CN118155820A - Hospital outpatient emergency service system and method based on cloud server - Google Patents
Hospital outpatient emergency service system and method based on cloud server Download PDFInfo
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Abstract
The invention discloses a hospital outpatient emergency service system and a method based on a cloud server, which belong to the field of medical care information.
Description
Technical Field
The invention belongs to the field of medical care information, and particularly relates to a hospital outpatient emergency service system and method based on a cloud server.
Background
The hospital outpatient emergency services are a series of countermeasures and protocols established to cope with emergencies, peak treatment periods, outbreaks of specific diseases, etc., and to ensure that patients are timely and effectively served by medical services, which generally include, but are not limited to, the following:
According to the requirements of the doctor, the number of medical staff is reasonably allocated, so that enough medical resources are ensured to deal with the influx of patients in the peak period; emergency materials such as medicines, medical instruments, protective articles and the like are prepared in advance, so that the emergency materials can be used rapidly in emergency; and (3) optimizing the flow: the procedures of registering, visiting, checking, taking medicine and the like are optimized, the waiting time of a patient is reduced, and the working efficiency is improved; triage system: an effective triage system is established, so that critical patients can be treated preferentially, and meanwhile, the treatment sequence of light patients is reasonably arranged.
In the prior art, the most suitable medical staff cannot be selected through comprehensive analysis of medical level data of medical staff and wound and patient data of a patient when medical emergency services are carried out, so that the recovery rate of the patient is reduced, the utilization rate of medical resources is further reduced, and the problems in the prior art (for example, in the invention patent with application publication numbers of CN115394396A and CN 112669946A) exist;
in order to solve the problems, the application designs a hospital outpatient emergency service system and a hospital outpatient emergency service method based on a cloud server.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a hospital outpatient emergency service system and a method based on a cloud server.
In order to achieve the above purpose, the present invention provides the following technical solutions: a hospital outpatient emergency service method based on a cloud server comprises the following specific steps:
The cloud server acquires patient injury data and physique characteristic data in the first aid process of the hospital, and acquires medical information of medical staff;
Constructing a patient injury analysis model, acquiring physique characteristic data of a patient and patient injury data, and importing the physique characteristic data and the patient injury data into the constructed patient injury analysis model for patient injury analysis;
constructing a medical staff diagnosis analysis model, acquiring medical information of the medical staff and patient injury data, and importing the medical information and the patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
Importing the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The cloud server sends a diagnosis command to the selected medical personnel, and the selected medical personnel perform emergency medical services.
The specific explanation is that the step of obtaining patient injury data and physique characteristic data in the first aid process of the hospital and obtaining medical information of medical staff comprises the following specific steps:
S11, the cloud server acquires physical characteristic data of a patient in the emergency treatment process of a hospital through a physical characteristic acquisition module, wherein the physical characteristic data comprise body temperature, heart rate and pulse information, and simultaneously acquires image data of a wounded position and wounded position data of the patient in the emergency treatment process of the hospital through an image acquisition module, wherein the image data comprise pixel value data of pixel points of the wounded position;
S12, the cloud server collects position information of medical staff, position data of treatment wounded parts, information of the number of people to be treated, treatment recovery rate and treatment recovery time data through the medical collection module;
it should be specifically stated that the patient injury analysis model includes the following specific matters:
S21, setting a monitoring period, acquiring physical characteristic data of a patient in a hospital emergency process in real time, and substituting the physical characteristic data of the patient acquired in the monitoring period into a physical characteristic abnormal coefficient calculation formula to calculate physical characteristic abnormal coefficients, wherein the physical characteristic abnormal coefficient calculation formula is as follows: wherein T is the time length of the monitoring period, n is the number of the physique features,/> For a specific value at time t of the ith physical characteristics,Closest/>, in a safe range corresponding to the age of the patient being the ith idioplasm featureValue of/>Maximum value in a safe range corresponding to the age of the patient as characteristic of the ith idioplasm,/>Minimum value in safety range corresponding to patient age for the ith idioplasm feature,/>The duty ratio coefficient of the ith body characteristic;
S22, acquiring pixel value data of each pixel point of a wounded position of a patient in real time, acquiring a pixel point which has the largest difference with the average pixel value of a wounded position of the patient in each pixel point of the wounded position as a central pixel point, acquiring the distance between each pixel point of the wounded position and the central pixel point, and importing the distance between each pixel point of the wounded position and the central pixel point and the pixel value of each pixel point into a wounded characteristic anomaly coefficient calculation formula to calculate wounded characteristic anomaly coefficients, wherein the wounded characteristic anomaly coefficient calculation formula is as follows: Wherein S is a set length standard value, y is an average pixel value of a patient' S non-wounded position, m is the number of wounded position pixels,/> For the distance between the pixel point of the jth wounded position and the central pixel point,The pixel value of the pixel point at the j-th wounded position;
s23, acquiring the calculated constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient, and leading the constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient into a patient disease abnormal value calculation formula to calculate the patient disease abnormal value, wherein the patient disease abnormal value calculation formula is as follows: Wherein/> The ratio of the abnormal coefficients of the physical characteristics;
here, it is to be noted that, here And/>Selecting patient data and constitution data of at least five thousand groups of patients, adopting an expert to seriously sort the illness states of the patients, importing the patient data and constitution data into a patient illness state abnormal value calculation formula to calculate patient illness state abnormal value, importing the calculated patient illness state abnormal value and patient illness state serious sorting result into fitting software to output/> conforming to the maximum sorting accuracyAnd/>Is a value of (2);
the method is characterized in that the patient injury is comprehensively analyzed through physique characteristic data and patient injury data of the patient, so that the patient injury analysis effect is improved;
Meanwhile, the monitoring period is flexibly set according to the condition change of the patient, and is preferably 1 minute;
the patient injury analysis model specifically comprises the following specific steps:
S24, acquiring an abnormal patient condition value of a previous monitoring period and an abnormal patient condition value of a current period, and acquiring a distance from a medical staff to a patient, and introducing the abnormal patient condition value of the previous monitoring period, the abnormal patient condition value of the current period and the distance from the medical staff to the patient into a patient condition predicted value calculation formula to calculate a patient condition predicted value of the patient when the medical staff arrives at the patient, wherein the patient condition predicted value calculation formula is as follows: Wherein s is the distance from the medical staff to the patient, v is the speed of the medical staff, T is the monitoring cycle time,/> For the patient's abnormal value of the present monitoring period,/>Is the patient's abnormal value of the previous monitoring period.
It should be specifically described herein that the medical staff diagnosis analysis model includes the following specific steps:
S31, acquiring treatment wound position data, treatment number information, treatment recovery rate and treatment recovery time data of idle medical staff, comparing the treatment wound position data of the idle medical staff with the patient wound position data, selecting the idle medical staff corresponding to the treatment wound position identical to the patient wound position data to be set as a first-choice idle medical staff, wherein the treatment wound position identical to the patient wound position data, such as the damage of leg muscles of the patient, is exemplified here, and the idle medical staff for the treatment of a movement system is required to be selected; for example, if the patient's nose is damaged, an idle healthcare worker who is required to select respiratory treatment;
S32, extracting treatment number information, treatment recovery rate and treatment recovery time data of the primarily selected idle medical staff, and importing the treatment number information, the treatment recovery rate and the treatment recovery time data of the primarily selected idle medical staff into a medical staff treatment value calculation formula to calculate a medical staff treatment value, wherein the medical staff treatment value calculation formula is as follows: Wherein/> Empirical duty cycle for the number of persons treated,/>Information of the number of treatment persons for initially selecting idle medical staff,/>Information on average number of patients with same positions of wounded parts in treatment of hospital,/>For the treatment of the recovery rate ratio coefficient,/>For the treatment recovery rate of the first choice of idle medical staff,/>For the average treatment recovery rate of the same positions of the wounded parts in the treatment of hospitals,/>For the same average recovery time of the treatment wounded parts of the hospital,/>The average recovery time of the first-choice idle medical staff is obtained.
It should be noted that the information on the number of persons treated, the rate of recovery of treatment and the time of recovery of treatment are used for comprehensively reflecting the medical level of medical staff, wherein,And/>The value-taking mode is as follows: acquiring medical data of 500 doctors, adopting 50 experts in the field to score and sort the medical levels of the doctors, guiding the medical data into a medical staff diagnosis value calculation formula to calculate a medical staff diagnosis value, and guiding the calculated medical staff diagnosis value and scoring ranking into fitting software to perform/>, which accords with the highest ranking accuracyAnd/>Outputting the value;
it should be specifically stated that the specific content of the medical personnel selection policy is as follows:
Substituting the calculated abnormal patient condition value and the medical personnel visit value into a medical personnel selection value calculation formula to calculate a medical personnel selection value, wherein the medical personnel selection value calculation formula is as follows: Comparing the calculated medical personnel selection value with a set medical personnel selection threshold value, and selecting a first selected idle medical personnel corresponding to the medical personnel selection value closest to the medical personnel selection threshold value as a selected medical personnel;
The most suitable medical staff is selected through comprehensive analysis of the medical level data of the medical staff and the wound and patient data of the patient, so that the recovery rate of the patient is improved, and the utilization rate of medical resources is further improved;
here, the medical staff selects the threshold value in the following manner: acquiring at least five thousand groups of patient condition data, selecting the most suitable medical personnel from a medical personnel selection library aiming at the patient condition data by 50 experts, importing the patient condition data and the medical personnel medical data into a medical personnel selection value calculation formula to calculate a medical personnel selection value, substituting the selected medical personnel and medical personnel selection value into fitting software, and outputting a medical personnel selection threshold conforming to the highest selection accuracy.
The utility model provides a hospital outpatient service emergency service system based on high in the clouds server, its based on above-mentioned hospital outpatient service emergency service method realization based on high in the clouds server, it specifically includes:
The data acquisition module is used for acquiring patient injury data and physique characteristic data in the first-aid process of the hospital by the cloud server and acquiring medical information of medical staff;
The patient injury analysis model module is used for constructing a patient injury analysis model, acquiring physique characteristic data of a patient and importing the patient injury data into the constructed patient injury analysis model for patient injury analysis;
The medical staff diagnosis analysis model construction module is used for constructing a medical staff diagnosis analysis model, acquiring medical information of medical staff and patient injury data and importing the medical information and patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
the medical personnel selection module is used for guiding the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The instruction issuing module is used for sending a diagnosis command to the selected medical personnel by the cloud server, and the selected medical personnel perform emergency medical services;
the control module is used for controlling the operation of the data acquisition module, the patient injury analysis model module, the medical staff diagnosis analysis model construction module, the medical staff selection module and the instruction issuing module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the hospital outpatient emergency service method based on the cloud server by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a hospital outpatient emergency service method based on a cloud server as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the cloud server is used for acquiring patient injury data and physique characteristic data in the first-aid process of a hospital, acquiring medical information of medical staff, constructing a patient injury analysis model, acquiring physique characteristic data of the patient and patient injury data, importing the physique characteristic data of the patient and the patient injury data into the constructed patient injury analysis model for patient injury analysis, constructing a medical staff diagnosis analysis model, importing the medical information of the medical staff and the patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis, importing a patient injury analysis result and a medical staff diagnosis analysis result into a medical staff selection strategy for medical staff selection, sending a diagnosis command to the selected medical staff by the cloud server, performing emergency medical service on the selected medical staff, comprehensively analyzing and selecting the most suitable medical staff through medical level data of the medical staff and the patient injury data, and further improving the utilization rate of medical resources.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic overall flow diagram of a hospital outpatient emergency service method based on a cloud server;
FIG. 2 is a schematic flow chart of a patient injury analysis model in a hospital outpatient service method based on a cloud server;
FIG. 3 is a schematic diagram of an overall framework of a hospital outpatient emergency service system based on a cloud server;
fig. 4 is a schematic diagram of an overall frame of an electronic device according to the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1-2, an embodiment of the present invention is provided: the technical problems solved by the embodiment are as follows: in the prior art, the most suitable medical staff cannot be selected through comprehensive analysis of medical level data of medical staff and wound and patient data of a patient when medical emergency services are carried out, so that the recovery rate of the patient is reduced, and the utilization rate of medical resources is further reduced;
A hospital outpatient emergency service method based on a cloud server comprises the following specific steps:
The cloud server acquires patient injury data and physique characteristic data in the first aid process of the hospital, and acquires medical information of medical staff;
Constructing a patient injury analysis model, acquiring physique characteristic data of a patient and patient injury data, and importing the physique characteristic data and the patient injury data into the constructed patient injury analysis model for patient injury analysis;
constructing a medical staff diagnosis analysis model, acquiring medical information of the medical staff and patient injury data, and importing the medical information and the patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
Importing the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The cloud server sends a diagnosis command to the selected medical personnel, and the selected medical personnel perform emergency medical services;
In this embodiment, it needs to be specifically described that the step of acquiring patient injury data and physical characteristic data in the first aid process of the hospital and simultaneously acquiring medical information of medical staff includes the following specific steps:
S11, the cloud server acquires physical characteristic data of a patient in the emergency treatment process of a hospital through a physical characteristic acquisition module, wherein the physical characteristic data comprise body temperature, heart rate and pulse information, and simultaneously acquires image data of a wounded position and wounded position data of the patient in the emergency treatment process of the hospital through an image acquisition module, wherein the image data comprise pixel value data of pixel points of the wounded position;
the following is a code example of using a cloud server written in language C to obtain physique feature data (including body temperature, heart rate and pulse information) of a patient in a hospital emergency process through a physical sign acquisition module:
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <netdb.h>
#include <sys/socket.h>
#include <arpa/inet.h>
#include <unistd.h>
int main() {
Socket object creation
int sock = socket(AF_INET, SOCK_STREAM, 0);
if (sock == -1) {
Perror ("socket creation failure");
exit(EXIT_FAILURE);
}
Defining server address information
struct sockaddr_in server_addr;
server_addr.sin_family = AF_INET;
Server_addr. Sin_port= htons (8888);// port number
Inet_ pton (AF_INET, "127.0.0.1", & server_addr.sin_addr);// server IP address
/(Connection server)
if (connect(sock, (struct sockaddr *)&server_addr, sizeof(server_addr)) < 0) {
Perror ("connection server failed");
exit(EXIT_FAILURE);
}
data request for/sending physical characteristics
Char request [20] = "body temperature";// request data format is "feature name" + "\n"
send(sock, request, strlen(request), 0);
Printf ("sent body temperature request\n");
Data responsive to the server is received/received
char buffer[256];
while (1) {
int len = recv(sock, buffer, sizeof(buffer), 0);
if (len == -1) {
Perror ("failed to receive data");
break;
} else if (len == 0) {
break;// connection is closed, exit the loop
} else {
Printf ("received data:% s\n", buffer);
the data received is/are processed, only the result is output, and the data is needed to be stored in a database or a file in practical application
If (buffer, "36.5") = 0) {// assuming that the received data is body temperature information, outputting the result and judging whether the result meets the expected value, and in practical application, judging and processing the data according to the actual situation is needed
Printf ("body temperature meets expected value \n");
} else {
printf ("body temperature does not correspond to the expected value \n");
}
}
}
Operations such as socket connection/resource release/closing
close(sock);
return 0;
}
S12, the cloud server collects position information of medical staff, treatment wounded parts position data, treatment number information, treatment recovery rate and treatment recovery time data through the medical collecting module, and the treatment wounded parts are divided according to human anatomy parts: human anatomy segmentation human body is generally classified into nine systems according to human anatomy:
1. motion system: is composed of bones, muscles, tendons, ligaments and joints, and can realize basic activities of human body such as walking, running, jumping, etc.;
2. the digestive system: is prepared from oral cavity, pharynx, esophagus, stomach, small intestine, large intestine, etc. The digestive system is mainly responsible for digestion and absorption of food;
3. The respiratory system: consists of nose, throat, lung, trachea, bronchus and the like, and is mainly responsible for breathing and air exchange;
4. urinary system: the urine collecting device consists of kidneys, ureters, bladders and urethra, and is mainly responsible for the generation and excretion of urine;
5. endocrine system: consists of pituitary gland, thyroid gland, pancreas gland, adrenal gland and the like, and is mainly responsible for regulating the growth, development, metabolism, water electrolyte balance and the like of human bodies;
6. The nervous system: consists of a central nervous system (including brain, spinal cord and the like) and a peripheral nervous system, and is mainly responsible for the regulation and management of various sensory and motor functions of a human body;
7. Reproduction system: men are composed of testis, seminiferous ducts, etc., and women are composed of ovaries, fallopian tubes, etc. The reproductive system is mainly responsible for reproduction and reproduction of offspring;
8. and (3) a circulation system: including the cardiovascular system and blood, the main functions of which are to provide the body with transport of substances and oxygen supply;
9. Sensory system: including the vision (eyes), hearing (ears) and other sense organs (such as olfactory organs, etc.), mainly responsible for sensing external information and feedback to the brain;
Meanwhile, the data acquired by the cloud server are only used in the system, and cannot be leaked in a manner of hacking and the like, so that the problem of secret leakage is not needed to be considered;
In this embodiment, the patient injury analysis model includes the following specific matters:
S21, setting a monitoring period, acquiring physical characteristic data of a patient in a hospital emergency process in real time, and substituting the physical characteristic data of the patient acquired in the monitoring period into a physical characteristic abnormal coefficient calculation formula to calculate physical characteristic abnormal coefficients, wherein the physical characteristic abnormal coefficient calculation formula is as follows: wherein T is the time length of the monitoring period, n is the number of the physique features,/> For the specific value of the ith idiosyncratic feature t moment,/>Closest/>, in a safe range corresponding to the age of the patient being the ith idioplasm featureValue of/>Maximum value in a safe range corresponding to the age of the patient as characteristic of the ith idioplasm,/>Minimum value in safety range corresponding to patient age for the ith idioplasm feature,/>The duty ratio coefficient of the ith body characteristic;
S22, acquiring pixel value data of each pixel point of a wounded position of a patient in real time, acquiring a pixel point which has the largest difference with the average pixel value of a wounded position of the patient in each pixel point of the wounded position as a central pixel point, acquiring the distance between each pixel point of the wounded position and the central pixel point, and importing the distance between each pixel point of the wounded position and the central pixel point and the pixel value of each pixel point into a wounded characteristic anomaly coefficient calculation formula to calculate wounded characteristic anomaly coefficients, wherein the wounded characteristic anomaly coefficient calculation formula is as follows: Wherein S is a set length standard value, y is an average pixel value of a patient' S non-wounded position, m is the number of wounded position pixels,/> For the distance between the pixel point of the jth wounded position and the central pixel point,The pixel value of the pixel point at the j-th wounded position;
s23, acquiring the calculated constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient, and leading the constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient into a patient disease abnormal value calculation formula to calculate the patient disease abnormal value, wherein the patient disease abnormal value calculation formula is as follows: Wherein/> The ratio of the abnormal coefficients of the physical characteristics;
here, it is to be noted that, here And/>Selecting patient data and constitution data of at least five thousand groups of patients, adopting an expert to seriously sort the illness states of the patients, importing the patient data and constitution data into a patient illness state abnormal value calculation formula to calculate patient illness state abnormal value, importing the calculated patient illness state abnormal value and patient illness state serious sorting result into fitting software to output/> conforming to the maximum sorting accuracyAnd/>Is a value of (2);
the method is characterized in that the patient injury is comprehensively analyzed through physique characteristic data and patient injury data of the patient, so that the patient injury analysis effect is improved;
Meanwhile, the monitoring period is flexibly set according to the condition change of the patient, and is preferably 1 minute;
it should be specifically noted that the patient injury analysis model further includes the following specific steps:
S24, acquiring an abnormal patient condition value of a previous monitoring period and an abnormal patient condition value of a current period, and acquiring a distance from a medical staff to a patient, and introducing the abnormal patient condition value of the previous monitoring period, the abnormal patient condition value of the current period and the distance from the medical staff to the patient into a patient condition predicted value calculation formula to calculate a patient condition predicted value of the patient when the medical staff arrives at the patient, wherein the patient condition predicted value calculation formula is as follows: Wherein s is the distance from the medical staff to the patient, v is the speed of the medical staff, T is the monitoring cycle time,/> For the patient's abnormal value of the present monitoring period,/>Patient condition outliers for the previous monitoring period;
In this embodiment, it should be specifically described that the medical staff diagnosis analysis model includes the following specific steps:
S31, acquiring treatment wound position data, treatment number information, treatment recovery rate and treatment recovery time data of idle medical staff, comparing the treatment wound position data of the idle medical staff with the patient wound position data, selecting the idle medical staff corresponding to the treatment wound position identical to the patient wound position data to be set as a first-choice idle medical staff, wherein the treatment wound position identical to the patient wound position data, such as the damage of leg muscles of the patient, is exemplified here, and the idle medical staff for the treatment of a movement system is required to be selected; for example, if the patient's nose is damaged, an idle healthcare worker who is required to select respiratory treatment;
S32, extracting treatment number information, treatment recovery rate and treatment recovery time data of the primarily selected idle medical staff, and importing the treatment number information, the treatment recovery rate and the treatment recovery time data of the primarily selected idle medical staff into a medical staff treatment value calculation formula to calculate a medical staff treatment value, wherein the medical staff treatment value calculation formula is as follows: Wherein/> Empirical duty cycle for the number of persons treated,/>Information of the number of treatment persons for initially selecting idle medical staff,/>Information on average number of patients with same positions of wounded parts in treatment of hospital,/>For the treatment of the recovery rate ratio coefficient,/>For the treatment recovery rate of the first choice of idle medical staff,/>For the average treatment recovery rate of the same positions of the wounded parts in the treatment of hospitals,/>For the same average recovery time of the treatment wounded parts of the hospital,/>The average recovery time of the first-choice idle medical staff is obtained.
It should be noted that the information on the number of persons treated, the rate of recovery of treatment and the time of recovery of treatment are used for comprehensively reflecting the medical level of medical staff, wherein,And/>The value-taking mode is as follows: acquiring medical data of 500 doctors, adopting 50 experts in the field to score and sort the medical levels of the doctors, guiding the medical data into a medical staff diagnosis value calculation formula to calculate a medical staff diagnosis value, and guiding the calculated medical staff diagnosis value and scoring ranking into fitting software to perform/>, which accords with the highest ranking accuracyAnd/>Outputting the value;
In this embodiment, the specific content of the medical personnel selection policy is as follows:
Substituting the calculated abnormal patient condition value and the medical personnel visit value into a medical personnel selection value calculation formula to calculate a medical personnel selection value, wherein the medical personnel selection value calculation formula is as follows: Comparing the calculated medical personnel selection value with a set medical personnel selection threshold value, and selecting a first selected idle medical personnel corresponding to the medical personnel selection value closest to the medical personnel selection threshold value as a selected medical personnel;
The most suitable medical staff is selected through comprehensive analysis of the medical level data of the medical staff and the wound and patient data of the patient, so that the recovery rate of the patient is improved, and the utilization rate of medical resources is further improved;
Here, the medical staff selects the threshold value in the following manner: acquiring at least five thousand groups of patient condition data, selecting the most suitable medical personnel from a medical personnel selection library aiming at the patient condition data by 50 experts, importing the patient condition data and the medical personnel medical data into a medical personnel selection value calculation formula to calculate a medical personnel selection value, substituting the selected medical personnel and medical personnel selection value into fitting software, and outputting a medical personnel selection threshold conforming to the highest selection accuracy;
it should be noted that the advantages of this embodiment compared with the prior art are: the method comprises the steps of obtaining patient injury data and physique feature data in a hospital emergency process through a cloud server, obtaining medical information of medical staff, constructing a patient injury analysis model, obtaining physique feature data of a patient and patient injury data, importing the patient injury analysis model constructed by the patient injury data to conduct patient injury analysis, constructing a medical staff diagnosis analysis model, importing the medical information of the medical staff and the patient injury data into the medical staff diagnosis analysis model to conduct medical staff diagnosis analysis, importing a patient injury analysis result and a medical staff diagnosis analysis result into a medical staff selection strategy to conduct medical staff selection, sending a diagnosis command to the selected medical staff by the cloud server, conducting emergency medical service on the selected medical staff, comprehensively analyzing and selecting the most suitable medical staff through medical level data of the medical staff and patient injury data, and further improving the utilization rate of medical resources while improving the recovery rate of the patient.
Example 2
As shown in fig. 3, a hospital outpatient emergency service system based on a cloud server is implemented based on the hospital outpatient emergency service method based on the cloud server, which specifically includes: the data acquisition module is used for acquiring patient injury data and physique characteristic data in the first-aid process of the hospital by the cloud server and acquiring medical information of medical staff;
The patient injury analysis model module is used for constructing a patient injury analysis model, acquiring physique characteristic data of a patient and importing the patient injury data into the constructed patient injury analysis model for patient injury analysis;
The medical staff diagnosis analysis model construction module is used for constructing a medical staff diagnosis analysis model, acquiring medical information of medical staff and patient injury data and importing the medical information and patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
the medical personnel selection module is used for guiding the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The instruction issuing module is used for sending a diagnosis command to the selected medical personnel by the cloud server, and the selected medical personnel perform emergency medical services;
the control module is used for controlling the operation of the data acquisition module, the patient injury analysis model module, the medical staff diagnosis analysis model construction module, the medical staff selection module and the instruction issuing module.
Example 3
As shown in fig. 4, the present embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the hospital outpatient emergency service method based on the cloud server by calling the computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a hospital outpatient emergency service method based on the cloud server provided by the foregoing method embodiment.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the hospital outpatient emergency service method based on the cloud server.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
Claims (9)
1. The hospital outpatient emergency service method based on the cloud server is characterized by comprising the following specific steps of:
The cloud server acquires patient injury data and physique characteristic data in the first aid process of the hospital, and acquires medical information of medical staff;
Constructing a patient injury analysis model, acquiring physique characteristic data of a patient and patient injury data, and importing the physique characteristic data and the patient injury data into the constructed patient injury analysis model for patient injury analysis;
constructing a medical staff diagnosis analysis model, acquiring medical information of the medical staff and patient injury data, and importing the medical information and the patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
Importing the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The cloud server sends a diagnosis command to the selected medical personnel, and the selected medical personnel perform emergency medical services.
2. The method for providing emergency service for hospital clinic based on cloud server as claimed in claim 1, wherein the step of obtaining patient injury data and physical characteristic data in the emergency procedure of hospital and obtaining medical information of medical staff comprises the following specific steps:
S11, the cloud server acquires physical characteristic data of a patient in the emergency treatment process of a hospital through a physical characteristic acquisition module, wherein the physical characteristic data comprise body temperature, heart rate and pulse information, and simultaneously acquires image data of a wounded position and wounded position data of the patient in the emergency treatment process of the hospital through an image acquisition module, wherein the image data comprise pixel value data of pixel points of the wounded position;
S12, the cloud server collects position information of all medical workers, position data of the wounded parts, information of the number of people to be treated, treatment recovery rate and treatment recovery time data through the medical collection module.
3. The hospital outpatient emergency service method based on the cloud server according to claim 2, wherein the patient injury analysis model comprises the following specific contents:
S21, setting a monitoring period, acquiring physical characteristic data of a patient in a hospital emergency process in real time, and substituting the physical characteristic data of the patient acquired in the monitoring period into a physical characteristic abnormal coefficient calculation formula to calculate physical characteristic abnormal coefficients, wherein the physical characteristic abnormal coefficient calculation formula is as follows: wherein T is the time length of the monitoring period, n is the number of the physique features,/> For the specific value of the ith idiosyncratic feature t moment,/>Closest/>, in a safe range corresponding to the age of the patient being the ith idioplasm featureValue of/>Maximum value in a safe range corresponding to the age of the patient as characteristic of the ith idioplasm,/>Minimum value in safety range corresponding to patient age for the ith idioplasm feature,/>The duty ratio coefficient of the ith body characteristic;
S22, acquiring pixel value data of each pixel point of a wounded position of a patient in real time, acquiring a pixel point which has the largest difference with the average pixel value of a wounded position of the patient in each pixel point of the wounded position as a central pixel point, acquiring the distance between each pixel point of the wounded position and the central pixel point, and importing the distance between each pixel point of the wounded position and the central pixel point and the pixel value of each pixel point into a wounded characteristic anomaly coefficient calculation formula to calculate wounded characteristic anomaly coefficients, wherein the wounded characteristic anomaly coefficient calculation formula is as follows: Wherein S is a set length standard value, y is an average pixel value of a patient' S non-wounded position, m is the number of wounded position pixels,/> Is the distance between the pixel point of the jth wounded position and the central pixel point,/>The pixel value of the pixel point at the j-th wounded position;
s23, acquiring the calculated constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient, and leading the constitution characteristic abnormal coefficient and the wound characteristic abnormal coefficient into a patient disease abnormal value calculation formula to calculate the patient disease abnormal value, wherein the patient disease abnormal value calculation formula is as follows: Wherein/> The ratio of the abnormal coefficients of the physical characteristics is calculated.
4. The hospital outpatient emergency service method based on the cloud server as claimed in claim 3, wherein said patient injury analysis model further comprises the following specific steps:
S24, acquiring an abnormal patient condition value of a previous monitoring period and an abnormal patient condition value of a current period, and acquiring a distance from a medical staff to a patient, and introducing the abnormal patient condition value of the previous monitoring period, the abnormal patient condition value of the current period and the distance from the medical staff to the patient into a patient condition predicted value calculation formula to calculate a patient condition predicted value of the patient when the medical staff arrives at the patient, wherein the patient condition predicted value calculation formula is as follows: Wherein s is the distance from the medical staff to the patient, v is the speed of the medical staff, T is the monitoring cycle time,/> For the patient's abnormal value of the present monitoring period,/>Is the patient's abnormal value of the previous monitoring period.
5. The hospital outpatient emergency service method based on the cloud server as claimed in claim 4, wherein the medical staff diagnosis analysis model comprises the following specific steps:
S31, acquiring treatment wound position data, treatment number information, treatment recovery rate and treatment recovery time data of idle medical staff, comparing the treatment wound position data of the idle medical staff with the wound position data of a patient, selecting the idle medical staff corresponding to the treatment wound position identical to the wound position data of the patient, and setting the idle medical staff as the first-choice idle medical staff;
S32, extracting treatment number information, treatment recovery rate and treatment recovery time data of the primarily selected idle medical staff, and importing the treatment number information, the treatment recovery rate and the treatment recovery time data of the primarily selected idle medical staff into a medical staff treatment value calculation formula to calculate a medical staff treatment value, wherein the medical staff treatment value calculation formula is as follows: Wherein/> Empirical duty cycle for the number of persons treated,/>Information of the number of treatment persons for initially selecting idle medical staff,/>Information on average number of patients with same positions of wounded parts in treatment of hospital,/>For the treatment of the recovery rate ratio coefficient,/>For the treatment recovery rate of the first choice of idle medical staff,/>For the average treatment recovery rate of the same positions of the wounded parts in the treatment of hospitals,/>For the same average recovery time of the treatment wounded parts of the hospital,/>The average recovery time of the first-choice idle medical staff is obtained.
6. The hospital outpatient emergency service method based on the cloud server as claimed in claim 5, wherein the specific contents of the medical personnel selection policy are as follows:
Substituting the calculated abnormal patient condition value and the medical personnel visit value into a medical personnel selection value calculation formula to calculate a medical personnel selection value, wherein the medical personnel selection value calculation formula is as follows: And comparing the calculated medical personnel selection value with a set medical personnel selection threshold value, and selecting the first selected idle medical personnel corresponding to the medical personnel selection value closest to the medical personnel selection threshold value as the selected medical personnel.
7. A cloud server-based hospital outpatient emergency service system, which is realized based on the cloud server-based hospital outpatient emergency service method according to any one of claims 1 to 6,
The method specifically comprises the following steps:
The data acquisition module is used for acquiring patient injury data and physique characteristic data in the first-aid process of the hospital by the cloud server and acquiring medical information of medical staff;
The patient injury analysis model module is used for constructing a patient injury analysis model, acquiring physique characteristic data of a patient and importing the patient injury data into the constructed patient injury analysis model for patient injury analysis;
The medical staff diagnosis analysis model construction module is used for constructing a medical staff diagnosis analysis model, acquiring medical information of medical staff and patient injury data and importing the medical information and patient injury data into the medical staff diagnosis analysis model for medical staff diagnosis analysis;
the medical personnel selection module is used for guiding the patient injury analysis result and the medical personnel diagnosis analysis result into a medical personnel selection strategy to select medical personnel;
The instruction issuing module is used for sending a diagnosis command to the selected medical personnel by the cloud server, and the selected medical personnel perform emergency medical services;
the control module is used for controlling the operation of the data acquisition module, the patient injury analysis model module, the medical staff diagnosis analysis model construction module, the medical staff selection module and the instruction issuing module.
8. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The method for hospital outpatient service based on the cloud server according to any one of claims 1 to 6, wherein said processor executes a computer program stored in said memory.
9. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform a hospital outpatient emergency service method based on a cloud server as claimed in any one of claims 1 to 6.
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