CN113034104A - Intelligent big data analysis method and system for medicine group purchasing - Google Patents

Intelligent big data analysis method and system for medicine group purchasing Download PDF

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CN113034104A
CN113034104A CN202110315163.9A CN202110315163A CN113034104A CN 113034104 A CN113034104 A CN 113034104A CN 202110315163 A CN202110315163 A CN 202110315163A CN 113034104 A CN113034104 A CN 113034104A
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medicine
purchasing
model
gpo
platform
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卓绮雯
孙涛
陈萌菲
李晓彤
朱仁
古冬青
刘淑佳
蔡少莹
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Shenzhen Quanyaowang Technology Co ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/26—Government or public services

Abstract

The invention discloses an intelligent big data analysis method for medicine group purchasing, which comprises the following steps: carrying out standardization processing, matching and association on the acquired medicine purchasing data to classify information of each medicine; constructing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions; and the visual intelligent analysis platform issues the content and the index of the functional model according to the application scene and the user authority through the cloud. The invention also provides an intelligent big data analysis system for medicine group purchasing. By building the intelligent big data analysis system for medicine group purchasing, the system can assist the supervision department to master the purchasing condition of the medical institution, the GPO catalogue, national acquisition nation talks, the reduction of the purchasing amount of the prevention and control stored medicines and the medicines with various classification attributes and the condition of saving the amount of money in real time, and the supervision department for helping better controls the purchasing behavior, so that the medical insurance fund is better utilized.

Description

Intelligent big data analysis method and system for medicine group purchasing
Technical Field
The invention relates to the technical field of big data analysis, in particular to an intelligent big data analysis method and system for medicine group purchasing.
Background
The procurement mode of the procurement organization of the GPO group has been operated for several years in China, and is started successively in various places in recent years, and the popularization influence of the GPO mode in China is the Shenzhen mode at most. GPO is a technology that increases the ability to negotiate prices for upstream suppliers (drugs, medical instruments, other medical services) by concentrating the procurement needs of various downstream medical institutions, thereby striving for greater discounts for medical institutions and reducing procurement costs. Therefore, the procurement of the medicine with the quantity is the key point of the GPO mode, hundreds of thousands of the medicine on the market in China, Shenzhen GPO performs dosage form integration on the medicine with the same general name and same catalog dosage form, selects the medicine with large procurement quantity commonly used in Shenzhen region to formulate a medicine catalog, and integrates the medicine with the large procurement quantity into more than twenty thousand medicine procurement quantities through catalog matching and dosage form conversion, so that the procurement of the medicine with the quantity is realized, and the medicine cost is effectively reduced.
The reduction of the cost of the medicine and the saving of money are main indexes for checking the GPO achievement. The GPO and other purchasing platforms in other areas are referred to at present and are simply and directly compared with the prices of the same products (the same common name, the same small dosage form, the same specification and the same manufacturer), the price reduction is the price direct reduction, the cost saving is the product of price difference and quantity, and the advantage of GPO dosage form integrated quantity purchasing cannot be reflected. By standardizing the medicine purchasing data of the original medical institutions and a unique medicine catalog matching principle and a medicine price reduction measuring and calculating method, the supervision department can master the GPO purchasing condition of each medical institution and the actual saved amount and reduction after the GPO is on line.
The original method is that the disordered data of the medical institutions are standardized in the EXCEL, then the universal names, the catalog formulations, the standard specifications and the standard production enterprises of the catalog are unified, the standardized purchase data of the GPO purchase platform are matched according to the unique catalog matching principle, the data amount of each area time period can reach hundreds of thousands, manual standardization is carried out, the catalog matching is carried out, two to three weeks are possibly required, the time periods are different, the areas are different, the medical institutions are different, the medicine catalog batches are different, the platform data are different, the work can be repeated, the previous amplitude reduction result can be registered only in a billing mode, when the final summary is needed, the amplitude reduction processing of the overlapping time period is very troublesome, and the error rate is high. The amplitude reduction measurement and calculation of two or three regions can also be manually dealt with, and with the implementation of regional group purchase of GPO, 21 regions are added, so that the requirement of amplitude reduction measurement and calculation of medical insurance offices in all regions cannot be met by the original working method.
Disclosure of Invention
The invention aims to solve the technical problem of solving the defects in the prior art and discloses an intelligent big data analysis method and system for medicine group purchasing, which are used for perfecting centralized purchasing work of medicines in public hospitals, reducing the deficiency and high price of the medicines, lightening the burden of medical expenses of citizens and promoting the healthy development of the medical industry.
The technical scheme adopted by the invention is as follows:
an intelligent big data analysis method for medicine group purchasing comprises the following steps:
carrying out standardization processing, matching and association on the acquired medicine purchasing data to classify information of each medicine;
constructing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions;
and the visual intelligent analysis platform issues the content and the index of the functional model according to the application scene and the user authority through the cloud.
The further technical proposal of the invention is that the acquired medicine purchasing data is processed by standardization, matching and associated with each medicine classification information; the method specifically comprises the following steps: for multi-source medicine purchasing data collected in multiple channels, automatic standardization is conducted on the medicine purchasing data through an intelligent standardization model, so that medicine purchasing data information is unified, automatic matching is conducted on medicine purchasing through an intelligent catalog matching model and a Shenzhen GPO platform, and mutual relations and logics of medicine purchasing are established.
Further, the drug procurement data comprises: common name, dosage form, specification attribute, packaging material and production enterprise element.
The method adopts the further technical scheme that a functional model is constructed and is cross-associated into a multifunctional comprehensive big data model by common dimensionality; the method specifically comprises the following steps: according to the standardized medicine information of purchase channel, it is different according to its application function, builds functional model, and wherein, functional model includes: the method comprises the steps of comparing a GPO (general purpose input/output) on-line amplitude reduction model after the GPO is on line in a measuring and calculating area, comparing a GPO on-line prediction amplitude reduction model in an expanded measuring area such as a GPO on-line average cost comparison model, a multiple purchasing platform same group of medicines, an amplitude reduction model after the price reduction of medicines collected by a country is measured and calculated, an amplitude reduction model after the price reduction of medicines is measured and calculated and compared with the price reduction model before the price reduction of the medicines, an amplitude reduction model before and after linkage is measured and calculated after the price reduction of the medicines is measured and calculated, a model for converting the purchase of the medicines without purchase on other platforms into GPO amplitude reduction after the GPO is on line, a GPO on-line city and other purchasing platforms any.
The method adopts the further technical scheme that a functional model is constructed and is cross-associated into a multifunctional comprehensive big data model by common dimensionality; specifically, the common dimensions include time, region, hospital, catalog lot, pharmacology classification, purchase source, before and after online, treatment field, price interval, extent reduction interval, money saving interval, daily average cost comparison, and the like.
As a further technical solution of the present invention, the method for publishing the content and the index of the functional model by the visual intelligent analysis platform according to the application scenario and the user right through the cloud specifically includes:
forming an index library through induction of historical data and index measurement and calculation;
setting indexes and threshold values by combining the dimensionality of the data model;
analyzing the standard reaching condition of the analysis content and index required by the functional model through data processing;
displaying in multiple forms through a visual intelligent analysis platform;
and issuing the content according to the application scene and the user permission through the cloud.
As a further technical solution of the present invention, the setting of the index and the threshold value in combination with the dimension of the data model specifically includes: and setting indexes by combining the dimensionality of the data model, and setting a threshold value according to the current policy and a statistical method, wherein the statistical method selects one of a same ratio, a ring ratio, an occupation ratio, a sequence, a weighted average, a difference value, a prediction trend and a price comprehensive index.
As a further technical solution of the present invention, the presentation in multiple forms by the visual intelligent analysis platform specifically includes: the visualized intelligent analysis platform is displayed in a webpage display and automatic analysis report form, wherein the webpage display comprises: the purchasing condition of the GPO platform, the amplitude reduction measuring and calculating condition of the GPO platform and the purchasing condition of the GPO platform and other platforms are compared; the automatic analysis report comprises a reduced-amplitude analysis report, a data analysis report of other regional platforms, a directory matching report and a reduced-amplitude analysis report of converting the other regional platforms into GPO.
The invention also provides an intelligent big data analysis system for medicine group purchasing, which comprises:
the data processing unit is used for carrying out standardized processing, matching and association on the acquired medicine purchasing data and classifying information of each medicine;
the model establishing unit is used for establishing a functional model and forming a multifunctional comprehensive big data model by cross-correlation of common dimensions;
and the index analysis visual display unit is used for the visual intelligent analysis platform to issue the content and the index of the functional model according to the application scene and the user permission through the cloud.
The invention has the beneficial effects that:
by building the intelligent big data analysis system for medicine group purchasing, the system can assist a supervision department to master the purchasing condition of medical institutions, GPO catalogs, national acquisition and national consultation, prevention and control of stored medicines and various classified attribute medicines in real time, the reduction of the range and the saving of money ranking of various administrative regions and various medical institutions, the condition of dosage form conversion, price comparison among multiple platforms in the market, the ratio of market share, the saving of money after conversion of the same common name and dosage form among the platforms, and the management and control of purchasing behaviors of the supervision department for help and help, so that medical insurance funds are better utilized. The method can be widely applied to the analysis of the big data of the medicines in areas where GPO group purchase and preparation are on line, and can also be used for the intelligent analysis and comparison of the big data of the medicines with multiple sources.
Drawings
FIG. 1 is a flow chart of an intelligent big data analysis method for group pharmaceutical procurement according to the invention;
FIG. 2 is a schematic diagram of an architecture of an intelligent big data analysis system for group pharmaceutical procurement according to the present invention;
FIG. 3 is a schematic diagram of the present invention for standardizing, matching and associating acquired drug procurement data with classification information of each drug;
FIG. 4 is a diagram of a standardized intelligent model of a medicine according to the present invention;
FIG. 5 is a diagram of a drug catalog matching intelligent model according to the present invention;
FIG. 6 is a diagram of the result of the optimal matching between directory A and directory B according to the present invention;
FIG. 7 is a flowchart illustrating the process of storing a standard drug dictionary and a matching drug dictionary in a drug information base according to the present invention;
FIG. 8 is a schematic diagram of building functional models to be cross-linked into a comprehensive big data model by common dimensions according to the present invention;
FIG. 9 is a flowchart of a method for calculating the price reduction range and the amount to be saved according to the present invention;
FIG. 10 is a flowchart of a second method for calculating the price reduction range and the amount to be saved according to the present invention;
FIG. 11 is a schematic diagram of dimensions, presentation forms and contents related to a big data intelligent analysis platform provided by the present invention;
FIG. 12 is a structural diagram of an intelligent big data analysis system for group pharmaceutical procurement according to the present invention;
FIG. 13 is a block diagram illustrating an exemplary embodiment of a big data analytics platform according to the present invention;
fig. 14 is a block diagram of another embodiment of the big data intelligent analysis platform according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Example one
Referring to fig. 1, an intelligent big data analysis method for group pharmaceutical procurement includes:
step 101, carrying out standardization processing, matching and association on the acquired medicine purchasing data to classify information of each medicine;
102, constructing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions;
103, the visual intelligent analysis platform issues the content and the index of the functional model according to different application scenes and user permissions through the cloud.
By building the intelligent big data analysis system for medicine group purchasing, the system can assist a supervision department to master the purchasing condition of medical institutions, GPO catalogs, national acquisition and national consultation, prevention and control of stored medicines and various classified attribute medicines in real time, the reduction of the range and the saving of money ranking of various administrative regions and various medical institutions, the condition of dosage form conversion, price comparison among multiple platforms in the market, the ratio of market share, the saving of money after conversion of the same common name and dosage form among the platforms, and the management and control of purchasing behaviors of the supervision department for help and help, so that medical insurance funds are better utilized.
The method can be widely applied to the analysis of the big data of the medicines in areas where GPO group purchase and preparation are on line, and can also be used for the intelligent analysis and comparison of the big data of the medicines with multiple sources.
In step 101, the acquired medicine purchasing data is subjected to standardization processing, matching and association with each medicine classification information; the method specifically comprises the following steps: for multi-source medicine purchasing data collected through multiple channels, multi-source medicine information is automatically standardized through an intelligent standardization model, so that the medicine purchasing data information is unified, medicines are automatically matched with Shenzhen GPO platform purchasing through an intelligent catalogue matching model, and the mutual relation and logic of medicine purchasing are established.
Referring to fig. 2, the multi-channel multi-source medicine purchasing data is in various descriptions and formats, and the intelligent standardization model is used for the medicine purchasing data such as: the automatic standardization of elements such as universal name, dosage form, specification attribute, packing material, manufacturing enterprise, and the like, solves the problem of medicine information sharing obstacle, and automatically matches the medicine purchased by the Shenzhen GPO platform through an intelligent catalog matching model, such as: analyzing the consistency of the medicines purchased by the universal name, the catalog dosage form and the Shenzhen GPO platform and bringing the medicines into a matching range, wherein the matched medicines can simultaneously compare with required data sources (dosage form, specification and production enterprises), screening the result of optimal matching with the medicines purchased by the Shenzhen GPO platform, and the standardized and matched medicine information can be stored in a medical institution acquisition information table of a medicine information base and is associated and classified with other information attributes of the medicine information base, so that the interrelation and the logic are established, then dimension information is screened according to the functions to be realized, various functional application modules are established, and the functional application modules are cross-associated with common dimensions, and finally a comprehensive big data model is formed; the comprehensive big data model comprises standardized multi-platform medicine purchasing information and relevant information attributes.
In step 103, the visual intelligent analysis platform issues the content and index of the functional model according to different application scenarios and user permissions through the cloud, and specifically includes: through induction and index measurement of historical data, an index library is formed, indexes and threshold values are set in combination with dimensionality of a data model, analysis content and index standard reaching conditions needed by a functional model are analyzed through data processing, the analysis content and the index standard reaching conditions are displayed in multiple forms through a visual intelligent analysis platform, and the content is issued through a cloud according to different application scenes and user permissions. And the data exceeding the threshold value can inform a local supervision department of data exception and processing suggestion in an email or short message mode.
Referring to fig. 3, a schematic diagram of the present invention for standardizing, matching and associating acquired drug purchase data with each drug classification information is shown; the collected multi-source medicine purchasing data firstly passes through an intelligent standardization model to obtain standardized data, then is automatically matched through an intelligent catalog matching model to obtain a matching relation corresponding to a GPO catalog, medicine key field information of a medicine information base participates in the process operation of the two models, finally, matching relation and standard medicine purchasing information are established, then the matching relation and the standard medicine purchasing information are stored in other channels purchasing information of a medical institution of the medicine information base, the price and the purchasing quantity of medicines in each area of up to 6 years are already contained through long-term accumulation, and then, according to the function requirements, the medicine classification information, the catalog batch information, the administrative area and each medical institution division information, the daily average dosage information matching attribute, the establishing relation and the logic of each professional field of the medicine information base are obtained, for example: the medicine classification information of the same medicine is different in different time periods, the medicine belongs to the medical insurance catalogue in 2018, and the medicine belongs to the non-medical insurance in 2020; the medicine originally used for treating hypertension in the aspect of treatment field is more widely used for urinary system diseases later; or the same medicine belongs to GPO catalogue, 36 countries talk about the medicine later, and the medicine is renewed to 97 countries in 2020, but because the original purchase contract is not executed yet, the order or the price in 36 countries talk about the medicine, the order and the price are strictly distinguished according to policy execution time, order placing time and price; from the aspect of information division of administrative areas and medical institutions, in addition to attribution of geographic positions, the medical institutions also divide according to the same rule, divide with the treatment field, purchase selection division of a purchasing platform and the like; the daily average dosage of the medicine is influenced by the specification of the medicine from the aspect of daily average dosage of the medicine, the daily dosage specified by effective components of the medicine is assumed to be unchanged, the larger the specification content is, the smaller the daily dosage is, and the daily average dosage cost of the order of the medicine can be calculated by calculating the daily dosage of the medicine and combining the quantity and the amount of the order, wherein the daily average dosage cost is an important index for comparing the daily cost of various medicines with different specifications, dosage forms and the like. And finally, building various functional application models by establishing the medicine information relationship and the calculation logic of the medicine order.
The domestic medicine catalogues contain the medicines currently marketed in China, and the standard information of the medicines is identified and distinguished on the basis of the domestic medicine catalogues, so that a medicine standardized dictionary is established. Referring to fig. 4, the intelligent model for drug standardization includes the following modules:
m1 identifies the word module: the medicine generally comprises elements such as a common name, a dosage form, specifications, specification attributes, packaging materials, manufacturing enterprises and the like; through analyzing each element of all medicines on the market in China, obtaining a text which is relatively fixed in the multiple description of the same medicine and embodies the characteristics of the text, using the text as a marking word of each element of the medicine, and further combining the text into a marking phrase of the medicine;
in the case of the common names of medicines, the identification words of the common names are generally the names, common or common alias names, trade names, etc. of the active ingredients thereof, such as azithromycin, VB, nordherin R, etc.
M2 distinguisher module: the same drug identification word group may correspond to different drugs in a standard drug database, and the essential differences among the drugs are obtained by comparing the drugs in detail and are used as differentiating words of the drugs;
for example, the identifying phrases "azithromycin, 0.125g, capsule, sika da heng" correspond to the standard drug 1 and the standard drug 2, the dosage forms of the drug 1 and the drug 2 are different, namely the capsule and the soft capsule, and the addition of the distinguishing phrase "soft" to the drug 2;
the collected medicine purchasing data from multiple sources is in multiple descriptions and formats, and firstly, the medicine purchasing data needs to enter an M3 information summarizing module of a medicine standardized intelligent model for preprocessing;
m3 information summarization module: splicing description texts of original medicine data, not distinguishing sequences, and summarizing original medicine information;
the description of the original medicine data is various, even the description sequence is disordered, the information of the original medicine data is summarized, and the sequence is not distinguished; such as: the original medicine A information is collected to form azithromycin-0.125 g (12.5 ten thousand units) -capsule-Xian Da Heng pharmaceutical Co., Ltd, and the original medicine B information is collected to form Aithromcn-12.5 ten thousand units-Lixi an Da Heng-hard glue film;
m4 standardization module: judging whether the summary information of the original medicines contains identification words and distinguishing words of the medicines in the standard medicine database, if the summary information of the original medicines contains and only contains one identification word and distinguishing word of the standard medicines, giving the information of the standard medicines to the data of the original medicines, and realizing the standardization of the medicines;
for example, the original drug A information summary ' azithromycin-0.125 g-capsule-Ci ' an Da Heng is available ' and the original drug C information ' azithromycin-0.125 g-capsule-Ci ' an Da Heng ' both contain 1 standard drug identification phrase ' azithromycin, 0.125g, capsule, Ci ' an ', and only contain 1 identification phrase in the standard drug dictionary, at this time, the original drugs A and C realize standardization corresponding to the standard drug information in the standard drug database;
m5 supplementary module: if the original medicine information contains more than 1 piece of standard medicine information, obtaining essential differences among medicines by comparing the standard medicines in detail, and using the essential differences as distinguishing words of the medicines;
for example, the summary information of original drug D "azithromycin dispersible tablet-0.1 g (10 ten thousand units) -tablet (dispersible tablet) -sichuan coln pharmaceutical industry gmbh" contains two identification phrases "azithromycin, 0.1g, tablet, sichuan coln" in a standard drug dictionary, and the dosage forms of two standard drugs containing the same identification phrase are analyzed and compared to be essentially different, the dosage forms are "tablet" and "dispersible tablet", and the distinguishing word "disperse" is added, at this time, the original drug D can correspond to the unique identification phrase and the distinguishing word in the standard drug database, thereby realizing standardization;
m6 maintenance module: the method comprises the steps that medicine description of each element of the medicine which is not standardized is analyzed, and if the medicine is a relatively fixed text which embodies the characteristics of the medicine, identification words and distinguishing words of the medicine are supplemented;
for example, the original medicine summary information does not find a corresponding identification phrase and can not realize standardization, if the analysis reason of the original medicine B summary information "Aithromcn-12.5 ten thousand units-tisiean da heng-hard glue" finds that the common name is an english name, the identification words of the medicine common name in the standard medicine dictionary are added with the content of the english name, and then the original medicine C can be found to correspond to the unique identification phrase in the standard medicine dictionary, so that standardization is realized;
the standard drug database or the original drug data are used for continuously carrying out standardized tests, and the identification words and the distinguishing words of the standard drug database can be gradually improved.
The domestic medicine catalogue set includes the medicines currently marketed in China, the characteristics and the forms of the medicine preparations are classified based on the domestic medicine catalogue set, the data elements are deconstructed, the attributes and the numerical values of the data elements are standardized, and a medicine matching dictionary is established.
Referring to fig. 5, the intelligent model for matching drug catalog includes the following modules:
m7 classification module: the medicines with the same preparation characteristics have similar information types covered by texts for describing the characteristics of the medicines; drugs are classified according to their pharmaceutical formulation characteristics, firstly according to chemical and biological products, Chinese patent drugs, and secondly according to formulation morphology, for example: solid preparations, liquid preparations, pills and the like, tablets and capsules belong to the solid preparations, and oral liquid and mixture belong to the liquid preparations;
m8 data meta-module: constructing a medicine data model for medicines on the market at home, wherein the medicine data model comprises data elements such as a universal name, a dosage form, a specification, specification attributes, a packaging material, a production enterprise and the like;
m9 data meta attribute module: according to the classification, the attribute and the value of each data element of different types of medicines are normalized; take the specification data elements of the medicine as an example: the specification attribute of the medicine belongs to chemical medicines and solid preparations, and is content (namely the amount of active ingredients of the medicine); the properties of the specifications of the medicines belonging to chemical medicines and liquid preparations are loading (capacity of the preparation), content and concentration;
the attributes of the same data elements of different types of medicines may be different, and the attribute comparison common to the data elements is selected during medicine comparison, so that the influence of non-key information can be ignored, and the essential comparison between two medicines is realized, for example: medicine 3 (metronidazole injection-20 ml: 0.1g) and medicine 4 (metronidazole-0.1 g for injection); the medicine 3 belongs to chemical medicines and liquid preparations, the medicine 4 belongs to chemical medicines and solid preparations, the common names of the two medicines are metronidazole and injection preparation, and the liquid preparation and the solid preparation have different specifications and different attributes; if all attributes of the specifications are compared, the specifications of the two medicines are different, but the active ingredients of the two medicines are the same in quantity and are 0.1g, and the essential difference of the medicines can be reflected by comparing the common attribute content of the specification data elements;
m10 preset condition 1 module: selecting the precondition and content of drug comparison;
for example, the dosage form, specification, and manufacturing company of the drug may be selected to be compared under the condition that the common names are the same or the common names and the classification of the dosage forms are the same. The dosage form classification here can be: oral sustained release dosage forms, oral liquid dosage forms, injections, etc.;
the medicine catalogues which are standardized by the Shenzhen GPO and the medicine catalogues which are standardized by the medicine purchasing data from multiple sources can be respectively used as an analysis catalog and a reference catalog module, and the medicine catalogues are reduced and brought into a matched medicine range.
M11 analyze catalog module: determining the range of the included contrast drugs according to the analysis catalog; drug catalogs that are typically other platforms that have been standardized;
m12 references the directory module: selecting a reference catalog and determining a reference medicine range; the medicine catalog of Shenzhen GPO platform is common;
m13 comparison module: according to the preset condition 1, newly adding formulation classification data elements, simultaneously selecting data elements (formulation, specification and production enterprises) required by drug comparison, comparing the common attributes of the same data elements of the two drugs, and assigning a value to each attribute comparison result, for example: the "same/yes" value is 1, the "different/no" value is 0, and the comparison result between the analysis directory a and the reference directory B is shown in fig. 6;
m14 preset condition 2 module: selecting a priority order of the directory matching; for example: under the condition of the same universal name-dosage form classification, the prior dosage forms are the same, and the subsequent enterprises are the same;
m15 matching module: according to the preset condition 2, sequentially combining the data elements participating in comparison and the comparison result values of the attributes of the data elements as matching sequence values, and screening the optimal matching;
taking drug 1 in analysis catalog A as an example, the dosage form is the same as drug 11 in reference catalog B, the specification and the enterprise are different, and the sequence value is 11010; unlike the reference catalog B, drug 12, which is different in specification, dosage form and enterprise, the sequence value is "11000"; the sequence values of the analysis catalog A, medicines 1 and the reference catalog B, medicines 11 are greater than those of the analysis catalog A, medicines 1 and the reference catalog B, medicines 12, so that the analysis catalog A, medicines 1 and the reference catalog B, medicines 11 are optimally matched under the preset condition; referring to fig. 6, the best matching result between the analysis directory a and the reference directory B is shown.
According to the change of the preset conditions 1 and 2, the optimal matching result between the analysis directory and the reference directory changes correspondingly.
And the standard medicine dictionary and the matched medicine dictionary participating in the intelligent medicine standardization model and the intelligent medicine matching model respectively enter the medicine information base for storage according to the key field information of medicines, and some newly marketed medicines serving as newly-added medicine information respectively enter the M1, the M2, the M7, the M8 and the M9 modules, are added to the standard medicine dictionary and the matched medicine dictionary, and then enter the medicine information base. Fig. 7 is a flowchart of storing the standard medicine dictionary and the matching medicine dictionary in the medicine information base.
Referring to fig. 8, a schematic diagram of a comprehensive big data model in which all functional models are constructed and cross-linked by common dimensions is provided;
in step 102, constructing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions; the method specifically comprises the following steps: according to the standardized medicine information of purchase channel, according to its application function difference, build functional model, include: the method comprises the steps of comparing a GPO (general purpose input/output) on-line amplitude reduction model after the GPO is on line in a measuring and calculating area, comparing a GPO on-line prediction amplitude reduction model in an expanded measuring area such as a GPO on-line average cost comparison model, a multiple purchasing platform same group of medicines, an amplitude reduction model after the price reduction of medicines collected by a country is measured and calculated, an amplitude reduction model after the price reduction of medicines is measured and calculated and compared with the price reduction model before the price reduction of the medicines, an amplitude reduction model before and after linkage is measured and calculated after the price reduction of the medicines is measured and calculated, a model for converting the purchase of the medicines without purchase on other platforms into GPO amplitude reduction after the GPO is on line, a GPO on-line city and other purchasing platforms any.
Wherein the common dimension relates to 21 dimensions: time (year of the rose), region (province, city, county, GDP classification, administrative region), hospital (class, category, attribute, treatment field), catalog lot, (base drug, medical insurance, prescription attribute), pharmacology classification (level four), purchase source, before and after online, treatment field, price interval, breadth reduction interval, money saving interval, daily cost comparison, etc.
The price reduction range and the amount saved measuring and calculating method have two methods: the method comprises the steps of predicting the amplitude reduction and the amount saved of GPO if the medicine of the A platform is purchased on the GPO platform by using a Laplace price index method before the online, and measuring the actual amplitude reduction and the actual amount saved of the medicine purchased by the GPO by using a Pasteur price index method after the online.
Referring to fig. 9, a first method for calculating the price reduction range and the amount saved: before the GPO platform is on-line, the amplitude is reduced and the amount can be saved is predicted: under the premise that the sales quantity of the platform A is not changed, the method calculates and calculates the purchase amount of each grouped variety for the price of the hospital by group purchase, further calculates and calculates the theoretical total sales amount of all the purchase groups, compares the theoretical total sales amount with the total medicine purchase amount of the platform A, and calculates the price reduction range; in order to facilitate the conversion of the medication quantity of different specifications under the same universal name and the same catalog dosage form in the catalog into the medication quantity of a transaction product, the concept of medication days is introduced, the medication days refer to the medication days of the product calculated according to the recommended daily dosage under the existing medication quantity, and the algorithm carries out reasonable quantity conversion on the products of different specifications and even different dosage forms; calculating the formula:
R＝∑[[∑(M0/T0)]*T1*P1]/∑S0*100％-100％；
note: r is the price reduction range;
M0the sale number of single products of the A platform hospital medication list;
T0the daily average dosage of the individual products of the medication list of the platform hospital A;
T1the daily average consumption of purchased and delivered products of the group;
P1group purchase of transaction products for hospital price;
S0platform A hospital pharmacy catalog each group actual sales amount.
1. Calculating the total days of medication of the same group
According to the purchase quantity (M) of each product in the A platform medicine data0) The daily average dosage (T)0) Calculating the total days of administration (D)0)。
Calculating the formula: d0＝∑In the same group(M0/T0)；
Note: d0: sum of days for administration of the same group.
2. Calculating theoretical purchasing quantity of group purchasing in each group
Sum of days for the same group using this variety (D)0) Multiplied by the daily average dosage (T) of the matched GPO product1) Obtaining the theoretical purchasing quantity (M) corresponding to the group purchasing transaction group1)；
Calculating the formula: m1＝D0*T1；
Note: m1: and (4) theoretical purchasing quantity of each group.
3. Calculating theoretical purchasing amount of each group purchasing
Purchasing theoretical purchasing quantity (M) by each group1) Procurement of hospital price (P) by Shenzhen market cluster1) Calculating the theoretical purchasing amount (S) of the group of group purchasing1)。
Calculating the formula: s1＝M1*P1
Note: s1: theoretical amount of purchase per group
4. Calculating the comprehensive amplitude reduction
Actual purchase amount of the drug according to the same group A platform (S)0) And theoretical purchase amount (S) of each group1) And calculating the price reduction range (R) of the group purchase catalogue deal variety relative to the A platform medicine purchase data.
The calculation formula is as follows: r ═ Σ S1/∑S0*100％-100％
Note: r: reduced amplitude of group purchase transaction varieties relative to A platform medicine purchase data
5. Calculating the savings amount
According to the actual purchase amount (Sigma S) of the medicine of the platform A0) And theoretical purchase amount (Sigma S) of group purchase1) Calculating the amount of money (S) that can be saved by purchasing the medicine using the group while maintaining the number of medicines used on the A platforma)。
The calculation formula is as follows: sa＝∑S0-∑S1
Note: sa: the group purchase is expected to save money.
The measuring and calculating method can be used for amplitude reduction before GPO platform online and money saving amount prediction, and can also be widely used for amplitude reduction measurement of conversion of non-purchased medicines after GPO online from purchasing on other platforms, measurement and calculation of amplitude reduction by comparing GPO price and any dimension price of medicines in various professional fields, and the like.
Referring to fig. 10, a second method for calculating the price reduction range and the amount saved: and (3) measuring and calculating the actual amplitude reduction and the amount saved after the GPO platform is on line: determining theoretical total purchase amount of all varieties of the A platform medical institution according to daily use amount of each medicine group purchased by the A platform on the original platform on the premise of determining actual purchase amount of the GPO platform, and calculating price reduction amplitude by comparing the total purchase amount of the medicines actually purchased by the A platform on the GPO platform with the theoretical total purchase amount; in order to calculate the daily drug amounts of different specifications under the same universal name and the same catalog dosage form, the concept of 'medication days' is specially introduced, wherein the 'medication days' refers to the medication days of the product calculated according to the recommended daily dosage under the existing medication quantity, and the algorithm carries out reasonable quantity conversion on the products of different specifications and even different dosage forms; calculating the formula:
R＝∑S1/∑[S0/∑(M0/T0)*(M1/T1)]*100％-100％；
note: r is the price reduction range;
M0the A platform purchases the purchase quantity of each product on the original platform;
M1the purchasing quantity of each product actually purchased by the platform A in the GPO;
T0the average daily consumption of each product purchased by the platform A on the original platform;
T1the average daily consumption of each product actually purchased by the platform A in the GPO;
S0theoretical purchase amount of each group of products purchased by the platform A on the original platform;
S1the A platform actually purchases money in GPO for each group of products.
1. Calculating the total number of days for the same group of medicine using the original purchasing platform of the A platform
According to the purchase quantity (M) of each product in the original purchase platform medicine data of the A platform0) The daily average dosage (T)0) Calculating the number of days (i.e. M) taken by the product0/T0) And further calculating the total days of the same group (D)0)。
Calculating the formula: d0＝∑In the same group(M0/T0)；
Note: d0: the total days of medicine use of the same group of the original purchasing platform of the platform A;
similarly, the purchase quantity (M) of each product in GPO actual purchase data according to the A platform1) The daily average dosage (T)1) Calculating the number of days taken by the product, and further calculating the total number of days taken by the same group (D)1)。
Calculating the formula: d1＝M1/T1；
Note: d1: the A platform actually purchases the medication days of the product in the GPO;
2. calculating the amount of daily medicine in each group
Using the original purchasing platform of the A platform to purchase the amount of money of each group (S)0) Divided by the sum of days taken in the same group (D)0) Obtaining the daily medication amount (P) of the same group of the original purchasing platform of the platform A0)。
Calculating the formula: p0＝S0/D0；
Note: p0: a, the platform purchases daily drug amounts of the same group of platforms;
3. calculating theoretical purchase amount of each group
Days of drug administration per product actually purchased at GPO with platform A (D)1) Multiplying the daily drug amount (P) of the same group of drugs by the matched A platform original purchasing platform0) Obtaining each group of theoretical purchase amount (S)R)。
Calculating the formula:
SR＝D1*P0；
SR: the A platform is the theoretical purchase amount of the same group of medicines of the original purchase platform.
4. Calculating the comprehensive amplitude reduction
Actual purchase amount (S) at GPO according to platform A1) And theoretical purchase amount (S) of same group of medicinesR) Calculating the price reduction amplitude (R) of the total sum of the medicines actually purchased by the platform A in the GPO relative to the theoretical total sum of the medicines actually purchased by the original purchasing platform of the platform A; the calculation formula is as follows:
R＝∑S1/∑SR*100％-100％；
r: and the price reduction range of the total amount of the actually purchased medicines of the platform A in the GPO relative to the original purchase amount of the platform A.
5. Calculating the actual savings
Actual purchase amount (S) at GPO according to platform A1) And the original platform theoretical purchase amount (S) calculated according to the original purchase priceR) Calculating the amount of money (S) that the group purchased medicine can actually save while maintaining the same actual medicine purchase amount of the GPO platform Aa)。
The calculation formula is as follows: sa＝∑SR-∑S1；
Note: sa: the amount of money can be saved actually by group purchasing;
attached:
calculating the average daily dosage:
firstly, calculating the daily average dosage (DDD/product dosage) according to the limited daily dosage (ATC/DDD) of the WHO official network;
such as: [ amitriptyline | oral ] administration
DDD: 75mg, pharmaceutical size: 25 mg;
then the daily average dosage is 75 mg/25 mg/3 tablets;
(II) if the WHO does not have the limited daily dose (ATC/DDD), calculating the daily average dose (the daily minimum dose + the daily maximum dose)/2 according to a medicine specification (usage dose);
wherein: daily minimum dose is daily minimum number of times of administration per minimum dose;
the maximum daily dosage is the maximum daily dosage times and the maximum daily dosage;
third, if the WHO does not have the limited daily dose (ATC/DDD), the instructions (usage and dosage) are unified and converted into the price of per gram/milligram/milliliter and the like, namely the unit comparable price, and the calculation of the daily average cost is carried out.
There are two calculation methods for the average daily cost:
the daily average cost is the daily average dosage and the minimum preparation unit price of the medicine;
② the daily average charge is the sum of medicine purchase and the number of days for using the medicine;
wherein the administration days are the minimum preparation amount divided by the daily average dosage.
The calculation method can be used for actual amplitude reduction after the GPO platform is online and can save money, and can also be widely used for comparison of daily medication money of multiple platforms, amplitude reduction calculation of medicaments in national acquisition, amplitude reduction calculation of Shenzhen GPO platform price linkage, price comparison calculation of GPO online city and other purchasing platforms at any time period, amplitude reduction calculation and the like.
Nine functional models are described below:
1) comparing the amplitude reduction model before the GPO of the measured area is on line with the amplitude reduction model before the GPO is on line: the method II is mainly used for measuring and calculating whether the regional cost and the comprehensive margin reduction are expected after GPO is on line compared with the regional cost and the comprehensive margin reduction before the GPO is on line, and the reason for the failure is that the medicine of an individual catalog batch is not executed in place or an individual medical institution is not executed according to the policy and the most used model of the regional supervision department.
2) An online prediction amplitude reduction model for measuring and calculating extended areas such as GPO (general purpose input/output) is as follows: the calculation method for the amplitude reduction and the saved amount adopts a method one, for some areas where GPOs are not on line or are on line with an intention, data of the area in the past year can be obtained, compared with the existing medicines on the shelf of the GPO, the calculation is supposed to be purchased in the GPO, the saved amount and the amplitude reduction are predicted, and the calculation method is generally used for automatically generating an amplitude reduction calculation report when a new area is expanded.
3) The daily average cost comparison model of the same group of medicines of multiple purchasing platforms: the daily average cost of the medicine can be calculated according to the daily medicine amount calculation method of the second method, when a plurality of medicine purchasing platforms exist in a local area, a supervision department can pay attention to which platform the medicine is lowest in purchasing cost, if medicines purchased on other platforms are converted into GPO purchasing, money and amplitude can be saved, medicine hospital institutions with low GPO platform price are selected to purchase on other platforms, and effective management and control basis can be provided for the supervision department.
4) The amplitude reduction model for measuring and calculating the reduction rate of the national collected medicines and the reduction rate of the national collected medicines is as follows: the method for measuring and calculating the reduction amplitude and the saved amount is a method II, the national centralized purchased medicine is a common medicine with large dosage and is used for measuring and calculating the saved amount and the reduction amplitude before and after the implementation of the national acquisition policy, the selected and unselected purchase quantity or the proportion of the purchase amount in the national acquired medicine, the situation that the national acquired medicine is purchased on multiple platforms, whether the national acquired medicine is strictly executed according to the policy or not, the completion rate of the national acquired medicine, the ranking in the whole province and the like are main indexes for the examination of a supervision department.
5) The decreasing model for measuring and calculating the decreasing rate of the national talking medicine after the price reduction and before the price reduction is as follows: the method for measuring and calculating the reduction amplitude and the saved amount is a method II, the national talk drugs are original research drugs, special effect drugs and new drugs, the method is different from the national talk drugs that most regions have no purchase originally, the purchase price before national price reduction is taken as a base number, the saved amount and the reduction amplitude before the national talk policy is implemented are measured and calculated, the selected and imitated purchase quantity or the proportion of the purchase amount in the national talk drugs, whether the national talk drugs are strictly executed according to policies under the multi-platform purchase condition, the national talk drug completion rate, the ranking in the whole province and the like are main indexes for the examination of supervision departments.
6) Calculating a linkage front and rear amplitude reduction model after GPO price linkage: the method II is adopted for measuring and calculating the reduction and the saving amount, the GPO can periodically capture the price of the national medicines, the price is lower than the price of the medicines on the shelves on the purchasing platform, price linkage can be implemented, manufacturers are mobilized to reduce the price, the model is used for measuring and calculating the reduction batch quantity and the quantity of the products of the GPO after the GPO is on line, the amount is saved, the comprehensive reduction is realized, and the real value of the GPO for group purchasing of medicines is reflected.
7) After GPO is on line, no-purchase medicine is purchased and converted into a GPO amplitude reduction model on other platforms: the method for calculating the amplitude reduction and the money saving is a method I, except first batch of gynecological emergency medicines, a medicine purchasing catalogue of the GPO is a large-variety medicine with large using amount and purchasing amount reaching the first 80%, the model is used for finding out the reason that some medicines have no purchasing orders, whether purchasing or distribution is carried out on other platforms or not, if the price is higher than that of other platforms, price linkage is carried out to mobilize manufacturer price reduction, and the problem of distribution or shortage is solved.
8) The GPO online city compares prices at any time with prices at other purchasing platforms to calculate a reduction model: the method for calculating the reduction and the saving amount is the second method, the price before the GPO is on line is unreasonable by a supervision department in the region with long time, but the administration structure of the region is changed due to long time on the GPO, the administration catalog is simplified, most of the administration departments select to purchase on a GPO platform, the administration data after the GPO is on line is compared with the administration data after the GPO is on line, and the reduction linked with the price of the GPO platform, so that the advantage of the GPO cannot be reflected.
9) The method comprises the following steps of (1) comparing the GPO price of the medicine in each professional field with any dimension price to calculate a reduction model: the calculation method for the daily use amount of the medicine is a first calculation method, the price of the GPO medicine can be compared by using average price or daily average cost, and the daily average cost of the medicine can be referred to a second calculation method. The model is used for comparing the price level of the used medicines in the areas or the professional fields of the medical institutions in the whole province, and is beneficial to the effective management and control of the supervision department, such as: the average price of the respiratory tract infection medicines in the three hospitals in the area A is 20 percent higher than the median of the similar medicines in the whole province, and the cost and the reduction range can be saved by calculating the price of the medicines with lower price range.
By combining the common dimension cross correlation among the 9 models, the comprehensive big data model is built just like a big data information network is built to cover the content of a multi-dimensional aspect, for example, a city is taken as the common dimension, all indexes and data of the 9 models can be correlated, the reduction of the online amount and the saved amount of the region can be obtained, the saved amount and the reduction of the price linkage can be known, and the average price level of the medicine used by the regional hospital can be known. The indexes of the model can take the average of three years as the standard reaching value of the batch of targets, can also set a threshold value by referring to policies or applying a statistical method, comprehensively evaluates the execution condition of GPO purchasing in a region, can also provide a big data analysis conclusion and report materials for a local supervision department by multi-dimensional comparison, and is convenient for the supervision department to make the next step of measures.
The comprehensive big data model is displayed on a large number of analysis platforms through the cloud server, a user can obtain an analysis result through the dimension required to be screened, and abnormal data exceeding a threshold value can be notified to a local medical insurance office in a mail or short message mode and operation suggestions are provided.
Referring to fig. 11, a schematic diagram of dimensions, a presentation form and contents related to a big data intelligent analysis platform provided by the present invention is shown;
in the embodiment of the present invention, setting the index and the threshold value in combination with the dimension of the data model specifically includes: and setting indexes by combining the dimensionality of the data model, and setting a threshold value according to the current policy and a statistical method, wherein the statistical method selects one of a same ratio, a ring ratio, an occupation ratio, a sequence, a weighted average, a difference value, a prediction trend and a price comprehensive index.
In the embodiment of the invention, the presentation is performed in multiple forms through a visual intelligent analysis platform, and the method specifically comprises the following steps: the visualized intelligent analysis platform is displayed in a webpage display and automatic analysis report form, wherein the webpage display comprises: the purchasing condition of the GPO platform, the amplitude reduction measuring and calculating condition of the GPO platform and the purchasing condition of the GPO platform and other platforms are compared; the automatic analysis report comprises a reduced-amplitude analysis report, a data analysis report of other regional platforms, a directory matching report and a reduced-amplitude analysis report of converting the other regional platforms into GPO.
Visualization presentation form: tables, bar charts, pie charts, scatter plots, radar plots, dashboards, and the like.
The webpage display analysis content comprises three modules, namely 1, GPO platform purchasing condition 2, GPO platform amplitude reduction measuring and calculating condition 3, and GPO platform purchasing condition comparison with other platforms. Indexes in the three modules can be compared with the reach value, the standard value, the average value and the threshold value of the index library, and any dimension index can be used for ranking. And informing the monitoring department of the set abnormal or standard exceeding data in a mail or short message mode and proposing an operation suggestion.
The automatic analysis report is an analysis report with reduced amplitude, a data analysis report of other platforms in the region, a directory matching report, an analysis report with reduced amplitude converted from other platforms in the region into GPO, and the like.
Example two
Referring to fig. 12, a structure diagram of an intelligent big data analysis system for pharmaceutical group procurement according to the present invention is shown;
as shown in fig. 12, an intelligent big data analysis system for group pharmaceutical procurement includes:
the data processing unit 201 is used for performing standardization processing, matching and associating various medicine classification information on the acquired medicine purchasing data;
the model establishing unit 202 is used for establishing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions;
and the index analysis visual display unit 203 is used for the visual intelligent analysis platform to issue the content and the index of the functional model according to different application scenes and user rights through the cloud.
Various changes and specific examples of the intelligent big data analysis method for pharmaceutical group procurement in the invention are also applicable to the intelligent big data analysis system for pharmaceutical group procurement in the embodiment, and through the detailed description of the intelligent big data analysis method for pharmaceutical group procurement, a person skilled in the art can clearly know the intelligent big data analysis system for pharmaceutical group procurement in the embodiment, so the detailed description is omitted here for the simplicity of the description.
EXAMPLE III
The embodiment explains the specific operation of the big data intelligent analysis platform:
referring to fig. 13, if we want to know the antibiotics purchasing situation of GPO purchasing platform in city a 9 month in 2020, we can select 9 months in 2020 through the time dimension, select city a in the region, select GPO platform for the purchasing platform, and select antibiotics for the drug attribute. Operation through some indexes and statistical methods;
the effective contract order quantity and the amount are compared with a comparison ring, and the antibiotic purchasing quantity or purchasing amount condition and trend of the GPO purchasing platform antibiotic in the city of 9 month A in 2020 and 9 month A are known;
the calculated amount and the theoretical procurement amount are included, the antibiotics of the GPO procurement platform of the city A in 9 months in 2020 are matched with the procurement catalogue (comparison group) of the antibiotics in any time period of the region, and the calculated amount and the theoretical procurement amount can be included according with the matching principle when the GPO is on line or in any time period before the GPO is on line;
the comprehensive amplitude reduction and the amount saving are compared with any platform at any time, the amplitude reduction and the amount saving are measured through matching with a control group, and the control result of a supervision department can be directly reflected through analyzing the percentage points of increase of the same ratio and the ring ratio of the amplitude reduction and the amount saving and the ranks of the percentage points in the medicine classification;
the specific gravity of the whole body is accounted, and the development trend of the antibiotics with the largest purchasing quantity can be predicted by analyzing the change of the specific gravity of the total purchasing quantity of the antibiotics on a GPO purchasing platform;
comparing daily average medication cost or price index, and analyzing the comparison between the daily average medication cost or price index of the city A and the daily average medication cost or price index of the same-level region of the whole province, so that supervision departments and medical institutions can be helped to select and purchase platforms and medicines with more price superiority;
the proportion of the purchasing channels can find out whether medical institutions purchase on platforms with favorable prices or remain in the original purchasing channels without conversion by analyzing the actual purchasing proportion of the antibiotics in the GPO catalogue on multiple platforms;
the catalog coverage rate is compared with the actual purchase rate, because the GPO platform is purchased with the quantity by the group, the medicine price has the absolute advantage, and by analyzing the GPO catalog coverage rate and the actual GPO purchase rate in the whole antibiotic medicine in the A market, a monitoring department can know the proportion which is not converted into the GPO platform for purchase, and the larger the proportion which is not converted is, the larger the influence on the reduction and the saved amount is;
other platforms are converted to GPO (general purpose input/output) purchasing, the amount of money saved can remind a supervision department of saving and reducing space on the assumption that medicines purchased on other platforms are converted to GPO purchasing, and the supervision department is promoted to increase the management and control strength.
Example four
Referring to fig. 14, the procurement of antineoplastic drugs in medical institutions such as city grade a in 2020 is analyzed, and antineoplastic drugs are selected by the time dimension of 9 months in 2020, the area dimension of city a, the hospital grade of grade a and the like, and the pharmacological classification. Through the measurement and calculation of indexes and statistical methods, the purchasing structure, purchasing channel, price index, daily average medication cost, medication days, money saving and reduction of different purchasing platforms and average level in the whole province of antitumor medication in hospitals such as third-class A city in 2020A are analyzed.
Directory attribute ratios, such as: national interviews and national adoption ratios and provincial rankings among hospitals of the same level, as the ratios of these inventory attributes can directly affect the average daily average cost of a drug;
purchasing channels and proportion, and comparing the level of the hospital in the same level of the whole province, because the selection of the purchasing channels is directly related to the reduction of the price and the saving of the money;
price index trends, such as: the price index trends of the two platforms are compared, the price indexes of the two platforms which are completely matched with the medicines are in a descending trend, and the price indexes of the medicines which are different from GPO specifications and manufacturers are in an ascending trend, so that after competition is introduced, GPO promotes the industry price reduction of the same product, and the average trend difference of the provinces is compared to see whether the situation is a case or not;
comparing daily average medication costs, for example, comparing daily average medication costs of medicines collected by countries with national and non-national relations, the price difference between the daily average medication costs is obvious, the maximum price reduction rate can reach 98%, and whether medical institutions strictly execute policies and the average level difference in the whole province can be seen from the purchasing selection of hospitals;
the medication days are compared, because of the diversity of medicines, the medicines have dosage forms such as branch/granule/drop/pill, and the purchase quantity among the medicines needs to be compared with the medication days, which is to analyze the purchase condition of the medicines from the dimension of the quantity;
for example, in 2020, the antineoplastic agents of medical institutions such as the third-class city A in the city A can be compared with the same dimension before the GPO in the city A is on line, so that the drug cost and the comprehensive reduction can be saved by measuring and calculating the on-line state, and the drug cost and the comprehensive reduction can be saved after the national conversation is executed by measuring and calculating the same dimension in 2019. Or to know the ranking case in the province;
the catalog coverage rate and the GPO actual execution condition are known, the GPO catalog coverage rate in other purchase platform orders is known to be compared with the GPO actual purchase condition, the GPO execution condition can be obtained, and the quality of the GPO execution condition is directly related to the cost reduction of the medicine, so that the GPO catalog coverage rate and the GPO actual execution condition are a main monitoring index of a supervision department;
the platform can reduce the amplitude and save the money after conversion, and the money and the amplitude can be saved after the conversion between the prediction platforms, thereby helping the supervision department to better select a purchasing platform and increase the monitoring strength of the conversion.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. An intelligent big data analysis method for medicine group purchasing is characterized by comprising the following steps:
carrying out standardization processing, matching and association on the acquired medicine purchasing data to classify information of each medicine;
constructing a functional model and cross-correlating the functional model into a multifunctional comprehensive big data model by using common dimensions;
and the visual intelligent analysis platform issues the content and the index of the functional model according to the application scene and the user authority through the cloud.
2. The intelligent big data analysis method for group purchase of drugs according to claim 1, wherein the obtained drug purchase data is standardized, matched and associated with each drug classification information; the method specifically comprises the following steps: for multi-source medicine purchasing data collected in multiple channels, automatic standardization is conducted on the medicine purchasing data through an intelligent standardization model, so that medicine purchasing data information is unified, automatic matching is conducted on medicine purchasing through an intelligent catalog matching model and a Shenzhen GPO platform, and mutual relations and logics of medicine purchasing are established.
3. The intelligent big data analysis method for group procurement of medicines according to claim 2, wherein the medicine procurement data comprises: common name, dosage form, specification attribute, packaging material and production enterprise element.
4. The intelligent big data analysis method for group procurement of pharmaceuticals according to claim 1, wherein the functional model is constructed and cross-linked by common dimension to form a multifunctional comprehensive big data model; the method specifically comprises the following steps: according to the standardized medicine information of purchase channel, it is different according to its application function, builds functional model, and wherein, functional model includes: the method comprises the steps of comparing a GPO (general purpose input/output) on-line amplitude reduction model after the GPO is on line in a measuring and calculating area, comparing a GPO on-line prediction amplitude reduction model in an expanded measuring area such as a GPO on-line average cost comparison model, a multiple purchasing platform same group of medicines, an amplitude reduction model after the price reduction of medicines collected by a country is measured and calculated, an amplitude reduction model after the price reduction of medicines is measured and calculated and compared with the price reduction model before the price reduction of the medicines, an amplitude reduction model before and after linkage is measured and calculated after the price reduction of the medicines is measured and calculated, a model for converting the purchase of the medicines without purchase on other platforms into GPO amplitude reduction after the GPO is on line, a GPO on-line city and other purchasing platforms any.
5. The intelligent big data analysis method for group procurement of pharmaceuticals according to claim 1, wherein the functional model is constructed and cross-linked by common dimension to form a multifunctional comprehensive big data model; specifically, the common dimensions include time, region, hospital, catalog lot, pharmacology classification, purchase source, before and after online, treatment field, price interval, extent reduction interval, money saving interval, daily average cost comparison, and the like.
6. The method according to claim 1, wherein the intelligent visualized intelligent analysis platform issues the content and indexes of the functional model according to the application scenario and the user right through a cloud, and specifically comprises:
forming an index library through induction of historical data and index measurement and calculation;
setting indexes and threshold values by combining the dimensionality of the data model;
analyzing the standard reaching condition of the analysis content and index required by the functional model through data processing;
displaying in multiple forms through a visual intelligent analysis platform;
and issuing the content according to the application scene and the user permission through the cloud.
7. The intelligent big data analysis method for group procurement of pharmaceuticals according to claim 6, wherein the dimension setting indicators and thresholds of the data model specifically include: and setting indexes by combining the dimensionality of the data model, and setting a threshold value according to the current policy and a statistical method, wherein the statistical method selects one of a same ratio, a ring ratio, an occupation ratio, a sequence, a weighted average, a difference value, a prediction trend and a price comprehensive index.
8. The method of claim 6, wherein the intelligent big data analysis for group procurement of pharmaceuticals is presented in multiple forms by a visual intelligent analysis platform, and comprises: the visualized intelligent analysis platform is displayed in a webpage display and automatic analysis report form, wherein the webpage display comprises: the purchasing condition of the GPO platform, the amplitude reduction measuring and calculating condition of the GPO platform and the purchasing condition of the GPO platform and other platforms are compared; the automatic analysis report comprises a reduced-amplitude analysis report, a data analysis report of other regional platforms, a directory matching report and a reduced-amplitude analysis report of converting the other regional platforms into GPO.
9. The method for analyzing big data of pharmaceutical group procurement according to any one of claims 1 to 8, wherein the system for analyzing big data of pharmaceutical group procurement comprises:
the data processing unit is used for carrying out standardized processing, matching and association on the acquired medicine purchasing data and classifying information of each medicine;
the model establishing unit is used for establishing a functional model and forming a multifunctional comprehensive big data model by cross-correlation of common dimensions;
and the index analysis visual display unit is used for the visual intelligent analysis platform to issue the content and the index of the functional model according to the application scene and the user permission through the cloud.
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CN117273617A (en) * 2023-11-17 2023-12-22 北京亿家老小科技有限公司 Big data-based supply chain and purchasing double-chain management platform
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