CN111028089B - Abnormal operation identification method and device, computer storage medium and electronic equipment - Google Patents
Abnormal operation identification method and device, computer storage medium and electronic equipment Download PDFInfo
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- CN111028089B CN111028089B CN201911166573.0A CN201911166573A CN111028089B CN 111028089 B CN111028089 B CN 111028089B CN 201911166573 A CN201911166573 A CN 201911166573A CN 111028089 B CN111028089 B CN 111028089B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The disclosure relates to the technical field of computers, and provides an abnormal operation identification method, an abnormal operation identification device, a computer storage medium and electronic equipment, wherein the abnormal operation identification method comprises the following steps: responding to the medicine taking operation of the target user, and acquiring the characteristic information of the target user and the state information of the medical insurance card used by the target user; acquiring the historical record times of the characteristic information of the target user in a preset time period; if the state information of the medical insurance card is in a frozen state or the number of times of history records is greater than a first preset threshold value, determining that the medicine taking operation is abnormal operation; and stopping the medicine taking process corresponding to the medicine taking operation. The abnormal operation identification method can not only identify abnormal operation in real time, but also improve identification accuracy.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to an abnormal operation identification method, an abnormal operation identification device, a computer storage medium, and an electronic apparatus.
Background
Along with the continuous improvement of the living standard of people, people gradually begin to accept various insurance projects, and particularly medical insurance for guaranteeing basic medical requirements is particularly valued by people. However, with the uncoordinated development of the popularity of the medical insurance and the mechanism of checking claims, abnormal use conditions of the medical insurance frequently occur, which brings unnecessary losses to medical insurance institutions such as national authorities and enterprises.
At present, related medical insurance institutions generally identify abnormal operators of medical insurance through traditional means such as a client blacklist recorded in a credit investigation system, and can not distinguish abnormal operation behaviors or abnormal operators in real time when a hospital takes medicines or a pharmacy purchases medicines. Thus, the real-time property of the prior art is insufficient, and the recognition accuracy is poor.
In view of the foregoing, there is a need in the art to develop a new abnormal operation identification method and apparatus.
It should be noted that the information disclosed in the foregoing background section is only for enhancing understanding of the background of the present disclosure.
Disclosure of Invention
The disclosure aims to provide an abnormal operation identification method, an abnormal operation identification device, a computer storage medium and electronic equipment, so that the technical problem of poor real-time performance of the method in the prior art is avoided at least to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided an abnormal operation identification method including: responding to the medicine taking operation of a target user, and acquiring characteristic information of the target user and state information of a medical insurance card used by the target user; acquiring the historical record times of the characteristic information of the target user in a preset time period; if the state information of the medical insurance card is in a frozen state or the historical record times are larger than a first preset threshold value, determining that the medicine taking operation is abnormal operation; and stopping the medicine taking flow corresponding to the medicine taking operation.
In an exemplary embodiment of the present disclosure, the characteristic information of the target user includes at least one of: face information, fingerprint information, voiceprint information.
In an exemplary embodiment of the present disclosure, the method further comprises: acquiring a historical medicine taking record of the target user based on the characteristic information of the target user; acquiring a disease type corresponding to the medicine contained in the historical medicine taking record; and if the number of the disease types is larger than a second preset threshold value, determining that the medicine taking operation of the target user is abnormal operation.
In an exemplary embodiment of the present disclosure, the method further comprises: acquiring prescription information of the target user; taking the current time as the current taking date of the target medicine contained in the prescription information; determining a first taking date of the target medicine from the historical medicine taking record, wherein the first taking date is the date of the last taking of the target medicine from the current taking date; determining the taking frequency of the target medicine according to the time interval between the first taking date and the current taking date; and when detecting that the taking frequency of the target medicine is greater than the standard taking frequency corresponding to the target medicine, determining that the medicine taking operation of the target user is abnormal operation.
In an exemplary embodiment of the present disclosure, the method further comprises: acquiring the number of medical insurance cards used by the target user based on the characteristic information of the target user; if the number of the medical insurance cards is larger than a third preset threshold, judging that the medicine taking operation is abnormal operation.
In an exemplary embodiment of the present disclosure, the method further comprises: acquiring the number of users of the medical insurance card in the preset time period; and if the number of users is greater than a fourth preset threshold value, judging that the medicine taking operation is abnormal operation.
In an exemplary embodiment of the present disclosure, the method further comprises: if the medicine taking operation is not abnormal operation, acquiring the diagnosis information of the target user, wherein the diagnosis information comprises medical expense information and medical insurance card information; calling an interface of a medical insurance system to enable the medical insurance system to determine medical insurance payment cost according to the medical expense information and the medical insurance card information; acquiring medical insurance payment cost returned by the medical insurance system, and determining self-payment cost according to the medical insurance payment cost information and the medical insurance payment cost; and settling the medical fee according to the self-fee and the medical insurance payment fee.
According to a second aspect of the present disclosure, there is provided an abnormal operation recognition apparatus including: the first acquisition module is used for responding to the medicine taking operation of a target user and acquiring the characteristic information of the target user and the state information of a medical insurance card used by the target user; the second acquisition module is used for acquiring the historical record times of the characteristic information of the target user in a preset time period; the identification module is used for determining that the medicine taking operation is abnormal operation if the state information of the medical insurance card is in a frozen state or the historical record times are larger than a first preset threshold value; and the flow suspension module is used for suspending the medicine taking flow corresponding to the medicine taking operation.
According to a third aspect of the present disclosure, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the abnormal operation identification method of the first aspect described above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the abnormal operation identification method of the first aspect described above via execution of the executable instructions.
As can be seen from the above technical solutions, the abnormal operation identification method, the abnormal operation identification device, the computer storage medium, and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
in the technical schemes provided by some embodiments of the present disclosure, on one hand, in response to a medicine taking operation of a target user, feature information of the target user and status information of a medical insurance card used by the target user are obtained, and historical record times of the feature information of the target user in a preset time period are obtained, so that identity of the target user can be accurately verified, and occurrence of abnormal behaviors such as medical insurance fraud is reduced. Further, if the state information of the medical insurance card is in a frozen state or the number of times of history records is greater than a first preset threshold, determining that the medicine taking operation is abnormal operation, and therefore real-time monitoring of a target user can be achieved, the technical problem that in the prior art, the instantaneity and the accuracy are poor due to the fact that identification is carried out only through a credit investigation system, a history blacklist system and the like is solved, the identification accuracy is improved, and abnormal behaviors such as medical insurance fraud are avoided. On the other hand, after the abnormal operation is identified, the medicine taking flow corresponding to the medicine taking operation is stopped in time, the legal rights and interests of medical insurance participants can be reasonably maintained, medical insurance resources can be used on the cutting edge, the medical insurance required personnel can be ensured to be more fully medical insurance, and the practicality of medical insurance is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a flow diagram of a method of abnormal operation identification in an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a flow diagram of a method of abnormal operation identification in another exemplary embodiment of the present disclosure;
FIG. 3 illustrates a flow diagram of a method of abnormal operation identification in yet another exemplary embodiment of the present disclosure;
FIG. 4 shows a flow diagram of a method of abnormal operation identification in yet another exemplary embodiment of the present disclosure;
FIG. 5 is a schematic overall flow diagram illustrating an abnormal operation identification method in an exemplary embodiment of the present disclosure;
fig. 6 is a schematic diagram showing the structure of an abnormal operation recognition apparatus in an exemplary embodiment of the present disclosure;
FIG. 7 illustrates a schematic diagram of a computer storage medium in an exemplary embodiment of the present disclosure;
fig. 8 illustrates a schematic structure of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.; the terms "first" and "second" and the like are used merely as labels, and are not intended to limit the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
At present, related medical insurance institutions generally identify abnormal operators of medical insurance through traditional means such as a client blacklist recorded in a credit investigation system, and can not distinguish abnormal operation behaviors or abnormal operators in real time when a hospital takes medicines or a pharmacy purchases medicines. Thus, the real-time property of the prior art is insufficient, and the recognition accuracy is poor.
In an embodiment of the present disclosure, an abnormal operation identification method is provided first, which overcomes, at least to some extent, the defect of poor accuracy of method identification of the abnormal operation identification method provided in the prior art.
Fig. 1 illustrates a flowchart of an abnormal operation recognition method in an exemplary embodiment of the present disclosure, and an execution subject of the abnormal operation recognition method may be a server that recognizes an abnormal operation.
Referring to fig. 1, an abnormal operation recognition method according to one embodiment of the present disclosure includes the steps of:
step S110, responding to the medicine taking operation of a target user, and acquiring characteristic information of the target user and state information of a medical insurance card used by the target user;
step S120, obtaining the historical record times of the characteristic information of the target user in a preset time period;
step S130, if the state information of the medical insurance card is in a frozen state or the number of times of the history records is larger than a first preset threshold value, determining that the medicine taking operation is abnormal operation;
step S140, the medicine taking process corresponding to the medicine taking operation is stopped.
In the technical scheme provided by the embodiment shown in fig. 1, on one hand, in response to a medicine taking operation of a target user, characteristic information of the target user and state information of a medical insurance card used by the target user are obtained, and historical record times of the characteristic information of the target user in a preset time period are obtained, so that identity of the target user can be accurately verified, and abnormal behaviors such as medical insurance fraud are reduced. Further, if the state information of the medical insurance card is in a frozen state or the number of times of history records is greater than a first preset threshold, determining that the medicine taking operation is abnormal operation, and therefore real-time monitoring of a target user can be achieved, the technical problem that in the prior art, the instantaneity and the accuracy are poor due to the fact that identification is carried out only through a credit investigation system, a history blacklist system and the like is solved, the identification accuracy is improved, and abnormal behaviors such as medical insurance fraud are avoided. On the other hand, after the abnormal operation is identified, the medicine taking flow corresponding to the medicine taking operation is stopped in time, the legal rights and interests of medical insurance participants can be reasonably maintained, medical insurance resources can be used on the cutting edge, the medical insurance required personnel can be ensured to be more fully medical insurance, and the practicality of medical insurance is improved.
The specific implementation of each step in fig. 1 is described in detail below:
in step S110, characteristic information of the target user and status information of the medical insurance card used by the target user are acquired in response to the medication intake operation of the target user.
In an exemplary embodiment of the present disclosure, when a medicine taking operation of a target user is received, feature information of the target user may be acquired, and status information of a medical insurance card used by the target user may be acquired.
In an exemplary embodiment of the present disclosure, the target user may be a patient visiting a hospital, or a patient family or co-worker, or the like. The medicine taking operation may be to scan the two-dimensional code of the charge bill (the charge bill provided by the charge person after the medicine fee is paid by the target user before the medicine taking operation) on the operation device (for example, the queuing and calling device provided with the scanning area) by the target user so as to perform the queuing and calling operation.
In an exemplary embodiment of the present disclosure, the feature information of the target user may be one or more of face information, fingerprint information, and voiceprint information of the target user. For example, a face collection device may be provided, and further, after detecting that the target user performs the medicine taking operation, the user is prompted to perform the face brushing operation, so as to obtain face information of the target user. For example, a fingerprint acquisition device may be further configured to acquire fingerprint information of the target user. For example, a voiceprint acquisition device may be further configured to acquire voiceprint information of the target user. It should be noted that, the acquiring mode of the feature information may be set according to the actual situation, which belongs to the protection scope of the present disclosure. By collecting the characteristic information of the target user, the identity of the target user can be effectively verified, and the occurrence of abnormal behaviors such as medical insurance fraud is reduced.
In the exemplary embodiment of the present disclosure, the medical insurance card, i.e., the social medical insurance card, is a medical insurance personal account special card, which uses a personal identification card as an identification code and stores detailed information such as a personal identification card number, a name, a sex, and payment of an account deposit. The status information of the medical insurance card may include: normal use conditions and frozen conditions. For example, when the medical insurance card of the insurer is normally used, the state information thereof may be a normal use state. When the medical insurance card of the insurer is lost, the insurer can freeze the medical insurance card so as to convert the state information of the medical insurance card into a frozen state.
In step S120, the number of histories of the feature information of the target user in the preset period is obtained.
In an exemplary embodiment of the present disclosure, the number of histories of characteristic information of a target user within a preset period of time (e.g., half a year) may be acquired. By way of example, the characteristic information of the target user is taken as face information to describe, so that the number of times of face brushing of the target user on the face acquisition device within half a year can be obtained and used as the number of times of the history record. It should be noted that, the specific duration of the preset time period may be set according to the actual situation, which belongs to the protection scope of the present disclosure.
In step S130, if the status information of the medical insurance card is in a frozen state or the number of times of the history is greater than a first preset threshold, the medicine taking operation is determined to be an abnormal operation.
In an exemplary embodiment of the present disclosure, after the history number and the status information of the medical insurance card are acquired, if the acquired status information is in a frozen state, the medicine taking operation may be determined to be an abnormal operation. Or, the obtained number of times of the history is larger than a first preset threshold (a preset numerical value which can be changed according to actual conditions, for example, 12), and when the number of times of the history is 12 times and the first preset threshold is 10 times, it may be determined that the number of times of the history is larger than the first preset threshold, and further, it is determined that the above-mentioned medicine taking operation is an abnormal operation.
In the exemplary embodiment of the disclosure, the identity of the target user can be accurately and effectively verified based on the state information of the medical insurance card, so that abnormal behaviors such as medical insurance fraud are reduced.
In an exemplary embodiment of the present disclosure, an exemplary flowchart of an abnormal operation identification method in another exemplary embodiment of the present disclosure may be illustrated with reference to fig. 2, fig. 2 specifically illustrates a flowchart of acquiring a historical medication intake record of a target user and identifying an abnormal operation according to a disease type of medication, and a specific embodiment is explained below with reference to fig. 2.
In step S201, a history of taking medicine of the target user is acquired based on the feature information of the target user.
In an exemplary embodiment of the present disclosure, a historical medication intake record of a target user may be obtained based on feature information of the target user, and specifically, the historical medication intake record may include: the number of times of taking the medicine, the amount of taking the medicine, the type of medicine contained in each taking, the name of the medicine contained in each taking, and the like.
In step S202, a disease type corresponding to the drug contained in the history drug administration record is acquired.
In an exemplary embodiment of the present disclosure, after the historical medication intake record is obtained, the medications included in the historical medication intake record may be obtained, and then, the disease types of the respective medication treatments may be counted and all the disease types may be summarized.
In step S203, if the number of disease types is greater than the second preset threshold, it is determined that the medicine taking operation of the target user is an abnormal operation.
In an exemplary embodiment of the present disclosure, after acquiring the disease type of the medication, if the disease type is greater than a second preset threshold (a preset numerical value that may be changed according to actual situations, for example, 10), for example, when the disease type of the medication is acquired to be 15, it may be determined that the disease type is greater than the second preset threshold, and further, it may be determined that the medication operation of the target user is an abnormal operation.
In an exemplary embodiment of the present disclosure, by way of example, reference may also be made to fig. 3, and fig. 3 is a flow chart illustrating a method for identifying abnormal operations in still another exemplary embodiment of the present disclosure, specifically illustrating a flow chart for acquiring prescription information of a target user and identifying abnormal operations according to the prescription information, and a specific embodiment will be explained below with reference to fig. 3.
In step S301, prescription information of a target user is acquired.
In exemplary embodiments of the present disclosure, prescription information of the target user may also be acquired. Illustratively, referring to the explanation related to step S110, after receiving the medication intake operation of the target user, prescription information of the target user may also be received, and illustratively, the target user may perform a scanning operation on prescription information prescribed by a doctor in a scanning area of the operation device, so that the operation device obtains the prescription information. The prescription refers to a medical document which is issued by registered medical practitioners and medical assistant doctors (hereinafter referred to as doctors) for patients in diagnosis and treatment activities, is checked, allocated and checked by a pharmaceutical professional technician (hereinafter referred to as pharmacist) who obtains the qualification of the pharmaceutical professional technical function, and is used as a medicine taking certificate of the patients, and is the basis of the medicine allocation of the pharmacist, thereby having legal, technical and economic responsibility.
In step S302, the current time is set as the current date of taking the target medicine included in the prescription information.
In an exemplary embodiment of the present disclosure, after the prescription information is acquired, the current time may be taken as the current taking date of the target medicine contained in the prescription information. Illustratively, the target drug may be drug A and the current date of access may be 10 months 20 days 2018.
In step S303, a first date of taking the target medicine is determined from the history of taking medicines, and the first date of taking is a date of taking the target medicine last time from the current date of taking.
In an exemplary embodiment of the present disclosure, referring to the explanation of step S201 described above, the first taking date of the target medicine (the date on which the target medicine was taken last time from the current taking date) may be determined from the history of taking medicines, and exemplary, the first taking date of the acquired medicine a is 2018, 10.
In step S304, the frequency of taking the target medicine is determined according to the time interval between the first taking date and the current taking date.
In an exemplary embodiment of the present disclosure, after the current taking date and the first taking date are obtained, it may be determined that a time interval between the two is 10 days, and further, it may be determined that the taking frequency of the target medicine is 10 days once.
In step S305, when it is detected that the taking frequency of the target medicine is greater than the standard taking frequency corresponding to the target medicine, it is determined that the medicine taking operation of the target user is an abnormal operation.
In an exemplary embodiment of the present disclosure, when it is detected that the taking frequency of the target medicine is greater than the corresponding standard taking frequency (for example, the standard taking frequency thereof is once for 15 days), it may be determined that the above taking frequency is greater than the standard taking frequency, and in turn, it may be determined that the taking operation of the target user is an abnormal operation.
In the exemplary embodiment of the present disclosure, the number of medical insurance cards used by the target user may also be obtained based on the feature information of the target user, and, for example, when the number of medical insurance cards used by the target user is 8 and greater than a third preset threshold (a preset numerical value that may be changed according to an actual situation, for example, 3 medical insurance cards), it may be determined that the medicine taking operation of the target user is an abnormal operation.
In the exemplary embodiment of the present disclosure, the number of users of the medical insurance card used by the target user in the preset time period may also be obtained, and, for example, when the number of users who use the medical insurance card is 20 in half a year, and is greater than a fourth preset threshold (a preset numerical value that may be changed according to actual conditions, for example, 5 people), it may be determined that the medicine taking operation of the target user is an abnormal operation.
In the exemplary embodiment of the disclosure, the real-time monitoring of the target user can be realized by identifying the abnormal operation based on the characteristic information of the target user and the related information of the medical insurance card used by the target user, so that the technical problems of poor real-time performance and accuracy caused by identifying only through a credit investigation system, a history blacklist system and the like in the prior art are solved, and the identification accuracy is improved, thereby avoiding the occurrence of abnormal behaviors such as medical insurance fraud and the like.
With continued reference to fig. 1, in step S140, the medication flow corresponding to the medication operation is aborted.
In an exemplary embodiment of the present disclosure, referring to the explanation related to the step S130, when it is determined that the medicine taking operation of the target user is abnormal, the medicine taking process corresponding to the medicine taking operation may be stopped, and an alarm prompt may be sent to a medicine taking center of the hospital to prompt the relevant medicine taking personnel to refuse to take medicine for the target user. Therefore, the legal rights and interests of medical insurance participants can be reasonably maintained, medical insurance resources can be used on the cutting edge, the personnel with medical insurance requirements can be ensured to obtain more sufficient medical insurance, and the practicability of medical insurance is improved.
In an exemplary embodiment of the present disclosure, for example, reference may be made to fig. 4, and fig. 4 is a flow chart illustrating an abnormal operation identification method in still another exemplary embodiment of the present disclosure, specifically illustrating a flow chart for settling medical costs when it is identified that a medicine taking operation of a target user is not an abnormal operation. A specific embodiment is explained below with reference to fig. 4.
In step S401, if the medication operation is not an abnormal operation, the diagnosis information of the target user is acquired, and the diagnosis information includes medical fee information and medical insurance card information.
In an exemplary embodiment of the present disclosure, if it is determined that the above-mentioned medicine taking operation is not an abnormal operation, the patient information of the target user may be obtained, and further, when the target user is a patient for patient, the medical fee information (including hospitalization fee, medical instrument use fee, etc.) and the medical insurance card information (name of the participant, medical insurance payment information, etc.) of the target user may be obtained.
In step S402, an interface of the medical insurance system is called to enable the medical insurance system to determine the medical insurance payment cost according to the medical expense information and the medical insurance card information.
In an exemplary embodiment of the present disclosure, after the visit information of the target user is acquired, the medical insurance system may be caused to determine a medical insurance pay-rate according to the medical expense information and the medical insurance card information. Wherein, the medical insurance payment cost can be directly paid by the medical insurance card.
In step S403, a medical insurance payment fee returned by the medical insurance system is acquired, and a self-fee is determined according to the medical fee information and the medical insurance payment fee.
In an exemplary embodiment of the present disclosure, after the acquisition of the medical insurance payment cost returned by the medical insurance system, the medical insurance payment cost information may be subtracted from the medical insurance payment cost to determine the self-cost of the target user.
In step S404, medical fee settlement is performed based on the self-fee and the medical insurance payment fee.
In an exemplary embodiment of the present disclosure, after the above-described self-fee and medical insurance payment fee are determined, medical fee settlement may be performed according to the above-described self-fee and medical insurance payment fee. Specifically, the expense settlement can be carried out on the medical insurance payment expense based on the interface of the medical insurance system and the medical insurance card information. The medical fee settlement may be performed on the self-fee based on other paytables (e.g., weChat, payment treasury, unionpay) and the like.
In an exemplary embodiment of the present disclosure, for example, reference may be made to fig. 5, where fig. 5 illustrates an overall flow diagram of an abnormal operation identification method in an exemplary embodiment of the present disclosure, and specifically illustrates an interaction flow diagram of a charging end, a medicine taking end, and a medical insurance system. A specific embodiment is explained below with reference to fig. 5.
In step S501, the charging terminal charges a fee;
in step S502, the medicine taking end receives a medicine taking operation of a target user;
in step S503, the medicine taking end acquires prescription information of the target user;
in step S504, the medicine taking end prompts the target user to perform a face brushing operation, and sends the obtained information to the medical insurance system;
in step S505, the medical insurance system records face brushing information, medical insurance card information and prescription information;
in step S506, the medical insurance system queries the historical data to identify the medicine taking operation of the target user;
in step S507, the medical insurance system obtains the identification result and sends the identification result to the medicine taking end;
in step S508, the medicine taking end receives the identification result sent by the medical insurance system;
in step S509, the medicine taking end determines whether the medicine taking operation of the target user is an abnormal operation according to the identification result;
in step S510, if the operation is abnormal, an abnormal prompt message is sent to the charging terminal, and the charging terminal performs a refund operation;
in step S511, if the operation is not abnormal, the medical insurance system performs medical insurance settlement;
in step S512, the hospital performs fee settlement;
in step S513, the drug delivery end dispenses a drug;
in step S514, the medicine taking is completed.
The present disclosure also provides an abnormal operation recognition apparatus, and fig. 6 shows a schematic structural diagram of the abnormal operation recognition apparatus in an exemplary embodiment of the present disclosure; as shown in fig. 6, the abnormal operation recognition apparatus 600 may include a first acquisition module 601, a second acquisition module 602, a recognition module 603, and a flow suspension module 604. Wherein:
the first obtaining module 601 is configured to obtain, in response to a medicine taking operation of a target user, feature information of the target user and status information of a medical insurance card used by the target user.
In an exemplary embodiment of the present disclosure, the characteristic information of the target user includes at least one of: the first acquisition module is used for responding to the medicine taking operation of the target user and acquiring the characteristic information of the target user and the state information of the medical insurance card used by the target user.
And a second obtaining module 602, configured to obtain the number of times of history records of the feature information of the target user in a preset time period.
In an exemplary embodiment of the present disclosure, the second obtaining module is configured to obtain a number of times of history of feature information of the target user in a preset period of time.
And the identifying module 603 is configured to determine that the medicine taking operation is an abnormal operation if the status information of the medical insurance card is in a frozen state or the number of times of the history record is greater than a first preset threshold.
In an exemplary embodiment of the present disclosure, the identification module is configured to determine that the medication intake operation is an abnormal operation if the status information of the medical insurance card is a frozen status or the number of times of history is greater than a first preset threshold.
In an exemplary embodiment of the present disclosure, the identification module is configured to obtain a historical medication intake record of the target user based on the feature information of the target user; acquiring a disease type corresponding to a medicine contained in a history medicine taking record; and if the number of the disease types is greater than a second preset threshold, determining that the medicine taking operation of the target user is abnormal operation.
In an exemplary embodiment of the present disclosure, the identification module is configured to obtain prescription information of a target user; taking the current time as the current taking date of the target medicine contained in the prescription information; determining a first taking date of the target medicine from the historical medicine taking record, wherein the first taking date is the date of the last taking of the target medicine from the current taking date; determining the taking frequency of the target medicine according to the time interval between the first taking date and the current taking date; and when the taking frequency of the target medicine is detected to be larger than the standard taking frequency corresponding to the target medicine, determining that the medicine taking operation of the target user is abnormal operation.
In an exemplary embodiment of the present disclosure, the identification module is configured to obtain, based on the feature information of the target user, a number of medical insurance cards used by the target user; if the number of the medical insurance cards is larger than the third preset threshold, judging that the medicine taking operation is abnormal operation.
In an exemplary embodiment of the present disclosure, the identification module is configured to obtain a number of users of the medical insurance card in the preset time period; if the number of users is greater than the fourth preset threshold, judging that the medicine taking operation is abnormal operation.
The flow suspension module 604 is configured to suspend the drug taking flow corresponding to the drug taking operation.
In an exemplary embodiment of the present disclosure, the flow suspension module is configured to suspend a drug delivery flow corresponding to a drug delivery operation.
In an exemplary embodiment of the present disclosure, the flow suspension module is further configured to obtain, if the medication operation is not an abnormal operation, diagnosis information of the target user, where the diagnosis information includes medical fee information and medical insurance card information; calling an interface of the medical insurance system to enable the medical insurance system to determine the medical insurance payment cost according to the medical expense information and the medical insurance card information; acquiring medical insurance payment cost returned by the medical insurance system, and determining self-payment cost according to the medical insurance payment cost and the medical insurance payment cost; and settling the medical fee according to the self-fee and the medical insurance payment fee.
The specific details of each module in the abnormal operation recognition device are described in detail in the corresponding abnormal operation recognition method, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer storage medium capable of implementing the above method is also provided. On which a program product is stored which enables the implementation of the method described above in the present specification. In some possible embodiments, the various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above-described method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 800 according to such an embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. Components of electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, and a bus 830 connecting the various system components, including the memory unit 820 and the processing unit 810.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps according to various exemplary embodiments of the present disclosure described in the above section of the present specification. For example, the processing unit 810 may perform the operations as shown in fig. 1: step S110, responding to the medicine taking operation of a target user, and acquiring the characteristic information of the target user and the state information of a medical insurance card used by the target user; step S120, obtaining the historical record times of the characteristic information of the target user in a preset time period; step S130, if the state information of the medical insurance card is in a frozen state or the historical record times are larger than a first preset threshold value, determining that the medicine taking operation is abnormal operation; step S140, suspending the drug taking process corresponding to the drug taking operation.
The storage unit 820 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 8201 and/or cache memory 8202, and may further include Read Only Memory (ROM) 8203.
The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and/or any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 over bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (9)
1. An abnormal operation identification method, characterized by comprising:
responding to the medicine taking operation of a target user, and acquiring characteristic information of the target user and state information of a medical insurance card used by the target user;
acquiring the historical record times of the characteristic information of the target user in a preset time period;
if the state information of the medical insurance card is in a frozen state or the historical record times are larger than a first preset threshold value, determining that the medicine taking operation is abnormal operation;
acquiring a historical medicine taking record of the target user based on the characteristic information of the target user; acquiring a disease type corresponding to the medicine contained in the historical medicine taking record; if the number of the disease types is larger than a second preset threshold, determining that the medicine taking operation of the target user is abnormal operation;
and stopping the medicine taking flow corresponding to the medicine taking operation.
2. The method of claim 1, wherein the characteristic information of the target user comprises at least one of: face information, fingerprint information, voiceprint information.
3. The method according to claim 1, wherein the method further comprises:
acquiring prescription information of the target user;
Taking the current time as the current taking date of the target medicine contained in the prescription information;
determining a first taking date of the target medicine from the historical medicine taking record, wherein the first taking date is the date of the last taking of the target medicine from the current taking date;
determining the taking frequency of the target medicine according to the time interval between the first taking date and the current taking date;
and when detecting that the taking frequency of the target medicine is greater than the standard taking frequency corresponding to the target medicine, determining that the medicine taking operation of the target user is abnormal operation.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring the number of medical insurance cards used by the target user based on the characteristic information of the target user;
if the number of the medical insurance cards is larger than a third preset threshold, judging that the medicine taking operation is abnormal operation.
5. The method according to claim 4, wherein the method further comprises:
acquiring the number of users of the medical insurance card in the preset time period;
and if the number of users is greater than a fourth preset threshold value, judging that the medicine taking operation is abnormal operation.
6. The method according to claim 1, wherein the method further comprises:
if the medicine taking operation is not abnormal operation, acquiring the diagnosis information of the target user, wherein the diagnosis information comprises medical expense information and medical insurance card information;
calling an interface of a medical insurance system to enable the medical insurance system to determine medical insurance payment cost according to the medical expense information and the medical insurance card information;
acquiring medical insurance payment cost returned by the medical insurance system, and determining self-payment cost according to the medical insurance payment cost information and the medical insurance payment cost;
and settling the medical fee according to the self-fee and the medical insurance payment fee.
7. An abnormal operation recognition apparatus, characterized by comprising:
the first acquisition module is used for responding to the medicine taking operation of a target user and acquiring the characteristic information of the target user and the state information of a medical insurance card used by the target user;
the second acquisition module is used for acquiring the historical record times of the characteristic information of the target user in a preset time period;
the identification module is used for determining that the medicine taking operation is abnormal operation if the state information of the medical insurance card is in a frozen state or the historical record times are larger than a first preset threshold value; acquiring a historical medicine taking record of the target user based on the characteristic information of the target user; acquiring a disease type corresponding to the medicine contained in the historical medicine taking record; if the number of the disease types is larger than a second preset threshold, determining that the medicine taking operation of the target user is abnormal operation;
And the flow suspension module is used for suspending the medicine taking flow corresponding to the medicine taking operation.
8. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the abnormal operation identification method according to any one of claims 1 to 6.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the abnormal operation identification method of any one of claims 1 to 6 via execution of the executable instructions.
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| CN109636623A (en) * | 2018-10-19 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Medical data method for detecting abnormality, device, equipment and storage medium |
| CN109544367A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of medical insurance card based on data processing is swiped the card processing method and relevant device |
| CN109509104A (en) * | 2018-10-27 | 2019-03-22 | 平安医疗健康管理股份有限公司 | Identity identifying method, device, server and medium based on recognition of face |
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| CN109615540A (en) * | 2018-12-13 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Recognition methods, device, terminal and the computer readable storage medium of medicine are purchased in violation of rules and regulations |
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| CN110309930A (en) * | 2019-05-22 | 2019-10-08 | 深圳壹账通智能科技有限公司 | Scheduled visits method, apparatus, equipment and storage medium based on recognition of face |
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