WO2022110053A1 - 医院门诊智能引导方法、装置和系统 - Google Patents

医院门诊智能引导方法、装置和系统 Download PDF

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Publication number
WO2022110053A1
WO2022110053A1 PCT/CN2020/132408 CN2020132408W WO2022110053A1 WO 2022110053 A1 WO2022110053 A1 WO 2022110053A1 CN 2020132408 W CN2020132408 W CN 2020132408W WO 2022110053 A1 WO2022110053 A1 WO 2022110053A1
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information
hospital
medical
examination
client
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PCT/CN2020/132408
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English (en)
French (fr)
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陈雪
李明
傅玲
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西门子（中国）有限公司
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Priority to PCT/CN2020/132408 priority Critical patent/WO2022110053A1/zh
Priority to CN202080107088.0A priority patent/CN116457891A/zh
Publication of WO2022110053A1 publication Critical patent/WO2022110053A1/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

Definitions

  • the invention relates to the field of industrial automation, in particular to an intelligent guidance method, device and system for outpatient consultation and inspection in a hospital.
  • queuing congestion level For hospital outpatient performance, queuing congestion level, queuing efficiency, queuing patient satisfaction, and patient waiting time are all core KPIs.
  • the problem we need to solve is how to reduce the waiting time of patients queuing to see the outpatient doctor and queuing for examination.
  • the appointment system allows a specific number of patients to perform appointments, allowing patients to allow distribution of their visit times within a window of one hour or half an hour.
  • the reservation system will ease the queuing time to a certain extent, but the degree of relaxation is far from enough.
  • the limitation of this scheme is that only the number of patients visiting the hospital does not increase the utilization rate of medical resources, and it only controls the waiting time of the patient to see the doctor by giving the patient a suitable appointment time for visiting the doctor, and lacks examinations in the hospital waiting time.
  • the appointment schedule lacks effective controls for random visits to patients without appointments.
  • Another solution of the prior art is to reconstruct the step flow of the outpatient department of the hospital, which mainly focuses on the layout design and the construction of the intensive care center.
  • the limitation of this scheme in the layout design is that it only optimizes the walking path of the patient outpatient clinic and reduces the situation of running back and forth, but this is only effective for the newly built hospital.
  • the intensive care center only incorporates some core functional units and procedures, such as stroke center, cancer center, etc.
  • a first aspect of the present invention provides an intelligent guidance method for outpatient examination in a hospital, which includes the following steps: S1, obtaining symptom description information from a client, and analyzing suspected symptoms and required examination and medical resources according to the symptom description information, Wherein, the symptom description information includes body parts and their symptoms; S2, allocate the medical resources and confirm their free time, and send the reservation result information to the client, wherein the medical resources include medical equipment, Consultation room, the appointment result information includes the suspected disease, the required examination and the required time, and the appointment time period; S3, after receiving the confirmation information of the appointment result information from the client, generate the patient's medical treatment schedule information and send it to the client, and lock the medical resources based on the information on the medical treatment schedule, wherein the information on the medical treatment schedule includes the required examination, the required duration, and the time to reach the hospital.
  • step S1 further includes the following steps: S11, obtaining symptom description information from the client, formatting the symptom description information, and retrieving the first symptom in the symptom description information from the ontology library; S12, analyze the disease corresponding to the first symptom and its required examination and medical resources based on the rule base according to the symptom description information, and when the corresponding symptom in the symptom description information cannot be diagnosed, query the hospital calendar to record its Matching of required examinations and medical resources.
  • step S2 also includes the following steps: fetching the required inspection and medical resources for the suspected disease, and performing modeling based on the required inspection and medical resources and the current occupation of medical resources in the hospital as input information, and through The algorithm description of the optimization objective is used to solve the model, to allocate the medical resources and confirm their free time, and to send the appointment result information to the client.
  • the intelligent guidance method for outpatient examination in a hospital further includes: S4 , acquiring status information of the patient after arriving at the hospital, and continuously updating the information on the medical treatment schedule based on the status information and the current status information of medical resources, and sending the information to the client.
  • the step S4 also includes the following steps: obtaining the status information of the patient after arriving at the hospital, combining the status information of the patient and the required inspection, the medical resource occupation and reservation situation, and the random patient and the constraints between the required inspection and resources. Relationships are modeled as input information, and the model is solved through an algorithmic description of the optimization objective to continuously update and send the schedule information to the client.
  • the optimization goal includes reducing the impact on delayed patients through interference management.
  • the management factors include random patients who come to the hospital for treatment without an appointment, and changes in available resources caused by equipment failure or lack of personnel.
  • a second aspect of the present invention provides an intelligent guidance system for outpatient examination in a hospital, comprising: a processor; and a memory coupled to the processor, the memory having instructions stored therein, the instructions being processed
  • the electronic device executes actions, and the actions include: S1: Acquire symptom description information from the client, and analyze the suspected disease and its required examination and medical resources according to the symptom description information, wherein the symptom The description information includes body parts and their symptoms; S2, allocate the medical resource and confirm its free time, and send the appointment result information to the client, wherein the medical resource includes medical equipment and a clinic, and the appointment The result information includes the suspected disease, the required examination and the required time, and the appointment time period; S3, after receiving the confirmation information of the appointment result information from the client, generate the patient's schedule information and send it to the client , and lock medical resources based on the information on the medical treatment schedule, wherein the information on the medical treatment schedule includes the required examination and its required duration and the time to reach the hospital.
  • the action S1 further includes: S11, obtaining symptom description information from the client, formatting the symptom description information, and retrieving the first symptom in the symptom description information from the ontology library; S12, according to the The symptom description information analyzes the disease corresponding to the first symptom and the required examination and medical resources based on the rule base, and when the corresponding symptom in the symptom description information cannot be diagnosed, the hospital records are queried to carry out the symptom and medical treatment. Matching of suspected conditions and their required examinations and medical resources.
  • the action S2 also includes: fetching the required examination and medical resources for the suspected disease, and performing modeling based on the required examination and medical resources and the current occupation of medical resources of the hospital as input information, and optimizing the target algorithm through the algorithm.
  • the model is described to solve the model to allocate the medical resources and confirm their free time, and send the appointment result information to the client.
  • the action further includes: S4 , acquiring the status information of the patient after arriving at the hospital, and continuously updating the medical treatment schedule information based on the status information and the current status information of the medical resources, and sending the information to the client.
  • the action S4 also includes: obtaining the state information after the patient arrives at the hospital, and combining the state information of the patient and the required inspection, the medical resource occupancy reservation situation and the random patient and the constraint relationship between the required inspection and resources as Modeling is performed on the input information, and the model is solved by optimizing the algorithmic description of the objective to continuously update and send the schedule information to the client.
  • the optimization goal includes reducing the impact on delayed patients through interference management.
  • the management factors include random patients who come to the hospital for treatment without an appointment, and changes in available resources caused by equipment failure or lack of personnel.
  • the third aspect of the present invention also provides an intelligent guidance device for hospital outpatient examination, which includes: an analysis device, which acquires symptom description information from a client, and analyzes suspected symptoms and their required examination and medical resources according to the symptom description information , wherein the symptom description information includes body parts and their symptoms and temperature; an allocation device that allocates the medical resources and confirms their free time, and sends the reservation result information to the client, wherein the medical The resources include medical equipment and consultation rooms, and the appointment result information includes suspected symptoms, required examinations and their required time, and an appointment time period; a confirmation device, which generates a confirmation message after receiving the confirmation information of the appointment result information from the client.
  • the patient's consultation schedule information is sent to the client, and medical resources are locked based on the patient's consultation schedule information, wherein the consultation schedule information includes required examinations, their required duration, and hospital arrival time.
  • a fourth aspect of the present invention provides a computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the present invention The method described in the first aspect of the invention.
  • a fifth aspect of the present invention provides a computer-readable medium having stored thereon computer-executable instructions that, when executed, cause at least one processor to perform the method of the first aspect of the present invention.
  • the present invention can optimize the distribution of hospital resources, that is, according to the appointment information, the hospital can know the distribution of patients in the next 24 or 48 hours, and the hospital can arrange the shift of employees, as well as medical materials and equipment accordingly, reduce waste of resources and improve response rate. .
  • the present invention can save the traditional waiting time for the first time, and can perform routine general consultation before going through the outpatient clinic, and the intelligent pre-diagnosis module can replace the suspected disease and the required examination items that the doctor tells the patient.
  • the invention can also shorten the waiting time for examination, pre-diagnosis and planning can arrange the patient's time, and the system can automatically calculate an appropriate time based on medical resource occupancy and queue length, which can greatly save waiting time.
  • the examination waiting time is shortened through real-time scheduling. Each time a patient completes an examination item, the system recommends where the patient should go for examination based on the calculation scheduling algorithm, which always guides the patient to the current minimum queue.
  • FIG. 1 is a schematic structural diagram of a hospital outpatient inspection guidance system according to a specific embodiment of the present invention
  • FIG. 2 is a graph showing the relationship between patients, symptoms, examinations and medical resources of a hospital outpatient examination guidance system according to a specific embodiment of the present invention
  • FIG. 3 is a flow chart of steps of a reservation stage of a hospital outpatient inspection guidance method according to a specific embodiment of the present invention
  • Fig. 4 is the step flow chart of the visiting stage of the hospital outpatient examination guidance method according to a specific embodiment of the present invention.
  • FIG. 5 is a schematic diagram of symptom input and reservation results of a hospital outpatient examination guidance system according to a specific embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a consulting room and its time neutral locking of a hospital outpatient examination guidance system according to a specific embodiment of the present invention
  • FIG. 7 is a schematic diagram of a consultation room and its time slot real-time arrangement of a hospital outpatient inspection guidance system according to a specific embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a consultation room and its time slot real-time arrangement of a hospital outpatient inspection guidance system according to a specific embodiment of the present invention
  • FIG. 9 is an explanatory diagram of the effect of a hospital outpatient inspection guidance system according to a specific embodiment of the present invention.
  • the present invention provides an intelligent guidance mechanism suitable for outpatient examination in a hospital, the core idea of which is to maximize customer value with the least waste of resources. Simply put, produce more value with fewer resources. With the intelligent guidance mechanism provided by the present invention, the problem of the patient's long waiting time for outpatient service and examination can be effectively alleviated.
  • the present invention includes a client and a server.
  • the client can be coupled to the client's smart phone, which is triggered based on an event.
  • the server side is applied to the server or cloud of the hospital.
  • the intelligent guidance system 100 for hospital outpatient examination is connected to the client 200 and the hospital information system 300 respectively.
  • the intelligent guidance system 100 for hospital outpatient examination mainly includes a patient information exchange device 110 , a pre-diagnosis device 120 , a planning device 130 and a hospital information exchange device 140 .
  • the reservation information exchange 111 and the hospital information exchange 112 respectively execute the patient reservation phase and the hospital visit phase.
  • the appointment information exchange 111 includes symptom acquisition means 111a, appointment feedback means 111b, and appointment determination and resource locking means 111c.
  • the pre-diagnosis device 120 includes a KBE (Knowledge-based Engineering) driver 121 and a knowledge base 122, wherein the knowledge base 122 includes an ontology base 122a and a rule base 122b.
  • KBE Knowledge-based Engineering
  • Step S1 is first performed to obtain symptom description information from the client, and analyze the suspected disease and its required examination and medical resources according to the symptom description information, wherein the symptom description information includes body parts and their symptoms, such as body temperature and pain sensation Wait.
  • the step S1 is to perform a pre-diagnosis, infer a suspected disease and its required examination and medical resources, and the step S1 further includes a sub-step S11 and a sub-step S12.
  • the symptom obtaining device 111a obtains the symptom description information from the client 200 and formats the symptom description information, and the KBE driver 121 retrieves the first symptom in the symptom description information from the ontology library 122a;
  • the analysis and query device 121b analyzes the symptom corresponding to the first symptom and the required examination and medical resources based on the rule base 122b according to the symptom description information.
  • the analysis and query device 121b inquires and inquires against the hospital records, finds the same symptoms, symptoms, examination types and medical resources, and then executes the matching of the required examination items and medical resources.
  • the symptom obtaining device 111a obtains symptom description information, such as body part, body temperature, cough, etc., from the client 200 .
  • symptom data are formatted and sent to the pre-diagnostic device 120 .
  • the reservation feedback device 111 b is responsible for formatting the reservation result information from the reservation device 131 and forwarding it to the client 200 .
  • the pre-diagnosis device 120 includes a KBE (Knowledge-based Engineering, knowledge-based engineering) driver 121 and a knowledge base 122, wherein the knowledge base 122 is a graph library, which includes an ontology library 122a and a rule library 122b.
  • Ontology library 122a describes abstract axioms, including types of patients, symptoms, conditions, examinations, resources, etc., and relationships with each other. For example, the interrelationships include patients and symptoms, patients and conditions, resources and examinations, and the like.
  • the ontology library 122a can be edited using tools such as the Protégé tool and saved in the format of a .owl file.
  • the KBE driver 121 retrieves a specific symptom in the symptom description information from the ontology library 122a, regards the ontology library 122a as a "dictionary" query, and performs inference.
  • the rule base 122b describes expert medical knowledge, mainly to establish the relationship between patients, symptoms, symptoms, examinations and resources.
  • the logic of the rule base 122b is that if a patient exhibits a particular symptom, and one of the conditions has the same symptom, then the patient has a probability of having that condition. If the condition requires specific tests, the patient needs to undergo those specific tests. If some resources of the hospital (including wards, personnel or equipment) can support these examinations, the patient needs to occupy these resources to perform the relevant examinations.
  • the entities included in the rule base 122b are disorders, symptoms, patients, examinations, and medical resources.
  • the relationship between symptoms and symptoms is “disease has symptoms”
  • the relationship between patients and symptoms is “patients show symptoms”
  • the relationship between symptoms and inspections is “disease requires inspection”
  • the relationship between medical resources and inspections is “medical resources support Check”
  • the above relations are all asserted axioms, and these asserted axioms are provided to the KBE driver 121 to query.
  • the relationship between the patient and the disease is "patient suffers from the disease”
  • the relationship between the patient and the examination is “the patient receives examination”
  • the relationship between the patient and the resource is “the resource is owned by the patient”. inferred.
  • the knowledge base 122 is used to store a large number of specific facts, such as entities such as patients, symptoms, symptoms, examinations, resources, etc., as well as their relationships with each other, and entity attributes.
  • entity attributes are exemplarily the patient's gender, age, and examination time. .
  • the above data can be obtained from the hospital's electronic medical record (Electronic Medical Record).
  • the knowledge base 122 may be independent of the ontology base 122a and the rule base 122b, and the knowledge base 122 can be replaced to suit any hospital's IT environment.
  • the rule base 122b only covers some basic and common relationships of symptoms, conditions and examinations.
  • the knowledge base 122 includes a number of patient histories, which can be used as a query source, providing some conditions that cannot be covered by the rules.
  • the KBE driver 121 is used to drive the data transfer and analysis process, which supports the execution of the above process for multiple patients at the same time.
  • the KBE driver 121 includes an instantiation device 121a, an analysis query device 121b, and an output device 121c.
  • the instantiating means 121a retrieves the symptoms of the patient from the ontology library 122a, that is, a single symptom is generated under the category of symptoms, and single resource information is obtained from the resource category.
  • the analysis and query device 121b adopts an inference engine (eg Drools) or an inference engine (eg Pellet, Hermit, etc.) to drive the rules to take effect: infer the symptoms of each patient, the examination steps required by each patient, and the resources required by each patient . If some patients show some symptoms that cannot be diagnosed by the analysis query device 121b, the hospital history records will be queried in the atlas database 122c (for example, whether there are patients in the hospital history records with the same condition) and based on the hospital history records Diagnosis as feedback, where the diagnosis includes the condition and its required investigations.
  • the output device 121c formats the diagnosed symptoms, examinations, and required medical resources, and outputs them to the planning device 130 for subsequent processing.
  • step S2 is performed, the medical resources are allocated and their free time is confirmed, and the appointment result information is sent to the client, wherein the medical resources include medical equipment and clinics, and the appointment result information includes suspected diseases, The required examinations and when they will be required, and the time slots for which an appointment can be made.
  • Step S2 is the execution reservation stage.
  • the step S2 further includes the following steps: fetching the required examination and medical resources for the suspected disease, and performing modeling based on the required examination and medical resources and the current occupation of medical resources of the hospital as input information, and optimizing the target
  • the algorithm description is used to solve the model to allocate the medical resources and confirm their free time, and send the appointment result information to the client.
  • the planning device 130 includes a reservation device 131 and a real-time scheduling device 132 .
  • the logic of the planning device 130 is to allocate time slots for medical resources to each patient.
  • the planning device 130 may use a computing module or a discrete event system (DES, Discrete Event System), which will describe the constraint relationship between the patient and medical resources in each examination step, and then optimize the target.
  • DES Discrete Event System
  • the planning device 130 can adopt many intelligent optimization algorithms, such as GA and PSO. Also, often these algorithms require refinement to increase solution speed and avoid local optima.
  • the reservation device 131 is used for the patient to perform reservation, and includes a modeling device 131a and a solving device 131b.
  • the modeling device 131a retrieves the patient's diagnostic information (mainly the examination and the time spent for each examination and the required medical resources) from the pre-diagnosis device 120 and obtains the current hospital resource occupancy as input information to perform modeling, and then
  • the solving device 131b solves the model through the algorithm description of the optimization target, and then modifies the appointment result on the patient's mobile phone APP based on this, informs the patient of an appropriate time to come to the hospital, and estimates the time slot for each examination of the patient.
  • step S3 is performed, after receiving the confirmation information of the appointment result information from the client, generating the patient's consultation schedule information and sending it to the client, and locking the medical resources based on the consultation schedule information, wherein the consultation Schedule information includes required tests and how long they will take, as well as arrival time at the hospital.
  • the reservation confirmation and resource locking device 111c starts to lock the medical resources after receiving the confirmation information of the reservation result information from the patient.
  • the appointment result information includes the suspected disease, the examination that the patient needs to perform, the estimated time of each examination, and the recommended time to arrive at the hospital, etc. If the reservation determination and resource locking device 111c determines that the reservation result information is executable based on the current state of the hospital (database), if the patient accepts the reservation result information, the mobile phone APP acting as the client 200 sends confirmation information to the server to lock the doctor and equipment and other resources.
  • the hospital information exchange 112 includes a registration device 112a, a payment device 112b, a patient status retrieval device 112c, and a schedule update device 112d. Specifically, when a patient arrives at the hospital, he first needs to register on the mobile APP based on the reservation result information and pay the required examination fee. Among them, the hospital information system 300 is connected with the registration device 112a and the payment device 112b. Then, the status information collected by the patient status retrieval means 112c is "payment completed, ready for examination".
  • the schedule update device 112d will obtain the latest schedule information from the real-time scheduling device 132, format the schedule information and then send it to the client's mobile phone APP, where the information update can be implemented at fixed time intervals or event-driven , eg the patient completes each procedure and triggers the next event.
  • the present invention also includes step S4, acquiring the status information of the patient after arriving at the hospital, and continuously updating the medical treatment schedule information based on the status information and the current status information of the medical resources and sending the information to the client.
  • the real-time scheduling means 132 is executed after the patient arrives at the hospital, which combines the real-time status of hospital resources and random patients who come to the hospital without an appointment to model the job scheduling.
  • random patients such as emergency patients, etc.
  • the failure of hospital equipment or the failure of doctors to arrive at the office can be regarded as a medical emergency in the industrial manufacturing process.
  • Machine failures or workers not arriving on the line are typical disturbance management issues in scheduling.
  • the nurse in the triage room will help such patients to register in the system, and obtain examination by inputting their symptoms into the pre-diagnosis device 120. and resources, and then put the above information into the query library of the real-time scheduling device 132.
  • Random patients who come to the hospital without an appointment may affect the resources and schedule of patients later in the time slot. Therefore, reducing the impact on the appointment patient needs to be considered when establishing the optimization objective, for example, considering the delay of appointment patient consultation after random patients who come to the hospital without appointment. In practical applications, we recommend strictly controlling the number of random patients who come to the hospital without an appointment, and encourage patients to use the appointment system to make appointments.
  • the step S4 also includes the following steps: acquiring the status information of the patient after arriving at the hospital, combining the status information of the patient and the required examinations, medical resource occupancy and reservation status, and random patients and the constraints between the required examinations and resources Relationships are modeled as input information, and the model is solved through an algorithmic description of the optimization objective to continuously update and send the schedule information to the client.
  • the optimization goal includes reducing the impact on delayed patients by managing factors.
  • the management factors include random patients who come to the hospital for treatment without an appointment, and changes in available resources caused by equipment failure or lack of personnel.
  • the real-time scheduling device 132 includes a modeling device 132a and a solving device 132b, and its workflow is as follows: First, the modeling device 132a combines patient status, examination, medical resource occupancy and reservation situation and constraints among random patients, examinations and resources relationship and an algorithm that describes the optimization objective that needs to include managing factors to reduce the impact on delayed patients. Among them, management factors include random patients who come to the hospital without an appointment, equipment failure or lack of personnel. The patient status mainly includes which step of the examination the patient has performed. Then, the solving means 132b solves the model. Finally, the new appointment result will be updated to the patient's mobile APP, informing the patient which is the next examination.
  • the hospital information exchange device 140 includes an IT system interface 141 and a sensing data acquisition device 142 .
  • the IT system interface 141 retrieves the shift information of doctors, nurses and other personnel from the hospital IT system and sends it to the planning device 130 as resource information.
  • an electronic tracker such as a smart bracelet.
  • the patient wears the electronic tracker, he can obtain the patient's location through the electronic tracker, where the patient is in the system and what resources the patient is occupying.
  • the registration is unlocked, otherwise, the registration is locked. After unlocking the registration, the patient can perform registration and payment on the mobile APP.
  • the sensing data acquisition device 142 is used to acquire the patient's status (location or geographic location) and update the patient's schedule through the electronic tracker, then acquire the medical resource occupancy status, acquire the unscheduled patient visits, and acquire the available medical resources for overall planning. arrange. If a sudden failure of a device is considered incomplete, it needs to be removed from the resource pool of the planning device 130, otherwise, real-time scheduling is performed. Then, the patient will check and visit according to the real-time schedule and update the status even if the visit is over, otherwise, the step of obtaining the status will be re-executed.
  • patient C before arriving at the hospital, patient C performs an appointment on the APP of the smartphone serving as the client 200, assuming that his symptom input includes "fever, vomiting, diarrhea".
  • the symptom acquisition device 111 a acquires the symptom data from the client 200 , and formats and transmits it to the pre-diagnosis device 120 .
  • the reservation feedback device 111b is responsible for formatting the reservation result information from the reservation device 131 and forwarding it to the client 200, wherein the reservation result information includes “stomach flu: routine urine examination, routine blood examination, X-ray examination” and recommendations to The time of the hospital "arrive time at 1:30 pm on October 22, 2020, examination time: 1 hour and 20 minutes”.
  • the mobile phone APP acting as the client 200 sends confirmation information to the server to lock the "check”. Room 3, Exam Room 4 and Exam Room n" and their time slots.
  • a hospital has an examination room 1 , an examination room 2 , an examination room 3 , an examination room 4 . . . an examination room n.
  • the current date is October 22, 2020, and the next day is October 23.
  • the hospital including patient A, patient B and patient C will make an appointment at 1:00 p.m. on October 22.
  • patient C starts the relevant examination in examination room 3 from t1, then does the relevant examination in examination room 4, and finally ends the relevant examination in examination room n at time t2, so the examination of patient C continues.
  • Time t 1 hour 20 minutes.
  • patient A needs to do relevant examinations in examination room 1, examination room 2 and examination room 3 respectively
  • patient B needs to do relevant examinations in examination room 2, examination room 4 and examination room n respectively.
  • the real-time scheduling device 132 retrieves the hospital data and obtains that patient B has cancelled the relevant appointment , while Patient D sought medical attention without an appointment due to an emergency.
  • Patient B is removed from the schedule and Patient D is scheduled.
  • patient A will have relevant examinations in examination room 1
  • patient D will have relevant examinations in examination room 2
  • patient A will have relevant examinations in examination room 3
  • patients D and C will have relevant examinations in examination room 3.
  • the relevant examination will be carried out in examination room 1 and patient C will be carried out in examination room n.
  • Patient C starts performing the relevant examination in examination room C at time t1 and completes the examination at 2:30 pm, at which time patient C needs to be removed from the arrangement of examination room 2 and examination room 4 shown in FIG. 6, and Patient D needs to be arranged in examination room 2 and examination room 4 next.
  • the updated real-time scheduling of the consultation room and its time slots is shown in FIG. 8 .
  • patient C ends the relevant examination in examination room 3, after which patient C goes to examination room n for examination according to the guidelines, and finally conducts relevant examinations in examination room 4, and ends all examinations at time t4 .
  • Patient C starts the real-time scheduling at time t3 and ends the examination at time t4, which takes t'.
  • patient A is examined in the examination room 3 and the examination room 1 after the patient C, respectively.
  • Patient D is examined in examination room 2 after patient A, then in examination room D and before patient C.
  • FIG. 9 is an explanatory diagram of the effect of a hospital outpatient inspection guidance system according to a specific embodiment of the present invention.
  • the upper figure is the time consumption of consultation in the prior art
  • the lower figure is the consumption of consultation time of the present invention
  • the time unit is minutes.
  • the registration (registration) in the hospital's routine procedures will take 10 minutes
  • the first consultation (including waiting in line) will take 30 minutes
  • the payment will take 10 minutes.
  • Each examination takes 15 minutes, 20 minutes, and 35 minutes respectively
  • the second consultation takes 10 minutes (including waiting in line)
  • the traditional process takes a total of 130 minutes.
  • the present invention has obvious advantages. If a patient comes to the hospital for the same disease and needs to do the same examination, the present invention saves a lot of time, at least 50%, compared with the prior art.
  • a second aspect of the present invention provides an intelligent guidance system for outpatient examination in a hospital, comprising: a processor; and a memory coupled to the processor, the memory having instructions stored therein, the instructions being processed
  • the electronic device performs actions, and the actions include: S1, obtaining symptom description information from the client, and analyzing the suspected disease and its required examination and medical resources according to the symptom description information, wherein the symptom
  • the description information includes body parts and their symptoms; S2, allocate the medical resource and confirm its free time, and send the appointment result information to the client, wherein the medical resource includes medical equipment and a clinic, and the appointment
  • the result information includes the suspected disease, the required examination and the required time, and the appointment time period; S3, after receiving the confirmation information of the appointment result information from the client, generate the patient's schedule information and send it to the client , and lock medical resources based on the information on the medical treatment schedule, wherein the information on the medical treatment schedule includes the required examination and its required duration and the time to reach the hospital.
  • the action S1 further includes: S11, obtaining symptom description information from the client, formatting the symptom description information, and retrieving the first symptom in the symptom description information from the ontology library; S12, according to the The symptom description information analyzes the disease corresponding to the first symptom and the required examination and medical resources based on the rule base, and when the corresponding symptom in the symptom description information cannot be diagnosed, the hospital records are queried to carry out the symptom and medical treatment. Matching of suspected conditions and their required examinations and medical resources.
  • the action S2 also includes: fetching the required examination and medical resources for the suspected disease, and performing modeling based on the required examination and medical resources and the current occupation of medical resources of the hospital as input information, and optimizing the target algorithm through the algorithm.
  • the model is described to solve the model to allocate the medical resources and confirm their free time, and send the appointment result information to the client.
  • the action further includes: S4 , acquiring the status information of the patient after arriving at the hospital, and continuously updating the medical treatment schedule information based on the status information and the current status information of the medical resources, and sending the information to the client.
  • the action S4 also includes: obtaining the state information after the patient arrives at the hospital, and combining the state information of the patient and the required inspection, the medical resource occupancy reservation situation and the random patient and the constraint relationship between the required inspection and resources as Modeling is performed on the input information, and the model is solved by optimizing the algorithmic description of the objective to continuously update and send the schedule information to the client.
  • the optimization goal includes reducing the impact on delayed patients through interference management.
  • the management factors include random patients who come to the hospital for treatment without an appointment, and changes in available resources caused by equipment failure or lack of personnel.
  • the third aspect of the present invention also provides an intelligent guidance device for hospital outpatient examination, which includes: an analysis device, which acquires symptom description information from a client, and analyzes suspected symptoms and their required examination and medical resources according to the symptom description information , wherein the symptom description information includes body parts and their symptoms and temperature; an allocation device that allocates the medical resources and confirms their free time, and sends the reservation result information to the client, wherein the medical The resources include medical equipment and consultation rooms, and the appointment result information includes suspected symptoms, required examinations and their required time, and an appointment time period; a confirmation device, which generates a confirmation message after receiving the confirmation information of the appointment result information from the client.
  • the patient's consultation schedule information is sent to the client, and medical resources are locked based on the patient's consultation schedule information, wherein the consultation schedule information includes required examinations, their required duration, and hospital arrival time.
  • a fourth aspect of the present invention provides a computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the present invention The method described in the first aspect of the invention.
  • a fifth aspect of the present invention provides a computer-readable medium having stored thereon computer-executable instructions that, when executed, cause at least one processor to perform the method of the first aspect of the present invention.
  • the present invention can optimize the distribution of hospital resources, that is, according to the reservation information, the hospital can know the distribution of patients in the next 24 or 48 hours, and the hospital can arrange staff shifts, as well as medical materials and equipment accordingly, reducing waste of resources and response time.
  • the present invention can reduce the time of traditional queuing for diagnosis and waiting for examination, routine general examination can be performed before arriving at the hospital, and the intelligent pre-diagnosis module can replace the suspected disease and required examination items told by the doctor to the patient, thereby eliminating the need for the first inspection. Second waiting time and consultation time.
  • the invention can also save waiting time, pre-diagnosis and planning can arrange the patient's time, and the system can automatically calculate an appropriate time based on medical resource occupancy and queue length, which can largely save waiting time.
  • the examination waiting time is shortened through real-time scheduling. Each time a patient completes an examination item, the system recommends where the patient should go for examination based on the calculation scheduling algorithm, which always guides the patient to the current minimum queue.

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Abstract

医院门诊检查的智能引导方法、装置和系统，其中，包括如下步骤：S1，从客户端（200）获取症状描述信息，并根据症状描述信息分析疑似病症及其所需检查和医疗资源，其中，症状描述信息包括身体部位及其症状以及温度；S2，分配医疗资源并确认其空档时间，并将预约结果信息发送给客户端（200），其中，医疗资源包括医疗设备、诊室，预约结果信息包括疑似病症、所需检查及其所需时间以及达到医院时间；S3，在收到客户端（200）对预约结果信息的确认信息以后，产生病人的就诊时间表信息并发送至客户端（200），并基于就诊时间表信息锁定医疗资源。能有效缩短病人在医院候诊以及等待检查的时间。

Description

医院门诊智能引导方法、装置和系统 技术领域
本发明涉及工业自动化领域，尤其涉及医院门诊看诊和检查的智能引导方法、装置和系统。
背景技术
对于医院门诊表现来说，排队拥挤水平、排队效率、排队病人的满意度以及病人等候时间都是核心KPI。
对于国内大部分医院都是十分拥挤的，病人并不满意于长时间的门诊等候。根据复旦大学在2017年所作的公开研究报告中显示，高达一半以上的病人在上海三级人民医院中门诊等候时间超过2个小时。在所有的门诊手续中，最长等候主要发生在病人排队见门诊医生的过程以及排队做检查的过程。
因此，我们需要解决的问题是如何减少病人排队见门诊医生的过程以及排队做检查的过程的等候时间。
为了减少病人等候时间和拥挤排队时间，许多医院最常采用的方法是使用预约系统。其中，预约系统允许特定数量的病人执行预约，允许病人在一个小时或半个小时的窗口时间内允许分布他们的到访时间。预约系统会一定程度缓和排队时间，但缓和程度远远不够。这种方案的局限性在于仅仅病人到访医院的数量，而不增加医疗资源利用率，并且只是通过给病人一个合适的预约到访时间来控制病人见医生的等候时间，而缺少在医院做检查的等候时间。此外，预约方案还缺乏对没有预约而随机到访病人的有效控制。
现有技术的另一个解决方案是重构医院门诊的步骤流程，其主要集中于布局设计和重病中心构建。这种方案在布局设计的局限性在于仅优化了病人门诊的步行路径，减少了来回奔波的情况，但这只是对于新建医院有效。此外，在这种方案中，重病中心只是合并了一些核心功能单元和程序，例如卒中中心、癌症中心等。
发明内容
本发明第一方面提供了医院门诊检查的智能引导方法，其中，包括如下步骤：S1，从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状；S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
进一步地，其还所述步骤S1还包括如下步骤：S11，从客户端获取症状描述信息并将该症状描述信息格式化，并从本体库中调取所述症状描述信息中的第一症状；S12，根据所述症状描述信息基于规则库分析该第一症状对应的病症及其所需检查和医疗资源，当所述症状描述信息中对应的症状并不能与诊断时，则查询医院历纪录其所需检查和医疗资源的匹配。
进一步地，其还所述步骤S2还包括如下步骤：调取疑似病症所需检查和医疗资源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
进一步地，医院门诊检查的智能引导方法还包括：S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
进一步地，所述步骤S4还包括如下步骤：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
进一步地，所述优化目标包括通过干扰管理来减少对延迟病人的影响。其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
本发明第二方面提供了一种医院门诊检查的智能引导系统，其中，包括：处理器；以及与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括： S1，从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状；S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
进一步地，所述动作S1还包括：S11，从客户端获取症状描述信息并将该症状描述信息格式化，并从本体库中调取所述症状描述信息中的第一症状；S12，根据所述症状描述信息基于规则库分析该第一症状对应的病症及其所需检查和医疗资源，当所述症状描述信息中对应的症状并不能与诊断时，则查询医院历纪录进行所述症状和疑似病症及其所需检查和医疗资源的匹配。
进一步地，所述动作S2还包括：调取疑似病症所需检查和医疗资源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
进一步地，所述动作还包括：S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
进一步地，所述动作S4还包括：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
进一步地，所述优化目标包括通过干扰管理来减少对延迟病人的影响。其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
本发明第三方面还提供了医院门诊检查的智能引导装置，其中，包括：分析装置，其从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状以及温度；分配装置，其分配所述医疗资源并确认其空档时间，并将 预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；确认装置，其在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
本发明第四方面提供了计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明能够优化医院资源分布，即根据预约信息医院能够获知病人在比如接下来的24或48小时的分布，医院可以据此安排员工的换班，以及医疗材料和设备，减少浪费资源和提高响应速率。并且，本发明能够省去传统第一次候诊的时间，通过门诊以前就可以执行常规通用的问诊，智能预诊断模块能够代替医生告诉病人的疑似疾病和所需检查项目。本发明还能缩短等待检查的时间，预诊断和计划能够安排病人的时间，系统能够基于医疗资源占用情况和队列长度自动计算出一个合适的时间,可以很大程度上节省等待的时间。通过实时调度缩短检查等待时间，病人每次完成一个检验项目，系统基于计算调度算法建议病人去哪里下检查，其总是引导病人到当前最少排队队列。
附图说明
图1是根据本发明一个具体实施例的医院门诊检查引导系统的结构示意图；
图2是根据本发明一个具体实施例的医院门诊检查引导系统的病人、症状、检查和医疗资源相互关系图谱；
图3是根据本发明一个具体实施例的医院门诊检查引导方法的预约阶段的步骤流程图；
图4是根据本发明一个具体实施例的医院门诊检查引导方法的就诊阶段 的步骤流程图；
图5是根据本发明一个具体实施例的医院门诊检查引导系统的症状输入和预约结果的示意图；
图6是根据本发明一个具体实施例的医院门诊检查引导系统的诊室及其时间空挡锁定的示意图；
图7是根据本发明一个具体实施例的医院门诊检查引导系统的诊室及其时间空挡实时安排的示意图；
图8是根据本发明一个具体实施例的医院门诊检查引导系统的诊室及其时间空挡实时安排的示意图；
图9是根据本发明一个具体实施例的医院门诊检查引导系统的效果说明图。
具体实施方式
以下结合附图，对本发明的具体实施方式进行说明。
本发明提供了一种适用于医院门诊检查的智能引导机制，其核心思想在于在最少的资源浪费情况下最大化客户价值。简单地说，用更少的资源产生更大的价值。利用本发明提供的智能引导机制，病人的门诊和检查等候时间过长的问题能够有效缓解。
其中，本发明包括客户端和服务器端。其中，客户端可耦合于客户的智能手机，其是基于事件触发的。其中，服务器端应用于医院的服务器或者云端。
如图1、图3和图4所示，医院门诊检查的智能引导系统100分别连接于客户端200和医院信息系统300。医院门诊检查的智能引导系统100主要包括病人信息交换装置110、预诊断装置120、计划装置130和医院信息交换装置140。其中，预约信息交换器111和医院信息交换器112分别执行病人预约阶段和到医院就诊阶段。预约信息交换器111包括症状获取装置111a、预约反馈装置111b和预约确定和资源锁定装置111c。预先诊断装置120包括一个KBE(Knowledge-based Engineering，基于知识的工程)驱动121和一个知识库122，其中，知识库122包括一个本体库122a和一个规则库122b。
在本发明第一方面提供的医院门诊检查的智能引导方法中：
首先执行步骤S1，从客户端获取症状描述信息，并根据所述症状描述信 息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状，如体温和痛感等。其中，所述步骤S1为执行预诊断，推断疑似病症及其所需检查和医疗资源，步骤S1还进一步地包括子步骤S11和子步骤S12。
在子步骤S11中，症状获取装置111a从客户端200获取症状描述信息并将该症状描述信息格式化，KBE驱动121从本体库122a中调取所述症状描述信息中的第一症状；
在子步骤S12中，分析查询装置121b根据所述症状描述信息基于规则库122b分析该第一症状对应的病症及其所需检查和医疗资源，当所述症状描述信息中对应的症状并不能与诊断时，则分析查询装置121b查询查询对照医院历纪录，找到相同症状，及其病症、检查类型和医疗资源，进而执行其所需检查项目和医疗资源的匹配。
具体地，在病人到达医院以前，需要在作为客户端200的智能手机APP中输入症状，症状获取装置111a从客户端200获取症状描述信息，例如身体部位、身体温度、咳嗽等。这些症状数据会被格式化并发送给预诊断装置120。预约反馈装置111b负责格式化从预约装置131来的预约结果信息并将其转发至客户端200。
预先诊断装置120包括一个KBE(Knowledge-based Engineering，基于知识的工程)驱动121和一个知识库122，其中，知识库122是一个图谱库，其包括一个本体库122a和一个规则库122b。本体库122a描述了抽象公理，包括病人、症状、病症、检查和资源等以及相互之间的关系的类型。例如，所述相互之间的关系包括病人和症状，病人和病症，资源和检查等。特别地，本体库122a能够使用Protégé工具等工具进行编辑，并且以.owl文件的格式保存。KBE驱动121从本体库122a中调取所述症状描述信息中的特定症状，将本体库122a视为“字典”查询并执行推断。
其中，规则库122b描述了专家医疗知识，主要为了建立病人、症状、病症、检查和资源之间的关系。规则库122b的逻辑是如果病人表现出了特定症状，其中一个病症具有相同症状，则病人有患这种病症的可能性。如果这种病症需要一些特定检查，则该病人需要接受这些特定检查。如果医院的一些资源(包括病房、人员或者设备)能够支持这些检查，则该病人则需要占有这些资源以执行相关检查。如图2所示，规则库122b中包括实体为病症、症 状、病人、检查和医疗资源。其中，病症和症状的关系是“病症具有症状”，病人和症状的关系是“病人表现出症状”，病症和检查的关系是“病症需要检查”，医疗资源和检查的关系是“医疗资源支持检查”，上述这些关系都是既定公理(asserted axioms)，这些断言公理都是提供给KBE驱动121查询的。其中，病人和病症的关系是“病人患有病症”，病人和检查的关系是“病人接受检查”，病人和资源的关系是“病人占有资源”，上述这些关系都是分析查询装置121b提供给推断出来的。
其中，知识库122用于保存大量具体事实，例如病人、症状、病症、检查、资源等实体以及相互之间的关系、实体属性，所述实体属性示例性地为病人性别、年龄、检查花费时间。上述数据都能够从医院的电子病历(Electronic Medical Record)获取。可选地，知识库122可独立于本体库122a和规则库122b，知识库122能够被替换以适应于任何医院的IT环境。规则库122b仅仅只覆盖症状、病症和检查的一些基本和共同关系。其中，知识库122包括许多病人的历史纪录，其能用于充当查询源，提供一些规则不能覆盖的病症。
其中，KBE驱动121用于驱动数据转移和分析过程，其支持同时针对多个病人执行上述过程。具体地，KBE驱动121包括实例化装置121a、分析查询装置121b和输出装置121c。实例化装置121a从本体库122a调取病人的症状，即在症状这个类别下产生单个症状，并且从资源类别下获取单个资源信息。分析查询装置121b采用一个推理引擎(例如Drools)或者一个推理机(例如Pellet、Hermit等)来驱动规则生效：推断每个病人的病症，每个病人所需的检查步骤以及每个病人需要的资源。如果一些病人表现出一些分析查询装置121b不能诊断的症状，则会在图谱数据库122c查询医院历史纪录(例如，会查询医院历史纪录中是否有病人具有同样的情况)并基于所述医院历史纪录的诊断作为反馈，其中，所述诊断包括病症及其所需检查。输出装置121c格式化诊断的病症、检查以及所需医疗资源，并输出给计划装置130做后续处理。
然后执行步骤S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段。步骤S2即为执行预约阶段。
具体地，所述步骤S2还包括如下步骤：调取疑似病症所需检查和医疗资 源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
具体地，所述计划装置130包括一个预约装置131和实时安排装置132。所述计划装置130的逻辑在于为每个病人分配医疗资源的时间空档。计划装置130可以采用计算模块或者离散事件系统(DES，Discrete Event System)，其会描述在每个检查步骤中病人和医疗资源的约束关系，然后优化目标。为了达到优化的目的，所述计划装置130可以采用许多智能优化算法，例如GA和PSO。并且，通常这些算法需要改进来增加求解速度并避免局部最优。
其中，预约装置131用于病人执行预约，其包括一个建模装置131a和求解装置131b。首先，建模装置131a从预先诊断装置120调取病人的诊断信息(主要是检查及每个检查的花费时间和所需医疗资源)和获取目前医院资源占用情况作为输入信息来执行建模，然后求解装置131b通过优化目标的算法描述来求解该模型，接着基于此修改病人手机APP上的预约结果，并告知病人来医院的一个合适时间，同时预估该病人每个检查的时间空档。
最后执行步骤S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时间以及到达医院时间。
具体地，病人在客户端200接收预约结果信息后，如果接受则表示预约成功，如果不接受则表示预约不成功。预约确定和资源锁定装置111c在收到病人对预约结果信息的确认信息后则开始执行锁定医疗资源。
其中，预约结果信息包括疑似疾病、病人需要执行的检查，每个检查的预估时间以及建议到达医院的时间等。预约确定和资源锁定装置111c如果基于医院目前的状况(数据库)判断预约结果信息是可执行的，如果病人接受预约结果信息则通过充当客户端200的手机APP发送确认信息给服务器来锁定医生、设备等资源。
具体地，医院信息交换器112包括注册装置112a、支付装置112b、病人状态检索装置112c和时间表更新装置112d。具体地，当病人到达医院其首先需要在手机APP上基于预约结果信息注册并且支付所需检查费用。其中，医院信息系统300连接有注册装置112a和支付装置112b。然后，病人状态检索 装置112c收集的状态信息是“支付完成，准备检查”。时间表更新装置112d会从实时调度装置132获取最新的时间表信息，并将所述时间表信息格式化然后发送给客户手机APP，其中信息更新可以以固定时间间隔实现，或者以事件驱动来实现，例如病人完成每个程序则触发下个事件。
本发明还包括步骤S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
具体地，如图1和图4所示，实时安排装置132是在病人达到医院后执行的，其结合医院资源的实时状态以及未预约就来医院就诊的随机病人来为作业安排建模。其中，未预约就来医院就诊的随机病人(例如急诊病人等)被视为在工业制造过程中的紧急插单，并且医院设备故障或者医生并未到达办公室能被视为在工业制造过程中的机器故障或者工人未到达生产线，这些都是在排程中的典型干扰管理问题。对于未预约就来医院就诊的随机病人来说，他们当然并不适用预约的手机APP，分诊室的护士会帮这样的病人在系统中登记，并且通过输入他们的症状到预先诊断装置120获取检查和资源，然后将上述信息放到实时安排装置132的查询库。未预约就来医院就诊的随机病人可能会影响时间空档稍后的病人的资源和时间安排。因此，当建立优化目标时需要考虑减少对预约病人的影响，例如，考虑在未预约就来医院就诊的随机病人之后的预约病人问诊的延后。在实际应用中，我们建议严格控制未预约就来医院就诊的随机病人数量，并鼓励病人们利用预约系统来预约就诊。
具体地，所述步骤S4还包括如下步骤：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
其中，所述优化目标包括通过管理因素来减少对延迟病人的影响。其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
其中，实时安排装置132包括一个建模装置132a和求解装置132b，其工作流程是：首先，建模装置132a结合病人状态、检查、医疗资源占用预约情况和随机病人、检查和资源之间的约束关系以及描述优化目标的算法来建模， 优化目标需要包括通过管理因素来减少对延迟病人的影响。其中，管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位等。病人状态主要包括病人进行到哪一步检查。然后，求解装置132b对该模型求解。最后，新的预约结果会更新到病人的手机APP，告知病人哪个是下个检查。
其中，医院信息交换装置140包括IT系统接口141和感应数据获取装置142。具体地，IT系统接口141从医院IT系统检索医生、护士和其他人员的换班信息并发送给计划装置130作为资源信息。病人按照预约时间到达医院时会收到一个电子跟踪器(例如智能手环)，病人佩戴电子跟踪器后，可以通过电子跟踪器获取病人位置，系统病人在哪里排队以及病人在占用哪些资源。当判断病人在医院时则解锁注册，否则就锁定注册。解锁注册后病人就可以在手机APP上执行注册和支付。感应数据获取装置142用于通过电子跟踪器获取病人状态(所在环节或地理位置)以及更新病人的日程安排，然后获取医疗资源占有状态和获取未预约病人来诊，以及获取可用的医疗资源统筹进行安排。如果一个设备突然故障视为未完成，则需要从计划装置130的资源池中移除，否则就进行实时安排。接着病人按照实时安排检查就诊并且更新状态就算就诊结束，否则就重新执行获取状态步骤。
根据本发明一个优选实施例，如图5所示，在到达医院以前病人C在充当客户端200的智能手机的APP上执行预约，假设其症状输入包括“发烧、呕吐、腹泻”。症状获取装置111a从客户端200获取该症状数据，并格式化发送给预诊断装置120。预约反馈装置111b负责格式化从预约装置131来的预约结果信息并将其转发至客户端200，其中，预约结果信息包括“肠胃感冒：尿常规检查、血常规检查、X光检查”以及建议到医院的时间“来院时间2020年10月22日下午1：30，检查时间：1个小时20分钟”。预约确定和资源锁定装置111c如果基于医院目前的状况(数据库)判断上述预约结果信息是可执行的，病人C接受预约结果信息则通过充当客户端200的手机APP发送确认信息给服务器来锁定“检查室3，检查室4和检查室n”及其时间空档。
如图6所示，假设医院具有检查室1、检查室2、检查室3、检查室4……检查室n。当前日期是2020年10月22日，下一天是10月23日。根据预约情况，在10月22日下午1点开始医院包括病人A、病人B和病人C预约就诊。如图所示，按照预约情况，病人C从t1开始在检查室3做相关检查，然后在检查室4做相关检查，最后在时间t2结束在检查室n的相关检查，因此 病人C的检查持续时间为t：1个小时20分钟。此外，病人A需要分别在检查室1、检查室2和检查室3做相关检查，病人B需要分别在检查室2、检查室4和检查室n做相关检查。
然而，当病人C到达医院以后完成了注册(特别地为挂号)和支付并且顺利按照预定时间在检查室3完成了相关检查，实时安排装置132检索了医院数据并获取了病人B取消了相关预约，而病人D由于紧急情况在未经预约情况下就医。当重新计算时间表时，则病人B则从安排中移除，并安排了病人D。如图7所示，在将要安排的列表上，病人A将在检查室1进行相关检查，病人D将在检查室2进行相关检查，病人A将在检查室3进行相关检查，病人D和C将在检查室1进行相关检查，病人C将在检查室n进行相关检查。病人C在t1时间开始在检查室C执行相关检查，并在下午2点30完成该检查，此时需要将病人C从图6所示的检查室2和检查室4的安排中移除，并且需要将病人D接下来安排在检查室2和检查室4。
在执行实时安排以后，更新后的诊室及其时间空挡实时安排如图8所示。其中，从t3即下午2点30开始，病人C结束在检查室3的相关检查，此后病人C根据指引到检查室n接受检查，最后在检查室4进行相关检查，并且在时间t4结束所有检查。病人C从时间t3开始进行实时安排以后在时间t4结束检查，耗时t’。此外，病人A分别在病人C以后在检查室3接受检查，在检查室1接受检查。病人D在病人A以后再检查室2接受检查，然后在检查室D并且在病人C以前接受检查。
图9是根据本发明一个具体实施例的医院门诊检查引导系统的效果说明图。如图9所示，上图是现有技术的问诊时间消耗，下图是本发明的问诊时间消耗，时间单位为分钟。具体地，如果病人因为同样病症来到医院需要做同样的检查，医院常规流程中的注册(挂号)需要10分钟，第一次问诊(含候诊排队)需要30分钟，支付需要10分钟，三个检查分别消耗15分钟、20分钟、35分钟，第二次问诊需要10分钟(含候诊排队)，因此传统流程一共需要130分钟。而采用本发明，同样做三个检查分别需要15分钟、10分钟和25分钟，最后问诊只需要7分钟，因此采用本发明一共需要57分钟。因此对比现有技术，本发明具有明显的优越性，如果病人因为同样病症来到医院需要做同样的检查本发明比现有技术节省了大量时间，至少50％。
本发明第二方面提供了一种医院门诊检查的智能引导系统，其中，包括： 处理器；以及与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括：S1，从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状；S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
进一步地，所述动作S1还包括：S11，从客户端获取症状描述信息并将该症状描述信息格式化，并从本体库中调取所述症状描述信息中的第一症状；S12，根据所述症状描述信息基于规则库分析该第一症状对应的病症及其所需检查和医疗资源，当所述症状描述信息中对应的症状并不能与诊断时，则查询医院历纪录进行所述症状和疑似病症及其所需检查和医疗资源的匹配。
进一步地，所述动作S2还包括：调取疑似病症所需检查和医疗资源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
进一步地，所述动作还包括：S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
进一步地，所述动作S4还包括：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
进一步地，所述优化目标包括通过干扰管理来减少对延迟病人的影响。其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
本发明第三方面还提供了医院门诊检查的智能引导装置，其中，包括：分析装置，其从客户端获取症状描述信息，并根据所述症状描述信息分析疑 似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状以及温度；分配装置，其分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；确认装置，其在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
本发明第四方面提供了计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明能够优化医院资源分布，即根据预约信息医院能够获知病人在比如接下来的24或48小时的分布，医院可以据此安排员工的换班，以及医疗材料和设备，减少浪费资源和响应时间。并且，本发明能够减少传统排队等待诊断和等待检查的时间，到院以前就可以执行常规通用的检查，智能预诊断模块能够代替医生告诉病人的疑似疾病和所需检查项目，从而省去第一次候诊及看诊时间。本发明还能节省等待时间，预诊断和计划能够安排病人的时间，系统能够基于医疗资源占用情况和队列长度自动计算出一个合适的时间,可以很大程度上节省等待的时间。通过实时调度缩短检查等待时间，病人每次完成一个检验项目，系统基于计算调度算法建议病人去哪里下检查，其总是引导病人到当前最少排队队列。
尽管本发明的内容已经通过上述优选实施例作了详细介绍，但应当认识到上述的描述不应被认为是对本发明的限制。在本领域技术人员阅读了上述内容后，对于本发明的多种修改和替代都将是显而易见的。因此，本发明的保护范围应由所附的权利要求来限定。此外，不应将权利要求中的任何附图标记视为限制所涉及的权利要求；“包括”一词不排除其它权利要求或说明书中未列出的装置或步骤；“第一”、“第二”等词语仅用来表示名称，而并不表示任何特定的顺序。

Claims (15)

  1. 医院门诊检查的智能引导方法，其中，包括如下步骤：
    S1，从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状；
    S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；
    S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
  2. 根据权利要求1所述的医院门诊检查的智能引导方法，其特征在于，其还所述步骤S1还包括如下步骤：
    S11，从客户端获取症状描述信息并将该症状描述信息格式化，并从本体库中调取所述症状描述信息中的第一症状；
    S12，根据所述症状描述信息基于规则库分析该第一症状对应的病症及其所需检查和医疗资源，
    当所述症状描述信息中对应的症状并不能与诊断时，则查询医院历纪录其所需检查和医疗资源的匹配。
  3. 根据权利要求1所述的医院门诊检查的智能引导方法，其特征在于，所述步骤S2还包括如下步骤：
    调取疑似病症所需检查和医疗资源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
  4. 根据权利要求1所述的医院门诊检查的智能引导方法，其特征在于，医院门诊检查的智能引导方法还包括：
    S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
  5. 根据权利要求1所述的医院门诊检查的智能引导方法，其特征在于， 所述步骤S4还包括如下步骤：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
  6. 根据权利要求5所述的医院门诊检查的智能引导方法，其特征在于，所述优化目标包括通过干扰管理来减少对延迟病人的影响，其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
  7. 医院门诊检查的智能引导系统，其中，包括：
    处理器；以及
    与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括：
    S1，从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状；
    S2，分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；
    S3，在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
  8. 根据权利要求7所述的医院门诊检查的智能引导系统，其特征在于，所述动作S1还包括：
    S11，从客户端获取症状描述信息并将该症状描述信息格式化，并从本体库中调取所述症状描述信息中的第一症状；
    S12，根据所述症状描述信息基于规则库分析该第一症状对应的病症及其所需检查和医疗资源，
    当所述症状描述信息中对应的症状并不能与诊断时，则查询医院历纪录进行所述症状和疑似病症及其所需检查和医疗资源的匹配。
  9. 根据权利要求7所述的医院门诊检查的智能引导系统，其特征在于，所述动作S2还包括：
    调取疑似病症所需检查和医疗资源，并基于所需检查和医疗资源以及医院目前的医疗资源占用情况作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端。
  10. 根据权利要求7所述的医院门诊检查的智能引导系统，其特征在于，所述动作还包括：
    S4，获取病人到达医院以后的状态信息，并基于所述状态信息和医疗资源当前状态信息持续更新就诊时间表信息并发送至客户端。
  11. 根据权利要求7所述的医院门诊检查的智能引导系统，其特征在于，所述动作S4还包括：获取病人到达医院以后的状态信息，结合所属状态信息及其所需检查、医疗资源占用预约情况和随机病人及其所需检查和资源之间的约束关系作为输入信息执行建模，并通过优化目标的算法描述来求解该模型，以持续更新就诊时间表信息并发送至客户端。
  12. 根据权利要求11所述的医院门诊检查的智能引导系统，其特征在于，所述优化目标包括通过干扰管理来减少对延迟病人的影响，其中，所述管理因素包括未预约就来医院就诊的随机病人，设备故障或者人员不到位导致的可用资源变化。
  13. 医院门诊检查的智能引导装置，其中，包括：
    分析装置，其从客户端获取症状描述信息，并根据所述症状描述信息分析疑似病症及其所需检查和医疗资源，其中，所述症状描述信息包括身体部位及其症状以及温度；
    分配装置，其分配所述医疗资源并确认其空档时间，并将预约结果信息所述发送给客户端，其中，所述医疗资源包括医疗设备、诊室，所述预约结果信息包括疑似病症、所需检查及其所需时间以及可以预约的来院时段；
    确认装置，其在收到客户端对预约结果信息的确认信息以后，产生该病人的就诊时间表信息并发送至客户端，并基于所述就诊时间表信息锁定医疗资源，其中，所述就诊时间表信息包括所需检查及其所需时长以及达到医院时间。
  14. 计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至6中任一项所述的方法。
  15. 计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至6中任一项所述的方法。
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