CN109636639B - Big data analysis-based medication detection method, device, equipment and storage medium - Google Patents
Big data analysis-based medication detection method, device, equipment and storage medium Download PDFInfo
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- CN109636639B CN109636639B CN201811529251.3A CN201811529251A CN109636639B CN 109636639 B CN109636639 B CN 109636639B CN 201811529251 A CN201811529251 A CN 201811529251A CN 109636639 B CN109636639 B CN 109636639B
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Abstract
The invention discloses a big data analysis-based medication detection method, which comprises the following steps: receiving a drug detection request, and acquiring a target area to be checked and a target drug; acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user; counting the actual dosage of each person to obtain the actual dosage of the target medicine in the target area; and judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion. The invention also discloses a medication detection device, equipment and a storage medium based on big data analysis. The regional medicine quantity detection method and the regional medicine quantity detection system realize the regional medicine usage quantity detection through the analysis of the regional medicine quantity so as to avoid the condition of medicine abuse.
Description
Technical Field
The invention relates to the technical field of medicines, in particular to a method, a device, equipment and a storage medium for medicine application detection based on big data analysis.
Background
Because the types of medicines are more and the diseases corresponding to the medicines are different, in order to effectively manage the medicines, each medicine related mechanism is provided with a medicine management system.
The current drug management system only carries out recording and inquiring on a simple drug and can not play a practical role in monitoring the drug, so that excessive medical service behaviors and illegal fraud behaviors can be generated under the drive of benefits, and the medical insurance fund is unreasonably lost; or drug abuse, which can cause certain damage to the health and society of the drug user. How to realize effective detection of medication becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The invention mainly aims to provide a medicine taking detection method, a medicine taking detection device, medicine taking detection equipment and a storage medium based on big data analysis, and aims to detect the regional medicine usage amount based on data analysis so as to avoid the abuse of medicines.
In order to achieve the above object, the present invention provides a big data analysis-based medication detection method, which comprises the following steps:
receiving a drug detection request, and acquiring a target area to be checked and a target drug;
acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user;
counting the actual dosage of each individual to obtain the actual dosage of the target drug in the target area;
and judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion.
Optionally, the step of obtaining medical insurance data of each user in the target area and calculating the actual personal dosage of the target drug according to the medical insurance data of each user includes:
acquiring medical insurance data of each user in the target area, analyzing the medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
and acquiring the reimbursement amount in each target medical insurance data, acquiring the preset unit price of the target medicine, and calculating the personal actual dosage of each target user according to the reimbursement amount and the preset unit price of the medicine, wherein the target user is a user corresponding to the target medical insurance data.
Optionally, the step of determining whether the drug dosage of the target drug in the target region is abnormal according to the actual drug dosage in the region, and outputting a drug audit result correspondingly includes:
determining the type of a disease cured by the target medicine, inquiring a preset disease database, and acquiring disease information corresponding to the type of the disease in the target area;
calculating the area theoretical dosage of the target area according to the disease information, and comparing the actual dosage of the area with the area theoretical dosage;
if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved;
and if the actual dosage in the region is not matched with the theoretical dosage in the region, judging that the dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine cannot be approved.
Optionally, the step of calculating a region theoretical dosage of the target region according to the disease information includes:
acquiring disease incidence, treatment rate and medication rate in the disease information, and acquiring population data of the target area;
performing product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area;
acquiring a preset use instruction of the target medicine, and determining a first theoretical dosage of the target medicine according to the preset use instruction;
and performing product operation on the first theoretical dosage and the theoretical dosage population number to obtain the regional theoretical dosage of the target region.
Optionally, the step of determining whether the drug dosage of the target drug in the target region is abnormal according to the actual drug dosage in the region, and outputting a drug audit result correspondingly includes:
inquiring a preset region characteristic database, acquiring a reference region with similar characteristics to the target region, and acquiring the synchronous dosage of the target medicine in the reference region;
determining the population ratio of the target area to the reference area, and multiplying the synchronous drug consumption by the population ratio to obtain the area theoretical drug consumption of the target drug in the target area;
comparing the actual dosage of the region with the theoretical dosage of the region;
if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved;
and if the actual medicine dosage of the region is not matched with the theoretical medicine dosage of the region, judging that the medicine dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine verification cannot be passed.
Optionally, after the step of determining whether the drug dosage of the target drug in the target region is abnormal according to the actual drug dosage in the region and outputting a drug audit result correspondingly, the method includes:
if the drug dosage of the target drug in the target area is abnormal, acquiring the actual drug dosage of the area and the theoretical drug dosage of the area;
and adding the actual dosage of the region and the theoretical dosage of the region to a preset log template to generate a medicine detection log.
Optionally, after the step of determining whether the drug dosage of the target drug in the target region is abnormal according to the actual drug dosage in the region and outputting a drug audit result correspondingly, the method includes:
if the medicine usage amount of the target medicine in the target area is abnormal, analyzing medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
acquiring the medication duration in the target medical insurance data, and performing product operation on the medication duration and the preset single dose of the target medicine to obtain a second theoretical dosage;
comparing each second theoretical dosage with the corresponding actual dosage of the individual;
and acquiring abnormal users of which the second theoretical dosage does not match with the corresponding individual actual dosage, and marking the abnormal users to prevent medical insurance fraud of the abnormal users.
In addition, in order to achieve the above object, the present invention further provides a medication detecting apparatus based on big data analysis, including:
the receiving module is used for receiving the medicine detection request and acquiring a target area to be checked and a target medicine;
the personal medicine application calculation module is used for acquiring medical insurance data of each user in the target area and calculating the personal actual medicine application amount of the target medicine according to the medical insurance data of each user;
the regional medicine consumption calculation module is used for counting the actual medicine consumption of each person to obtain the regional actual medicine consumption of the target medicine in the target region;
and the conclusion output module is used for judging whether the medicine consumption of the target medicine in the target area is abnormal or not according to the actual medicine consumption of the area and correspondingly outputting a medicine checking conclusion.
In addition, in order to realize the purpose, the invention also provides a medication detection device based on big data analysis;
the big data analysis-based medication detection device comprises: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the computer program when executed by the processor implements the steps of the big data analysis based medication intake detection method as described above.
In addition, to achieve the above object, the present invention also provides a computer storage medium;
the computer storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the big data analysis based medication detection method as described above.
The embodiment of the invention provides a method, a device, equipment and a computer storage medium for medicine application detection based on big data analysis, which are used for acquiring a target area to be checked and a target medicine by receiving a medicine detection request; acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user; counting the actual dosage of each individual to obtain the actual dosage of the target drug in the target area; and judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion. The server analyzes the regional medical quantity, namely, the server acquires the medical insurance data of each user in the target region, calculates the personal actual dosage of the target medicine according to the medical insurance data of each user, collects the personal actual dosage of the target medicine to obtain the regional actual dosage of the target region, and judges whether the medicine dosage of the target medicine in the target region is abnormal or not according to the regional actual dosage so as to realize the detection of the regional medicine dosage and avoid the abuse of the medicine.
Drawings
FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a big data analysis-based medication detection method according to the present invention;
fig. 3 is a functional module schematic diagram of an embodiment of a medication detection device based on big data analysis according to the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Because the prior art does not have a method for detecting regional medication, the invention provides a solution, a medicine detector triggers a regional medicine detection request, a server receives the medicine detection request, and a target region to be checked and a target medicine are obtained; acquiring medical insurance data of each user in the target area, calculating the actual personal dosage of the target medicine according to the medical insurance data of each user, and counting the actual personal dosage to obtain the actual regional dosage of the target medicine in the target area; and judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion. The regional medicine quantity detection method and the regional medicine quantity detection system realize the regional medicine usage quantity detection through the analysis of the regional medicine quantity so as to avoid the condition of medicine abuse.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a server (also called big data analysis-based medication detection apparatus, where the big data analysis-based medication detection apparatus may be formed by a single big data analysis-based medication detection device, or may be formed by combining other devices with the big data analysis-based medication detection device) in a hardware operating environment according to an embodiment of the present invention.
The server in the embodiment of the invention refers to a computer for managing resources and providing services for users, and is generally divided into a file server, a database server and an application server. The computer or computer system running the above software is also referred to as a server. Compared with a common PC (personal computer) personal computer, the server has higher requirements on stability, safety, performance and the like; as shown in fig. 1, the server may include: the processor 1001 includes, for example, a Central Processing Unit (CPU), a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002, a chipset, a disk system, hardware such as a network, and the like. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., WIFI interface, WIreless FIdelity, WIFI interface). The memory 1005 may be a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the server may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, and a WiFi module; the input unit is compared with a display screen and a touch screen; the network interface can be selected from the wireless interface except WiFi, bluetooth, a probe and the like. Those skilled in the art will appreciate that the server architecture shown in FIG. 1 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the computer software product is stored in a storage medium (storage medium: also called computer storage medium, computer medium, readable storage medium, computer readable storage medium, or direct storage medium, such as RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention, and as a computer storage medium, the memory 1005 may include an operating system, a network communication module, a user interface module, and a computer program.
In the server shown in fig. 1, the network interface 1004 is mainly used for connecting to a background database and performing data communication with the background database; the user interface 1003 is mainly used for connecting a client (client, also called user side or terminal, the terminal in the embodiment of the present invention may be a fixed terminal or a mobile terminal, for example, an intelligent air conditioner with networking function, an intelligent electric lamp, an intelligent power supply, an intelligent sound box, an automatic driving car, a PC, a smart phone, a tablet computer, an electronic book reader, a portable computer, etc., the terminal includes sensors such as a light sensor, a motion sensor, and other sensors, which are not described herein again), and performs data communication with the client; and the processor 1001 may be configured to call up a computer program stored in the memory 1005 and execute the steps of the big data analysis-based medication detection method provided by the following embodiments of the present invention.
In a first embodiment of the big data analysis-based medication detection method according to the present invention, the big data analysis-based medication detection method includes:
receiving a drug detection request, and acquiring a target area to be checked and a target drug;
acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user;
counting the actual dosage of each person to obtain the actual dosage of the target medicine in the target area;
and judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion.
In this embodiment, the server receives the drug detection request, performs auditing on target drugs in a target area, and determines whether the target drugs in the target area have drug administration abnormality, so as to effectively avoid a regional drug abuse situation, specifically including:
referring to fig. 2, in a first embodiment of the big data analysis-based medication detection method according to the present invention, the big data analysis-based medication detection method includes:
and step S10, receiving a medicine detection request, and acquiring a target area to be checked and a target medicine.
The method comprises the steps that a medicine detection request is triggered on a terminal by a medicine detector, a server (called a medicine management platform) receives the medicine detection request and obtains medicine identification information to be checked (the medicine identification information can refer to a medicine name or a predefined medicine number), the server determines a target medicine to be checked according to the medicine identification to be checked, after the target medicine to be checked is determined, the server obtains a target area to be checked (called a target area and a target city), due to the fact that the weather, the consumption level, the crowd characteristic and other dimensions of each area are different, the disease type and the disease proportion of each area are different, the used medicines are different, and the area is used as the dimension when medicine checking is carried out.
It should be noted that: in this embodiment, multiple drug audits may be performed simultaneously, that is, a drug inspector sets and determines multiple target drugs, but for convenience of understanding, one drug audit is taken as an example for explanation, and in this embodiment, a special drug: for the explanation, the examination of the torilizumab injection (yamero) is taken as an example, a drug detector triggers a drug detection request of the torilizumab injection (yamero), a server receives the drug detection request, and the server acquires a target area to be examined and a target drug to be examined: yameiluo.
And step S20, acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user.
The server acquires medical insurance data of each user in the target area, namely, the server queries a preset medical insurance management platform, and the server acquires the medical insurance data of each user in the target area from the medical insurance management platform, wherein the medical insurance data includes but is not limited to: medical insurance identification, medical insurance reimbursement time, medical insurance limit, medical insurance consumption type and the like, so that the server calculates the individual actual dosage of the target medicine according to medical insurance data of each user.
Specifically, the embodiment provides an implementation manner for calculating the actual dosage of a person, which includes:
step S21, acquiring medical insurance data of each user in the target area, analyzing the medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
the server acquires medical insurance data of each user in the target area, analyzes the medical insurance data of each user to obtain target medical insurance data related to a target medicine, namely, the medical insurance data of each user does not relate to the target medicine, analyzes the medical insurance data of each user to acquire the medical insurance data related to the target medicine as the target medical insurance data, for example, the server acquires the medical insurance data of each person in the target area and analyzes the medical insurance data of each person, the server acquires the target medical insurance data related to the target medicine, for example, 30 thousands of medical insurance participators in the area, acquires the medical insurance data of 30 thousands of persons, and uses the medical insurance data related to Yaolu in the medical insurance data of 30 thousands of persons as the target medical insurance data; namely, the server acquires medical insurance data of 522 users purchasing Tulizumab injection (Yamerou) by using medical insurance cards in a target area as target medical insurance data, wherein the target medical insurance data comprises admission time, discharge time, medicine purchase types, medicine use time, medicine use dosage and reimbursement amount.
And S22, acquiring the reimbursement amount in each target medical insurance data, acquiring the preset unit price of the target medicine, and calculating the actual personal dosage of each target user according to the reimbursement amount and the preset unit price of the medicine.
The server acquires the reimbursement amount in each target medical insurance data, acquires preset unit prices of the target medicines (the preset unit prices of the medicines refer to preset unit prices of the target medicines), and calculates the personal actual dosage of each target user (the target user refers to a user corresponding to the target medical insurance data) according to the reimbursement amount, the preset unit prices of the medicines and a preset reimbursement proportion (the preset reimbursement proportion refers to a preset medical insurance reimbursement proportion); namely, the server divides the reimbursement amount by the preset reimbursement proportion to obtain the total price of the purchased medicines, and the server divides the total price of the purchased medicines by the unit price of the target medicines to obtain the personal actual dosage of the target user.
For example, the server acquires target medical insurance data of a single person, determines that the target medical insurance data contains total reimbursement amount of the tositumumab injection (Jacobs) as 109746 yuan, reimbursement proportion as 70%, and actual purchase amount as 156780, and acquires pre-stored drug standard information of the tositumumab injection (Jacobs), wherein the drug standard information comprises: if the average unit price of 80mg/4ml is 2010 yuan, the dosage of the data drug is 8mg/kg recommended by adults, and the data drug is intravenously instilled 1 time every 4 weeks (assuming that the standard weight is 60 kg); the server calculates the individual actual dosage of the single tobramycin injection (Yamerol) according to the target medical insurance information and the drug standard information.
And S30, counting the actual dosage of each individual to obtain the actual dosage of the target medicine in the target region.
And the server accumulates the actual dosage of each person, and the sum obtained by calculating the actual dosage of each person is used as the area actual dosage of the target medicine in the target area.
And S40, judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage in the area, and correspondingly outputting a medicine checking conclusion.
The server judges whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area by different modes,
the implementation mode is as follows: the server determines the area theoretical dosage of the target area, compares the area actual dosage with the area theoretical dosage to judge whether the medicine dosage of the target medicine in the target area is abnormal or not, namely, the area theoretical dosage of the target medicine in the target area is obtained (wherein the area theoretical dosage of the target area can be determined in different ways), and compares the area actual dosage with the area theoretical dosage; if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved; and if the actual dosage in the region is not matched with the theoretical dosage in the region, judging that the dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine cannot be approved.
The implementation mode two is as follows: and obtaining the historical dosage of the target area in the past year, comparing the actual dosage of the area with the historical dosage to judge whether the dosage of the target medicine in the target area is abnormal or not, and correspondingly outputting a medicine checking conclusion.
In the embodiment, the server analyzes the regional medical quantity, that is, the server obtains medical insurance data of each user in the target region, calculates the individual actual dosage of the target drug according to the medical insurance data of each user, and obtains the regional actual dosage of the target region in a summary manner, and the server judges whether the drug dosage of the target drug in the target region is abnormal according to the regional actual dosage, so that the regional drug dosage is detected to avoid the drug abuse condition.
Further, on the basis of the first embodiment of the present invention, a second embodiment of the medication detection method based on big data analysis according to the present invention is provided.
This embodiment is a refinement of step S40 in the first embodiment, and a specific implementation manner for determining whether the drug usage amount of the target drug in the target area is abnormal is provided in this embodiment, where the medication detection method based on big data analysis includes:
step S41, determining the disease type cured by the target medicine, inquiring a preset disease database, and acquiring the disease information corresponding to the disease type in the target area.
In this embodiment, the server obtains an indication corresponding to a target drug, determines a disease type cured by the target drug, queries a preset disease database (the preset disease database refers to a preset data amount for recording relevant information of each disease), and obtains disease information corresponding to the disease type in the target area from the preset disease database. Namely, the server acquires disease incidence, treatment rate and medication rate in the disease information and acquires population data of the target area.
For example, the target drug approved by the user is tocilizumab injection (yamerozoite), and the cured diseases of tocilizumab injection (yamerozoite) are: rheumatoid arthritis. The server obtains the probability of rheumatoid arthritis occurrence in the area A as 0.0036, the diagnosis rate treatment rate of 52%, the medicine utilization rate of 85%, the treatment rate of antirheumatic drugs (DMARD) of 90%, the proportion of patients with poor response to DMARD treatment of 55%, the utilization rate of Tuzhuzumab injection (Yamerol) of 100%, the medication duration, standard medication information and the like as the disease information.
And S42, calculating the region theoretical dosage of the target region according to the disease information, and comparing the region actual dosage with the region theoretical dosage.
The server calculates the region theoretical dosage of the target region according to the disease information, and specifically comprises the following steps:
step a, acquiring disease incidence, treatment rate and medication rate in the disease information, and acquiring population data of the target area;
b, performing product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area;
and c, acquiring a preset use instruction of the target medicine, determining a first theoretical dosage of the target medicine according to the preset use instruction, and performing product operation on the first theoretical dosage and the number of theoretical medicine-taking population to obtain the regional theoretical dosage of the target region.
The server acquires disease incidence, treatment rate and medication rate in the disease information, and acquires population data of the target area; the server performs product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area; the server obtains the drug use instruction of the target drug, can determine a first theoretical drug use amount according to the drug use instruction (the first theoretical drug use amount refers to the individual theoretical drug use amount determined by the server according to the drug use instruction), and multiplies the theoretical drug use population number by the first theoretical drug use amount to obtain the regional theoretical drug use amount of the target region.
For example, the server obtains a probability of rheumatoid arthritis occurrence in a target area of 0.0036, a diagnosis rate treatment rate of 52%, a drug usage rate of 85%, a DMARD treatment rate of 90%, a DMARD poor response patient ratio of 55%, a tosubumab injection (jamerosal) usage rate of 100%, a drug administration duration and standard drug administration information (recommended adult dose of 8mg/kg, 1 intravenous drip every 4 weeks (assuming standard body weight of 60 kg), and determines a theoretical reasonable usage amount based on the disease information because the usage amount of the tosubumab injection (jamerosal) is relatively fixed.
Further, after the area theoretical dosage is obtained, the server compares the area actual dosage with the area theoretical dosage to judge whether the dosage of the target drug in the target area is abnormal according to a comparison result, specifically:
and S43, if the actual medicine consumption of the region is matched with the theoretical medicine consumption of the region, judging that the medicine consumption of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved.
And the server determines that the actual medicine consumption of the region is matched with the theoretical medicine consumption of the region, namely the difference between the actual medicine consumption of the region and the theoretical medicine consumption of the region does not exceed a preset range, and the server judges that the medicine consumption of the target medicine in the target region is normal and correspondingly outputs a conclusion that the medicine is approved.
And S44, if the actual dosage in the region is not matched with the theoretical dosage in the region, judging that the dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine cannot be approved.
And the server determines that the actual medicine consumption of the region is not matched with the theoretical medicine consumption of the region, namely the actual medicine consumption of the region and the theoretical medicine consumption of the region exceed a preset range, and judges that the medicine consumption of the target medicine in the target region is abnormal and correspondingly outputs a conclusion that the medicine cannot be audited.
In this embodiment, the server determines the region theoretical dosage of the target region according to the disease information corresponding to the target drug in the target region, so as to realize the abnormal judgment of the drug dosage of the target drug in the target region, and thus, the drug detection of the target region is more intelligent and accurate.
Further, on the basis of the first embodiment of the present invention, a third embodiment of the method for detecting drug use based on big data analysis of the present invention is provided.
This embodiment is a refinement of step S40 in the first embodiment, and in this embodiment, another implementation manner for determining whether the drug dosage of the target drug in the target area is abnormal is provided, and the medication detection method based on big data analysis includes:
step S45, inquiring a preset region characteristic database, acquiring a reference region with similar characteristics to the target region, and acquiring the synchronous dosage of the target medicine in the reference region;
the server inquires a preset region characteristic database, wherein the preset region characteristic database is a database recorded with characteristic information (the characteristic information comprises but is not limited to regional climate, regional geographical position, regional diet characteristic, regional consumption level and crowd characteristic), the server acquires the characteristic data of a target region, determines a reference region with similar (the similarity of the characteristics can be measured through the dimensions of the climate, the consumption level, the crowd characteristic and the like) characteristics with the target region according to the characteristic data of the target region, acquires the synchronous dosage of a target medicine in the reference region after acquiring the reference region, and acquires the synchronous dosage of a reference city, wherein the synchronous dosage can be acquired in a hospital system (or a medical system) in the reference city.
Step S46, determining the population ratio of the target area to the reference area, and multiplying the synchronous drug consumption by the population ratio to obtain the area theoretical drug consumption of the target drug in the target area;
the server acquires the population number of a target area, acquires the population number of the reference area, and performs ratio operation on the population number of the target area and the population number of the reference area to obtain the population ratio of the target area to the reference area; and then, the server multiplies the synchronous drug consumption by the population proportion to obtain a drug consumption pre-estimated value, and the drug consumption pre-estimated value is used as the region theoretical drug consumption of the target drug in the target region.
Step S47, comparing the actual dosage of the region with the theoretical dosage of the region; and determining whether the medicine usage of the target medicine in the target area is abnormal according to the comparison result, and correspondingly outputting a medicine audit conclusion.
Specifically, the server compares the actual medicine consumption of the region with the theoretical medicine consumption of the region, and if the actual medicine consumption of the region is matched with the theoretical medicine consumption of the region, the server determines that the medicine consumption of the target medicine in the target region is normal and correspondingly outputs a conclusion that the medicine is approved; and if the actual medicine dosage of the region is not matched with the theoretical medicine dosage of the region, judging that the medicine dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine verification cannot be passed.
In the embodiment, in order to avoid the situation of abnormal medicine consumption in a part of areas during medicine consumption monitoring, the server determines the theoretical medicine consumption in the target area according to the medicine consumption in the reference city, so that the judgment of abnormal medicine consumption in the target area is realized, and the medicine detection in the target area is more intelligent and accurate.
Further, on the basis of the above embodiments, a fourth embodiment of the medication detecting method based on big data analysis according to the present invention is provided.
In this embodiment, after determining that the amount of the target drug in the target area is abnormal, a corresponding detection log may be generated. The medication detection method based on big data analysis comprises the following steps:
step S50, if the medicine dosage of the target medicine in the target area is abnormal, acquiring the actual medicine dosage of the area and the theoretical medicine dosage of the area;
and determining that the medicine dosage of the target medicine in the target area is abnormal in the server, and acquiring the actual medicine dosage of the area and the theoretical medicine dosage of the area by the server.
And S60, adding the actual dosage of the area and the theoretical dosage of the area to a preset log template to generate a medicine detection log.
The server adds the actual dosage of the area and the theoretical dosage of the area to a preset log template (the preset log template refers to a preset drug detection log blank template), and a drug detection log is generated; the server in this embodiment can form the corresponding detection log with the information that detects for drug detection personnel conveniently look over more.
Further, on the basis of the above embodiments, a fifth embodiment of the medication detecting method based on big data analysis according to the present invention is provided.
In this embodiment, after determining that the amount of the target drug in the target area is abnormal, in this embodiment, for an abnormal user, specifically, the medication detection method based on big data analysis includes:
step S70, if the medicine usage amount of the target medicine in the target area is abnormal, analyzing the medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
when the server determines that the medicine usage amount of the target medicine in the target area is abnormal, the server analyzes the medical insurance data of each user, obtains target medical insurance data related to the target medicine, obtains the medical insurance data of each person in the target area, analyzes the medical insurance data of each person, and obtains the target medical insurance data related to the target medicine from the medical insurance data of each person.
S80, acquiring the medication duration time in the target medical insurance data, and performing product operation on the medication duration time and the preset single dose of the target medicine to obtain a second theoretical dosage;
the server acquires the medication duration in the target medical insurance data; for example, the server obtains the discharge time and the admission time in the target medical insurance data to obtain the total hospitalization time, and takes the hospitalization time as the medication duration, and the server performs a product operation on the medication duration and a preset single dose of the target drug (the preset single dose refers to a preset single use dose of the target drug) to obtain the second theoretical dosage.
It should be noted that the second theoretical dosage in this embodiment and the first theoretical dosage in the above embodiment are both the theoretical dosages of the individual, and are not to be distinguished from each other in order to avoid ambiguity in reading.
And S90, comparing the second theoretical dosage with the corresponding actual personal dosage, acquiring abnormal users with the second theoretical dosage not matched with the corresponding actual personal dosage, and marking the abnormal users to prevent medical insurance fraud of the abnormal users.
And comparing each second theoretical dosage with the corresponding actual dosage of the individual by the server to obtain abnormal users with the second theoretical dosage not matched with the corresponding actual dosage of the individual, and marking the abnormal users by the server to prevent medical insurance fraud of the abnormal users.
After the server determines that the medicine usage of the target medicine in the target region is abnormal, the server further determines an abnormal user causing the abnormal medicine usage, and marks the abnormal user to prevent medical insurance fraud of the abnormal user.
In addition, referring to fig. 3, an embodiment of the present invention further provides a big data analysis-based medication detection apparatus, where the big data analysis-based medication detection apparatus includes:
the receiving module 10 is configured to receive a drug detection request, and obtain a target area to be checked and a target drug;
the personal medicine application calculation module 20 is configured to obtain medical insurance data of each user in the target area, and calculate an actual personal medicine application amount of the target medicine according to the medical insurance data of each user;
a regional medication calculating module 30, configured to count actual medication amounts of the individuals to obtain a regional actual medication amount of the target drug in the target region;
and the conclusion output module 40 is used for judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage in the area, and correspondingly outputting a medicine audit conclusion.
Optionally, the personal medication administration calculation module 20 includes:
the acquisition and analysis unit is used for acquiring medical insurance data of each user in the target area, analyzing the medical insurance data of each user and acquiring target medical insurance data related to the target medicine;
and the personal medicine consumption calculating unit is used for acquiring the reimbursement amount in each target medical insurance data, acquiring the preset unit price of the target medicine, and calculating the personal actual medicine consumption of each target user according to the reimbursement amount and the preset unit price of the medicine, wherein the target user is a user corresponding to the target medical insurance data.
Optionally, the conclusion output module 40 includes:
the query submodule is used for determining the type of the disease cured by the target medicine, querying a preset disease database and acquiring disease information corresponding to the type of the disease in the target area;
the comparison judgment submodule is used for calculating the region theoretical dosage of the target region according to the disease information and comparing the actual dosage of the region with the region theoretical dosage;
the first output sub-module is used for judging that the medicine consumption of the target medicine in the target area is normal if the actual medicine consumption of the area is matched with the theoretical medicine consumption of the area, and correspondingly outputting a conclusion that the medicine is approved;
and the second output submodule is used for judging that the medicine consumption of the target medicine in the target area is abnormal if the actual medicine consumption in the area is not matched with the theoretical medicine consumption in the area, and correspondingly outputting a conclusion that the medicine audit does not pass.
Optionally, the comparing and determining sub-module includes:
the acquisition unit is used for acquiring disease incidence, treatment rate and medication rate in the disease information and acquiring population data of the target area;
the population number determining unit is used for performing product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area;
and the calculation unit is used for acquiring a preset use instruction of the target medicine, determining a first theoretical dosage of the target medicine according to the preset use instruction, and performing product operation on the first theoretical dosage and the number of theoretical medicine-taking population to obtain the regional theoretical dosage of the target region.
Optionally, the conclusion output module 40 includes:
the query acquisition sub-module is used for querying a preset region characteristic database, acquiring a reference region with similar characteristics to the target region and acquiring the synchronous dosage of the target medicine in the reference region;
the theoretical determination submodule is used for determining the population proportion of the target area and the reference area, and multiplying the synchronous drug consumption by the population proportion to obtain the area theoretical drug consumption of the target drug in the target area;
the comparison submodule is used for comparing the actual dosage of the region with the theoretical dosage of the region; if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved; and if the actual dosage in the region is not matched with the theoretical dosage in the region, judging that the dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine cannot be approved.
Optionally, the medication detection device based on big data analysis includes:
the abnormity determining module is used for acquiring the actual dosage of the region and the theoretical dosage of the region if the dosage of the target medicine in the target region is abnormal;
and the data output unit is used for adding the actual dosage of the region and the theoretical dosage of the region to a preset log template to generate a medicine detection log.
Optionally, the medication detection device based on big data analysis includes:
the analysis acquisition module is used for analyzing medical insurance data of each user and acquiring target medical insurance data related to the target medicine if the medicine dosage of the target medicine in the target area is abnormal;
the acquisition and calculation module is used for acquiring the medication duration time in the target medical insurance data and performing product operation on the medication duration time and the preset single dose of the target medicine to obtain a second theoretical dosage;
the comparison marking module is used for comparing each second theoretical dosage with the corresponding actual dosage; and acquiring abnormal users with the second theoretical dosage not matched with the corresponding individual actual dosage, and marking the abnormal users to prevent medical insurance fraud of the abnormal users.
The steps implemented by each functional module of the medication detection device based on big data analysis can refer to each embodiment of the medication detection method based on big data analysis, and are not described herein again.
In addition, the embodiment of the invention also provides a computer storage medium.
The computer storage medium stores thereon a computer program that, when executed by a processor, implements operations in the big data analysis-based medication intake detection method provided by the above-described embodiments.
It should be noted that, in this document, relational terms such as first and second, and the like are only used for distinguishing one entity/operation/object from another entity/operation/object, and do not necessarily require or imply any actual relationship or order between these entities/operations/objects; the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or system that comprises the element.
For the apparatus embodiment, since it is substantially similar to the method embodiment, it is described relatively simply, and reference may be made to some description of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, in that elements described as separate components may or may not be physically separate. Some or all modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or the portions contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (8)
1. A big data analysis-based medication detection method is characterized by comprising the following steps:
receiving a drug detection request, and acquiring a target area to be checked and a target drug;
acquiring medical insurance data of each user in the target area, and calculating the personal actual dosage of the target medicine according to the medical insurance data of each user;
counting the actual dosage of each person to obtain the actual dosage of the target medicine in the target area;
judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area, and correspondingly outputting a medicine checking conclusion;
the step of judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage of the area and correspondingly outputting a medicine checking conclusion comprises the following steps:
determining the type of a disease cured by the target medicine, inquiring a preset disease database, and acquiring disease information corresponding to the type of the disease in the target area;
calculating the region theoretical dosage of the target region according to the disease information, and comparing the region actual dosage with the region theoretical dosage;
if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved;
if the actual medicine dosage of the region is not matched with the theoretical medicine dosage of the region, judging that the medicine dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine audit cannot pass;
the step of calculating the region theoretical dosage of the target region according to the disease information comprises the following steps:
acquiring disease incidence, treatment rate and medication rate in the disease information, and acquiring population data of the target area;
performing product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area;
acquiring a preset use instruction of the target medicine, and determining a first theoretical dosage of the target medicine according to the preset use instruction;
and performing product operation on the first theoretical dosage and the number of the theoretical medicine-using population to obtain the regional theoretical dosage of the target region.
2. The big data analysis-based medication detection method according to claim 1, wherein the step of obtaining medical insurance data of each user in the target area and calculating the actual personal dosage of the target drug according to the medical insurance data of each user comprises:
acquiring medical insurance data of each user in the target area, analyzing the medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
and acquiring the reimbursement amount in each target medical insurance data, acquiring the preset unit price of the target medicine, and calculating the personal actual dosage of each target user according to the reimbursement amount and the preset unit price of the medicine, wherein the target user is the user corresponding to the target medical insurance data.
3. The big data analysis-based medication detection method according to claim 1, wherein the step of determining whether the medication dosage of the target medication in the target region is abnormal according to the actual medication dosage in the region, and outputting a medication audit conclusion correspondingly comprises:
querying a preset region characteristic database, acquiring a reference region with similar characteristics to the target region, and acquiring the synchronous dosage of the target medicine in the reference region;
determining the population ratio of the target area to the reference area, and multiplying the synchronous drug consumption by the population ratio to obtain the area theoretical drug consumption of the target drug in the target area;
comparing the actual dosage of the region with the theoretical dosage of the region;
if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved;
and if the actual medicine dosage of the region is not matched with the theoretical medicine dosage of the region, judging that the medicine dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine verification cannot be passed.
4. The big data analysis-based medication detection method according to any one of claims 1 or 3, wherein after the step of determining whether the medication dosage of the target medication in the target region is abnormal according to the actual medication dosage in the region and outputting a corresponding medication audit result, the method comprises:
if the medicine dosage of the target medicine in the target area is abnormal, acquiring the actual medicine dosage of the area and the theoretical medicine dosage of the area;
and adding the actual dosage of the area and the theoretical dosage of the area to a preset log template to generate a medicine detection log.
5. The big data analysis-based medication detection method according to claim 1, wherein after the step of determining whether the medication dosage of the target medication in the target region is abnormal according to the actual medication dosage in the region and outputting a corresponding medication audit result, the method comprises:
if the medicine usage amount of the target medicine in the target area is abnormal, analyzing medical insurance data of each user, and acquiring target medical insurance data related to the target medicine;
acquiring the medication duration time in the target medical insurance data, and performing product operation on the medication duration time and the preset single dose of the target medicine to obtain a second theoretical dosage;
comparing each second theoretical dosage with the corresponding actual dosage of the individual;
and acquiring abnormal users with the second theoretical dosage not matched with the corresponding individual actual dosage, and marking the abnormal users to prevent medical insurance fraud of the abnormal users.
6. A big data analysis-based medication detection device is characterized by comprising:
the receiving module is used for receiving the medicine detection request and acquiring a target area to be checked and a target medicine;
the personal medicine application calculation module is used for acquiring medical insurance data of each user in the target area and calculating the personal actual medicine application amount of the target medicine according to the medical insurance data of each user;
the regional medicine consumption calculation module is used for counting the actual medicine consumption of each person to obtain the regional actual medicine consumption of the target medicine in the target region;
the conclusion output module is used for judging whether the medicine dosage of the target medicine in the target area is abnormal or not according to the actual medicine dosage in the area and correspondingly outputting a medicine audit conclusion;
the conclusion output module: the system is also used for determining the type of a disease cured by the target medicine, querying a preset disease database and acquiring disease information corresponding to the type of the disease in the target area; calculating the area theoretical dosage of the target area according to the disease information, and comparing the actual dosage of the area with the area theoretical dosage; if the actual dosage of the region is matched with the theoretical dosage of the region, judging that the dosage of the target medicine in the target region is normal, and correspondingly outputting a conclusion that the medicine is approved; if the actual dosage in the region is not matched with the theoretical dosage in the region, judging that the dosage of the target medicine in the target region is abnormal, and correspondingly outputting a conclusion that the medicine cannot be approved;
the conclusion output module is further used for acquiring disease incidence, treatment rate and medication rate in the disease information and acquiring population data of the target area; performing product operation on the disease incidence, the treatment rate, the medication rate and the population data to obtain the theoretical medication population number of the target area; acquiring a preset use instruction of the target medicine, and determining a first theoretical dosage of the target medicine according to the preset use instruction; and performing product operation on the first theoretical dosage and the number of the theoretical medicine-using population to obtain the regional theoretical dosage of the target region.
7. A big data analysis-based medication detection apparatus, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the computer program when executed by the processor implements the steps of the big data analysis based medication detection method of any of claims 1 to 5.
8. A computer storage medium, characterized in that the computer storage medium has stored thereon a computer program which, when being executed by a processor, implements the steps of the big data analysis based medication intake detection method according to any one of claims 1 to 5.
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