CN115188458A - Medical information management method and device based on cloud computing - Google Patents
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Abstract
The invention provides a medical information management method and device based on cloud computing, which are characterized in that a topological graph formed by all hospitals is obtained based on medical information of the cloud computing, a comprehensive evaluation value is calculated based on a field value of each hospital node in the topological graph, and a target hospital set with a front comprehensive evaluation value is screened out based on the comprehensive evaluation value, so that the medical experience of a patient can be greatly improved. Then, receiving data are respectively counted based on each hospital node in the target hospital set, and then a fault hospital set with delayed diagnosis in the patient diagnosis process and a to-be-scheduled hospital set with non-zero remaining hospital capacity of each hospital node at the current time are determined in real time based on the receiving data. Then, the fault hospital set and the to-be-scheduled hospital set carry out scheduling management on the patients, the problem that the waiting time of the patients is long is solved, blind diagnosis of the patients is effectively reduced, and the condition that the waiting area is jammed is relieved.
Description
Technical Field
The invention relates to the technical field of medical configuration resource information management, in particular to a medical information management method and device based on cloud computing.
Background
The medical resource information is a general term of production elements for providing medical services, and generally includes personnel, medical expenses, medical institutions, medical beds, medical facilities and equipment, knowledge skills, information and the like, the medical resource allocation refers to allocation and flow of treatment resources in hospitals or between hospitals, uneven medical resource allocation still remains a major problem to be solved, and the medical resource scheduling system is a system for solving the problem.
However, in the existing medical resource scheduling system, the scheduling of medical resources is still not reasonable enough in use, and meanwhile, the medical resources are not reasonably scheduled, so that when medical needs and idle medical resources exist at the same time, the medical needs and the idle medical resources cannot be effectively configured. The lack of scheduling of the acquisition of medical resources on such lines results in a low efficiency and accuracy of the acquisition of medical resources by physicians and patients. Therefore, a system for scheduling medical resources in an online manner based on cloud-computing medical information is currently required.
Disclosure of Invention
The invention aims to provide a medical information management method and device based on cloud computing to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the application provides a medical information management method based on cloud computing, which comprises the following steps:
acquiring a hospital set to be treated, wherein the hospital set to be treated is a topological graph of all hospital nodes extending to the periphery by taking the position of a patient as the center; each hospital node comprises corresponding field information, wherein the field information comprises service quality information, journey time information and medical level information;
calculating based on the field information corresponding to each hospital node in the hospital set to be treated, and determining a target hospital set, wherein the target hospital set is obtained by screening the hospital set to be treated;
respectively counting treatment data based on each hospital node in the target hospital set, wherein the treatment data comprises patient treatment data and hospital remaining capacity data;
respectively determining a failure hospital set and a hospital set to be scheduled based on all the treatment data, wherein each hospital node in the failure hospital set comprises failure information, and the failure information comprises patients with delayed treatment and the average time of delayed treatment; the hospital set to be scheduled is the hospital node with the residual capacity of the hospital in a preset range by taking the position of a patient as the center;
and scheduling the hospital for the patient to see a doctor based on the failure hospital set and the hospital set to be scheduled so as to manage the medical resource configuration information.
The invention has the beneficial effects that:
in the application, the medical information based on cloud computing extends to the periphery by taking the current position of a patient as a midpoint, a topological graph formed by all hospitals is obtained, and a comprehensive evaluation value is computed based on the field value of each hospital node in the topological graph. The comprehensive evaluation value considers the influence of three factors of the service quality information, the route time information and the medical level information, and screens out a target hospital set with the front comprehensive evaluation value based on the comprehensive evaluation value, so that the medical experience of a patient can be improved to a great extent. Then, receiving data are respectively counted based on each hospital node in the target hospital set, and then a fault hospital set with delayed diagnosis in the patient diagnosis process and a to-be-scheduled hospital set with non-zero remaining hospital capacity of each hospital node at the current time are determined in real time based on the receiving data. Then, patients with delay in hospitalization are transferred to hospitals with non-zero residual capacity nearby to be treated, the flow of the patients in one day can be distributed to each hospital as evenly as possible, the problem of long waiting time of the patients can be solved while the pressure of doctor consultation is relieved, and the conditions of blind hospitalization of the patients and congestion in a waiting area are effectively reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flow chart of a cloud computing-based medical information management method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a medical information management apparatus based on cloud computing according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a large data storage device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
Example 1:
the embodiment provides a medical information management method based on cloud computing.
Referring to fig. 1, it is shown that the method comprises steps S1-S5, wherein:
s1, acquiring a hospital set to be treated, wherein the hospital set to be treated is a topological graph of all hospital nodes extending to the periphery by taking the position of a patient as a center; each hospital node comprises corresponding field information, wherein the field information comprises service quality information, journey time information and medical level information.
It can be understood that, in this step, all hospitals are found by extending around the current position of the patient to be treated, the hospitals are used as network nodes, paths between different hospitals are represented by connecting lines as network edges, and a treatment hospital set is constructed as shown in formula (1) and formula (2):
wherein,
in order to be integrated in the hospital for treatment,
the method comprises the following steps that A is an edge set of hospital nodes, and A is a topological graph of the hospital nodes; n is the total number of all hospital nodes in the hospital node topological graph;
indicating the location of the patient in the patient,
hospital section for showing patient hospitalizingPoint;
is represented by (
，
) The edge of (2). Based on each hospital node in the hospital node topological graph, corresponding field information such as service quality information, journey time information and medical level information is obtained respectively. In this embodiment, the service quality information may be obtained by scoring the hospital service by a plurality of hospitalizing patients, and taking an average value as the service quality information of the hospital. The medical level information can be comprehensively scored according to factors such as the technical level of doctors and the level of medical equipment of the hospital and the like of a plurality of experts, and the average value of the comprehensive evaluation of the plurality of experts is taken as the medical level information. The distance time information can be used for inquiring the expected time spent by instruments such as a GPS positioning instrument and the like to serve as distance time information, and the field information is used as a side weight value of a corresponding hospital node.
The method for acquiring the travel time information includes step S11, step S12, and step S13.
S11, acquiring first information, second information and third information based on each hospital node; the first information is time-use information when a patient reaches the hospital node without obstacles; the second information is the probability of traffic jam when the current time point of the patient reaches the hospital node; the third information is an average occlusion time of a path taken by a patient to reach the hospital node.
It can be understood that, in this step, a travel path is determined according to the current position of the patient and each hospital node (in this embodiment, the shortest distance is used as the criterion, in other embodiments, the shortest obstruction time is used as the criterion, and no limitation is specifically made), and statistics is performed based on the historical data corresponding to each path to obtain the first information, the second information, and the third information.
And S12, obtaining an obstacle time parameter based on the product of the second information and the third information.
It can be understood that, in this step, considering the traffic jam on the commute and off the commute and some roads, the time taken by the current time and the possible jam on the current road can be reflected in real time by the obstacle time parameter.
And S13, obtaining the distance time information based on the sum of the obstacle time parameter and the first information.
It will be appreciated that in this step, the total time taken to reflect the current trip is reflected in real time based on the sum of the barrier time parameter and the first information.
S2, calculating based on the field information corresponding to each hospital node in the hospital set to be treated, and determining a target hospital set, wherein the target hospital set is obtained after screening based on the hospital set to be treated.
It is understood that, in this step, a comprehensive evaluation value of each hospital node is calculated by summing up according to the field information corresponding to each hospital node, and then a preset number of hospitals in the front are selected to form a target hospital set based on the descending order of the comprehensive evaluation values.
Further, in order to improve the adaptability between the patient selection habit and the medical information management platform in consideration of the fact that the interest points of each patient himself/herself in selecting a hospital are different, step S2 further includes step S21, step S22 and step S23.
S21, acquiring a first degree of interest, a second degree of interest and a third degree of interest of a patient, wherein the first degree of interest is an estimated value of the degree of interest of the patient on service quality; the second degree of interest is an estimated value of the degree of interest of the patient for travel time; the third degree of interest is an estimate of the patient's degree of interest for each of the hospital nodes.
It is understood that in this step, the first, second and third degrees of care are determined based on the points of care that each patient is more interested in attending themselves.
And S22, calculating based on the first degree of relevance, the second degree of relevance, the third degree of relevance and the field information to obtain a comprehensive evaluation value.
It can be understood that, in this step, according to the comprehensive evaluation value, the patient can conveniently select a hospital more meeting the expected desire of the patient in the later period according to the needs of the patient under the condition that the service quality information, the route time information and the medical level information are different, so that the personalized selection is realized, and the experience of the patient is improved.
Further, the above-described calculation method of the comprehensive evaluation value includes step S221, step S222, step S223, and step S224.
And S221, obtaining the clinic experience cost value based on the product of the first degree of concern and the service quality information.
And step S222, obtaining a time cost value based on the product of the second degree of interest and the journey time information.
And S223, obtaining a cure cost value based on the product of the third degree of relevance and the medical treatment level information.
Step S224, obtaining the comprehensive evaluation value based on the sum of the time cost value, the visit experience cost value and the cure cost value.
As can be seen from steps S221 to S224, the calculation of the comprehensive evaluation value is as shown in equation (3):
wherein,
is a first degree of concern;
is a second degree of concern;
is the third degree of closeness;
is the service quality information;
the distance time information is obtained;
is medical level information.
And S23, performing descending order arrangement based on the size of the comprehensive evaluation value, and screening the front hospital nodes according to a preset screening number to obtain the target hospital set.
It is understood that, in this step, the larger the composite evaluation value, the more suitable it meets the actual needs of the current patient, and by using it as the screening criterion, the former hospital node is screened as the target hospital set of the current patient.
And S3, respectively counting treatment data based on each hospital node in the target hospital set, wherein the treatment data comprises patient treatment data and hospital residual capacity data.
It is understood that, in this step, based on each hospital node in the target set, the patient visit data (including the personal information of the patient to be visited and the time used for visiting, etc.) and the remaining capacity data of the hospital (the remaining denomination is reserved for the visit in this embodiment, and a certain treatment may be the remaining denomination in other embodiments, etc.) are collected respectively by using various sensors and other devices.
S4, respectively determining a failure hospital set and a hospital set to be scheduled based on all the treatment data, wherein each hospital node in the failure hospital set comprises failure information, and the failure information comprises patients with delayed treatment and the time of patients with delayed treatment; the set of hospitals to be scheduled is the hospital nodes with the residual capacity of the hospitals in a preset range by taking the positions of patients as centers.
It can be understood that, in the present step, in the medical treatment process, according to the patient medical treatment data memorability statistics in the patient receiving data, based on the preset per-person medical treatment time as the judgment basis, the medical treatment delay conditions of each hospital node are counted to form a failure hospital set, so as to describe the phenomenon that the hospital node has medical treatment delay and needs to perform resource scheduling. And, the patients with delayed visit and the visit delay per capita time are counted based on each hospital node in the faulty hospital set. And then, with the current hospital node where the patient is located as the center, all the hospital nodes within the preset distance and with the residual hospital capacity not equal to zero are collected to form a hospital collection to be scheduled.
And S5, scheduling the hospital for the patient to see a doctor based on the fault hospital set and the hospital set to be scheduled so as to manage medical resource configuration information.
It can be understood that, in this step, according to the faulty hospital aggregate and the hospital aggregate to be scheduled, the patient with delayed treatment is recommended to the hospital node with non-zero residual capacity in the nearby hospital nearby for treatment, so that the pressure of the number of patients in treatment in some hospital nodes can be relieved, meanwhile, the waiting time of the patient is reduced, and the utilization rate of medical resources is improved.
In detail, step S5 includes step S51 and step S52.
And S51, based on the fault hospital set, performing descending order arrangement on each fault information according to the visit delay per capita time to obtain a delay time sequence.
It can be understood that, in this step, the larger the visit delay is, the more patients with undiagnosed patients are accumulated later, the longer the waiting time is, and the patients with longest waiting time are treated preferentially by arranging the patients in descending order, so that the anxiety of the patients can be relieved, and the experience of the patients can be improved.
Step S52, personnel scheduling: and taking the first hospital node corresponding to the most front in the delay time sequence as a center, determining a second hospital node closest to the first hospital node in the hospital set to be scheduled, scheduling the patients with delayed hospitalization in the first hospital node to the second hospital until the remaining hospital capacity of the second hospital node is zero, and repeating the personnel scheduling operation until all the patients with delayed hospitalization are recommended completely.
It can be understood that, in this step, based on the patient with a delayed visit corresponding to the first hospital node closest to the first hospital node in the delay time series, the patient is recommended to the second hospital node with non-zero remaining hospital capacity nearby until the remaining hospital capacity of the second hospital node is zero, and then the staff scheduling operation is performed, so as to obtain the remaining delay time series. And repeating the personnel scheduling operation according to the residual delay time sequence, and finishing the recommendation of all the hospitalizing delay patients.
Example 2:
as shown in fig. 2, the present embodiment provides a cloud computing-based medical information management apparatus, which includes an obtaining module 710, a computing module 720, a first statistical module 730, a second statistical module 740, and a scheduling module 750, where:
the obtaining module 710: the hospital acquisition system is used for acquiring a hospital set to be treated, wherein the hospital set to be treated is a topological graph of all hospital nodes extending around a patient position as a center; each hospital node comprises corresponding field information, and the field information comprises service quality information, journey time information and medical level information.
Preferably, the acquisition module 710 comprises a first acquisition unit 711, a first sub-unit 712 and a second sub-unit 713, wherein:
the first acquisition unit 711: the hospital node is used for acquiring first information, second information and third information on the basis of each hospital node; the first information is time-use information when a patient reaches the hospital node without obstacles; the second information is the probability of traffic jam when the current time point of the patient reaches the hospital node; the third information is an average occlusion time of a path taken by a patient to reach the hospital node.
The first subunit 712: for deriving an obstacle time parameter based on a product of the second information and the third information.
Second subunit 713: the distance time information is obtained based on the sum of the obstacle time parameter and the first information.
The calculation module 720: the system is used for calculating based on the field information corresponding to each hospital node in the hospital set to be visited and determining a target hospital set, wherein the target hospital set is obtained after screening based on the hospital set to be visited.
Preferably, the calculation module 720 comprises a third sub-unit 721, a fourth sub-unit 722 and a first ordering unit 723, wherein:
third subunit 721: the system comprises a first degree of interest, a second degree of interest and a third degree of interest of a patient, wherein the first degree of interest is an evaluation value of the degree of interest of the patient on service quality; the second degree of interest is an estimated value of the degree of interest of the patient for travel time; the third degree of interest is an estimate of the patient's degree of interest for each of the hospital nodes.
Fourth subunit 722: and the comprehensive evaluation value is obtained by calculation based on the first degree of relevance, the second degree of relevance, the third degree of relevance and the field information.
Preferably, said fourth subunit 722 comprises a first 7221, a second 7222, a third 7223 and a fourth 7224 computing unit, wherein:
the first calculation unit 7221: the system is used for obtaining the visit experience cost value based on the product of the first degree of care and the service quality information;
second calculation unit 7222: the system is used for obtaining a time cost value based on the product of the second degree of interest and the journey time information;
third calculation unit 7223: for deriving a cure cost value based on a product of the third degree of relevance and the medical level information;
fourth calculation unit 7224: for deriving the composite assessment value based on a sum of the temporal cost value, the treatment experience cost value, and the cure cost value.
The first sorting unit 723: and the hospital node sorting module is used for performing descending order based on the size of the comprehensive evaluation value and screening the hospital nodes at the front according to the preset screening number to obtain the target hospital set.
The first statistics module 730: the hospital node counting system is used for respectively counting treatment data based on each hospital node in the target hospital set, and the treatment data comprises patient treatment data and hospital residual capacity data.
The second statistics module 740: the hospital scheduling system is used for respectively determining a failure hospital set and a to-be-scheduled hospital set based on all the visit data, wherein each hospital node in the failure hospital set comprises failure information, and the failure information comprises a patient with a delayed visit and the mean time of the patient with the delayed visit; the hospital set to be scheduled is the hospital node with the remaining capacity of the hospital within a preset range by taking the position of a patient as a center.
The scheduling module 750: and the hospital scheduling system is used for scheduling the hospital for the patient to see a doctor based on the failure hospital set and the hospital set to be scheduled so as to manage the medical resource configuration information.
In detail, the scheduling module 750 includes a second sorting unit 751 and an adjusting unit 752, wherein:
the second sorting unit 751: the system is used for carrying out descending order arrangement on each fault information according to the visit delay per capita time based on the fault hospital set to obtain a delay time sequence;
the adjusting unit 752: for personnel scheduling: and taking the first hospital node corresponding to the most front in the delay time sequence as a center, determining the second hospital node closest to the first hospital node in the hospital set to be scheduled, recommending the patient with the delayed hospitalization in the first hospital node to the second hospital node for hospitalization, and repeating the personnel scheduling operation until all the patients with the delayed hospitalization are recommended completely until the remaining hospital capacity of the second hospital node is zero.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3:
corresponding to the above method embodiment, the present embodiment further provides a big data storage device 800, and a big data storage device 800 described below and a cloud computing-based medical information management method described above may be referred to in correspondence.
FIG. 3 is a block diagram illustrating a large data storage device 800 according to an exemplary embodiment. As shown in fig. 3, the large data storage device 800 may include: a processor 801, a memory 802. The big data storage device 800 may also include one or more of a multimedia component 803, an I/O interface 804, and a communications component 805.
The processor 801 is configured to control the overall operation of the large data storage device 800, so as to complete all or part of the steps in the cloud computing-based medical information management method. Memory 802 is used to store various types of data to support operations on the mass data storage device 800, such data may include, for example, instructions for any application or method operating on the mass data storage device 800, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the big data storage device 800 and other devices, and the corresponding communication component 805 includes: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the mass data storage Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described cloud computing-based medical information management method.
In another exemplary embodiment, there is also provided a computer storage medium including program instructions which, when executed by a processor, implement the steps of the cloud computing-based medical information management method described above. For example, the computer storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the big data storage device 800 to perform the cloud computing-based medical information management method described above.
Example 4:
corresponding to the above method embodiment, a storage medium is also provided in this embodiment, and a storage medium described below and a cloud computing-based medical information management method described above may be referred to in correspondence with each other.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the cloud computing-based medical information management method of the above-described method embodiments.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A medical information management method based on cloud computing is characterized by comprising the following steps:
acquiring a hospital set to be treated, wherein the hospital set to be treated is a topological graph of all hospital nodes extending to the periphery by taking the position of a patient as the center; each hospital node comprises corresponding field information, wherein the field information comprises service quality information, journey time information and medical level information;
calculating based on the field information corresponding to each hospital node in the hospital set to be visited, and determining a target hospital set, wherein the target hospital set is obtained after screening based on the hospital set to be visited;
respectively counting treatment data based on each hospital node in the target hospital set, wherein the treatment data comprises patient treatment data and hospital residual capacity data;
respectively determining a failure hospital set and a hospital set to be scheduled based on all the treatment data, wherein each hospital node in the failure hospital set comprises failure information, and the failure information comprises patients with delayed treatment and the average time of patients with delayed treatment; the hospital set to be scheduled is the hospital node with the remaining capacity of the hospital in a preset range by taking the position of a patient as a center;
and scheduling the hospital for the patient to see a doctor based on the failure hospital set and the hospital set to be scheduled so as to manage the medical resource configuration information.
2. The cloud-computing-based medical information management method according to claim 1, wherein the method for acquiring the journey time information includes:
acquiring first information, second information and third information based on each hospital node; the first information is time-use information when a patient reaches the hospital node without obstacles; the second information is the probability of traffic jam when the current time point of the patient reaches the hospital node; the third information is the average blockage time of the path taken by the patient to reach the hospital node;
obtaining an obstacle time parameter based on a product of the second information and the third information;
and obtaining the journey time information based on the sum of the obstacle time parameter and the first information.
3. The cloud computing-based medical information management method of claim 1, wherein computing based on the field information corresponding to each hospital node in the set of hospitals to see a doctor and determining a target set of hospitals comprises:
acquiring a first degree of interest, a second degree of interest and a third degree of interest of a patient, wherein the first degree of interest is an estimated value of the degree of interest of the patient on service quality; the second degree of interest is an estimated value of the degree of interest of the patient for travel time; the third degree of interest is an estimated value of the degree of interest of the patient to each hospital node;
calculating based on the first degree of relevance, the second degree of relevance, the third degree of relevance and the field information to obtain a comprehensive evaluation value;
and performing descending order based on the size of the comprehensive evaluation value, and screening the hospital nodes at the front according to a preset screening number to obtain the target hospital set.
4. The medical information management method based on cloud computing according to claim 3, wherein calculating based on the first degree of concern, the second degree of concern, the third degree of concern, and the field information to obtain a composite evaluation value includes:
obtaining a treatment experience cost value based on the product of the first degree of care and the service quality information;
obtaining a time cost value based on the product of the second degree of interest and the journey time information;
obtaining a cure cost value based on a product of the third degree of relevance and the medical level information;
and obtaining the comprehensive evaluation value based on the sum of the time cost value, the visit experience cost value and the cure cost value.
5. The cloud-computing-based medical information management method of claim 1, wherein scheduling patient hospitalizations hospitals to manage medical resource configuration information based on the failed hospital set and the to-be-scheduled hospital set comprises:
based on the fault hospital set, performing descending order arrangement on each fault information according to the visit delay per capita time to obtain a delay time sequence;
and (3) personnel scheduling: and taking the first hospital node corresponding to the most front position in the delay time sequence as a center, determining a second hospital node closest to the first hospital node in the hospital set to be scheduled, recommending the patient with delayed treatment in the first hospital node to the second hospital node for treatment, and repeating the personnel scheduling operation until the residual hospital capacity of the second hospital node is zero, and finishing the recommendation of all the patients with delayed treatment.
6. A medical information management apparatus based on cloud computing, comprising:
an acquisition module: the hospital acquisition system is used for acquiring a hospital set to be treated, wherein the hospital set to be treated is a topological graph of all hospital nodes extending around a patient position as a center; each hospital node comprises corresponding field information, wherein the field information comprises service quality information, journey time information and medical level information;
a calculation module: the system comprises a field information acquisition module, a field information acquisition module and a field information acquisition module, wherein the field information acquisition module is used for acquiring field information corresponding to each hospital node in the to-be-treated hospital set;
a first statistics module: the hospital node counting system is used for respectively counting treatment data based on each hospital node in the target hospital set, wherein the treatment data comprises patient treatment data and hospital residual capacity data;
a second statistical module: the hospital scheduling system is used for respectively determining a failure hospital set and a to-be-scheduled hospital set based on all the visit data, wherein each hospital node in the failure hospital set comprises failure information, and the failure information comprises a patient with a delayed visit and the mean time of the patient with the delayed visit; the hospital set to be scheduled is the hospital node with the remaining capacity of the hospital in a preset range by taking the position of a patient as a center;
a scheduling module: and the hospital scheduling system is used for scheduling the hospital for patient to see a doctor based on the failure hospital set and the hospital set to be scheduled so as to manage medical resource configuration information.
7. The cloud-computing-based medical information management apparatus according to claim 6, wherein the acquisition module includes:
a first acquisition unit: the hospital node is used for acquiring first information, second information and third information on the basis of each hospital node; the first information is time-use information when a patient reaches the hospital node without obstacles; the second information is the probability of traffic jam when the current time point of the patient reaches the hospital node; the third information is the average blockage time of the path taken by the patient to reach the hospital node;
a first subunit: obtaining an obstacle time parameter based on a product of the second information and the third information;
a second subunit: the distance time information is obtained based on the sum of the obstacle time parameter and the first information.
8. The cloud-computing-based medical information management apparatus according to claim 6, wherein the computing module includes:
a third subunit: the system comprises a first degree of interest, a second degree of interest and a third degree of interest of a patient, wherein the first degree of interest is an evaluation value of the degree of interest of the patient on service quality; the second degree of interest is an estimated value of the degree of interest of the patient for travel time; the third degree of interest is an estimated value of the degree of interest of the patient to each hospital node;
a fourth subunit: the field information acquisition unit is used for calculating based on the first degree of relevance, the second degree of relevance, the third degree of relevance and the field information to obtain a comprehensive evaluation value;
a first sequencing unit: and the hospital node sorting module is used for performing descending order based on the size of the comprehensive evaluation value and screening the hospital nodes at the front according to the preset screening number to obtain the target hospital set.
9. The cloud-computing-based medical information management apparatus according to claim 8, wherein the fourth subunit includes:
the first calculation unit: the system is used for obtaining the treatment experience cost value based on the product of the first degree of care and the service quality information;
a second calculation unit: the system is used for obtaining a time cost value based on the product of the second degree of interest and the journey time information;
a third calculation unit: for deriving a cure cost value based on a product of the third degree of relevance and the medical level information;
a fourth calculation unit: for obtaining the comprehensive evaluation value based on the sum of the time cost value, the visit experience cost value and the cure cost value.
10. The cloud-computing-based medical information management apparatus according to claim 6, wherein the scheduling module includes:
a second sorting unit: the system is used for carrying out descending order arrangement on each fault information according to the visit delay per capita time based on the fault hospital set to obtain a delay time sequence;
an adjusting unit: for personnel scheduling: and taking the first hospital node corresponding to the most front position in the delay time sequence as a center, determining a second hospital node closest to the first hospital node in the hospital set to be scheduled, recommending the patient with delayed treatment in the first hospital node to the second hospital node for treatment, and repeating the personnel scheduling operation until the residual hospital capacity of the second hospital node is zero, and finishing the recommendation of all the patients with delayed treatment.
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