emergency level of the disease.

[0091] Each doctor's emergency treatment level has a corresponding actual treatment score. The actual treatment score of an expert doctor may be higher than that of an ordinary doctor, and the actual treatment score of an ordinary doctor is higher than that of an intern. Doctors who can perform emergency treatment are generally doctors with certain experience. The actual treatment scores of these doctors are higher than the preset treatment scores, among which the preset treatment scores are determined based on the emergency level of the disease. Therefore, the target medical group composed of the first target emergency doctors are generally top doctors at the expert level.

[0092] The doctor scheduling method of this embodiment selects a doctor with the same emergency treatment level as the emergency level of the disease in the target hospital where the patient is located as the first target emergency doctor, and at the same time screens the doctor according to the actual treatment score of the first target emergency doctor to obtain a target medical group that provides emergency treatment for the patient. The doctors in the medical group are all top doctors, which greatly meets the medical needs of the patient and realizes the flexible scheduling of doctors in the target hospital according to the emergency level of the patient's disease.

[0093] In an optional embodiment, if Figure 4As shown, the above step S45 includes:

[0094] S451. Obtain the patient's emergency surgery schedule.

[0095] S452: Eliminate doctors who are busy during the emergency surgery schedule from the first target emergency doctors to obtain the first remaining doctors.

[0096] S453 obtains the preset number of doctors corresponding to the disease emergency level;

[0097] S454 determines whether the actual number of doctors among the first remaining doctors is greater than or equal to the preset number of doctors.

[0098] If yes, execute step S455;

[0099] S455. Output the doctor information of the first remaining doctors to form a target medical group.

[0100] Among them, the preset number of doctors is determined based on the difficulty of emergency surgery corresponding to the actual disease diagnosis results.

[0101] When a patient needs emergency surgery, the number of doctors required for the surgery needs to be determined based on the difficulty of the emergency surgery corresponding to the patient's actual disease diagnosis results, that is, the preset number of doctors corresponding to the emergency level of the disease needs to be determined. The number of doctors required for different emergency surgery difficulties may be different.

[0102] Each doctor has his or her own corresponding work schedule, so it is necessary to eliminate the doctors who are busy during the emergency surgery schedule from the first target emergency doctors to obtain the first remaining doctors. If the actual number of doctors in the first remaining doctors meets the preset number of doctors, the doctor information of the first remaining doctors is output to form a target medical team for emergency treatment of patients.

[0103] In an optional embodiment, if Figure 4 As shown, if it is determined that the actual number of doctors of the first remaining doctor is less than the preset number of doctors, the scheduling method further includes:

[0104] S456. In the remaining hospitals except the target hospital, select doctors with the same emergency treatment level as the emergency level of the disease as the second target emergency doctor.

[0105] Among them, the remaining hospitals are located within the set area.

[0106] S457. Eliminate the doctors who are busy during the emergency surgery schedule from the second target emergency doctors to obtain the second remaining doctors.

[0107] S458. Obtain the actual treatment score of each second remaining doctor, and select doctors whose actual treatment score is greater than or equal to the preset treatment score from the second remaining doctors to obtain third remaining doctors.

[0108] S459, obtaining the difference between the preset number of doctors and the actual number of doctors, and selecting doctors who meet the difference in number from the third remaining doctors to form the fourth remaining doctors.

[0109] S450. Output the doctor information of the first remaining doctor and the fourth remaining doctor to form a target medical group.

[0110] If the actual number of the first remaining doctors in the target hospital is less than the preset number of doctors, it means that the number of doctors in the target hospital is insufficient and there is a doctor gap. It is necessary to select doctors with the same emergency treatment level as the disease emergency level from the remaining hospitals except the target hospital as the second target emergency doctors, eliminate the doctors who are busy during the emergency surgery schedule to obtain the second remaining doctors, and select doctors whose actual treatment scores are greater than or equal to the preset treatment scores from the second remaining doctors to obtain the third remaining doctors, so that the third remaining doctors are also top doctors; from the third remaining doctors, select doctors who meet the number difference to form the fourth remaining doctors, fill the doctor gap through the fourth remaining doctors, and then output the doctor information of the first remaining doctors and the fourth remaining doctors to form the target medical group.

[0111] The doctor scheduling method of this embodiment can select doctors from the remaining hospitals except the target hospital in a timely manner when the number of doctors in the target hospital where the patient is located cannot meet the number of doctors required for emergency surgery and there is a shortage of emergency doctors. This can more flexibly meet the medical needs of the patient, ensure the number and quality of doctors required for emergency surgery, and realize the flexible scheduling of doctors in various hospitals according to the emergency level of the patient's disease.

[0112] In an optional implementation, the above step S459 includes:

[0113] The actual treatment scores of the third remaining doctors were ranked from high to low;

[0114] Obtain the difference in the number of people, and select doctors who are ranked high and meet the difference in the number of people to form the fourth remaining doctor.

[0115] The actual number of the third remaining doctors may be large. Therefore, the actual treatment scores of the third remaining doctors are ranked from high to low, and the doctors with high rankings and who meet the number difference are selected to form the fourth remaining doctors. This allows the fourth remaining doctors to further ensure the quality of doctors while meeting the shortage of emergency doctors, and realizes the flexible dispatch of doctors in various hospitals according to the patient's disease emergency level.

[0116] In an optional embodiment, if Figure 5 As shown, the above step S2 includes:

[0117] S21. Based on the diagnosis feature extraction model, a correspondence between historical diagnosis feature data of a number of historical disease diagnosis data and historical diagnosis results is pre-constructed.

[0118] S22. Acquire the actual diagnosis data of the patient, and input the actual diagnosis data into the diagnosis feature extraction model to obtain the actual diagnosis feature data of the patient.

[0119] S23. Based on the actual diagnostic feature data and the corresponding relationship, obtain the actual disease diagnosis result of the patient.

[0120] The diagnostic feature extraction model can be a deep learning machine model that can realize deep feature extraction of data. The diagnostic feature extraction model is constructed through a large amount of historical diagnostic data and the historical diagnostic results corresponding to each historical diagnostic data to obtain a diagnostic feature extraction model. Specifically, in the model construction stage, the input of the diagnostic feature extraction model is the historical disease diagnosis data, and the output is the historical diagnosis results corresponding to the historical diagnostic feature data; in the model use stage, the input of the diagnostic feature extraction model is the actual diagnostic data, and the output is the actual diagnostic feature data corresponding to the actual diagnostic data.

[0121] The diagnostic feature extraction model can comprehensively extract feature data and output actual diagnostic feature data corresponding to actual diagnostic data. Based on the diagnostic feature extraction model, the corresponding relationship between historical diagnostic feature data and historical diagnostic results of several historical disease diagnostic data can be pre-constructed.

[0122] Specifically, the correspondence between historical diagnostic feature data of several historical disease diagnosis data and historical diagnostic results is pre-constructed in the database. For example, if the historical diagnosis result is myocarditis, a correspondence between myocarditis and the diagnostic feature data of myocarditis can be established.

[0123] The doctor scheduling method of this embodiment can pre-construct the correspondence between historical diagnostic feature data and historical diagnostic results of several historical disease diagnosis data based on the diagnostic feature extraction model, and then obtain the patient's actual disease diagnosis result based on the patient's actual diagnostic feature data and the corresponding relationship, thereby achieving accurate and rapid determination of the patient's actual disease diagnosis result.

[0124] In an optional embodiment, if Figure 6 As shown, the above step S23 includes:

[0125] S231. Calculate the similarity between the actual diagnosis feature data and each historical diagnosis feature data to obtain a number of actual similarity values.

[0126] S232: Select historical diagnostic feature data corresponding to an actual similarity value that meets a preset similarity threshold as target diagnostic feature data.

[0127] S233. Based on the corresponding relationship, the historical diagnosis result corresponding to the target diagnostic feature data is used as the actual disease diagnosis result of the patient.

[0128] Calculate the similarity between the actual diagnostic feature data and each historical diagnostic feature data to obtain several actual similarity values, for example, 5 actual similarity values ​​are obtained, and the first actual similarity value to the fifth actual similarity value are 10%, 15%, 20%, 30%, and 90%, respectively; if the preset similarity threshold is 85%, the fifth actual similarity value of 90% is greater than the preset similarity threshold of 85%, then the historical diagnostic feature data corresponding to the fifth actual similarity value is used as the target diagnostic feature data; if the historical diagnosis result corresponding to the target diagnostic feature data is acute gastroenteritis, then the patient's actual disease diagnosis result is acute gastroenteritis.

[0129] The similarity can also be called matching degree.

[0130] The above numerical values ​​are only exemplary, and those skilled in the art can flexibly set the preset similarity threshold according to actual conditions.

[0131] The doctor scheduling method of this embodiment calculates the similarity between the actual diagnostic feature data and each historical diagnostic feature data to obtain a number of actual similarity values, selects the historical diagnostic feature data corresponding to the actual similarity values ​​that meet the preset similarity threshold as the target diagnostic feature data, and based on the corresponding relationship, uses the historical diagnostic results corresponding to the target diagnostic feature data as the actual disease diagnosis results of the patient; the actual disease diagnosis results of the patient can be accurately obtained.

[0132] Example 2

[0133] Corresponding to the above-mentioned doctor scheduling method embodiment, the present disclosure also provides an embodiment of a doctor scheduling system, which includes:

[0134] Information storage module 1, used to pre-store doctor information corresponding to each doctor in a set area;

[0135] The doctor information includes the doctor's basic personal information, the doctor's emergency treatment level, and the actual treatment score corresponding to the emergency treatment level;

[0136] Diagnosis result acquisition module 2, used to obtain the patient's actual disease diagnosis result;

[0137] The first aid level determination module 3 is used to determine the patient's disease first aid level based on the actual disease diagnosis result;

[0138] The doctor scheduling module 4 is used to schedule doctors in a set area based on the disease emergency level and doctor information to form a target medical team for emergency treatment of patients.

[0139] In an optional implementation, the first aid level determination module 3 includes:

[0140] A disease type determination unit 31 is used to obtain the actual disease type corresponding to the actual disease diagnosis result;

[0141] The emergency level determination unit 32 is used to determine the patient's disease emergency level based on the risk level of the actual disease type.

[0142] In an optional embodiment, the doctor scheduling module 4 includes:

[0143] A hospital acquisition unit 41 is used to acquire a target hospital where the patient is located;

[0144] Among them, the target hospital is located in the set area;

[0145] A first doctor selection unit 42 is used to select a doctor with the same emergency treatment level as the disease emergency level in the target hospital as a first target emergency doctor;

[0146] A treatment score obtaining unit 43 is used to obtain an actual treatment score corresponding to each first target emergency doctor;

[0147] A first judging unit 44 is used to judge whether the actual treatment score is greater than or equal to the preset treatment score;

[0148] A first doctor information output unit 45, configured to output doctor information of a first target emergency doctor to form a target medical group when the actual treatment score is greater than or equal to a preset treatment score;

[0149] Among them, the preset treatment score is determined based on the emergency level of the disease.

[0150] In an optional embodiment, the first doctor information output unit 45 is specifically used to obtain the patient's emergency surgery schedule; from the first target emergency doctors, eliminate the doctors who are busy during the emergency surgery schedule to obtain the first remaining doctors; obtain the preset number of doctors corresponding to the emergency level of the disease; determine whether the actual number of doctors of the first remaining doctors is greater than or equal to the preset number of doctors; if so, output the doctor information of the first remaining doctors to form a target medical group; wherein the preset number of doctors is determined based on the difficulty of the emergency surgery corresponding to the actual disease diagnosis result.

[0151] In an optional implementation, if the first doctor information output unit 45 determines that the actual number of doctors of the first remaining doctors is less than the preset number of doctors, the doctor scheduling module 4 further includes:

[0152] The second doctor information output unit 46 is used to select doctors with the same emergency treatment level as the emergency level of the disease from the remaining hospitals except the target hospital as the second target emergency doctors; wherein the remaining hospitals are located in the set area; from the second target emergency doctors, doctors who are busy during the emergency surgery schedule are eliminated to obtain the second remaining doctors; the actual treatment score of each second remaining doctor is obtained, and doctors whose actual treatment score is greater than or equal to the preset treatment score are selected from the second remaining doctors to obtain the third remaining doctors; the difference between the preset number of doctors and the actual number of doctors is obtained, and doctors who meet the difference in number are selected from the third remaining doctors to form the fourth remaining doctors; and the doctor information of the first remaining doctors and the fourth remaining doctors is output to form a target medical group.

[0153] In an optional implementation, the second doctor information output unit 46 is specifically used to sort the actual treatment scores of the third remaining doctors from high to low; obtain the difference in the number of people, and select the doctors who are ranked high and meet the difference in the number of people to form the fourth remaining doctors.

[0154] In an optional implementation, the diagnosis result acquisition module 2 includes:

[0155] A correspondence building unit 21 is used to pre-build a correspondence between historical diagnosis feature data and historical diagnosis results of a number of historical disease diagnosis data based on a diagnosis feature extraction model;

[0156] Wherein, the diagnostic feature extraction model is used to extract actual diagnostic feature data corresponding to the input actual diagnostic data;

[0157] The feature data acquisition unit 22 is used to acquire the actual diagnosis data of the patient, input the actual diagnosis data into the diagnosis feature extraction model, and obtain the actual diagnosis feature data of the patient;

[0158] The diagnosis result determination unit 23 is used to obtain the actual disease diagnosis result of the patient based on the actual diagnosis feature data and the corresponding relationship.

[0159] In an optional embodiment, the diagnosis result determination unit 23 is specifically used to calculate the similarity between the actual diagnostic feature data and each historical diagnostic feature data to obtain a number of actual similarity values; select the historical diagnostic feature data corresponding to the actual similarity value that meets the preset similarity threshold as the target diagnostic feature data; based on the corresponding relationship, use the historical diagnostic result corresponding to the target diagnostic feature data as the actual disease diagnosis result of the patient.

[0160] The doctor dispatch system of this embodiment pre-stores the doctor information corresponding to each doctor in a set area, obtains the patient's actual disease diagnosis result; determines the patient's disease emergency level based on the actual disease diagnosis result; dispatches the doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient; through the mutual cooperation between various modules and units, the doctors in the set area can be flexibly dispatched according to the patient's disease emergency level while meeting the patient's medical needs.

[0161] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution.

[0162] Example 3

[0163] This embodiment provides an electronic device, Figure 8 This is a structural diagram of an electronic device provided in this embodiment, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, the doctor scheduling method in the above-mentioned embodiment 1 is implemented. Figure 8 The electronic device 70 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0164] like Figure 8 As shown, the electronic device 70 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 70 may include, but are not limited to: at least one processor 71, at least one memory 72, and a bus 73 connecting different system components (including the memory 72 and the processor 71).

[0165] The bus 73 includes a data bus, an address bus, and a control bus.

[0166] The memory 72 may include a volatile memory, such as a random access memory (RAM) 721 and / or a cache memory 722 , and may further include a read-only memory (ROM) 723 .

[0167] The memory 72 may also include a program tool 725 (or utility) having a set (at least one) of program modules 724, such program modules 724 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0168] The processor 71 executes various functional applications and data processing by running the computer programs stored in the memory 72, such as the doctor scheduling method in the above-mentioned embodiment 1.

[0169] The electronic device 70 may also communicate with one or more external devices 74. Such communication may be performed via an input / output (I / O) interface 75. Furthermore, the model generating electronic device 70 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 76. Figure 8 As shown, the network adapter 76 communicates with other modules of the electronic device 70 via the bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 70, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0170] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0171] Example 4

[0172] The embodiment of the present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the doctor scheduling method in the above-mentioned embodiment 1.

[0173] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0174] In a possible implementation, the present disclosure may also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps in the doctor scheduling method in the above-mentioned embodiment 1.

[0175] Among them, the program code for executing the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0176] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A doctor scheduling method, characterized in that: The doctor scheduling method comprises: Pre-store the doctor information corresponding to each doctor in the set area; The doctor information includes the doctor's basic personal information, the doctor's emergency treatment level, and the actual treatment score corresponding to the emergency treatment level; Obtain the patient’s actual disease diagnosis results; Determine the patient's disease emergency level based on the actual disease diagnosis result; Based on the disease emergency level and the doctor information, doctors in the set area are dispatched to form a target medical team for providing emergency treatment to the patient.

2. The doctor scheduling method according to claim 1, characterized in that: The step of determining the patient's disease emergency level based on the actual disease diagnosis result comprises: Obtaining the actual disease type corresponding to the actual disease diagnosis result; The disease emergency level of the patient is determined based on the risk level of the actual disease type.

3. The doctor scheduling method according to claim 1, characterized in that: The step of dispatching doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient includes: Obtain the target hospital where the patient is located; Wherein, the target hospital is located in the set area; In the target hospital, a doctor with the same emergency treatment level as the emergency level of the disease is selected as the first target emergency doctor; Obtain the actual treatment score corresponding to each first target emergency doctor; Determining whether the actual treatment score is greater than or equal to a preset treatment score; If yes, outputting the doctor information of the first target emergency doctor to form the target medical team; Among them, the preset treatment score is determined based on the emergency level of the disease.

4. The doctor scheduling method according to claim 3, characterized in that: The step of outputting the doctor information of the first target emergency doctor to form the target medical group includes: Obtaining the emergency surgery schedule of the patient; Eliminate doctors who are busy during the emergency surgery schedule from the first target emergency doctors to obtain first remaining doctors; Obtain the preset number of doctors corresponding to the emergency level of the disease; Determining whether the actual number of doctors of the first remaining doctors is greater than or equal to the preset number of doctors; If yes, output the doctor information of the first remaining doctors to form the target medical group; Among them, the preset number of doctors is determined based on the difficulty of the emergency surgery corresponding to the actual disease diagnosis result.

5. The doctor scheduling method according to claim 4, characterized in that: If the actual number of doctors of the first remaining doctors is less than the preset number of doctors, the doctor scheduling method further includes: In the remaining hospitals except the target hospital, select doctors with the same emergency treatment level as the emergency level of the disease as the second target emergency doctors; Wherein, the remaining hospitals are located within the set area; Eliminate doctors who are busy during the emergency surgery schedule from the second target emergency doctors to obtain second remaining doctors; Obtaining the actual treatment score of each second remaining doctor, and selecting doctors whose actual treatment score is greater than or equal to the preset treatment score from the second remaining doctors to obtain third remaining doctors; Obtaining the difference between the preset number of doctors and the actual number of doctors, and selecting doctors meeting the difference in number from the third remaining doctors to form a fourth remaining doctor; The doctor information of the first remaining doctors and the fourth remaining doctors is output to form the target medical group.

6. The doctor scheduling method according to claim 1, characterized in that: The step of obtaining the actual disease diagnosis result of the patient comprises: Based on the diagnostic feature extraction model, the corresponding relationship between historical diagnostic feature data and historical diagnostic results of several historical disease diagnostic data is pre-constructed; Wherein, the diagnostic feature extraction model is used to extract actual diagnostic feature data corresponding to the input actual diagnostic data; Acquiring actual diagnosis data of the patient, and inputting the actual diagnosis data into the diagnosis feature extraction model to obtain the actual diagnosis feature data of the patient; Based on the actual diagnosis feature data and the corresponding relationship, the actual disease diagnosis result of the patient is obtained.

7. The doctor scheduling method according to claim 6, characterized in that: The step of obtaining the actual disease diagnosis result of the patient based on the actual diagnosis feature data and the corresponding relationship comprises: Calculating the similarity between the actual diagnostic feature data and each of the historical diagnostic feature data to obtain a number of actual similarity values; Selecting historical diagnostic feature data corresponding to an actual similarity value that meets a preset similarity threshold as target diagnostic feature data; Based on the corresponding relationship, the historical diagnosis result corresponding to the target diagnosis feature data is used as the actual disease diagnosis result of the patient.

8. A doctor scheduling system, characterized in that: The doctor scheduling system includes: An information storage module, used to pre-store doctor information corresponding to each doctor in a set area; The doctor information includes the doctor's basic personal information, the doctor's emergency treatment level, and the actual treatment score corresponding to the emergency treatment level; A diagnosis result acquisition module is used to obtain the patient's actual disease diagnosis result; An emergency level determination module, used to determine the patient's disease emergency level based on the actual disease diagnosis result; The doctor scheduling module is used to schedule doctors in the set area based on the disease emergency level and the doctor information to form a target medical team for emergency treatment of the patient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that: When the processor executes the computer program, the doctor scheduling method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the doctor scheduling method according to any one of claims 1 to 7 is implemented.

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