CN116110602A - Information processing method and system applied to medical community - Google Patents
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Abstract
The invention belongs to the technical field of information processing, and discloses an information processing method and system applied to medical communities, wherein the method comprises the following steps: acquiring clinic information from n medical institution servers; classifying the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, setting a corresponding critical threshold value for the classified illness state quantity, comparing and analyzing the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marking the illness state as normal illness state and abnormal illness state; generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode by the corresponding medical institution servers according to the pre-stored contact mode; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an information processing method and system applied to medical communities.
Background
The medical community is a system for realizing medical community information management and sharing by utilizing computer technology and Internet technology, and the system can be connected with a plurality of medical institutions to communicate with doctors so as to realize information sharing.
The existing medical community information processing system is used for forming a treatment scheme database by collecting and integrating medical record data from different medical institutions and carrying out data analysis, so that medical staff can more quickly and accurately formulate the treatment scheme to improve the working efficiency of doctors and medical institutions and the nursing quality of patients;
the lack of analysis of the epidemic situation of the disease, the rapid increase of the number of patients after the spread of the disease and the corresponding drug deficiency, because the medical institution fails to know in advance and extract the preparation, the medical staff is in a hurry to deal with, the nursing quality is reduced, so that the medical co-body information processing system can not better serve the medical institution and the medical staff.
In view of the above, the present application provides an information processing method and system applied to medical community, which can better serve a medical community server to connect a plurality of medical institutions and medical staff.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide an information processing method and system applied to a medical community.
In order to achieve the above purpose, the present invention provides the following technical solutions: an information processing method applied to a medical community is applied to a medical community server, and the method comprises the following steps:
acquiring and storing outpatient information from n medical institution servers, wherein the outpatient information comprises examination information and medicine information, the examination information comprises diagnosis results of patient conditions, and the medicine information comprises consumable data corresponding to the diagnosis results;
classifying the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, setting a corresponding critical threshold value for the classified illness state quantity, comparing and analyzing the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marking the illness state as normal illness state and abnormal illness state according to the comparison and analysis result;
obtaining consumable data corresponding to abnormal conditions in unit time, generating consumable coefficients according to the consumption quantity of prescription drugs and the consumption quantity of medical instruments in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if abnormal medical product consumption is generated, associating the corresponding abnormal conditions with the abnormal medical product consumption;
generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode by the corresponding medical institution servers according to the pre-stored contact mode; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
Further, the specific process of marking the disease as normal and abnormal disease is as follows:
if the corresponding number of the illness states is greater than or equal to the critical threshold value corresponding to the illness state, defining the illness state as abnormal illness state; if the corresponding number of conditions is less than the critical threshold corresponding to the condition, the condition is defined as normal.
Further, the specific process of generating the consumption line number according to the prescription drug consumption number and the medical device consumption number in the consumable data is as follows:
the prescription drug consumption number is denoted as q, the medical instrument consumption number is denoted as w, according to the formula: x=a1×q+a2×w, X is a consumable coefficient, and a1 and a2 are weighting constants related to the number of prescription drug consumption and the number of medical device consumption, respectively, and are both greater than 0.
Further, the specific process of determining whether to generate abnormal medical product consumption is as follows:
comparing the consumable coefficient with a preset consumable coefficient threshold value for analysis, and if the consumable coefficient is greater than or equal to the consumable line number threshold value, generating abnormal medical product consumption;
if the consumable product coefficient is smaller than the consumable line number threshold, no abnormal medical product consumption is generated.
Further, the abnormal medical consumption is associated with frequent abnormality, observed abnormality and occasional abnormality, and diffusion information is generated based on the abnormal medical consumption and abnormal condition having frequent abnormality at the same time.
Further, r consumable product coefficients X of a period after a unit time are obtained, a consumable product coefficient set is established, an average value and a discrete coefficient in the consumable product coefficient set are calculated, and the corresponding abnormal medical product consumption is divided into sporadic abnormality, observation abnormality and frequent abnormality according to the average value and the discrete coefficient, wherein the medical product consumption degree corresponding to the frequent abnormality is larger than the medical product consumption degree corresponding to the observation abnormality, and the medical product consumption degree corresponding to the observation abnormality is larger than the medical product consumption degree corresponding to the sporadic abnormality.
Further, the specific process of calculating the average value and the discrete coefficient in the consumable coefficient set is as follows:
the average value is marked as Pr and,
the method comprises the steps of carrying out a first treatment on the surface of the xi represents the different consumable coefficients in the consumable line number set, and r is the number of consumable coefficients in the consumable line number set; marking discrete coefficients +.>
。
Further, the specific processes of classifying the corresponding abnormal medical product consumption into sporadic abnormality, observed abnormality and frequent abnormality according to the average value and the discrete coefficient are as follows:
comparing the average value Pr with a consumption strain number threshold value for analysis, and comparing the discrete coefficient KJ with a preset discrete coefficient threshold value for analysis;
if the average value Pr is greater than or equal to the consumable coefficient threshold value and the discrete coefficient KJ is greater than or equal to the discrete coefficient threshold value, marking the condition A;
if the average value Pr is greater than or equal to the consumable coefficient threshold value, and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a B condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is larger than or equal to the discrete coefficient threshold value, marking as a C condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a D condition;
the consumption of abnormal medical products corresponding to the B condition is classified as frequent abnormality; the abnormal medical product consumption corresponding to the condition A is divided into abnormal observation; and (5) equally dividing abnormal medical product consumption corresponding to the C condition and the D condition into sporadic abnormalities.
An information processing system applied to a medical community, which is applied to a medical community server, the system comprising:
the data acquisition module acquires and stores outpatient information from n medical institution servers, wherein the outpatient information comprises examination information and medicine information, the examination information comprises diagnosis results of patient conditions, and the medicine information comprises consumable data corresponding to the diagnosis results;
the data management module classifies the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, sets a corresponding critical threshold value for the classified illness state quantity, compares and analyzes the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marks the illness state as normal illness state and abnormal illness state according to the comparison and analysis result;
obtaining consumable data corresponding to abnormal conditions in unit time, generating consumable coefficients according to the consumption quantity of prescription drugs and the consumption quantity of medical instruments in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if abnormal medical product consumption is generated, associating the corresponding abnormal conditions with the abnormal medical product consumption;
the mark processing module is used for generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption at the same time, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode according to a pre-stored contact mode by the corresponding medical institution servers; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
The invention discloses an information processing method and a system applied to medical community, which have the technical effects and advantages that:
according to the disease epidemic situation of the comprehensive analysis of abnormal illness state and consumable data, secondly, the relevant data analysis of a plurality of medical institutions is synthesized through the medical community server, the obtained analysis result is more comprehensive and more accurate, a plurality of medical institutions and medical staff can be connected for the medical community server, the disease epidemic situation is analyzed in advance, timely sharing and informing are achieved in advance, and the better service medical community server is connected with the plurality of medical institutions and the medical staff.
Drawings
FIG. 1 is a schematic diagram of an information processing system applied to a medical community according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an information processing system applied to a medical community in a second embodiment of the present invention;
FIG. 3 is a schematic diagram of an information processing system applied to a medical community in accordance with a third embodiment of the present invention;
FIG. 4 is a schematic diagram of an information processing method applied to a medical community according to the present invention;
fig. 5 is a schematic diagram of connection between servers according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, an information processing system for a medical community according to the present embodiment is applied to a medical community server, and the system includes: the system comprises a data acquisition module 1, a data management module 2 and a mark processing module 3.
The data acquisition module 1 acquires and stores outpatient service information from n medical institution servers, wherein the outpatient service information comprises examination information and medicine information, and the medical institution server authorizes the data acquisition module 1 in the medical community server in a specific acquisition mode so as to obtain acquisition data authority.
The examination information comprises diagnosis results of patient conditions, the medicine information comprises consumable data corresponding to the diagnosis results, the consumable data comprises the consumption quantity of prescription medicines, the consumption quantity of medical instruments and the name of the prescription medicines, the consumable data is provided by medical staff, and the consumable data and the diagnosis results are uploaded to a medical institution server in a one-to-one correspondence.
The data management module 2 classifies the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, sets a corresponding critical threshold value for the classified illness state quantity, compares and analyzes the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marks the illness state as normal illness state and abnormal illness state according to the comparison and analysis results.
And acquiring consumable data corresponding to the abnormal condition in unit time, generating consumable coefficients according to the number of prescription medicine consumption and the number of medical instrument consumption in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if so, associating the corresponding abnormal condition with the abnormal medical product consumption.
The specific process of marking the illness as normal illness and abnormal illness is as follows:
if the corresponding number of the illness states is greater than or equal to the critical threshold value corresponding to the illness state, defining the illness state as abnormal illness state; if the corresponding number of conditions is less than the critical threshold corresponding to the condition, the condition is defined as normal.
The specific process of generating the consumption line number according to the prescription medicine consumption number and the medical appliance consumption number in the consumable data is as follows:
the prescription drug consumption number is denoted as q, the medical instrument consumption number is denoted as w, according to the formula: x=a1×q+a2×w, X is a consumable coefficient, and a1 and a2 are weighting constants related to the number of prescription drug consumption and the number of medical device consumption, respectively, and are both greater than 0.
The specific procedure for determining whether to generate abnormal medical product consumption is as follows:
comparing the consumable coefficient with a preset consumable coefficient threshold value for analysis, and if the consumable coefficient is greater than or equal to the consumable line number threshold value, generating abnormal medical product consumption; if the consumable product coefficient is smaller than the consumable line number threshold, no abnormal medical product consumption is generated.
The mark processing module 3 generates diffusion information of the illness state corresponding to the consumption of the abnormal medical products and sends the diffusion information to n medical institution servers, and the corresponding medical institution servers send the diffusion information to receiving ends corresponding to the contact modes according to the prestored contact modes, wherein the receiving ends are carried by medical staff; the diffusion information comprises a corresponding illness state name and consumable data corresponding to the illness state, and medical staff can purchase in advance and prepare in advance according to the illness state and the consumable data corresponding to the illness state.
The efficacy effectiveness of prescription drugs and non-prescription drugs is guaranteed. Wherein, the over-the-counter medicine is mainly used for treating various common mild diseases which are easy to self-diagnose and self-treat for consumers. The consumption of prescription drugs is considered together with the abnormal illness state, so that the abnormal illness state caused by common mild illness can be eliminated, and the accuracy of the medical community information processing system on the abnormal illness state analysis can be improved.
According to the disease epidemic situation of the comprehensive analysis of abnormal illness state and consumable data, secondly, the relevant data analysis of a plurality of medical institutions is synthesized through the medical community server, the obtained analysis result is more comprehensive and more accurate, a plurality of medical institutions and medical staff can be connected for the medical community server, the disease epidemic situation is analyzed in advance, timely sharing and informing are achieved in advance, and the better service medical community server is connected with the plurality of medical institutions and the medical staff.
Example two
Referring to fig. 2, the present embodiment further improves the design on the basis of implementation, further improves the accuracy of the abnormal condition analysis, and provides an information processing system applied to the medical community, which further includes a data depth management module 4, wherein the conditions corresponding to the abnormal medical product consumption include frequent abnormality, observation abnormality and occasional abnormality, and the mark processing module 3 generates diffusion information according to the abnormal medical product consumption and the abnormal condition with the frequent abnormality.
The data depth management module 4 acquires r consumable coefficient X of a later period of unit time, establishes a consumable coefficient set, calculates an average value and a discrete coefficient in the consumable line number set, and classifies corresponding abnormal medical product consumption into sporadic abnormality, observation abnormality and frequent abnormality according to the average value and the discrete coefficient, wherein the medical product consumption degree corresponding to the frequent abnormality is greater than the medical product consumption degree corresponding to the observation abnormality, and the medical product consumption degree corresponding to the observation abnormality is greater than the medical product consumption degree corresponding to the sporadic abnormality.
The specific process of calculating the average value and the discrete coefficient in the consumption strain number set is as follows:
the average value is marked as Pr and,
the method comprises the steps of carrying out a first treatment on the surface of the xi represents the different consumable coefficients in the consumable line number set, and r is the number of consumable coefficients in the consumable line number set; marking discrete coefficients +.>
。
The specific processes of classifying the corresponding abnormal medical product consumption conditions into sporadic abnormalities, observed abnormalities and frequent abnormalities according to the average value and the discrete coefficient are as follows:
comparing the average value Pr with a consumption strain number threshold value for analysis, and comparing the discrete coefficient KJ with a preset discrete coefficient threshold value for analysis; if the average value Pr is greater than or equal to the consumable coefficient threshold value and the discrete coefficient KJ is greater than or equal to the discrete coefficient threshold value, marking the condition A; if the average value Pr is greater than or equal to the consumable coefficient threshold value, and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a B condition; if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is larger than or equal to the discrete coefficient threshold value, marking as a C condition; if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a D condition;
the consumption of abnormal medical products corresponding to the B condition is classified as frequent abnormality; the abnormal medical product consumption corresponding to the C condition and the D condition is uniformly divided into occasional abnormalities; abnormal medical product consumption corresponding to the case A is classified as observed abnormality.
The abnormal medical product consumption is not frequent, diffusion information can not be generated, the abnormal medical product consumption is frequent, the diffusion information needs to be generated in time, the observation abnormality is between the abnormal abnormality and the frequent abnormality, the observation is continued for a period of time, and whether the diffusion information is generated is judged according to the observation result.
The mark processing module 3 generates diffusion information according to the abnormal medical product consumption and abnormal illness state with frequent abnormality, and further improves the accuracy of analysis of the abnormal illness state and the accuracy of generation of diffusion information of the information processing system of the medical community.
Example III
Referring to fig. 3, the present embodiment further improves the design on the basis of implementation two, and provides an information processing system applied to medical communities, and further includes an index formulation module 5.
When the mark processing module 3 generates diffusion information, the index making module 5 makes corresponding production indexes according to consumable data in the diffusion information, and sends the corresponding production indexes to i consumable production servers; the corresponding consumable product production server sends production indexes to the mobile terminal corresponding to the contact number according to the pre-stored contact number, the mobile terminal is carried by a production responsible person, the corresponding production responsible person can master accurate disease epidemic conditions conveniently, the number of produced medical products is formulated reasonably to cope with the follow-up disease epidemic development conditions, and the better service medical community server is connected with a plurality of medical institutions and medical staff.
Corresponding production indexes are formulated according to the consumable data in the diffusion information, and the prescription drug name is taken as an example, and the specific formulation process is as follows:
dividing the unit time into k time periods, wherein k is an integer greater than 1, extracting consumable data corresponding to abnormal illness conditions in the k time periods, sequentially classifying prescription medicine consumption amounts in the consumable data one by one according to prescription medicine names to obtain consumption amounts corresponding to the classified prescription medicine names, arranging the k time periods according to a time sequence, calculating the growth rate of the consumption amounts corresponding to the prescription medicine names classified by adjacent time periods in the k time periods, establishing a growth rate set of the k growth rates obtained by calculation, calculating an average growth rate in the growth rate set, marking the last time period in the k time periods as a cut-off time period, and marking consumption amounts corresponding to the prescription medicine names corresponding to the cut-off time periods with consumption bases; and calculating according to the consumption base and the average growth rate to obtain the consumption quantity corresponding to the prescription drug name in the next time period of the cut-off time period.
Specifically, the consumption number corresponding to the prescription drug name in the next time period of the deadline is calculated according to the growth formula, XHi =hi (1+li), XHi represents the consumption number corresponding to different prescription drug names, li represents the average growth rate corresponding to different prescription drug names, and Hi represents the consumption base corresponding to different prescription drug names.
The modules are connected through wired signals or wireless signals to realize data transmission among the modules, and the servers are connected through wired signals or wireless signals to realize data transmission among the servers, as shown in fig. 5.
Example IV
Referring to fig. 4, the detailed descriptions of the first, second and third embodiments are omitted, and an information processing method applied to a medical community is provided, and the method is applied to a medical community server, and includes:
acquiring and storing outpatient information from n medical institution servers, wherein the outpatient information comprises examination information and medicine information, the examination information comprises diagnosis results of patient conditions, and the medicine information comprises consumable data corresponding to the diagnosis results;
classifying the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, setting a corresponding critical threshold value for the classified illness state quantity, comparing and analyzing the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marking the illness state as normal illness state and abnormal illness state according to the comparison and analysis result;
obtaining consumable data corresponding to abnormal conditions in unit time, generating consumable coefficients according to the consumption quantity of prescription drugs and the consumption quantity of medical instruments in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if abnormal medical product consumption is generated, associating the corresponding abnormal conditions with the abnormal medical product consumption;
generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode by the corresponding medical institution servers according to the pre-stored contact mode; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
Further, the specific process of marking the disease as normal and abnormal disease is as follows:
if the corresponding number of the illness states is greater than or equal to the critical threshold value corresponding to the illness state, defining the illness state as abnormal illness state; if the corresponding number of conditions is less than the critical threshold corresponding to the condition, the condition is defined as normal.
Further, the specific process of generating the consumption line number according to the prescription drug consumption number and the medical device consumption number in the consumable data is as follows:
the prescription drug consumption number is denoted as q, the medical instrument consumption number is denoted as w, according to the formula: x=a1×q+a2×w, X is a consumable coefficient, and a1 and a2 are weighting constants related to the number of prescription drug consumption and the number of medical device consumption, respectively, and are both greater than 0.
Further, the specific process of determining whether to generate abnormal medical product consumption is as follows:
comparing the consumable coefficient with a preset consumable coefficient threshold value for analysis, and if the consumable coefficient is greater than or equal to the consumable line number threshold value, generating abnormal medical product consumption;
if the consumable product coefficient is smaller than the consumable line number threshold, no abnormal medical product consumption is generated.
Further, the abnormal medical consumption is associated with frequent abnormality, observed abnormality and occasional abnormality, and diffusion information is generated based on the abnormal medical consumption and abnormal condition having frequent abnormality at the same time.
Further, r consumable product coefficients X of a period after a unit time are obtained, a consumable product coefficient set is established, an average value and a discrete coefficient in the consumable product coefficient set are calculated, and the corresponding abnormal medical product consumption is divided into sporadic abnormality, observation abnormality and frequent abnormality according to the average value and the discrete coefficient, wherein the medical product consumption degree corresponding to the frequent abnormality is larger than the medical product consumption degree corresponding to the observation abnormality, and the medical product consumption degree corresponding to the observation abnormality is larger than the medical product consumption degree corresponding to the sporadic abnormality.
Further, the specific process of calculating the average value and the discrete coefficient in the consumable coefficient set is as follows:
the average value is marked as Pr and,
the method comprises the steps of carrying out a first treatment on the surface of the xi represents the different consumable coefficients in the consumable line number set, and r is the number of consumable coefficients in the consumable line number set; marking discrete coefficients +.>
。
Further, the specific processes of classifying the corresponding abnormal medical product consumption into sporadic abnormality, observed abnormality and frequent abnormality according to the average value and the discrete coefficient are as follows:
comparing the average value Pr with a consumption strain number threshold value for analysis, and comparing the discrete coefficient KJ with a preset discrete coefficient threshold value for analysis;
if the average value Pr is greater than or equal to the consumable coefficient threshold value and the discrete coefficient KJ is greater than or equal to the discrete coefficient threshold value, marking the condition A;
if the average value Pr is greater than or equal to the consumable coefficient threshold value, and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a B condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is larger than or equal to the discrete coefficient threshold value, marking as a C condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a D condition;
the consumption of abnormal medical products corresponding to the B condition is classified as frequent abnormality; the abnormal medical product consumption corresponding to the condition A is divided into abnormal observation; and (5) equally dividing abnormal medical product consumption corresponding to the C condition and the D condition into sporadic abnormalities.
Further, when the diffusion information is generated, corresponding production indexes are formulated according to the consumable data in the diffusion information, and the corresponding production indexes are sent to the i consumable production servers; and the corresponding consumable product production server sends the production index to the mobile terminal corresponding to the contact number according to the prestored contact number.
Further, corresponding production indexes are formulated according to the consumable data in the diffusion information, and the specific formulation process is as follows:
dividing the unit time into k time periods, wherein k is an integer greater than 1, extracting consumable data corresponding to abnormal illness conditions in the k time periods, sequentially classifying prescription medicine consumption amounts in the consumable data one by one according to prescription medicine names to obtain consumption amounts corresponding to the classified prescription medicine names, arranging the k time periods according to a time sequence, calculating the growth rate of the consumption amounts corresponding to the prescription medicine names classified by adjacent time periods in the k time periods, establishing a growth rate set of the k growth rates obtained by calculation, calculating an average growth rate in the growth rate set, marking the last time period in the k time periods as a cut-off time period, and marking consumption amounts corresponding to the prescription medicine names corresponding to the cut-off time periods with consumption bases; and calculating according to the consumption base and the average growth rate to obtain the consumption quantity corresponding to the prescription drug name in the next time period of the cut-off time period.
Further, the consumption number corresponding to the prescription drug name in the next time period of the deadline is calculated according to the growth formula, XHi =hi (1+li), XHi represents the consumption number corresponding to different prescription drug names, li represents the average growth rate corresponding to different prescription drug names, and Hi represents the consumption base corresponding to different prescription drug names.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from one website site, computer, server, or data center over a wired network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. An information processing method applied to a medical community, which is characterized by being applied to a medical community server, comprising the following steps:
acquiring and storing outpatient information from n medical institution servers, wherein the outpatient information comprises examination information and medicine information, the examination information comprises diagnosis results of patient conditions, and the medicine information comprises consumable data corresponding to the diagnosis results;
classifying the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, setting a corresponding critical threshold value for the classified illness state quantity, comparing and analyzing the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marking the illness state as normal illness state and abnormal illness state according to the comparison and analysis result;
obtaining consumable data corresponding to abnormal conditions in unit time, generating consumable coefficients according to the consumption quantity of prescription drugs and the consumption quantity of medical instruments in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if abnormal medical product consumption is generated, associating the corresponding abnormal conditions with the abnormal medical product consumption;
generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode by the corresponding medical institution servers according to the pre-stored contact mode; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
2. The method for processing information applied to medical community according to claim 1, wherein the specific process of marking the illness as normal illness and abnormal illness is as follows:
if the corresponding number of the illness states is greater than or equal to the critical threshold value corresponding to the illness state, defining the illness state as abnormal illness state; if the corresponding number of conditions is less than the critical threshold corresponding to the condition, the condition is defined as normal.
3. The information processing method for medical community according to claim 2, wherein the specific process of generating the consumption line number according to the prescription medicine consumption number and the medical instrument consumption number in the consumable data is as follows:
the prescription drug consumption number is denoted as q, the medical instrument consumption number is denoted as w, according to the formula: x=a1×q+a2×w, X is a consumable coefficient, and a1 and a2 are weighting constants for the correlation between the number of prescription drug consumption and the number of medical device consumption, respectively.
4. The information processing method applied to a medical community according to claim 3, wherein the specific process of determining whether to generate abnormal medical product consumption is as follows:
comparing the consumable coefficient with a preset consumable coefficient threshold value for analysis, and if the consumable coefficient is greater than or equal to the consumable line number threshold value, generating abnormal medical product consumption;
if the consumable product coefficient is smaller than the consumable line number threshold, no abnormal medical product consumption is generated.
5. The information processing method according to claim 4, wherein the abnormal medical consumption is associated with a common abnormality, an observed abnormality and a sporadic abnormality, and the diffusion information is generated based on the abnormal medical consumption and the abnormal condition having the common abnormality at the same time.
6. The information processing method applied to medical communities according to claim 5, wherein r consumable product coefficients X of a period after a unit time are acquired, a consumable product coefficient set is established, an average value and a discrete coefficient in the consumable product coefficient set are calculated, and the conditions corresponding to abnormal medical product consumption are classified into sporadic anomalies, observed anomalies and frequent anomalies according to the average value and the discrete coefficient, wherein the medical product consumption degree corresponding to frequent anomalies is greater than the medical product consumption degree corresponding to observed anomalies, and the medical product consumption degree corresponding to observed anomalies is greater than the medical product consumption degree corresponding to sporadic anomalies.
7. The information processing method applied to medical community according to claim 6, wherein the specific process of calculating the average value and the discrete coefficient in the consumption line number set is as follows:
the average value is marked as Pr and,
the method comprises the steps of carrying out a first treatment on the surface of the xi represents the different consumable coefficients in the consumable line number set, and r is the number of consumable coefficients in the consumable line number set; marking discrete coefficients +.>
。
8. The information processing method for medical community according to claim 7, wherein the specific processes of classifying the corresponding abnormal medical product consumption into sporadic abnormalities, observed abnormalities and frequent abnormalities according to the average value and the discrete coefficient are as follows:
comparing the average value Pr with a consumption strain number threshold value for analysis, and comparing the discrete coefficient KJ with a preset discrete coefficient threshold value for analysis;
if the average value Pr is greater than or equal to the consumable coefficient threshold value and the discrete coefficient KJ is greater than or equal to the discrete coefficient threshold value, marking the condition A;
if the average value Pr is greater than or equal to the consumable coefficient threshold value, and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a B condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is larger than or equal to the discrete coefficient threshold value, marking as a C condition;
if the average value Pr is smaller than the consumable coefficient threshold value and the discrete coefficient KJ is smaller than the discrete coefficient threshold value, marking as a D condition;
the consumption of abnormal medical products corresponding to the B condition is classified as frequent abnormality; the abnormal medical product consumption corresponding to the condition A is divided into abnormal observation; and (5) equally dividing abnormal medical product consumption corresponding to the C condition and the D condition into sporadic abnormalities.
9. An information processing system applied to a medical community, which is realized based on the information processing method applied to the medical community according to any one of claims 1 to 8, wherein the system is applied to a medical community server, and the system comprises:
the data acquisition module acquires and stores outpatient information from n medical institution servers, wherein the outpatient information comprises examination information and medicine information, the examination information comprises diagnosis results of patient conditions, and the medicine information comprises consumable data corresponding to the diagnosis results;
the data management module classifies the corresponding illness states in the diagnosis results in unit time to obtain classified illness state quantity, sets a corresponding critical threshold value for the classified illness state quantity, compares and analyzes the corresponding illness state quantity with the critical threshold value corresponding to the illness state, and marks the illness state as normal illness state and abnormal illness state according to the comparison and analysis result;
obtaining consumable data corresponding to abnormal conditions in unit time, generating consumable coefficients according to the consumption quantity of prescription drugs and the consumption quantity of medical instruments in the consumable data, comparing the consumable coefficients with a preset consumable threshold value, judging whether abnormal medical product consumption is generated, and if abnormal medical product consumption is generated, associating the corresponding abnormal conditions with the abnormal medical product consumption;
the mark processing module is used for generating diffusion information of the illness state corresponding to the abnormal illness state and the abnormal medical product consumption at the same time, transmitting the diffusion information to n medical institution servers, and transmitting the diffusion information to a receiving end corresponding to the contact mode according to a pre-stored contact mode by the corresponding medical institution servers; the diffusion information includes a corresponding illness state name and consumable data corresponding to the illness state.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN109636652A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Purchase monitoring method, monitoring service end and the storage medium of medicine abnormal behavior |
| CN112102957A (en) * | 2020-09-02 | 2020-12-18 | 四川骏逸富顿科技有限公司 | Epidemic disease early warning method and early warning system thereof |
| CN112365997A (en) * | 2020-11-10 | 2021-02-12 | 陈昱帆 | New strategy for epidemic early warning, prevention and control |
| CN112466473A (en) * | 2020-09-28 | 2021-03-09 | 北京左医科技有限公司 | Automatic early warning method and system for newly-discovered infectious diseases |
| CN112907045A (en) * | 2021-02-02 | 2021-06-04 | 方翔 | Intelligent medical data acquisition method based on 5G Internet of things |
| CN113052199A (en) * | 2019-12-28 | 2021-06-29 | 中移(成都)信息通信科技有限公司 | Method, device and equipment for training disease detection model and computer storage medium |
| CN113316640A (en) * | 2018-11-09 | 2021-08-27 | 世代生物公司 | Modified closed-end DNA (CEDNA) comprising a symmetrical modified inverted terminal repeat |
| CN114119313A (en) * | 2021-11-08 | 2022-03-01 | 皖南医学院 | Intelligent management and monitoring system for big data of medicine for chronic diseases |
| CN114334169A (en) * | 2022-03-07 | 2022-04-12 | 四川大学 | Medical object category decision method and device, electronic equipment and storage medium |
| CN114464321A (en) * | 2022-02-10 | 2022-05-10 | 安徽硕牌通信科技有限公司 | Intelligent medical system based on big data |
| CN114529086A (en) * | 2022-02-21 | 2022-05-24 | 卫宁健康科技集团股份有限公司 | Medicine data processing method, device and equipment |
-
2023
- 2023-04-13 CN CN202310394511.5A patent/CN116110602B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN113316640A (en) * | 2018-11-09 | 2021-08-27 | 世代生物公司 | Modified closed-end DNA (CEDNA) comprising a symmetrical modified inverted terminal repeat |
| US20210388379A1 (en) * | 2018-11-09 | 2021-12-16 | Generation Bio Co. | Modified closed-ended dna (cedna) comprising symmetrical modified inverted terminal repeats |
| CN109636652A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Purchase monitoring method, monitoring service end and the storage medium of medicine abnormal behavior |
| CN113052199A (en) * | 2019-12-28 | 2021-06-29 | 中移(成都)信息通信科技有限公司 | Method, device and equipment for training disease detection model and computer storage medium |
| CN112102957A (en) * | 2020-09-02 | 2020-12-18 | 四川骏逸富顿科技有限公司 | Epidemic disease early warning method and early warning system thereof |
| CN112466473A (en) * | 2020-09-28 | 2021-03-09 | 北京左医科技有限公司 | Automatic early warning method and system for newly-discovered infectious diseases |
| CN112365997A (en) * | 2020-11-10 | 2021-02-12 | 陈昱帆 | New strategy for epidemic early warning, prevention and control |
| CN112907045A (en) * | 2021-02-02 | 2021-06-04 | 方翔 | Intelligent medical data acquisition method based on 5G Internet of things |
| CN114119313A (en) * | 2021-11-08 | 2022-03-01 | 皖南医学院 | Intelligent management and monitoring system for big data of medicine for chronic diseases |
| CN114464321A (en) * | 2022-02-10 | 2022-05-10 | 安徽硕牌通信科技有限公司 | Intelligent medical system based on big data |
| CN114529086A (en) * | 2022-02-21 | 2022-05-24 | 卫宁健康科技集团股份有限公司 | Medicine data processing method, device and equipment |
| CN114334169A (en) * | 2022-03-07 | 2022-04-12 | 四川大学 | Medical object category decision method and device, electronic equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| YONGQING NAN 等: ""A machine learning method to monitor China\'s AIDS epidemics with data from Baidu trends"", 《PLOS ONE》, vol. 13, no. 7, pages 1 - 12 * |
| 周芳检 等: ""大数据时代的重大突发公共卫生事件预警创新"", 《云南民族大学学报(哲学社会科学版)》, vol. 37, no. 5, pages 114 - 120 * |
| 肖晴 等: ""药房功能模块优化在基层医院药事管理中的应用"", 《中国当代医药》, vol. 30, no. 3, pages 156 - 158 * |
| 贺哲;翁雨雄;王莉霞;张义丹;傅新巧;喻姣花;李迪;周彬;孙晖;: "重大突发公共卫生事件中医疗机构的信息化应对――以江汉方舱医院为例", 医学与社会, no. 09, pages 96 - 100 * |
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