WO2020119401A1 - 医师违规执业的监控方法、监控服务端及存储介质 - Google Patents

医师违规执业的监控方法、监控服务端及存储介质 Download PDF

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WO2020119401A1
WO2020119401A1 PCT/CN2019/119209 CN2019119209W WO2020119401A1 WO 2020119401 A1 WO2020119401 A1 WO 2020119401A1 CN 2019119209 W CN2019119209 W CN 2019119209W WO 2020119401 A1 WO2020119401 A1 WO 2020119401A1
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physician
medicine
category
identifier
preset
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PCT/CN2019/119209
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English (en)
French (fr)
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陈明东
黄越
胥畅
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平安医疗健康管理股份有限公司
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Publication of WO2020119401A1 publication Critical patent/WO2020119401A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Definitions

  • the present application relates to the technical field of medical monitoring, in particular to a monitoring method for doctors practicing illegally, a monitoring server, and a computer-readable storage medium.
  • the main purpose of this application is to provide a monitoring method, monitoring server and computer-readable storage medium for doctors' illegal practice, aiming to solve the technical problem of large workload and easy omission due to the huge medical data and manual verification of illegal prescriptions .
  • this application provides a monitoring method for doctors' illegal practice, including the steps of:
  • the prescription information including a physician ID and a drug ID;
  • the physician information including the physician qualification category corresponding to the physician ID;
  • the illegal practice mark is set to be associated with the doctor mark.
  • the present application also provides a monitoring server, including:
  • a receiving module the receiving module is used to receive prescription information sent by a server of a medical institution, and the prescription information includes a physician ID and a drug ID;
  • a query module the query module is used to query a drug type corresponding to the drug identifier in a preset drug classification table; the query module is also used to query doctor information corresponding to the doctor identifier from pre-stored doctor practice data ,
  • the physician information includes a physician qualification category corresponding to the physician identification;
  • a judgment module the judgment module is used for judging whether the medicine type matches the doctor qualification category according to a preset medicine type and doctor qualification category mapping table;
  • An identification module the identification module is used to determine that a doctor corresponding to the doctor's logo is illegally practicing when the type of medicine does not match the doctor's qualification category.
  • the present application also provides a monitoring server.
  • the monitoring server includes: a communication module, a memory, a processor, and a computer stored on the memory and capable of running on the processor. Read instructions, when the computer-readable instructions are executed by the processor to implement the steps of the monitoring method for the doctor's illegal practice as described above.
  • the present application also provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, the computer-readable instructions executed by the processor to achieve the above Steps of monitoring methods for doctors' illegal practice.
  • a monitoring method, monitoring server, and computer-readable storage medium for doctors practicing illegally proposed in this application can obtain the drug type and doctor qualification category based on the prescription information and doctor information issued by the doctor, by judging the drug type and doctor qualification category Whether it matches, to determine whether the doctor prescribes a violation across disciplines to protect the interests of patients.
  • FIG. 1 is a schematic structural diagram of a hardware operating environment involved in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a first embodiment of a monitoring method for a doctor’s illegal practice applied for;
  • FIG. 3 is a schematic flowchart of a second embodiment of a monitoring method for a doctor’s illegal practice
  • FIG. 4 is a schematic flowchart of a third embodiment of a monitoring method for a doctor’s illegal practice applied for;
  • FIG. 5 is a schematic flowchart of a fourth embodiment of a monitoring method for a doctor’s illegal practice applied for;
  • FIG. 6 is a schematic flowchart of a sixth embodiment of a monitoring method for a doctor’s illegal practice applied for;
  • FIG. 7 is a schematic flowchart of a seventh embodiment of a monitoring method for a doctor’s illegal practice.
  • FIG. 1 is a schematic diagram of a hardware structure of a monitoring server 100 in various embodiments of the present application.
  • the monitoring server 100 may be a server that is communicatively connected to a terminal for medical expenses reimbursement by an insured person or a medical institution. It can also be a monitoring service platform dedicated to data monitoring that is connected to the server and the terminal for medical expense reimbursement.
  • the monitoring server 100 provided by this application includes components such as a communication module 10, a memory 20, and a processor 30. Wherein, the processor 30 is respectively connected to the memory 20 and the communication module 10, and the memory 20 stores computer-readable instructions, and the computer-readable instructions are simultaneously executed by the processor 30.
  • the communication module 10 can be connected to external communication equipment through a network.
  • the communication module 10 can receive requests from external communication devices, and can also broadcast events, instructions, and information to the external communication devices.
  • the external communication device may be a server, a mobile phone, a computer, a charging terminal of a medical institution, a prescription terminal issued by a medical institution, or the like.
  • the memory 20 can be used to store software programs and various data.
  • the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • the processor 30 is a control center of the monitoring server 100, and uses various interfaces and lines to connect the various parts of the entire monitoring server 100, by running or executing software programs and/or modules stored in the memory 20, The data in the memory 20 executes various functions and processing data of the monitoring server 100, thereby performing overall monitoring on the monitoring server 100.
  • the above-mentioned monitoring server 100 may further include a circuit control module for connecting to a power source to ensure the normal operation of other components.
  • the monitoring server 100 may also include a display module for displaying system interfaces, medical data, pre-stored maximum price lists, etc., to facilitate real-time operation and control by workers.
  • the structure of the monitoring server 100 shown in FIG. 1 does not constitute a limitation on the monitoring server 100, and may include more or fewer components than the illustration, or a combination of certain components, or different Parts arrangement.
  • Step S100 Receive prescription information sent by a server of a medical institution, where the prescription information includes a physician ID and a drug ID;
  • the medical institution may be a hospital, nursing home, outpatient department, clinic, health center, and first aid station for disease diagnosis and treatment.
  • the medical institution may also be a pharmacy that legally sells drugs.
  • the prescription information is other
  • pharmacies only upload the information contained in the prescriptions held by customers when they purchase medicines.
  • Application software is installed on the server of the medical institution, so that the server of the medical institution sends medical data to the monitoring server regularly or in real time.
  • the monitoring server can directly obtain medical data reported by the application software from its own memory; when the monitoring server is a dedicated monitoring service platform, it can send a request to the medical institution server to obtain medical data, or the medical institution server actively sends medical data To the monitoring service platform.
  • the doctor's logo may specifically be a doctor's ID card number, qualification certificate number, etc., which can distinguish the doctors.
  • the drug label may specifically be the drug name, drug code, etc.
  • the drug label is the drug code, because the regional health management departments have formulated corresponding drug codes and drugs for different drugs, dosage forms, indications, etc. to distinguish each drug , Making the drug code unique, and the accuracy of identification is higher than that of the drug name.
  • Step S100 includes:
  • the preset bidirectional recurrent neural network (Bidirectional recurrent neural network (BRNN) is a neural network model with a feedback structure, the word vector is input into the preset bidirectional recursive neural network model, so that the preset bidirectional recursive neural network model matches the word vector sequence Encode and output a sentence matrix, where each row of the sentence matrix represents the meaning of each word in the context.
  • Attention model Model is used to select information that is more critical to the current task goal from a large number of information, and the preset attention model is used to extract valid data from the sentence vector matrix and convert the valid data into sentence vectors ,
  • the sentence vector is a standardized field corresponding to the non-standard field in the original prescription information. For the standardized fields, you can also extract other required information, such as: patient identification, prescription type, etc.
  • Step S200 query the type of medicine corresponding to the medicine identifier in the preset medicine classification table
  • the preset drug classification table is set by a person skilled in the art with reference to the "Drug List", "National Essential Drug List” and other documents.
  • the preset drug classification table includes the drug identification, the drug type corresponding to the drug identification, the medication subject, and the medication sub-subject.
  • Chinese patent medicines use Chinese medicinal materials as raw materials. Under the guidance of Chinese medicine theory, they are processed into certain dosage forms of Chinese medicine products according to prescribed prescriptions and preparation processes.
  • Chinese herbal medicine can be Chinese herbal medicine decoctions that can be directly used in the clinical practice of Chinese medicine after being processed and processed according to Chinese medicine theories and traditional Chinese medicine processing methods, or can be granules made by extracting and concentrating single Chinese herbal decoction pieces for clinical prescription of Chinese medicine.
  • Western medicine includes organic chemicals, inorganic chemicals and biological products.
  • Step S300 querying the physician information corresponding to the physician ID in the pre-stored physician practice data, the physician information including the physician qualification category corresponding to the physician ID;
  • the pre-stored physician practice data contains information about each physician who is qualified to carry out diagnosis and treatment activities.
  • the physician information specifically includes the physician qualification category, physician identification, physician name, and practicing medical institution.
  • the physician qualification categories include clinical, traditional Chinese medicine, oral cavity and Public health, of course, those skilled in the art can set the categories included in the doctor qualification category according to the specific rules of the area where the doctor works.
  • Step S400 judging whether the medicine type matches the doctor qualification category according to the preset medicine type and doctor qualification category mapping table;
  • the preset mapping table of drug types and physician qualification categories is preset by a person skilled in the art according to needs, in which clinical medicine corresponding to Western medicine and traditional Chinese medicine corresponding to Chinese herbal medicine are set.
  • step S500 when the type of the medicine does not match the doctor's qualification category, an illegal practice mark is set to be associated with the doctor's mark.
  • the physician qualification category of physician A whose physician ID is 001 is clinical.
  • the prescription information corresponding to the prescription issued by physician A contains physician ID 001 and American ginseng Y-IX002; query the drug type corresponding to Y-IX002 in the preset drug classification table to obtain Y-IX002 corresponding Chinese herbal decoction; according to the preset drug type and
  • the physician qualification category mapping table judges that the clinical and Chinese herbal decoctions do not correspond, and judges that Doctor A is practicing illegally.
  • the method further includes: generating a prohibition instruction and sending the prohibition instruction to the medical institution server to prohibit the medical institution server from sending a prescription containing the physician ID Information until the physician receives the due punishment.
  • the monitoring method provided in this application can obtain the type of medicine corresponding to the drug identifier in the prescription and the doctor qualification category corresponding to the doctor identifier based on the prescription information issued by the doctor, so as to judge whether the doctor violates the regulation by judging whether the drug type corresponds to the doctor qualification category Prescriptions are issued to protect the interests of patients.
  • the preset drug classification table further includes a medication subject corresponding to the drug identifier
  • the doctor information further includes the doctor identifier Corresponding clinical practice category; after the step S400, including:
  • Step S600 when the medicine type matches the physician qualification category, query the medicine subject corresponding to the medicine identifier in the preset medicine classification table;
  • Step S700 confirming the clinical practice category corresponding to the physician identification according to the physician information
  • the clinical practice category is set with reference to the existing secondary clinical medicine disciplines. In other embodiments, those skilled in the art may also increase or decrease the types of clinical practice categories on this basis.
  • the medicine subject is the indication according to the description of the medicine, and the medicine is further divided to correspond to the classification of the clinical medicine secondary discipline. For example, the subject of pediatric ibuprofen suppositories is pediatrics; the subject of amitinidine is psychiatry and mental hygiene.
  • Step S800 judging whether the medicine subject matches the clinical practice category
  • Step S900 When the medication subject does not match the clinical practice category, an abnormality flag is set to associate the physician flag.
  • doctor A's corresponding physician qualification category is clinical
  • the clinical practice category is pediatrics.
  • the prescription information prescribed by doctor A for patients includes: 1 box of gynecological Qianjin tablets.
  • the physician qualification category "clinical” corresponds to "Chinese patent medicines”
  • the clinical practice category "Pediatrics” and "gynecological medicines” are not Correspondingly, an abnormality flag is set to associate with the physician flag.
  • the prescription information further includes the prescription type; after the step S900, it includes:
  • Step S910 Determine whether the prescription type of the prescription information is an outpatient prescription
  • step S920 when the prescription type is an outpatient prescription, an illegal practice mark is set to be associated with the physician mark.
  • the prescription types include outpatient prescriptions and hospitalization prescriptions. Because the patient can only live in the corresponding department according to the most serious disease, but at the same time the patient may also suffer from other diseases, for example: the patient is admitted to surgery because of a fracture, but at the same time the patient also has cardiovascular disease and needs to take medical medicine, so The resident doctor will also prescribe a certain amount of medicine for internal medicine.
  • the monitoring method provided by this application can be used to identify the abnormal identifier associated with the doctor, and further verification is required by the verification staff.
  • the outpatient physician who issues outpatient prescriptions can only prescribe medicines within the range prescribed by the registered subjects of the license, when the prescription type is an outpatient prescription, it can be directly judged that the doctor corresponding to the doctor's logo is practicing illegally.
  • the prescription information further includes a patient identification, and after the step S800, it includes:
  • Step S810 when the medication subject matches the clinical practice category, confirm the patient identification according to the prescription information
  • Step S820 querying the diagnosis and treatment information corresponding to the patient identification according to the pre-stored medical record data, the diagnosis and treatment information includes information about the type of disease the patient is suffering from;
  • the pre-stored medical record data may specifically be the medical record data of the visiting patient pre-stored in the server of the medical institution, or the resident medical record data pre-stored in the memory of the medical supervision department, or the medical record data pre-stored in the monitoring server, specifically recorded Patient identification, disease type information corresponding to the patient identification, medical records, medication records and other medical information.
  • Step S830 Obtain the indication corresponding to the drug identifier according to the preset medicine and indication mapping table
  • the preset medicine and indication mapping table is set by a person skilled in the art according to the indications and the adapted population recorded in the medicine instruction manual.
  • the preset medicine and indication mapping table includes the medicine identification and the indication corresponding to the medicine identification.
  • Step S840 Determine whether there is at least one match between the indication and the disease type information
  • Step S850 when none of the indication and the disease type information match, the abnormality identifier is set to be associated with the physician identifier.
  • an abnormal identifier is set to associate with the physician identifier.
  • a recurrent neural network recurrent neural net work, RNN
  • the two-way RNN model is used to encode the vector into a sentence vector matrix, so as to match the fields related to the medical department category, medication sub-subject, and disease type information in the medical data to the corresponding medical department In the standardized fields of category, medication sub-subject, and disease type information.
  • the prescription information further includes a patient identification; the monitoring method further includes:
  • Step S10 querying the disease type information corresponding to the patient identification according to the pre-stored medical record data
  • Step S20 query the essential drugs corresponding to the disease type information according to the preset essential drugs list;
  • the preset list of essential medicines is set by a person skilled in the art according to the indications and groups of people described in the drug manual.
  • the preset list of essential medicines includes disease type information and essential drugs corresponding to the disease type information.
  • Step S30 Determine whether each of the medicine identifiers in the prescription information corresponds to the necessary medicine
  • Step S40 when each of the drug identifiers does not correspond to the necessary drugs, the abnormality flag is set to associate the physician identifier and the patient identifier.
  • hypothyroidism requires regular check of hormone levels.
  • the diagnosis and treatment information of the patient A includes the type of hypothyroidism, but the prescription information does not include the hormone test, and then an abnormal mark is set to associate the physician ID and the patient ID.
  • the preset drug classification table further includes a medication sub-subject corresponding to the drug identification, and the physician information also includes the practice department category; After the step S800, it includes:
  • Step S910 when the medication subject matches the clinical practice category, query the medication sub-subject corresponding to the drug identifier according to the preset drug classification table;
  • Step S920 Acquire the medical department category corresponding to the physician ID according to the pre-stored physician practice data
  • the category of the practice medicine department is specifically to further classify the clinical practice category according to different pathogenesis principles of the disease.
  • internal medicine is divided into respiratory medicine, digestive medicine, cardiovascular medicine, hematology, etc.
  • the medicine sub-subject is to further subdivide the medicine sub-subject.
  • the medicine sub-subjects obtained by further subdividing the medicines in the medicine subjects are: medicine for respiratory medicine, medicine for digestive medicine, medicine for cardiovascular medicine, medicine for blood medicine, etc.
  • Step S930 judging whether the medication sub-subject corresponds to the category of the practice medicine department according to a mapping table of preset medication sub-subjects and physician qualification categories;
  • step S940 when the medication sub-subject does not correspond to the category of the medical practice department, the abnormal identifier is set to be associated with the physician identifier.
  • the monitoring method provided in this embodiment can distinguish physicians from practicing in the same practice subject and across departments.
  • the physician information further includes a registered medical institution identification; the step S910 includes:
  • Step S911 when the medication subject matches the clinical practice category, obtain the registered medical institution ID corresponding to the doctor ID according to the doctor information;
  • Step S912 query the level corresponding to the medical institution ID according to the pre-stored medical institution information
  • Step S913 Determine whether the level is higher than a preset level
  • Step S914 When the level is higher than the preset level, query the medication sub-subject corresponding to the drug identifier according to the preset drug classification table.
  • the preset level is set by those skilled in the art according to the actual situation.
  • the prescriptions made by the physicians of the medical institutions that meet the level requirements are then judged. Therefore, it is avoided that the categories of medical practice departments correspond to the sub-categories of medications for the doctors of all medical institutions, and the workload is reduced.
  • This application also provides a monitoring server, including:
  • a receiving module 10 the receiving module 10 is used to receive prescription information sent by a server of a medical institution, and the prescription information includes a physician ID and a drug ID;
  • the query module 20 is used to query the drug type corresponding to the drug identifier in a preset drug classification table; the query module 20 is also used to query the corresponding doctor identifier in the pre-stored physician practice data Physician information, the physician information includes a physician qualification category corresponding to the physician identification;
  • a judgment module 30, the judgment module 30 is configured to judge whether the medicine type matches the doctor qualification category according to a preset medicine type and doctor qualification category mapping table;
  • An identification module 40 which is used to set an illegal practice identification to be associated with the physician identification when the drug type does not match the physician qualification category.
  • the query module 20 is further configured to query the medicine subject corresponding to the medicine identifier in the preset medicine classification table when the medicine type matches the physician qualification category;
  • the physician information confirms the clinical practice category corresponding to the physician ID;
  • the judgment module 30 is also used to judge whether the medication subject matches the clinical practice category
  • the identification module 40 is further configured to set an abnormal identification to associate the physician identification when the medication subject does not match the clinical practice category.
  • the judgment module 30 is further used to judge whether the prescription type of the prescription information is an outpatient prescription
  • the identification module is also used to determine when the prescription type is an outpatient prescription, the physician corresponding to the physician identification is practicing illegally.
  • the query module 20 is further configured to confirm the patient ID according to the prescription information when the medication subject matches the clinical practice category; to query the corresponding patient ID according to the pre-stored medical record data Diagnosis and treatment information, where the diagnosis and treatment information includes information about the patient's disease type; the indication corresponding to the drug identification is obtained according to a preset drug and indication mapping table;
  • the judgment module 30 is also used to judge whether there is at least one match between the indication and the disease type information
  • the identification module 40 is further configured to set the abnormal identification to be associated with the physician identification when none of the indication and the disease type information match.
  • the query module 20 is further configured to query the necessary drugs corresponding to the disease type information according to a preset list of necessary drugs;
  • the judging module 30 is also used to judge whether each of the medicine identifiers in the prescription information corresponds to the necessary medicine;
  • the identification module 40 is further configured to set the abnormal mark to associate the physician ID and the patient ID when each of the drug IDs does not correspond to the necessary drugs.
  • the query module 20 is further configured to query the medicine sub-subject corresponding to the medicine identifier according to the preset medicine classification table when the medicine subject matches the clinical practice category; Obtain the medical practice category corresponding to the physician identification by pre-stored physician practice data;
  • the judging module 30 is further configured to judge whether the medication sub-subject corresponds to the category of the medical practice department according to a mapping table of preset medication sub-subjects and physician qualification categories;
  • the identification module 40 is further configured to set an abnormal identification to associate the physician identification when the medication sub-subject does not correspond to the category of the practice department.
  • the query module 30 is further configured to, when the medication subject matches the clinical practice category, obtain the registered medical institution ID corresponding to the doctor ID according to the doctor information, and according to the pre-stored medical institution Information query the corresponding level of the medical institution identification;
  • the judgment module 30 judges whether the level is higher than a preset level
  • the query module 30 is further configured to query the medicine sub-subject corresponding to the medicine identifier according to the preset medicine classification table when the rank is higher than the preset rank.
  • the monitoring server 100 includes a communication module 10, a memory 20, and a processor 30, wherein the processor 30 is connected to the memory 20 and the communication module 10, respectively, and the memory Computer-readable instructions are stored on the 20, and when the computer-readable instructions are executed by the processor 20, the steps of the monitoring method of the doctor's illegal practice as described above are realized.
  • the present application also proposes a computer-readable storage medium on which computer-readable instructions are stored.
  • the computer-readable storage medium may specifically be a non-volatile computer-readable storage medium.
  • the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or part that contributes to the existing technology, and the computer software product is stored in a storage medium (such as ROM/RAM as described above) , Magnetic disk, optical disk), including several instructions to make a terminal device (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to perform the method described in each embodiment of the present application.

Abstract

本申请公开了一种医师违规执业的监控方法、服务端及存储介质，该方法包括步骤：接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；在预设药品分类表中查询与所述药品标识对应的药品类型；在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。

Description

医师违规执业的监控方法、监控服务端及存储介质
本申请要求于2019年12月13日提交中国专利局、申请号为201811530811.7、发明名称为“医师违规执业的监控方法、监控服务端及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及医疗监控技术领域，尤其涉及医师违规执业的监控方法、监控服务端及计算机可读存储介质。
背景技术
为保障就医诊疗的安全性，对医师执业进行划分，在某一学科执业的医师，仅能根据学科划分开具相应的药物。现有部分医疗机构，由于监管失职，出现违规开具处方的行为，例如：不具有开具西药处方资格的医师开具西药，或不具有开具中草药处方资格的医师开具中草药等情况，给患者的生命造成极大的隐患。监管工作人员进行核查时，需要对大量的医疗数据进行排查，以分析识别是否存在违规开具处方行为，这样不仅需要消耗大量人力，而且容易遗漏，导致识别不全面。
发明内容
本申请的主要目的在于提供一种医师违规执业的监控方法、监控服务端及计算机可读存储介质，旨在解决由于医疗数据巨大，人工核查违规开具处方行为的工作量大、容易遗漏的技术问题。
为实现上述目的，本申请提供一种医师违规执业的监控方法，包括步骤：
接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
在预设药品分类表中查询与所述药品标识对应的药品类型；
在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
此外，为实现上述目的，本申请还提供一种监控服务端，包括：
接收模块，所述接收模块用于接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
查询模块，所述查询模块用于在预设药品分类表中查询与所述药品标识对应的药品类型；所述查询模块还用于在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
判断模块，所述判断模块用于根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
标识模块，所述标识模块用于当所述药品类型与所述医师资格类别不匹配时，判断所述医师标识对应的医师违规执业。
此外，为实现上述目的，本申请还提供一种监控服务端，所述监控服务端包括：通信模块、存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机可读指令，所述计算机可读指令被所述处理器执行时实现如上所述的医师违规执业的监控方法的步骤。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如上所述的医师违规执业的监控方法的步骤。
本申请提出的一种医师违规执业的监控方法、监控服务端及计算机可读存储介质，可根据医师开具的处方信息和医师信息，获得药品类型和医师资格类别，通过判断药品类型和医师资格类别是否匹配，以判断医师是否跨科目违规开具处方，以维护了患者的利益。
附图说明
图1是本申请实施例方案涉及的硬件运行环境的结构示意图；
图2为本申请医师违规执业的监控方法第一实施例的流程示意图；
图3为本申请医师违规执业的监控方法第二实施例的流程示意图；
图4为本申请医师违规执业的监控方法第三实施例的流程示意图；
图5为本申请医师违规执业的监控方法第四实施例的流程示意图；
图6为本申请医师违规执业的监控方法第六实施例的流程示意图；
图7为本申请医师违规执业的监控方法第七实施例的流程示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
请参照图1，图1为本申请各个实施例中的监控服务端100的硬件结构示意图，所述监控服务端100可以是与参保人或医疗机构办理医疗费用报销的终端通信连接的服务器，也可以是与服务器以及办理医疗费用报销的终端通信连接的专用于数据监控的监控服务平台。本申请所提供的监控服务端100包括通信模块10、存储器20及处理器30等部件。其中，所述处理器30分别与所述存储器20和所述通信模块10连接，所述存储器20上存储有计算机可读指令，所述计算机可读指令同时被处理器30执行。
通信模块10，可通过网络与外部通讯设备连接。通信模块10可以接收外部通讯设备发出的请求，还可广播事件、指令及信息至所述外部通讯设备。所述外部通讯设备可以是服务器、手机、电脑、医疗机构收费终端及医疗机构开具处方终端等。存储器20，可用于存储软件程序以及各种数据。此外，存储器20可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。处理器30，是监控服务端100的控制中心，利用各种接口和线路连接整个监控服务端100的各个部分，通过运行或执行存储在存储器20内的软件程序和/或模块，以及调用存储在存储器20内的数据，执行监控服务端100的各种功能和处理数据，从而对监控服务端100进行整体监控。尽管图1未示出，但上述监控服务端100还可以包括电路控制模块，用于与电源连接，保证其他部件的正常工作等。上述监控服务端100还可以包括显示模块，用于显示系统界面、医疗数据、预存最高限价表等，方便工作人员进行实时操作和控制。
本领域技术人员可以理解，图1中示出的监控服务端100结构并不构成对监控服务端100的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
基于上述硬件结构，提出本申请方法各个实施例。
参照图2，在本申请医师违规执业的监控方法的第一实施例中，包括步骤：
步骤S100，接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
具体地，医疗机构可以是进行疾病诊断、治疗的医院、疗养院、门诊部、诊所、卫生所以及急救站，医疗机构还可以是合法出售药品的药房，当医疗机构为药房时，处方信息为其他医院、诊所、卫生所等具有医疗机构执业许可的机构医师开具的处方，药房仅将顾客购药时持有的处方中包含的信息进行上传。在医疗机构服务器上安装有应用软件，以使得医疗机构服务器定时或实时向监控服务端发送医疗数据。监控服务端可以直接从自身存储器中获取应用软件上报的医疗数据；当监控服务端是专用的监控服务平台时，可以向医疗机构服务器发送请求以获取医疗数据，或由医疗机构服务器主动发送医疗数据给所述监控服务平台。医师标识具体可以是医师身份证号、资格证号等可区别各个医师的标识。药品标识具体可以是药品名称、药品编码等，优选地，药品标识为药品编码，由于各地区卫生管理部门针对不同药品、剂型、适应症等制定了相应的药品编码与药品对应，以区分各个药品，使得药品编码具有唯一性，并且相较于药品名称识别准确率高。
不同机构发送的处方信息多种多样，对处方信息进行预处理，以得到所需的医师标识、药品标识、处方类型、患者标识等准确字段。具体可通过预设双向递归神经网络对处方信息中无法识别、与预设检验规则不符的不标准字段进行处理，也可以对处方信息的内所有字段进行处理。步骤S100包括：
将所述处方信息向量化，获得向量序列；
通过预设双向递归神经网络对所述处方信息的字段编码为句子向量矩阵；
通过预设注意力模型从所述句子向量矩阵中提取上下文向量，根据所述上下文向量将所述句子向量矩阵压缩为句向量，并将所述句向量作为标准化字段；
根据所述标准化字段提取医师标识和药品标识。
所述预设双向递归神经网络(Bidirectional recurrent neuralnetwork，BRNN)是一种具有反馈结构的神经网络模型，将所述词向量输入至所述预设双向递归神经网络模型中，以使所述预设双向递归神经网络模型对所述词向量序列进行编码，并输出句子矩阵，所述句子矩阵的每一行表示每个词语在上下文中所表达的意思。注意力模型(Attention Model)用于从众多信息中选择出对当前任务目标更关键的信息，而所述预设注意力模型用于从所述句子向量矩阵中提取有效数据，并将所述有效数据转化为句向量，该句向量就是与原始处方信息中不规范字段对应的标准化字段。针对标准化字段还可以提取其它所需信息，例如：患者标识、处方类型等。
步骤S200，在预设药品分类表中查询与所述药品标识对应的药品类型；
在本实施中，预设药品分类表为本领域技术人员参照《药品目录》、《国家基本药物目录》等文献进行设置。预设药品分类表包括药品标识、与药品标识对应的药品类型、用药科目、用药子科目等。
药品类型包括中成药、中草药和西药，中成药是以中药材为原料，在中医药理论指导下，按规定的处方和制剂工艺将其加工制成一定剂型的中药制品。中草药可以是经过按中医药理论、中药炮制方法，经过加工炮制后可直接用于中医临床的中药饮片，还可以是由单味中药饮片经提取浓缩制成的、供中医临床配方用的颗粒。西药包括有机化学药品，无机化学药品和生物制品。
步骤S300，在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
预存医师执业数据中保存有各个具有开展诊疗活动资格的医师信息，医师信息具体包括医师资格类别、医师标识、医师姓名、从业医疗机构等，在本申请中医师资格类别包括临床、中医、口腔和公共卫生，当然本领域技术人员可根据医师从业地区具体规则设置医师资格类别包括的种类。
步骤S400，根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
预设药品类型与医师资格类别映射表为本领域技术人员根据需要预先设置，其中设置有西药对应临床、中草药对应中医等。
步骤S500，当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
在本实施例中，当所述药品类型与所述医师资格类别匹配时，不做处理。
例如：医师标识为001的医师A的医师资格类别为临床。当医师A开具处方对应的处方信息中含有医师标识001、西洋参Y-IX002；在预设药品分类表中查询Y-IX002对应的药品类型，得到Y-IX002对应中药饮片；根据预设药品类型与医师资格类别映射表判断临床和中药饮片不对应，判断医师A违规执业。
优选地，设置违规执业标识与所述医师标识关联后，还包括：生成禁止指令，并向所述医疗机构服务器发送所述禁止指令，以禁止所述医疗机构服务器发送包含所述医师标识的处方信息，直至该医师接收应有的处罚。
本申请提供的监控方法可根据医师开具的处方信息，获得处方中药品标识对应的药品类型，以及获得医师标识对应的医师资格类别，从而通过判断药品类型与医师资格类别是否对应，判断医师是否违规开具处方，以维护了患者的利益。
参照图3，在本申请医师违规执业的监控方法的第二实施例中，所述预设药品分类表还包括与所述药品标识对应的用药科目，所述医师信息还包括与所述医师标识对应的临床执业类别；所述步骤S400之后，包括：
步骤S600，当所述药品类型与所述医师资格类别匹配时，在所述预设药品分类表中查询所述药品标识对应的用药科目；
步骤S700，根据所述医师信息确认所述医师标识对应的所述临床执业类别；
在本实施例中临床执业类别参考现有临床医学二级学科进行设置，在其他实施例中，本领域技术人员也可以在此基础上增加或减少临床执业类别的种类。用药科目为根据药品说明的适应症，对药品进行进一步划分，以使用药科目与临床医学二级学科的分类对应。例如：小儿布洛芬栓的用药科目为儿科；阿咪替丁的用药科目为精神病与精神卫生学等。
步骤S800，判断所述用药科目与所述临床执业类别是否匹配；
步骤S900，当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识。
在本实施例中，当所述用药科目与所述临床执业类别匹配时，不做处理。例如：A医师对应的医师资格类别为临床，临床执业类别为儿科，A医师为患者开出的处方信息中包括：妇科千金片1盒。将妇科千金片与预设药品分类表进行对应，得到妇科千金片与中成药、妇科用药对应；医师资格类别“临床”与“中成药”对应；临床执业类别“儿科”与“妇科用药”不对应，设置异常标识关联所述医师标识。
在本实施例中，可识别医师执业证注册科目与开具处方不一致行为，从而可进一步判断医师是否违规执业。
参照图4，在本申请医师违规执业的监控方法的第三实施例中，所述处方信息还包括处方类型；所述步骤S900之后，包括：
步骤S910，判断所述处方信息的处方类型是否为门诊处方；
步骤S920，当所述处方类型为门诊处方时，设置违规执业标识与所述医师标识关联。
具体地，处方类型包括门诊处方和住院处方。由于患者仅能根据最严重的病种住入相应的科室，但同时患者有可能还患有其他疾病，例如：患者因骨折住入外科，但同时患者还患有心血管疾病需要服用内科用药，所以住院医师还会开具一定量的内科用药，这种情况下，通过本申请提供的监控方法可针对该医师关联异常标识，待核查工作人员进一步核查。又由于开具门诊处方的门诊医师仅能针对执业证注册科目规定的范围内开具药物，所以当所述处方类型为门诊处方时，可直接判断所述医师标识对应的医师违规执业。
参照图5，在本申请医师违规执业的监控方法的第四实施例中，所述处方信息还包括患者标识，所述步骤S800之后，包括：
步骤S810，当所述用药科目与所述临床执业类别匹配时，根据所述处方信息确认患者标识；
步骤S820，根据预存病历数据查询与所述患者标识对应的诊疗信息，所述诊疗信息包括患者所患病种信息；
预存病历数据具体可以是预存于医疗机构服务器内的就诊患者的病历数据，或是预存于医疗监管部门的存储器上的居民病历数据，还可以是预存于监控服务端内的病历数据，具体记载了患者标识、与所述患者标识对应的病种信息、就诊记录、用药记录等诊疗信息。
步骤S830，根据预设药品与适应症映射表获取与所述药品标识对应的适应症；
预设药品与适应症映射表为本领域技术人员根据药品说明书记载的适应症、适应人群进行设置，预设药品与适应症映射表包括药品标识、与药品标识对应的适应症。
步骤S840，判断是否存在至少一项所述适应症与所述病种信息匹配；
步骤S850，当所述适应症与所述病种信息均不匹配时，设置所述异常标识关联所述医师标识。
由于药品对应的适应症可能不止一种，所以当所述病种信息与所述适应症的任一项均不对应时，设置异常标识关联所述医师标识。通过本实施例提供的监控方法，可识别出医师开具处方与患者疾病无关的药物，维护患者利益，同时防止医师与患者合谋获取非必须药物的医保报销。医务工作人员在录入时，处方信息、预存病历数据、行医科室类别设置内容复杂多变。在本申请提供的监控方法中，针对处方信息、预存病历数据、行医科室类别等数据中不规范的字段，在考虑词本身、语义距离的要求下，利用递归神经网络（recurrent neural net work，RNN）分析文本内容，如行医科室类别、用药子科目、病种信息等。将文本用一个向量的序列表示之后，使用双向RNN模型将向量编码为一个句子向量矩阵，从而将医疗数据中的与行医科室类别、用药子科目、病种信息相关的字段匹配到相应的行医科室类别、用药子科目、病种信息标准化字段中。
在本申请医师违规执业的监控方法的第五实施例中，所述处方信息还包括患者标识；所述监控方法还包括：
步骤S10，根据预存病历数据查询与所述患者标识对应的病种信息；
步骤S20，根据预设必要药品列表查询所述病种信息对应的必要药品；
预设必要药品列表为本领域技术人员根据药品说明书记载的适应症、适应人群进行设置，预设必要药品列表包括病种信息、与病种信息对应的必要药品。
步骤S30，判断所述处方信息中的各所述药品标识是否与所述必要药品对应；
步骤S40，当各所述药品标识与所述必要药品均不对应时，设置所述异常标记关联所述医师标识和所述患者标识。
在本实施例中，当各所述药品标识与所述必要药品均对应时，不做处理。
例如:甲状腺低下需要定时检查激素含量。A患者的诊疗信息中包括甲状腺低下的病种，但处方信息不包括激素检查，则设置异常标记关联该医师标识和患者标识。
通过本实施例提供的监控方法，能识别医师为套取医保报销费用，出具假处方行为。
参照图6，在本申请医师违规执业的监控方法的第六实施例中，所述预设药品分类表还包括与所述药品标识对应的用药子科目，所述医师信息还包括行医科室类别；所述步骤S800之后，包括：
步骤S910，当所述用药科目与所述临床执业类别匹配时，根据所述预设药品分类表查询所述药品标识对应的用药子科目；
步骤S920，根据所述预存医师执业数据获取所述医师标识对应的行医科室类别；
所述行医科室类别具体为根据病种发病原理不同对临床执业类别进行进一步分类。例如：内科学科分为呼吸内科、消化内科、心血管内科、血液内科等。用药子科目为对用药科目进行进一步的细分。例如对用药科目内科用药进行进一步细分得到的用药子科目为：呼吸内科用药、消化内科用药、心血管内科用药、血液内科用药等。
步骤S930，根据预设用药子科目与医师资格类别映射表判断所述用药子科目与所述行医科室类别是否对应；
步骤S940，当所述用药子科目与所述行医科室类别不对应，设置所述异常标识关联所述医师标识。
本实施例提供的监控方法，能区别医师在相同执业科目内，跨科室执业。
参照图7，在本申请医师违规执业的监控方法的第七实施例中，所述医师信息还包括注册医疗机构标识；所述步骤S910，包括：
步骤S911，当所述用药科目与所述临床执业类别匹配时，根据医师信息获取所述医师标识对应的注册医疗机构标识；
步骤S912，根据预存医疗机构信息查询所述医疗机构标识对应的等级；
由于医疗机构的环境、医疗水平具有差异，所以对医疗机构进行分级管理。越高级别的医疗机构在治疗科室上划分越细致，才需要对医师行医科室类别进行区分。
步骤S913，判断所述等级是否高于预设等级；
步骤S914，当所述等级高于所述预设等级，根据所述预设药品分类表查询所述药品标识对应的用药子科目。
当所述等级低或等于所述预设等级，不做处理。预设等级为本领域技术人员根据实际情况自行设置。通过预先判断医疗机构的等级，再对达到等级要求的医疗机构的医师开具的处方进行判断。从而避免针对所有医疗机构的医师进行行医科室类别与用药子目录对应，减小工作量。
本申请还提供一种监控服务端，包括：
接收模块10，所述接收模块10用于接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
查询模块20，所述查询模块20用于在预设药品分类表中查询与所述药品标识对应的药品类型；所述查询模块20还用于在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
判断模块30，所述判断模块30用于根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
标识模块40，所述标识模块40用于当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
在另一实施例中，所述查询模块20还用于当所述药品类型与所述医师资格类别匹配时，在所述预设药品分类表中查询所述药品标识对应的用药科目；根据所述医师信息确认所述医师标识对应的所述临床执业类别；
所述判断模块30还用于判断所述用药科目与所述临床执业类别是否匹配；
所述标识模块40还用于当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识。
在又一实施例中，所述判断模块30还用于判断所述处方信息的处方类型是否为门诊处方；
所述标识模块还用于40当所述处方类型为门诊处方时，判断所述医师标识对应的医师违规执业。
在又一实施例中，所述查询模块20还用于当所述用药科目与所述临床执业类别匹配时，根据所述处方信息确认患者标识；根据预存病历数据查询与所述患者标识对应的诊疗信息，所述诊疗信息包括患者所患病种信息；根据预设药品与适应症映射表获取与所述药品标识对应的适应症；
所述判断模块30还用于判断是否存在至少一项所述适应症与所述病种信息匹配；
所述标识模块40还用于当所述适应症与所述病种信息均不匹配时，设置所述异常标识关联所述医师标识。
在又一实施例中，所述查询模块20还用于根据预设必要药品列表查询所述病种信息对应的必要药品；
所述判断模块30还用于判断所述处方信息中的各所述药品标识是否与所述必要药品对应；
所述标识模块40还用于当各所述药品标识与所述必要药品均不对应时，设置所述异常标记关联所述医师标识和所述患者标识。
在又一实施例中，所述查询模块20还用于当所述用药科目与所述临床执业类别匹配时，根据所述预设药品分类表查询所述药品标识对应的用药子科目；根据所述预存医师执业数据获取所述医师标识对应的行医科室类别；
所述判断模块30还用于根据预设用药子科目与医师资格类别映射表判断所述用药子科目与所述行医科室类别是否对应；
所述标识模块40还用于当所述用药子科目与所述行医科室类别不对应，设置异常标识关联所述医师标识。
在又一实施例中，所述查询模块30还用于当所述用药科目与所述临床执业类别匹配时，根据所述医师信息获取所述医师标识对应的注册医疗机构标识，根据预存医疗机构信息查询所述医疗机构标识对应的等级；
所述判断模块30判断所述等级是否高于预设等级；
所述查询模块30还用于当所述等级高于所述预设等级，根据所述预设药品分类表查询所述药品标识对应的用药子科目。
请再次结合图1，在一实施例中，监控服务端100包括通信模块10、存储器20及处理器30，其中，所述处理器30分别与所述存储器20和通信模块10连接，所述存储器20上存储有计算机可读指令，所述计算机可读指令被所述处理器20执行时实现如上所述的医师违规执业的监控方法的步骤。
本申请还提出一种计算机可读存储介质，其上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如上述医师违规执业的监控方法的步骤。其中，计算机可读存储介质具体可以为非易失性计算机可读存储介质。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种医师违规执业的监控方法，其中，包括步骤：
    接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
    在预设药品分类表中查询与所述药品标识对应的药品类型；
    在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
    根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
    当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
  2. 如权利要求1所述的医师违规执业的监控方法，其中，所述接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识的步骤包括：
    将所述处方信息向量化，获得向量序列；
    通过预设双向递归神经网络对所述处方信息的字段编码为句子向量矩阵；
    通过预设注意力模型从所述句子向量矩阵中提取上下文向量，根据所述上下文向量将所述句子向量矩阵压缩为句向量，并将所述句向量作为标准化字段；
    根据所述标准化字段提取医师标识和药品标识。
  3. 如权利要求1所述的医师违规执业的监控方法，其中，所述预设药品分类表还包括与所述药品标识对应的用药科目，所述医师信息还包括与所述医师标识对应的临床执业类别；所述根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配的步骤之后，包括：
    当所述药品类型与所述医师资格类别匹配时，在所述预设药品分类表中查询所述药品标识对应的用药科目；
    根据所述医师信息确认所述医师标识对应的所述临床执业类别；
    判断所述用药科目与所述临床执业类别是否匹配；
    当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识。
  4. 如权利要求3所述的医师违规执业的监控方法，其中，所述处方信息还包括处方类型；所述当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识的步骤之后，包括：
    判断所述处方信息的处方类型是否为门诊处方；
    当所述处方类型为门诊处方时，设置所述违规执业标识与所述医师标识关联。
  5. 如权利要求3所述的医师违规执业的监控方法，其中，所述处方信息还包括患者标识；所述判断所述用药科目与所述临床执业类别是否匹配的步骤之后，包括：
    当所述用药科目与所述临床执业类别匹配时，根据所述处方信息确认患者标识；
    根据预存病历数据查询与所述患者标识对应的病种信息；
    根据预设药品与适应症映射表获取与所述药品标识对应的适应症；
    判断是否存在至少一项所述适应症与所述病种信息匹配；
    当所述适应症与所述病种信息均不匹配时，设置所述异常标识关联所述医师标识。
  6. 如权利要求3所述的医师违规执业的监控方法，其中，所述预设药品分类表还包括与所述药品标识对应的用药子科目，所述医师信息还包括行医科室类别；所述处方信息还包括患者标识；所述判断所述用药科目与所述临床执业类别是否匹配的步骤之后，包括：
    当所述用药科目与所述临床执业类别匹配时，根据所述预设药品分类表查询所述药品标识对应的用药子科目；
    根据所述预存医师执业数据获取所述医师标识对应的行医科室类别；
    根据预设用药子科目与医师资格类别映射表判断所述用药子科目与所述行医科室类别是否对应；
    当所述用药子科目与所述行医科室类别不对应时，设置所述异常标识关联所述医师标识。
  7. 如权利要求6所述的医师违规执业的监控方法，其中，所述医师信息还包括注册医疗机构标识；所述当所述用药科目与所述临床执业类别匹配时，根据所述预设药品分类表查询所述药品标识对应的用药子科目的步骤，包括：
    当所述用药科目与所述临床执业类别匹配时，根据所述医师信息获取所述医师标识对应的注册医疗机构标识；
    根据预存医疗机构信息查询所述医疗机构标识对应的等级；
    判断所述等级是否高于预设等级；
    当所述等级高于所述预设等级，根据所述预设药品分类表查询所述药品标识对应的用药子科目。
  8. 如权利要求1所述的医师违规执业的监控方法，其中，所述处方信息还包括患者标识；所述监控方法还包括：
    根据预存病历数据查询与所述患者标识对应的病种信息；
    根据预设必要药品列表查询所述病种信息对应的必要药品；
    判断所述处方信息中的各所述药品标识是否与所述必要药品对应；
    当各所述药品标识与所述必要药品均不对应时，设置所述异常标记关联所述医师标识和所述患者标识。
  9. 一种监控服务端，其中，包括：
    接收模块，所述接收模块用于接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
    查询模块，所述查询模块用于在预设药品分类表中查询与所述药品标识对应的药品类型；所述查询模块还用于在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
    判断模块，所述判断模块用于根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
    标识模块，所述标识模块用于当所述药品类型与所述医师资格类别不匹配时，判断所述医师标识对应的医师违规执业。
  10. 一种监控服务端，其中，所述监控服务端包括：通信模块、存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机可读指令，所述计算机可读指令被所述处理器执行时实现如下步骤。
    接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
    在预设药品分类表中查询与所述药品标识对应的药品类型；
    在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
    根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
    当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
  11. 如权利要求10所述的监控服务端，其中，所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    将所述处方信息向量化，获得向量序列；
    通过预设双向递归神经网络对所述处方信息的字段编码为句子向量矩阵；
    通过预设注意力模型从所述句子向量矩阵中提取上下文向量，根据所述上下文向量将所述句子向量矩阵压缩为句向量，并将所述句向量作为标准化字段；
    根据所述标准化字段提取医师标识和药品标识。
  12. 如权利要求10所述的监控服务端，其中，所述预设药品分类表还包括与所述药品标识对应的用药科目，所述医师信息还包括与所述医师标识对应的临床执业类别；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    当所述药品类型与所述医师资格类别匹配时，在所述预设药品分类表中查询所述药品标识对应的用药科目；
    根据所述医师信息确认所述医师标识对应的所述临床执业类别；
    判断所述用药科目与所述临床执业类别是否匹配；
    当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识。
  13. 如权利要求12所述的医师违规执业的监控方法，其中，所述处方信息还包括处方类型；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    判断所述处方信息的处方类型是否为门诊处方；
    当所述处方类型为门诊处方时，设置所述违规执业标识与所述医师标识关联。
  14. 如权利要求12所述的监控服务端，其中，所述处方信息还包括患者标识；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    当所述用药科目与所述临床执业类别匹配时，根据所述处方信息确认患者标识；
    根据预存病历数据查询与所述患者标识对应的病种信息；
    根据预设药品与适应症映射表获取与所述药品标识对应的适应症；
    判断是否存在至少一项所述适应症与所述病种信息匹配；
    当所述适应症与所述病种信息均不匹配时，设置所述异常标识关联所述医师标识。
  15. 如权利要求12所述的监控服务端，其中，所述预设药品分类表还包括与所述药品标识对应的用药子科目，所述医师信息还包括行医科室类别；所述处方信息还包括患者标识；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    当所述用药科目与所述临床执业类别匹配时，根据所述预设药品分类表查询所述药品标识对应的用药子科目；
    根据所述预存医师执业数据获取所述医师标识对应的行医科室类别；
    根据预设用药子科目与医师资格类别映射表判断所述用药子科目与所述行医科室类别是否对应；
    当所述用药子科目与所述行医科室类别不对应时，设置所述异常标识关联所述医师标识。
  16. 一种计算机可读存储介质，其中，所述计算机可读存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如下步骤。
    接收医疗机构服务器发送的处方信息，所述处方信息包括医师标识和药品标识；
    在预设药品分类表中查询与所述药品标识对应的药品类型；
    在预存医师执业数据中查询与所述医师标识对应的医师信息，所述医师信息包括与所述医师标识对应的医师资格类别；
    根据预设药品类型与医师资格类别映射表判断所述药品类型与所述医师资格类别是否匹配；
    当所述药品类型与所述医师资格类别不匹配时，设置违规执业标识与所述医师标识关联。
  17. 如权利要求16所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    将所述处方信息向量化，获得向量序列；
    通过预设双向递归神经网络对所述处方信息的字段编码为句子向量矩阵；
    通过预设注意力模型从所述句子向量矩阵中提取上下文向量，根据所述上下文向量将所述句子向量矩阵压缩为句向量，并将所述句向量作为标准化字段；
    根据所述标准化字段提取医师标识和药品标识。
  18. 如权利要求16所述的计算机可读存储介质，其中，所述预设药品分类表还包括与所述药品标识对应的用药科目，所述医师信息还包括与所述医师标识对应的临床执业类别；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    当所述药品类型与所述医师资格类别匹配时，在所述预设药品分类表中查询所述药品标识对应的用药科目；
    根据所述医师信息确认所述医师标识对应的所述临床执业类别；
    判断所述用药科目与所述临床执业类别是否匹配；
    当所述用药科目与所述临床执业类别不匹配时，设置异常标识关联所述医师标识。
  19. 如权利要求18所述的计算机可读存储介质，其中，所述处方信息还包括处方类型；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    判断所述处方信息的处方类型是否为门诊处方；
    当所述处方类型为门诊处方时，设置所述违规执业标识与所述医师标识关联。
  20. 如权利要求18所述的计算机可读存储介质，其中，所述处方信息还包括患者标识；所述计算机可读指令被所述处理器执行时，还实现如下步骤：
    当所述用药科目与所述临床执业类别匹配时，根据所述处方信息确认患者标识；
    根据预存病历数据查询与所述患者标识对应的病种信息；
    根据预设药品与适应症映射表获取与所述药品标识对应的适应症；
    判断是否存在至少一项所述适应症与所述病种信息匹配；
    当所述适应症与所述病种信息均不匹配时，设置所述异常标识关联所述医师标识。
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