CN112289395A - Clinical diagnosis and treatment follow-up normalization method and system - Google Patents
Clinical diagnosis and treatment follow-up normalization method and system Download PDFInfo
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Abstract
The invention discloses a follow-up normalization method and a follow-up normalization system for clinical diagnosis and treatment, wherein the method comprises the following steps: obtaining the group information of the interviewees; obtaining the position information of each interviewee in the interviewee group according to the interviewee group information; obtaining the visited key point information of each visited person in the visited person group according to the visited person group information; inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result; acquiring professional information of a follow-up visitor; and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors. The technical problem that the follow-up result is not ideal and true due to the fact that the professional information of the follow-up person is not consistent with the illness information of the visited group is solved.
Description
Technical Field
The invention relates to the technical field of clinical follow-up visits, in particular to a clinical diagnosis and treatment follow-up visit standardization method and system.
Background
The patient follow-up visit management is carried out on the patient leaving the hospital, so that the patient condition information and the rehabilitation condition of the patient can be mastered, a doctor can conveniently track and observe the patient, the first-hand data can be mastered to carry out statistical analysis and experience accumulation, the hospital can be helped to improve the service level before and after the doctor, and the patient can be better served.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
when the patient leaves the hospital for follow-up visit, the follow-up visit result is not ideal and true because the professional information of the follow-up visit person does not accord with the illness information of the visited group.
Disclosure of Invention
The embodiment of the application provides a clinical diagnosis and treatment follow-up standardized method and system, solves the technical problem that the follow-up result is unsatisfactory and unrealistic due to the fact that the professional information of the follow-up person does not accord with the illness information of the visited group, achieves the purpose of carrying out follow-up on the follow-up person matched with the interviewee with the proper professional, enables the follow-up result to be real and reliable, and further achieves the technical effect of effective communication between the doctor and the patient.
The embodiment of the application provides a follow-up visit standardization method for clinical diagnosis and treatment, wherein the method comprises the following steps: obtaining the group information of the interviewees; obtaining the position information of each interviewee in the interviewee group according to the interviewee group information; obtaining the visited key point information of each visited person in the visited person group according to the visited person group information; inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result; acquiring professional information of a follow-up visitor; and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
On the other hand, the application also provides a clinical diagnosis and treatment follow-up normalization system, wherein the system comprises: a first obtaining unit: the first obtaining unit is used for obtaining the interviewee population information; a second obtaining unit: the second obtaining unit is used for obtaining the position information of each interviewee in the interviewee group according to the interviewee group information; a third obtaining unit: the third obtaining unit is used for obtaining the visited key point information of each visited person in the visited person group according to the visited person group information; a first input unit: the first input unit is used for inputting the position information and the visited key point information into a neural network model, and grouping the visited persons to obtain a first grouping result; a fourth obtaining unit: the fourth obtaining unit is used for obtaining professional information of the followed person; a first distribution unit: the first allocation unit is used for allocating corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the interviewees are grouped according to the position information and the interviewee key information of the interviewees, and meanwhile, the professional information of the follow-up interviewees is combined to match the suitable interviewees for follow-up interviewing, so that the follow-up interviewing result achieves the established effect, is real and effective, is convenient for doctors to master the illness state of the interviewees in real time, and further has the better technical effect of serving patients.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a clinical diagnosis and treatment follow-up normalization method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a clinical diagnosis and treatment follow-up normalization system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first input unit 14, a fourth obtaining unit 15, a first allocation unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, a bus interface 305.
Detailed Description
The embodiment of the application provides a clinical diagnosis and treatment follow-up standardized method and system, solves the technical problem that the follow-up result is unsatisfactory and unrealistic due to the fact that the professional information of the follow-up person does not accord with the illness information of the visited group, achieves the purpose of carrying out follow-up on the follow-up person matched with the interviewee with the proper professional, enables the follow-up result to be real and reliable, and further achieves the technical effect of effective communication between the doctor and the patient.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The patient follow-up visit management is carried out on the patient leaving the hospital, so that the patient condition information and the rehabilitation condition of the patient can be mastered, a doctor can conveniently track and observe the patient, the first-hand data can be mastered to carry out statistical analysis and experience accumulation, the hospital can be helped to improve the service level before and after the doctor, and the patient can be better served. When the patient leaves the hospital for follow-up visit, the follow-up visit result is not ideal and true because the professional information of the follow-up visit person does not accord with the illness information of the visited group.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a follow-up visit standardization method for clinical diagnosis and treatment, wherein the method comprises the following steps: obtaining the group information of the interviewees; obtaining the position information of each interviewee in the interviewee group according to the interviewee group information; obtaining the visited key point information of each visited person in the visited person group according to the visited person group information; inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result; acquiring professional information of a follow-up visitor; and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
For better understanding of the above technical solutions, the following detailed descriptions will be provided in conjunction with the drawings and the detailed description of the embodiments.
Example one
As shown in fig. 1, an embodiment of the present application provides a clinical diagnosis and treatment follow-up normalization method, where the method further includes:
step S100: obtaining the group information of the interviewees;
specifically, the interviewee group information is the sum of individuals who are visited in clinical diagnosis and treatment, a hospital can carry out information access application on patients who have been treated in the hospital once so as to obtain the information of the patients, the patients agree to apply for access, the hospital can obtain the information of the patients, further, the information required by clinical follow-up is fully prepared, and the illness state information and the rehabilitation condition of the patients are mastered through the clinical follow-up.
Step S200: obtaining the position information of each interviewee in the interviewee group according to the interviewee group information;
specifically, the position information is the position information of each interviewee in a group receiving follow-up visits, because the position information of each interviewee is different, the follow-up visits can send position access information to the patient, after the patient passes the position access information, the system automatically obtains the position information of the patient, then statistical analysis is carried out on the position information, and the interviewees are reasonably matched according to the position information.
Step S300: obtaining the visited key point information of each visited person in the visited person group according to the visited person group information;
specifically, the important visited information is the important visited information of each visited group in the visited group, and it can be further understood that if the visited person suffers from heart disease when he visits the doctor, the important visited information is the heart, and the heart beat information and the like can be obtained.
Step S400: inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result;
specifically, the position information and the visited key information are known, the position information and the visited key information can be input into a neural network model, the neural network model is a data training model, input data can be continuously trained, a training result is more accurate, the visited persons are grouped, a first grouping result is obtained, namely the visited persons with the similar positions and the visited keys are grouped into one group, and the visited persons can conveniently visit.
Step S500: acquiring professional information of a follow-up visitor;
specifically, the professional information is the research direction of the follow-up, and it is further understood that the professional information of the follow-up can be the specialties of internal medicine, surgery, pediatrics, obstetrics and gynecology, oncology and the like.
Step S600: and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
Specifically, given the first grouping result and the professional information of the respondents, the respondents in different groups can be assigned with corresponding respondents according to the first grouping result and the professional information of the respondents, and it can be further understood that when the first grouping result is a patient with a cardiac history, the matched corresponding respondent should be a professional in research cardiology, and the like.
The step S400 of inputting the location information and the visited key information into a neural network model, and grouping the visited persons to obtain a first grouping result further includes:
step S410: inputting the position information and the visited key point information as input data into a neural network model, wherein the neural network model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the location information, the visited key point information and identification information for identifying a grouping result;
step S420: obtaining first output information of the neural network model, wherein the first output information is a first grouping result of a visitor population.
Specifically, in order to obtain more accurate grouping information, the position information and the visited key point information are used as input data and input into a neural network model for continuous training, so that the output training result can be more accurate. The first training model is a Neural network model, namely a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. In the embodiment of the application, the position information and the visited key point information are used as input data, the input data is input into a neural network model for continuous training, and the grouping result information of the identification is used for training the neural network model.
Further, the process of training the neural network model is substantially a process of supervised learning. The plurality of groups of training data are specifically: the location information, the visited emphasis information, and identification information for identifying a grouping result. The neural network model outputs first grouping result information of a group of the interviewees by inputting the position information and the interviewed key information, the output information and the grouping result information with the identification function are verified, if the output information is consistent with the requirement of the grouping result information with the identification function, the data supervised learning is finished, and then the next group of data supervised learning is carried out; if the output information is not consistent with the requirement of the grouping result information with the identification function, the neural network learning model adjusts itself until the output result of the neural network learning model is consistent with the requirement of the grouping result information with the identification function, and then the supervised learning of the next group of data is carried out. The neural network learning model is continuously corrected and optimized through training data, the accuracy of the neural network learning model for processing the information is improved through the process of supervised learning, and the technical effect that the first grouping result of the interviewee group is more accurate is achieved.
Further, the embodiment of the application further comprises:
step S710: obtaining follow-up visit time limit information of a first interviewee;
step S720: obtaining the last diagnosis and treatment time of a first interviewee, wherein the first interviewee is an interviewee in an interviewee group;
step S730: acquiring first reminding information according to the last diagnosis and treatment time of the first interviewee and the follow-up visit time limit information;
step S740: and reminding the first interviewee of needing follow-up visit according to the first reminding information.
Specifically, in order to make the follow-up visit of clinical diagnosis and treatment more standardized, the follow-up visit time limit information of a first interviewee can be obtained, the follow-up visit time limit information is a time period in which the interviewee needs follow-up visit according to the condition of the disease, namely how often the interviewee visits, and the last diagnosis and treatment time of the first interviewee can be obtained, the first interviewee is an interviewee in an interviewee group, the last diagnosis and treatment time is the last diagnosis and treatment time of the first interviewee, and first reminding information is obtained according to the last diagnosis and treatment time of the first interviewee and the follow-up visit time limit information, the first reminding information is used for reminding the first interviewee of needing follow-up visit, and follow-up visit reminding is carried out according to the last diagnosis and treatment time of the interviewee, so that the follow-up visit of a patient is guaranteed to be within a specified time limit, and the follow-up visit result is real and reliable.
Further, the embodiment of the application further comprises:
step S810: obtaining a disease type of a first interviewee, the first interviewee being an interviewee in an interviewee population;
step S820: obtaining disease severity information of the first interviewee;
step S830: constructing a follow-up basic requirement according to the disease type of the first interviewee and the disease severity information of the first interviewee;
step S840: obtaining a first followed-up visitor corresponding to the first followed-up visitor;
step S850: obtaining first monitoring information, wherein the first monitoring information comprises follow-up process monitoring of the first visitor by the first visitor;
step S860: judging whether the follow-up process of the first visitor to the first visitor meets the follow-up basic requirement or not according to the first monitoring information;
step S870: and if the follow-up process of the first visitor to the first interviewee does not accord with the follow-up basic requirement, obtaining second reminding information, wherein the second reminding information is used for reminding the first visitor that the follow-up process of the first visitor to the first interviewee does not accord with the follow-up basic requirement.
Specifically, in order to further standardize the clinical diagnosis and treatment follow-up visit of the patient, the disease type of a first interviewee can be obtained, the first interviewee is an interviewee in an interviewee population, for example, the first interviewee can be a new coronary pneumonia epidemic situation rehabilitee, the disease type can be a new coronary pneumonia virus infection, the disease severity information of the first interviewee can be further obtained, the infection degree of the new coronary pneumonia virus can be further understood, a follow-up basic requirement is further constructed according to the disease type of the first interviewee and the disease severity information of the first interviewee, the follow-up basic requirement is specific to the disease state, for example, when the new coronary pneumonia epidemic situation exists, a protective measure should be made to avoid problems such as secondary infection, and meanwhile, first monitoring information is obtained, and the first monitoring information comprises the follow-up process monitoring of the first interviewee on the first interviewee, and then, the follow-up process monitoring is judged, whether the follow-up process of the first followed-up visitor to the first followed-up visitor meets the follow-up basic requirement or not is judged, namely, whether a protective measure is made or not is judged, and the like is also judged, when the follow-up process of the first followed-up visitor to the first followed-up visitor does not meet the follow-up basic requirement, namely, the first followed-up visitor does not make the protective measure, second reminding information is obtained, the second reminding information is used for reminding the first followed-up visitor that the follow-up process of the first followed-up visitor does not meet the follow-up basic requirement, and the follow-up basic requirement is matched according to the disease type and the severity of the patient, so that the technical effects of more reasonable and normative follow-up of the patient and improving the follow-up efficiency are achieved.
Further, the embodiment of the application further comprises:
step S910: obtaining a first set of visited populations;
step S920: obtaining a disease category and a disease severity for the first group of visited populations;
step S930: acquiring professional information and professional grade information of a second visitor;
step S940: judging whether the second followed-up visitor meets the follow-up visit requirements of the first group of visited groups or not according to the professional information of the second followed-up visitor and the professional grade information;
step S950: obtaining a first deficient specialty of a second visitor if the second visitor is unable to meet the follow-up requirements of the first group of visited populations;
step S960: obtaining a third follow-up visitor according to the first lack specialty of the second follow-up visitor, wherein the third follow-up visitor belongs to the first lack specialty;
step S970: obtaining a first allocation instruction;
step S980: assigning the third and second visitors to the first set of visited populations according to the first assignment instruction.
Specifically, in order to match a suitable professional follow-up visitor with the interviewee for follow-up visit, a first group of interviewed population can be obtained, the first group of interviewed population is the interviewed population, the disease type and the disease severity of the first group of interviewed population are further obtained, the disease type is the disease type of the first group of interviewed population, professional information and professional grade information of a second interviewer can be further obtained, the professional information is the professional research information of the second interviewer, whether the disease type of the first group of interviewed population is matched or not is judged, the professional grade information is the professional research grade of the second interviewer, whether the grade of the professional research grade meets the standard or not is judged, and whether the second interviewed population meets the follow-up visit requirement of the first group of interviewed population or not is judged according to the professional information of the second interviewer and the professional grade information, it can be further understood that for some patients requiring intensive observation, a doctor with higher professional qualification grade is required to perform follow-up visits, a doctor who enters a department at first cannot complete the follow-up visit requirements, or a medical aspect complies with medical compliance, a professional doctor should perform follow-up visits on the disease field studied by the doctor, if the second follow-up visitor cannot meet the follow-up visit requirements of the first group of visited populations, i.e. the specialties are not matched or the grades are not enough, a first deficient specialty of a second follow-up visitor is obtained, for example, the first group of visited populations has heart disease, and the professional study of the second follow-up visitor is infectious disease, the follow-up visit requirements are not met, the first deficient specialty can be understood as cardiology specialty, a third follow-up visitor can be obtained according to the first deficient specialty of the second follow-up visitor, and the third follow-up visitor belongs to the first deficient specialty, namely, the professional research of the third followed-up person is cardiology, and then a first distribution instruction is obtained, wherein the first distribution instruction is used for distributing the third followed-up person and the second followed-up person to the first group of visited groups, so that the illness state information of the first group of visited groups is matched with the specialties of the followed-up persons, the follow-up persons who are matched with the specialties of the followed-up persons are visited, and the technical effects of standardizing the clinical diagnosis and treatment follow-up are achieved.
Further, the embodiment of the application further comprises:
step S1010: obtaining mobility capability information of a second interviewee;
step S1020: and determining a first follow-up mode according to the action capability information.
Specifically, the method can also obtain the action capability information of a second interviewee, wherein the action capability information is used for judging whether the second interviewee has normal action capability or not, and further determining a first follow-up method according to the action capability information.
Further, the embodiment of the application further comprises:
step S1030: obtaining location information for a first group of visited populations;
step S1040: determining the coverage area range of the first group of visited groups according to the position information of the first group of visited groups;
step S1050: determining a first trip mode according to the coverage area range;
step S1060: and obtaining third reminding information according to the first travel mode, wherein the third reminding information is used for recommending a visitor to select the first travel mode.
Specifically, the location information of a first group of visited groups may be obtained, the location information is the location of the first group of visited groups, and then the coverage area range of the first group of visited groups is determined according to the location information of the first group of visited groups, that is, when the first group of visited groups is located in a certain community, other communities around the community are locked, so that the coverage area range is determined, and a first travel mode is determined according to the coverage area range, which may be further understood as that when the coverage area range is convenient to transport, a vehicle may be driven to go, if the transportation is inconvenient, only a person may walk to go, and the like, and then third reminding information is obtained according to the first travel mode, and the third reminding information is used to recommend a visitor to select the first travel mode, and by matching the travel mode according to the coverage area range of the visited groups, the technical effects of improving follow-up efficiency and further enabling follow-up to be more standardized are achieved.
Further, the embodiment of the application further comprises:
step S1110: obtaining first follow-up visit time information;
step S1120: obtaining first route time information, wherein the first route time information is time length information of position information from the visitor to the first group of visited groups;
step S1130: acquiring first reminding information according to the first follow-up visit time information and the first route time information;
step S1140: and reminding the first group of visited groups to prepare for follow-up visit according to the first reminding information.
Specifically, first follow-up time information can be obtained, the first follow-up time information is follow-up time information agreed by a follow-up person and a visited person, first route time information is obtained, the first route time information is time length information of the position information of the first group visited group from the follow-up person, namely the time length spent by the follow-up person on the road, first reminding information is obtained according to the first follow-up time information and the first route time information, the first reminding information is used for reminding the first group visited group to make follow-up preparation, the follow-up progress is prevented from being influenced because the follow-up person arrives at the destination and the visited person is not at home or does not make preparation, and the technical effects of saving follow-up time and improving follow-up efficiency are achieved by reminding the follow-up preparation of the visited person.
To sum up, the clinical diagnosis and treatment follow-up normalization method and system provided by the embodiment of the application have the following technical effects:
1. the interviewees are grouped according to the position information and the interviewee key information of the interviewees, and meanwhile, the professional information of the follow-up interviewees is combined to match the suitable interviewees for follow-up interviewing, so that the follow-up interviewing result achieves the established effect, is real and effective, is convenient for doctors to master the illness state of the interviewees in real time, and further has the better technical effect of serving patients.
2. The follow-up visitor corresponding to the major repair specialty is matched according to the illness type of the interviewee, the travel mode of the follow-up visitor is matched according to the coverage area range of the interviewee, unreasonable follow-up visit requirements in the follow-up visit process are timely reminded, more reasonable service for patients is achieved, the follow-up visit of clinical diagnosis and treatment is standardized, and the technical effect of follow-up visit efficiency is improved.
Example two
Based on the same inventive concept as the clinical diagnosis and treatment follow-up normalization method in the foregoing embodiment, the present invention further provides a clinical diagnosis and treatment follow-up normalization system, as shown in fig. 2, the system includes:
the first obtaining unit 11: the first obtaining unit 11 is used for obtaining interviewee population information;
the second obtaining unit 12: the second obtaining unit 12 is configured to obtain location information of each interviewee in the interviewee group according to the interviewee group information;
the third obtaining unit 13: the third obtaining unit 13 is configured to obtain visited key point information of each visited person in the visited person group according to the visited person group information;
first input unit 14: the first input unit 14 is configured to input the location information and the visited key point information into a neural network model, and group the visited persons to obtain a first grouping result;
the fourth obtaining unit 15: the fourth obtaining unit 15 is used for obtaining professional information of the followed person;
first distribution unit 16: the first allocation unit 16 is configured to allocate corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
Further, the system further comprises:
a second input unit: the second input unit is configured to input the position information and the visited key point information as input data into a neural network model, where the neural network model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the location information, the visited key point information and identification information for identifying a grouping result;
a fifth obtaining unit: the fifth obtaining unit is configured to obtain first output information of the neural network model, where the first output information is a first grouping result of the interviewee population.
Further, the system further comprises:
a sixth obtaining unit: the sixth obtaining unit is used for obtaining the follow-up visit time limit information of the first visitor;
a seventh obtaining unit: the seventh obtaining unit is configured to obtain a last diagnosis and treatment time of a first interviewee, where the first interviewee is an interviewee in an interviewee group;
an eighth obtaining unit: the eighth obtaining unit is configured to obtain first reminding information according to the last diagnosis and treatment time of the first interviewee and the follow-up visit time limit information;
the first reminding unit: the first reminding unit is used for reminding the first interviewee of needing follow-up visit according to the first reminding information.
Further, the system further comprises:
a ninth obtaining unit: the ninth obtaining unit is for obtaining a disease type of a first interviewee, the first interviewee being an interviewee in an interviewee population;
a tenth obtaining unit: the tenth obtaining unit is used for obtaining the disease severity information of the first interviewee;
a first building unit: the first construction unit is used for constructing a follow-up basic requirement according to the disease type of the first interviewee and the disease severity information of the first interviewee;
an eleventh obtaining unit: the eleventh obtaining unit is configured to obtain a first followed-up person corresponding to the first followed-up person;
a twelfth obtaining unit: the twelfth obtaining unit is configured to obtain first monitoring information, where the first monitoring information includes a follow-up process monitoring of the first visitor by the first visitor;
a first judgment unit: the first judging unit is used for judging whether the follow-up process of the first visitor to the first visitor meets the follow-up basic requirement or not according to the first monitoring information;
a thirteenth obtaining unit: the thirteenth obtaining unit is configured to obtain second reminding information if the follow-up procedure of the first followed-up visitor to the first followed-up visitor does not meet the follow-up basic requirement, where the second reminding information is used to remind the first followed-up visitor that the follow-up procedure of the first followed-up visitor to the first followed-up visitor does not meet the follow-up basic requirement.
Further, the system further comprises:
a fourteenth obtaining unit: the fourteenth obtaining unit is for obtaining a first set of visited populations;
a fifteenth obtaining unit: the fifteenth obtaining unit is used for obtaining the disease category and disease severity of the first group of visited populations;
a sixteenth obtaining unit: the sixteenth obtaining unit is used for obtaining professional information and professional grade information of a second visitor;
a second judgment unit: the second judging unit is used for judging whether the second followed-up visitor meets the follow-up visit requirements of the first group of visited groups or not according to the professional information and the professional grade information of the second followed-up visitor;
a seventeenth obtaining unit: the seventeenth obtaining unit is used for obtaining a first lack of expertise of a second visitor if the second visitor cannot meet the follow-up requirements of the first group of visited populations;
an eighteenth obtaining unit: the eighteenth obtaining unit is configured to obtain a third followed-up person according to the first lack specialty of the second followed-up person, where the third followed-up person belongs to the first lack specialty;
a nineteenth obtaining unit: the nineteenth obtaining unit is used for obtaining a first allocation instruction;
a second allocation unit: the second allocation unit is configured to allocate the third followed-up person and the second followed-up person to the first group of visited populations according to the first allocation instruction.
Further, the system further comprises:
a twentieth obtaining unit: the twentieth obtaining unit is configured to obtain the mobility capability information of the second interviewee;
a first determination unit: the first determination unit is used for determining a first follow-up mode according to the action capability information.
Further, the system further comprises:
a twenty-first obtaining unit: the twenty-first obtaining unit is used for obtaining the position information of the first group of visited groups;
a second determination unit: the second determining unit is used for determining the coverage area range of the first group of visited groups according to the position information of the first group of visited groups;
a third determination unit: the third determining unit is configured to determine a first travel mode according to the coverage area range;
a twenty-second obtaining unit: the twenty-second obtaining unit is configured to obtain third reminding information according to the first travel mode, where the third reminding information is used to recommend a visitor to select the first travel mode.
Various changes and specific examples of the clinical diagnosis and treatment visit normalization method in the first embodiment of fig. 1 are also applicable to the clinical diagnosis and treatment visit normalization system in the present embodiment, and through the detailed description of the clinical diagnosis and treatment visit normalization method, a person skilled in the art can clearly know the implementation method of the clinical diagnosis and treatment visit normalization system in the present embodiment, so for the brevity of the description, detailed description is not repeated.
EXAMPLE III
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the clinical diagnosis and treatment follow-up normalization method in the embodiment, the invention also provides a clinical diagnosis and treatment follow-up normalization system, wherein a computer program is stored on the system, and when the program is executed by a processor, the steps of any one of the methods of the clinical diagnosis and treatment follow-up normalization method are realized.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the application provides a follow-up visit standardization method for clinical diagnosis and treatment, wherein the method comprises the following steps: obtaining the group information of the interviewees; obtaining the position information of each interviewee in the interviewee group according to the interviewee group information; obtaining the visited key point information of each visited person in the visited person group according to the visited person group information; inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result; acquiring professional information of a follow-up visitor; and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A clinical diagnosis and treatment follow-up visit normalization method, wherein the method comprises the following steps:
obtaining the group information of the interviewees;
obtaining the position information of each interviewee in the interviewee group according to the interviewee group information;
obtaining the visited key point information of each visited person in the visited person group according to the visited person group information;
inputting the position information and the visited key information into a neural network model, and grouping the visitors to obtain a first grouping result;
acquiring professional information of a follow-up visitor;
and distributing corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
2. The method of claim 1, wherein said inputting said location information and said visited focal point information into a neural network model, grouping said visited persons, obtaining a first grouping result, comprises:
inputting the position information and the visited key point information as input data into a neural network model, wherein the neural network model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the location information, the visited key point information and identification information for identifying a grouping result;
obtaining first output information of the neural network model, wherein the first output information is a first grouping result of a visitor population.
3. The method of claim 1, wherein the method comprises:
obtaining follow-up visit time limit information of a first interviewee;
obtaining the last diagnosis and treatment time of a first interviewee, wherein the first interviewee is an interviewee in an interviewee group;
acquiring first reminding information according to the last diagnosis and treatment time of the first interviewee and the follow-up visit time limit information;
and reminding the first interviewee of needing follow-up visit according to the first reminding information.
4. The method of claim 1, wherein the method comprises:
obtaining a disease type of a first interviewee, the first interviewee being an interviewee in an interviewee population;
obtaining disease severity information of the first interviewee;
constructing a follow-up basic requirement according to the disease type of the first interviewee and the disease severity information of the first interviewee;
obtaining a first followed-up visitor corresponding to the first followed-up visitor;
obtaining first monitoring information, wherein the first monitoring information comprises follow-up process monitoring of the first visitor by the first visitor;
judging whether the follow-up process of the first visitor to the first visitor meets the follow-up basic requirement or not according to the first monitoring information;
and if the follow-up process of the first visitor to the first interviewee does not accord with the follow-up basic requirement, obtaining second reminding information, wherein the second reminding information is used for reminding the first visitor that the follow-up process of the first visitor to the first interviewee does not accord with the follow-up basic requirement.
5. The method of claim 1, wherein the method comprises:
obtaining a first set of visited populations;
obtaining a disease category and a disease severity for the first group of visited populations;
acquiring professional information and professional grade information of a second visitor;
judging whether the second followed-up visitor meets the follow-up visit requirements of the first group of visited groups or not according to the professional information of the second followed-up visitor and the professional grade information;
obtaining a first deficient specialty of a second visitor if the second visitor is unable to meet the follow-up requirements of the first group of visited populations;
obtaining a third follow-up visitor according to the first lack specialty of the second follow-up visitor, wherein the third follow-up visitor belongs to the first lack specialty;
obtaining a first allocation instruction;
assigning the third and second visitors to the first set of visited populations according to the first assignment instruction.
6. The method of claim 1, wherein the method comprises:
obtaining mobility capability information of a second interviewee;
and determining a first follow-up mode according to the action capability information.
7. The method of claim 5, wherein the method comprises:
obtaining location information for a first group of visited populations;
determining the coverage area range of the first group of visited groups according to the position information of the first group of visited groups;
determining a first trip mode according to the coverage area range;
and obtaining third reminding information according to the first travel mode, wherein the third reminding information is used for recommending a visitor to select the first travel mode.
8. A clinical visit normalization system, wherein the system comprises:
a first obtaining unit: the first obtaining unit is used for obtaining the interviewee population information;
a second obtaining unit: the second obtaining unit is used for obtaining the position information of each interviewee in the interviewee group according to the interviewee group information;
a third obtaining unit: the third obtaining unit is used for obtaining the visited key point information of each visited person in the visited person group according to the visited person group information;
a first input unit: the first input unit is used for inputting the position information and the visited key point information into a neural network model, and grouping the visited persons to obtain a first grouping result;
a fourth obtaining unit: the fourth obtaining unit is used for obtaining professional information of the followed person;
a first distribution unit: the first allocation unit is used for allocating corresponding visitors to the visitors in different groups according to the first grouping result and the professional information of the visitors.
9. A clinical visit normalization apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method of any one of claims 1-7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090138282A1 (en) * | 2007-11-28 | 2009-05-28 | Chuck Lee | System and Method for Tracking and Maintaining Vascular Access Medical Records |
| CN106296524A (en) * | 2016-08-19 | 2017-01-04 | 北京大学人民医院 | Follow-up method and system |
| CN106446591A (en) * | 2016-11-26 | 2017-02-22 | 烟台凯富医疗科技有限公司 | Intelligent follow-up visit management system based on medical system |
| CN107436988A (en) * | 2016-05-26 | 2017-12-05 | 上海蝶科软件有限公司 | A kind of implementation method of hospital's Supervise |
| CN109493958A (en) * | 2018-10-23 | 2019-03-19 | 平安医疗健康管理股份有限公司 | A kind of follow-up ways to draw up the plan, device, server and medium |
| CN109616164A (en) * | 2018-10-18 | 2019-04-12 | 湖州市中心医院 | A kind of medical follow up system and its follow-up method |
| CN109887611A (en) * | 2019-02-13 | 2019-06-14 | 北京大学第三医院（北京大学第三临床医学院） | A kind of medical management system and mobile terminal |
-
2020
- 2020-12-29 CN CN202011587528.5A patent/CN112289395A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090138282A1 (en) * | 2007-11-28 | 2009-05-28 | Chuck Lee | System and Method for Tracking and Maintaining Vascular Access Medical Records |
| CN107436988A (en) * | 2016-05-26 | 2017-12-05 | 上海蝶科软件有限公司 | A kind of implementation method of hospital's Supervise |
| CN106296524A (en) * | 2016-08-19 | 2017-01-04 | 北京大学人民医院 | Follow-up method and system |
| CN106446591A (en) * | 2016-11-26 | 2017-02-22 | 烟台凯富医疗科技有限公司 | Intelligent follow-up visit management system based on medical system |
| CN109616164A (en) * | 2018-10-18 | 2019-04-12 | 湖州市中心医院 | A kind of medical follow up system and its follow-up method |
| CN109493958A (en) * | 2018-10-23 | 2019-03-19 | 平安医疗健康管理股份有限公司 | A kind of follow-up ways to draw up the plan, device, server and medium |
| CN109887611A (en) * | 2019-02-13 | 2019-06-14 | 北京大学第三医院（北京大学第三临床医学院） | A kind of medical management system and mobile terminal |
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