CN114664422A - Medicine shortage risk monitoring and early warning method and system - Google Patents

Medicine shortage risk monitoring and early warning method and system Download PDF

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CN114664422A
CN114664422A CN202210310944.3A CN202210310944A CN114664422A CN 114664422 A CN114664422 A CN 114664422A CN 202210310944 A CN202210310944 A CN 202210310944A CN 114664422 A CN114664422 A CN 114664422A
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王若尘
沈忆光
王硕
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Chinese Funded Medical Emergency Support Platform Co ltd
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Abstract

The invention relates to a medicine shortage risk monitoring and early warning method and system, and belongs to the technical field of medicine shortage risk monitoring. The method comprises the steps of firstly obtaining circulation data of the medicine to be monitored N months before a prediction time point, then calculating a supply average value and a demand average value of the medicine to be monitored according to the circulation data, respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the medicine to be monitored one month after the prediction time point by using a Poisson distribution formula. And finally, calculating a medicine shortage index of the medicine to be monitored one month after the prediction time according to the supply prediction value and the demand prediction value, grading the risk according to the medicine shortage index, and determining the risk grade of the medicine to be monitored one month after the prediction time, so that a market regulation rule formed for keeping supply and demand balance in a normal state can be found by using large medicine circulation data resources, a means can be provided for early discovery of medicine shortage risks, and a medicine shortage early warning gateway is moved forward.

Description

Medicine shortage risk monitoring and early warning method and system
Technical Field
The invention relates to the technical field of medicine shortage risk monitoring, in particular to a medicine shortage risk monitoring and early warning method and system based on big data processing and calculation.
Background
At present, the monitoring of the medicine shortage information mainly comes from production stopping reports of production enterprises and active reports of medical institutions, the two monitoring dimensions are positioned at two ends of a medicine supply chain, and the production stopping reports of the medicine production enterprises can effectively deal with shortages caused by insufficient production and supply but cannot identify shortage risks caused by other market factors; medical institutions are located at the end of drug supply chains, and actively report drug shortage information, so that the drug shortage is less sensitive to market factors, and the feedback is delayed when the drug shortage occurs.
Therefore, a method and system for detecting the risk of drug shortage as early as possible are needed.
Disclosure of Invention
The invention aims to provide a medicine shortage risk monitoring and early warning method and system, which can determine the medicine shortage risk grade in a prediction mode so as to discover the medicine shortage risk as early as possible.
In order to achieve the purpose, the invention provides the following scheme:
a drug shortage risk monitoring and early warning method, the method comprising:
acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
calculating the average supply value and the average demand value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the to-be-monitored medicine one month after the prediction time point by using a Poisson distribution formula;
calculating a medicine shortage index of the medicine to be monitored one month after the prediction time point according to the supply prediction value and the demand prediction value;
and carrying out risk grading according to the medicine shortage index, and determining the risk grade of the medicine to be monitored in one month after the prediction time point.
A drug shortage risk monitoring and pre-warning system, the system comprising:
the circulation data acquisition module is used for acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
the average value calculating module is used for calculating the supply average value and the demand average value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
the prediction module is used for respectively taking the supply average value and the demand average value as input and predicting a supply predicted value and a demand predicted value of the to-be-monitored medicine one month after the prediction time point by utilizing a Poisson distribution formula;
the medicine shortage index calculation module is used for calculating a medicine shortage index of the medicine to be monitored one month after the prediction time point according to the supply prediction value and the demand prediction value;
and the risk grading module is used for carrying out risk grading according to the medicine shortage index and determining the risk grade of the medicine to be monitored one month after the prediction time point.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a medicine shortage risk monitoring and early warning method and system, which are used for firstly acquiring circulation data of a medicine to be monitored in N months before a prediction time point, then calculating a supply average value and a demand average value of the medicine to be monitored in N months before the prediction time point according to the circulation data, respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the medicine to be monitored in one month after the prediction time point by using a Poisson distribution formula. And finally, calculating a medicine shortage index of the medicine to be monitored one month after the prediction time according to the supply prediction value and the demand prediction value, grading the risk according to the medicine shortage index, and determining the risk grade of the medicine to be monitored one month after the prediction time, so that a market regulation rule formed for keeping supply and demand balance in a normal state can be searched by using large medicine circulation data resources, a means can be provided for early discovery of medicine shortage risk, and a gate of medicine shortage early warning is moved forward.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a process flow diagram of a process provided in example 1 of the present invention;
FIG. 2 is a method schematic of the method provided in example 1 of the present invention;
fig. 3 is a system block diagram of a system provided in embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a medicine shortage risk monitoring and early warning method and system, which can determine the medicine shortage risk grade in a prediction mode so as to discover the medicine shortage risk as early as possible.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
the embodiment is used for providing a medicine shortage risk monitoring and early warning method, as shown in fig. 1 and fig. 2, the method includes:
s1: acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
before S1, the method of this embodiment further includes data governance to construct a database for storing circulation data of each drug, which specifically includes:
(1) acquiring medicine circulation data and medicine information data of a plurality of medicine business enterprises, wherein the medicine circulation data comprises circulation data such as medicine purchase data, medicine sale data and medicine month end inventory data;
(2) and (4) carrying out association matching on the medicine circulation data and the medicine information data in an sql mode to obtain circulation data of each medicine, and storing the circulation data of each medicine in a database.
Specifically, the medicine circulation data and the medicine information data are set to comprise four fields of a national medicine standard word, a specification, a medicine universal name and a dosage form, and then the medicine circulation data and the medicine information data are associated and matched in an sql mode according to the national medicine standard word, the specification, the medicine universal name and the dosage form.
In addition, for the medicine circulation data which cannot be associated and matched with the medicine information data, the inquiry is carried out on the internet according to the national medicine standard, the specification, the medicine universal name and the dosage form, the medicine information data corresponding to the medicine circulation data is determined, and the association and matching are completed.
More specifically, the data management steps include: the method comprises the steps of mastering medicine circulation data (medicine circulation data, namely purchase-sale-inventory data, including circulation data of medicine purchase, sale, inventory and the like) of 799 medicine business enterprises in China and main information data (namely medicine information data) related to medicines, wherein the two kinds of data respectively have four fields of national standard characters, specifications, general names of medicines and dosage forms, the medicine circulation data and the medicine information data are subjected to correlation matching in an sql mode according to the four fields to realize correspondence and are stored in a kudu database, and part of medicine circulation data which are not correlated are subjected to internet manual inquiry and data correlation storage according to the information of the national standard characters, the specifications, the general names of medicines, the dosage forms and the like to obtain circulation data of each medicine, and a database is built.
In this embodiment, a location attribute of circulation data is assigned according to a geographical location of a pharmaceutical business enterprise, and a time attribute of circulation data is assigned according to a time point at which the circulation data occurs, so that the obtained circulation data of each medicine is circulation data generated by each medicine at different times and different places, and the circulation data can be stored according to a time sequence.
After building the database storing the circulation data of each medicine, since the circulation data each has the time attribute and the location attribute, S1 may include: and (4) screening the types, time and places of the medicines in the database to obtain circulation data of the medicines to be monitored N months before the prediction time.
S2: calculating the average supply value and the average demand value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
specifically, S2 may include:
(1) respectively accumulating purchase data of the to-be-monitored medicines in each month N months before the prediction time point to obtain supply data of the to-be-monitored medicines in each month; respectively accumulating sales data of the to-be-monitored medicines in each month N months before the prediction time point to obtain demand data of the to-be-monitored medicines in each month;
in this embodiment, the drug is sold and delivered, the drug is delivered as a market demand, the drug is purchased and delivered, and the drug is delivered as a supply. Therefore, for each month N months before the prediction time point, the purchase data in the month are accumulated to obtain the supply data of the month, and the sales data in the month are accumulated to obtain the demand data of the month.
(2) Averaging the supply data of the medicine to be monitored in each month to obtain the average supply value of the medicine to be monitored in N months before the prediction time point; and averaging the demand data of the medicine to be monitored in each month to obtain the average demand value of the medicine to be monitored in N months before the prediction time point.
Wherein, averaging the supply data of the to-be-monitored medicine in each month to obtain the supply average value of the to-be-monitored medicine N months before the prediction time point may include: judging whether the supply data of the medicine to be monitored in each month is not zero; if yes, performing geometric averaging on the supply data of the medicine to be monitored in each month to obtain the average supply value of the medicine to be monitored in N months before the prediction time point; if not, performing arithmetic average on the supply data of the to-be-monitored medicines in each month to obtain the supply average value of the to-be-monitored medicines in N months before the prediction time point.
Calculating the average demand value may be similar to calculating the average supply value, and averaging the demand data of the drug to be monitored in each month to obtain the average demand value of the drug to be monitored in N months before the forecast time point may include: judging whether the demand data of the medicine to be monitored in each month is not zero; if yes, performing geometric averaging on the demand data of the medicine to be monitored in each month to obtain the average demand value of the medicine to be monitored in N months before the prediction time point; and if not, performing arithmetic average on the demand data of the medicine to be monitored in each month to obtain the average demand value of the medicine to be monitored in N months before the prediction time point.
The formula for the geometric mean is:
Figure BDA0003567103230000051
wherein X is a supply average; x1Supply data for month 1; xNData for month N supply.
Figure BDA0003567103230000052
Wherein Y is a demand average; y is1Month 1 demand data; y isNThe data is the demand data of the Nth month.
The formula for the arithmetic mean is as follows:
Figure BDA0003567103230000053
Figure BDA0003567103230000054
more specifically, N may be 3, and in this embodiment, the supply value, the demand value, and the current month inventory value of each drug in each province, each month, may be calculated according to the data in the database obtained by the data governance, so that for the drug to be monitored, the province and the month are selected through the prediction time point, and the supply value and the demand value of the drug to be monitored in 3 months before the prediction time point may be obtained, and then the supply average value and the demand average value of 3 months are obtained according to the supply value and the demand value of the adjacent 3 months, and finally the data is stored in the kudu database.
Wherein, if the data of three months are not empty, calculating according to geometric mean, namely:
Figure BDA0003567103230000055
Figure BDA0003567103230000056
if the individual data is null, taking the value of zero and taking the arithmetic mean value of the three data, namely:
the average supply level (supply average) of the past 3 months is (first month supply amount + second month supply amount + third month supply amount)/3.
Average demand level (average of demand) in the past 3 months (first month demand + second month demand + third month demand)/3.
The calculation of the geometric average quantity of supply and demand 3 months before the prediction time point can be completed by utilizing the formula.
S3: respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the medicine to be monitored one month after the prediction time point by using a Poisson distribution formula;
according to the formula of the poisson distribution, the supply and demand predicted values can be obtained under the condition that the future release probability is 1 or infinitely approaches to 1. Therefore, the predicted supply and demand values of the next month are respectively obtained according to the average supply and demand values of the adjacent N months in S2. Specifically, the supply average value of S2 is obtained through Java, the supply predicted value of the next month is calculated through an R language existing function (qpois function: reversible calculation Poisson distribution formula), the demand average value of S2 is obtained through Java, and the demand predicted value of the next month is calculated through the R language existing function (qpois function: reversible calculation Poisson distribution formula), so that the supply and demand prediction of the next month at the prediction time point is completed.
S4: calculating a medicine shortage index of the medicine to be monitored one month after the prediction time point according to the supply predicted value and the demand predicted value;
s4 may include: summing the supply predicted value and the current inventory of the medicine to be monitored at the prediction time point to obtain a sum value; subtracting the sum value from the demand predicted value to obtain a difference value; and calculating the ratio of the difference value to the demand predicted value to obtain a medicine shortage index of the medicine to be monitored one month after the predicted time point.
The present embodiment originally provides a "Drug Shortage Index", which can quantitatively measure the Drug Shortage risk, and the calculation formula of the Drug Shortage Index (DSI) is: the medicine shortage index is (predicted value of predicted future one-month demand-predicted value of predicted future one-month supply-current stock)/predicted value of future one-month demand. Wherein the predicted supply predicted value for one month in the future and the predicted demand predicted value for one month in the future are obtained at S3, and the current inventory amount is a known amount.
The present embodiment may also calculate the medicine shortage index in the next month based on the calculation formula of the medicine shortage index by using data of Java calculation S3.
S5: and carrying out risk grading according to the medicine shortage index, and determining the risk grade of the medicine to be monitored one month after the prediction time point.
Specifically, S5 may include:
(1) judging whether the medicine shortage index is smaller than or equal to a first preset value or not to obtain a first judgment result;
(2) if the first judgment result is yes, the risk level of the medicine to be monitored in a month after the prediction time point is low;
(3) if the first judgment result is negative, judging whether the medicine shortage index is smaller than or equal to a second preset value or not, and obtaining a second judgment result; wherein the second preset value is greater than the first preset value;
(4) if the second judgment result is yes, the risk grade of the medicine to be monitored in one month after the prediction time point is medium risk;
(5) if the second judgment result is negative, the risk level of the medicine to be monitored in one month after the prediction time point is high risk.
More specifically, the first preset value may be 0, and the second preset value may be 0.5, then S5 may include: when the DSI is less than or equal to 0, the shortage risk is low risk; when the DSI is more than 0 and less than or equal to 0.5, the shortage risk is the stroke risk; when the DSI is more than 0.5 and less than or equal to 1, the shortage risk is high risk, and the risk grading is carried out on the medicine to be monitored based on the DSI value.
And saving the corresponding risk level and data information in the mysql database through Java processing according to the result obtained in the step S5.
The method provided by the embodiment utilizes medicine circulation big data resources to find the market regulation rule formed for keeping supply and demand balance in a normal state, can provide a means for early detection of medicine shortage risk, and moves the gate of medicine shortage early warning forward. Meanwhile, the full integration of industrial medicine circulation big data is unprecedentedly realized, and the circulation link is supplemented in the medicine shortage monitoring work through the application of the circulation big data, so that a production end and a demand end are connected in series.
Example 2:
the embodiment is used for providing a medicine shortage risk monitoring and early warning system, as shown in fig. 3, the system includes:
the circulation data acquisition module M1 is used for acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
the average value calculating module M2 is used for calculating the average supply value and the average demand value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
the predicting module M3 is used for respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the medicine to be monitored one month after the predicting time point by using a Poisson distribution formula;
a medicine shortage index calculation module M4, configured to calculate, according to the supply predicted value and the demand predicted value, a medicine shortage index of the medicine to be monitored in a month after the prediction time point;
and the risk grading module M5 is used for carrying out risk grading according to the medicine shortage index and determining the risk grade of the medicine to be monitored in one month after the prediction time point.
The emphasis of each embodiment in the present specification is on the difference from the other embodiments, and the same and similar parts among the various embodiments may be referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A medicine shortage risk monitoring and early warning method is characterized by comprising the following steps:
acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
calculating the average supply value and the average demand value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
respectively taking the supply average value and the demand average value as input, and predicting a supply predicted value and a demand predicted value of the medicine to be monitored one month after the prediction time point by using a Poisson distribution formula;
calculating a medicine shortage index of the medicine to be monitored one month after the prediction time point according to the supply prediction value and the demand prediction value;
and carrying out risk grading according to the medicine shortage index, and determining the risk grade of the medicine to be monitored one month after the prediction time point.
2. The method of claim 1, wherein prior to obtaining circulation data for the drug to be monitored N months prior to the predicted time point, the method further comprises:
acquiring medicine circulation data and medicine information data of a plurality of medicine business enterprises; the medicine circulation data comprises medicine purchasing data, medicine selling data and medicine monthly end inventory data;
and performing correlation matching on the medicine circulation data and the medicine information data in an sql mode to obtain circulation data of each medicine, and storing the circulation data of each medicine in a database.
3. The method according to claim 2, wherein the associating and matching the medicine circulation data and the medicine information data in an sql manner specifically comprises:
setting the medicine circulation data and the medicine information data to comprise national medicine standard characters, specifications, medicine universal names and dosage forms;
and performing correlation matching on the medicine circulation data and the medicine information data in an sql mode according to the national medicine standard, the specification, the medicine universal name and the dosage form.
4. The method according to claim 3, wherein for the medicine circulation data which cannot be associated and matched with the medicine information data, the medicine information data corresponding to the medicine circulation data is determined by querying the internet according to the national medicine standard, the specification, the medicine common name and the dosage form, and the association and matching are completed.
5. The method according to claim 2, wherein the acquiring the circulation data of the drug to be monitored N months before the prediction time point specifically comprises:
and screening the types, time and places of the medicines in the database to obtain circulation data of the medicines to be monitored N months before the prediction time.
6. The method of claim 1, wherein calculating the average supply and average demand values of the drug to be monitored N months prior to the forecast time point according to the circulation data specifically comprises:
respectively accumulating the purchase data of the medicine to be monitored in each month N months before the prediction time point to obtain the supply data of the medicine to be monitored in each month; respectively accumulating the sales data of the to-be-monitored medicines in each month N months before the prediction time point to obtain the demand data of the to-be-monitored medicines in each month;
averaging the supply data of the medicine to be monitored in each month to obtain the average supply value of the medicine to be monitored in N months before the prediction time point; and averaging the demand data of the medicine to be monitored in each month to obtain the average demand value of the medicine to be monitored in N months before the forecasting time point.
7. The method of claim 6, wherein the averaging the supply data of the drug to be monitored in each month to obtain the average supply value of the drug to be monitored in N months before the predicted time point specifically comprises:
judging whether the supply data of the medicine to be monitored in each month is not zero;
if so, performing geometric average on the supply data of the medicine to be monitored in each month to obtain the supply average value of the medicine to be monitored in N months before the prediction time point;
and if not, performing arithmetic average on the supply data of the medicine to be monitored in each month to obtain the supply average value of the medicine to be monitored in N months before the prediction time point.
8. The method according to claim 1, wherein the calculating the drug shortage index of the drug to be monitored one month after the forecast time according to the supply forecast value and the demand forecast value specifically comprises:
summing the supply predicted value and the current inventory of the medicine to be monitored at the predicted time point to obtain a sum value;
subtracting the sum value from the demand predicted value to obtain a difference value;
and calculating the ratio of the difference value to the demand predicted value to obtain a medicine shortage index of the medicine to be monitored one month after the prediction time point.
9. The method of claim 1, wherein the risk stratification is performed according to the drug shortage index, and wherein the determining the risk stratification of the drug to be monitored one month after the prediction time point comprises:
judging whether the medicine shortage index is smaller than or equal to a first preset value or not to obtain a first judgment result;
if the first judgment result is yes, the risk level of the medicine to be monitored in a month after the prediction time point is low risk;
if the first judgment result is negative, judging whether the medicine shortage index is smaller than or equal to a second preset value or not, and obtaining a second judgment result; the second preset value is greater than the first preset value;
if the second judgment result is yes, the risk grade of the medicine to be monitored in one month after the prediction time point is medium risk;
and if the second judgment result is negative, the risk grade of the medicine to be monitored in one month after the prediction time point is high risk.
10. A drug shortage risk monitoring and early warning system, the system comprising:
the circulation data acquisition module is used for acquiring circulation data of the medicine to be monitored N months before the prediction time point; the prediction time point comprises provinces and time points; the circulation data comprises purchase data and sales data of each month in N months;
the average value calculating module is used for calculating the supply average value and the demand average value of the medicine to be monitored N months before the forecasting time point according to the circulation data;
the prediction module is used for respectively taking the supply average value and the demand average value as input and predicting a supply prediction value and a demand prediction value of the medicine to be monitored one month after the prediction time point by utilizing a Poisson distribution formula;
the medicine shortage index calculation module is used for calculating a medicine shortage index of the medicine to be monitored in one month after the prediction time point according to the supply prediction value and the demand prediction value;
and the risk grading module is used for carrying out risk grading according to the medicine shortage index and determining the risk grade of the medicine to be monitored one month after the prediction time point.
CN202210310944.3A 2022-03-28 2022-03-28 Medicine shortage risk monitoring and early warning method and system Pending CN114664422A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063079A (en) * 2022-06-28 2022-09-16 重庆长安汽车股份有限公司 Monitoring and early warning system and method for chip supply chain

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063079A (en) * 2022-06-28 2022-09-16 重庆长安汽车股份有限公司 Monitoring and early warning system and method for chip supply chain

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