CN109767157B - Medicine sales system based on cloud computing and big data - Google Patents
Medicine sales system based on cloud computing and big data Download PDFInfo
- Publication number
- CN109767157B CN109767157B CN201811556275.8A CN201811556275A CN109767157B CN 109767157 B CN109767157 B CN 109767157B CN 201811556275 A CN201811556275 A CN 201811556275A CN 109767157 B CN109767157 B CN 109767157B
- Authority
- CN
- China
- Prior art keywords
- medicine
- module
- patient
- logistics
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a medical sales system based on cloud computing and big data, which comprises a data acquisition module, a big data processing module, a DTP service cloud platform, a medicine tracing module, a health management module, a demand prediction module and a logistics decision module. The medicine tracing module provides a visual interface for a medicine supply chain member; the health management module provides medicine purchase service and medicine tracking reminding service for the patient; the demand prediction module predicts the medicine demands of patients, and then gathers mass individual demand data in the area to predict the total medicine demands in the area; the logistics decision module is used for optimizing logistics decisions including medicine distribution decisions, medicine ordering decisions and inventory transfer decisions. The invention reduces the circulation links of medical products, can effectively predict the demands of patients, realizes the decision optimization of medical supply chain logistics, is beneficial to improving the medical service level and improves the satisfaction degree of patients.
Description
Technical Field
The invention belongs to the field of medicine sales, and particularly relates to a medicine sales system based on cloud computing and big data.
Background
The deficiency of the price of the medicine is always a difficult problem which puzzles the development of medical service industry in China, and the reason is that the medicine has too long circulation links and too many intermediate links. In such a background, DTP (Direct to Patient) mode has evolved. The DTP mode is a medicine marketing mode which directly faces patients, namely, a pharmacy directly authorizes medicines to retail pharmacies and hospital pharmacies to serve as sales agents, an intermediate agent link is omitted, and the patients can acquire the medicines from the retail pharmacies or the hospital pharmacies after taking prescriptions, and obtain high-quality services such as professional medication guidance, medication tracking reminding and the like.
The DTP mode was originally initiated in the united states, the 90 s of the 19 th century, and united states pharmaceutical manufacturers began to sell drugs directly in the face of consumers. At the beginning of the 21 st century, with the shrinkage of profit margin in the united states medical service industry, drug distribution enterprises are prompted to start exploring new drug marketing modes, and gradually develop to professional pharmacies with high profit, and rapid development of DTP modes in the united states is promoted. The DTP mode has now evolved in the united states to be a mature pharmaceutical sales mode. In 2000, china formally developed DTP business. Although the domestic DTP mode and the corresponding management system are continuously perfected, the development of the DTP mode is still in the primary exploration stage. In China, the existing DTP mode mainly comprises a medicine logistics distribution system, a patient data management system and an information sharing system. The systems are mutually independent, the information transmission is not realized in real time in the whole process, and the supporting effect of technologies such as cloud computing, big data and the like on the medicine DTP mode is not fully exerted.
Disclosure of Invention
The invention aims to: aiming at the problem that the DTP mode in the prior art cannot fully meet the requirements of safe medication, economic medication and convenient medication of patients, the medical DTP sales system based on cloud computing and big data is provided.
The technical scheme is as follows: in order to solve the technical problems, the invention provides a medical DTP sales system based on cloud computing and big data, which is characterized by comprising a data acquisition module, a big data processing module, a DTP service cloud platform, a medicine tracing module, a health management module, a demand prediction module and a logistics decision module;
the data acquisition module acquires data information of each node of the medical supply chain and performs preprocessing;
the big data processing module performs screening, association analysis, processing and the like on the acquired data, so as to mine the change rules of the data on the time and space levels and realize the increment of the data;
the DTP service cloud platform includes a shared database, a shared pool of computing resources, and various data interfaces. The shared database provides storage and reading services for 'value added' data generated by the big data processing module, and each application module can access the database through a data interface so as to perform cloud computing by utilizing a computing resource sharing pool (comprising a network, a server, storage, application software and the like) in the cloud platform;
the medicine tracing module provides a visual interface for the medicine supply chain members so as to realize effective inquiry of basic information such as medicine names, types, notes and the like and medicine track tracing in time and space layers;
the health management module provides medicine purchase service for the patient on one hand, the patient can select a medicine purchase mode, a medicine distribution mode, medicine distribution time and the like, and on the other hand, the patient is reminded to take medicines in a quantitative manner on time, and patient health management data including information such as medicine taking effect of the patient, doctor's advice compliance of the patient, life style of the patient and the like are collected in real time;
the demand prediction module firstly calculates the relation between the medicine demand of the patient and the health management data of the patient based on a multiple regression model, further predicts the medicine demand of the patient by utilizing the real-time health data of the patient generated by the health management module, and then gathers mass individual demand data in the area to predict the total medicine demand in the area;
the logistics decision module optimizes logistics decisions based on the drug track generated by the drug tracing module and the regional demand prediction information generated by the demand prediction module, and the logistics decision module comprises a drug delivery decision, a drug ordering decision and an inventory transfer decision.
Further, the medical supply chain includes a pharmacy, a hospital clinic, a retail pharmacy, a hospital pharmacy, a third party logistics, and a plurality of subjects for patients.
Further, the data information of the pharmacy in the data acquisition module comprises the name, type, quality guarantee period, notice, historical logistics track, wholesale price, order quantity and the like; the hospital consulting room information includes: doctor prescription, prescription flow direction, patient treatment frequency and other diagnosis information; the retail pharmacy and hospital pharmacy information includes: location, business hours, drug inventory, drug retail price, etc.; the third party logistics information includes: the logistics environment, the logistics speed, the logistics price and the like; the patient information includes information such as medication intake preference, logistics preference (cost-first or time-first), delivery time preference, medication habit, and the like.
Further, the medicine distribution decision in the logistics decision module is a third party logistics decision medicine distribution route according to the information such as logistics mode preference, distribution time preference and the like of the patient; the medicine ordering decision optimizes decisions such as medicine ordering frequency and ordering amount of retail pharmacies and hospital pharmacies according to information such as predicted regional medicine demands, patient medicine taking mode preference, medicine taking habit and the like; the inventory transfer decision decides whether to transfer the medicines among the medicine libraries and the corresponding transfer capacity according to the historical inventory information of retail medicine stores and hospital pharmacies and the preference of the medicine taking modes of patients.
Compared with the prior art, the invention has the advantages that:
according to the medical DTP sales system based on cloud computing and big data, which is disclosed by the invention, by means of a medical DTP service cloud platform, linkage is better realized among pharmacy, hospitals, retail pharmacies, medical staff and patients, the links of medical circulation are reduced, medicines are directly delivered to the patients from the pharmacy, and the medical circulation cost is reduced; and the demand of the patient can be effectively predicted, so that the logistic decision optimization of a medicine supply chain is realized, the medicine service level is improved, and the satisfaction degree of the patient is improved.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is a diagram showing the steps of selling and distributing medicines according to the present invention.
Detailed Description
The invention is further elucidated below in connection with the drawings and the detailed description. The described embodiments of the invention are only some, but not all, embodiments of the invention. Based on the embodiments of the present invention, other embodiments that may be obtained by those of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
As shown in fig. 1, the medical DTP sales system based on cloud computing and big data is characterized by comprising a data acquisition module, a big data processing module, a DTP service cloud platform, a medicine tracing module, a health management module, a demand prediction module and a logistics decision module;
the data acquisition module acquires data information of each node of the medical supply chain and performs preprocessing;
the big data processing module performs screening, association analysis, processing and the like on the acquired data, so as to mine the change rules of the data on the time and space levels and realize the increment of the data;
the DTP service cloud platform includes a shared database, a shared pool of computing resources, and various data interfaces. The shared database provides storage and reading services for 'value added' data generated by the big data processing module, and each application module can access the database through a data interface so as to perform cloud computing by utilizing a computing resource sharing pool (comprising a network, a server, storage, application software and the like) in the cloud platform;
the medicine tracing module provides a visual interface for the medicine supply chain members so as to realize effective inquiry of basic information such as medicine names, types, notes and the like and medicine track tracing in time and space layers;
the health management module provides medicine purchase service for the patient on one hand, the patient can select a medicine purchase mode, a medicine distribution mode, medicine distribution time and the like, and on the other hand, the patient is reminded to take medicines in a quantitative manner on time, and patient health management data including information such as medicine taking effect of the patient, doctor's advice compliance of the patient, life style of the patient and the like are collected in real time;
the demand prediction module firstly calculates the relation between the medicine demand of the patient and the health management data of the patient based on a multiple regression model, further predicts the medicine demand of the patient by utilizing the real-time health data of the patient generated by the health management module, and then gathers mass individual demand data in the area to predict the total medicine demand in the area;
the logistics decision module optimizes logistics decisions based on the drug track generated by the drug tracing module and the regional demand prediction information generated by the demand prediction module, and the logistics decision module comprises a drug delivery decision, a drug ordering decision and an inventory transfer decision.
The medical supply chain includes a pharmacy, a hospital clinic, a retail pharmacy, a hospital pharmacy, a third party logistics, and a plurality of subjects for patients.
The data acquisition module is used for acquiring the data information of the pharmacy, wherein the data information comprises the name, type, shelf life, notice, historical logistics track, wholesale price, ordering quantity and the like of the medicine; the hospital consulting room information includes: doctor prescription, prescription flow direction, patient treatment frequency and other diagnosis information; the retail pharmacy and hospital pharmacy information includes: location, business hours, drug inventory, drug retail price, etc.; the third party logistics information includes: the logistics environment, the logistics speed, the logistics price and the like; the patient information includes information such as medication intake preference, logistics preference (cost-first or time-first), delivery time preference, medication habit, and the like.
The drug delivery decision in the logistics decision module is a third party logistics decision drug delivery route according to the information such as logistics mode preference, delivery time preference and the like of the patient; the medicine ordering decision optimizes decisions such as medicine ordering frequency and ordering amount of retail pharmacies and hospital pharmacies according to information such as predicted regional medicine demands, patient medicine taking mode preference, medicine taking habit and the like; the inventory transfer decision decides whether to transfer the medicines among the medicine libraries and the corresponding transfer capacity according to the historical inventory information of retail medicine stores and hospital pharmacies and the preference of the medicine taking modes of patients.
Referring to fig. 2, the specific steps of medicine sales and distribution of the medicine DTP sales system based on cloud computing and big data include:
(1) After a patient is hospitalized, a doctor inputs basic information of the patient, diagnosis results, doctor prescriptions and the like into an electronic medical record, and a system automatically loads medical institution names (departments, doctors) and geographical position information of the medical institutions into the electronic medical record and uploads the electronic medical record to a DTP service cloud platform;
(2) The patient selects a drug purchase channel (retail pharmacy or hospital pharmacy). If the patient selects a retail drug store channel, the DTP service cloud platform searches drug store information for selling the drugs, sorts the listed retail drug stores according to the retail price or geographic position of the drugs, and then the patient selects a retail drug store for purchasing the drugs, and then the DTP service cloud platform transmits the electronic medical record to the retail drug store; if the patient selects a hospital pharmacy channel, the DTP service cloud platform transmits the electronic medical record to the hospital pharmacy;
(3) The patient selects the mode of medication (self-taking or professional dispensing). If the patient selects professional delivery, the patient needs to select a delivery mode and delivery time on the patient side of the DTP service cloud platform and fill in a delivery address, and a hospital pharmacy (or a retail pharmacy) transmits order information to a third party logistics;
(4) Payment is carried out when the patient entity shop takes the medicine or the patient entity shop directly carries out online payment;
(5) The patient takes the medicine (or the third party stream reaches the medicine) by himself;
(6) The doctor sets up patient's warning of taking medicine through DTP service cloud platform doctor's end, and patient inputs information such as taking medicine condition, effect of taking medicine after taking medicine.
Claims (5)
1. A medical sales system based on cloud computing and big data is characterized in that:
the system comprises a data acquisition module, a big data processing module, a DTP service cloud platform, a medicine tracing module, a health management module, a demand prediction module and a logistics decision module;
wherein the data acquisition module: collecting data information of each node of a medical supply chain and preprocessing;
big data processing module: screening, association analysis and processing are carried out on the collected data, so that the change rules of the time and space layers of the data are mined, and the value-added of the data is realized;
the DTP service cloud platform includes a shared database, a computing resource shared pool, and various data interfaces: the shared database provides storage and reading services for 'value added' data generated by the big data processing module, and each application module can access the database through a data interface so as to perform cloud computing by utilizing a computing resource sharing pool in the cloud platform;
medicine traceability module: providing a visual interface for the members of the medicine supply chain to realize the effective inquiry of the basic information of the medicine name, type and notice and the medicine track tracking at the time and space level;
health management module: on one hand, the medicine purchasing service is provided for the patient, the patient can select a medicine purchasing mode, a medicine distribution mode and medicine distribution time, and on the other hand, the patient is reminded to take medicines in a quantitative manner on time, and patient health management data including the medicine taking effect of the patient, doctor's advice compliance of the patient and life style information of the patient are collected in real time; if the patient selects a retail pharmacy channel, the DTP service cloud platform searches out pharmacy information for selling the medicines, and sorts the listed retail pharmacy according to the retail price or geographic position of the medicines;
the demand prediction module: firstly, calculating the relation between the medicine requirements of the patient and the health management data of the patient based on a multiple regression model, further predicting the medicine requirements of the patient by utilizing the real-time health data of the patient generated by the health management module, and then summarizing mass individual requirement data in the area to predict the total medicine requirements in the area;
the logistics decision module: optimizing logistics decisions based on the drug track generated by the drug tracing module and the regional demand prediction information generated by the demand prediction module, wherein the logistics decisions comprise drug delivery decisions, drug ordering decisions and inventory transfer decisions;
the drug delivery decision in the logistics decision module is a third party logistics decision drug delivery route according to the logistics mode preference and the delivery time preference information of the patient;
the medicine ordering decision optimizes medicine ordering frequency and ordering quantity decision of retail pharmacies and hospital pharmacies according to predicted medicine demands in the areas, preference of the medicine taking modes of patients and medicine taking habit information;
the inventory transfer decision decides whether to transfer the medicines among the medicine libraries and the corresponding transfer capacity according to the historical inventory information of retail medicine stores and hospital pharmacies and the preference of the medicine taking modes of patients.
2. The cloud computing and big data based pharmaceutical sales system of claim 1, wherein the pharmaceutical supply chain comprises a pharmacy, a hospital clinic, a retail pharmacy, a hospital pharmacy, a third party logistics, and a plurality of subjects for patients.
3. The cloud computing and big data based pharmaceutical sales system of claim 1, wherein the pharmaceutical manufacturer data information in the data collection module includes pharmaceutical name, type, shelf life, notes, historic logistics trail, wholesale price and order quantity.
4. The cloud computing and big data based medical marketing system of claim 1, wherein the hospital consulting room information in the data acquisition module comprises: doctor prescription, prescription flow direction and diagnosis and treatment information of patient diagnosis frequency; the patient information includes medication intake preference, logistical preference, distribution time preference, and medication habits.
5. The cloud computing and big data based medical marketing system of claim 1, wherein the retail pharmacy and hospital pharmacy information in the data collection module comprises: location, business hours, drug inventory, and drug retail price; the third party logistics information includes: the logistics environment, the logistics speed and the logistics price.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811556275.8A CN109767157B (en) | 2018-12-19 | 2018-12-19 | Medicine sales system based on cloud computing and big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811556275.8A CN109767157B (en) | 2018-12-19 | 2018-12-19 | Medicine sales system based on cloud computing and big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109767157A CN109767157A (en) | 2019-05-17 |
| CN109767157B true CN109767157B (en) | 2023-07-28 |
Family
ID=66451378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811556275.8A Active CN109767157B (en) | 2018-12-19 | 2018-12-19 | Medicine sales system based on cloud computing and big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109767157B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110322331A (en) * | 2019-07-18 | 2019-10-11 | 徐志颖 | A kind of medicine marketing method based on cloud computing and big data |
| CN111428930A (en) * | 2020-03-24 | 2020-07-17 | 中电药明数据科技(成都)有限公司 | GBDT-based medicine patient using number prediction method and system |
| CN112102955B (en) * | 2020-09-07 | 2024-03-15 | 武汉科瓴智能科技有限公司 | Patient disease prediction control system and method based on Gaussian mixture model |
| CN113379332A (en) * | 2021-07-20 | 2021-09-10 | 四川药之道科技有限公司 | Medicine marketing synergy management system |
| CN116504417B (en) * | 2023-06-28 | 2023-09-12 | 南京易联阳光信息技术股份有限公司 | Decision-making auxiliary method and platform based on medical big data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855406A (en) * | 2012-09-14 | 2013-01-02 | 重庆市科学技术研究院 | Intelligent medicine purchasing system based on remote medical system and medical advice compliance monitoring |
| CN102880976A (en) * | 2012-09-14 | 2013-01-16 | 重庆大学 | Intelligent medicine purchase system based on regional medical cooperation and doctor advice compliance monitoring |
| WO2014025215A2 (en) * | 2012-08-08 | 2014-02-13 | 주식회사 래디쉬 | System for automatically instructing medication based on prescription |
| CN108038773A (en) * | 2018-02-02 | 2018-05-15 | 东南大学 | A kind of medical treatment ＆ health big data method of servicing based on multi engine integrated technology |
-
2018
- 2018-12-19 CN CN201811556275.8A patent/CN109767157B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014025215A2 (en) * | 2012-08-08 | 2014-02-13 | 주식회사 래디쉬 | System for automatically instructing medication based on prescription |
| CN102855406A (en) * | 2012-09-14 | 2013-01-02 | 重庆市科学技术研究院 | Intelligent medicine purchasing system based on remote medical system and medical advice compliance monitoring |
| CN102880976A (en) * | 2012-09-14 | 2013-01-16 | 重庆大学 | Intelligent medicine purchase system based on regional medical cooperation and doctor advice compliance monitoring |
| CN108038773A (en) * | 2018-02-02 | 2018-05-15 | 东南大学 | A kind of medical treatment ＆ health big data method of servicing based on multi engine integrated technology |
Non-Patent Citations (1)
| Title |
|---|
| 医药分开视角下我国DTP药房模式的发展;王丹丹 等;《卫生经济研究》;20180705(第07期);第12-14页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109767157A (en) | 2019-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109767157B (en) | Medicine sales system based on cloud computing and big data | |
| Gebicki et al. | Evaluation of hospital medication inventory policies | |
| CN102855406B (en) | Based on the intelligent medicine purchase system of tele-medicine system and the monitoring of the doctor's advice property deferred to | |
| US8478611B2 (en) | System and method for clinical strategy for therapeutic pharmacies | |
| Porter | What is value in health care | |
| US20170185723A1 (en) | Machine Learning System for Creating and Utilizing an Assessment Metric Based on Outcomes | |
| Kanavos et al. | The pharmaceutical distribution chain in the European Union: structure and impact on pharmaceutical prices | |
| CN108665212A (en) | inventory management system based on cloud platform | |
| JP6181787B2 (en) | Medical information management system | |
| CA2564926A1 (en) | A system for determining medical resource utilization characteristics | |
| Temple et al. | Implementation and evaluation of carousel dispensing technology in a university medical center pharmacy | |
| CN110322331A (en) | A kind of medicine marketing method based on cloud computing and big data | |
| Kazemzadeh et al. | Design and analysis of a health care supply chain management | |
| US8626529B1 (en) | Systems and methods for identifying risk evaluation and mitigation strategies (REMS) compliance | |
| Kochakkashani et al. | Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19 | |
| KR20210050084A (en) | pharmaceutical distribution platform | |
| CN108665284A (en) | Supply chain cloud platform system | |
| US20220359067A1 (en) | Computer Search Engine Employing Artificial Intelligence, Machine Learning and Neural Networks for Optimal Healthcare Outcomes | |
| Saha et al. | Modelling and analysis of healthcare inventory management systems | |
| Simoens et al. | Review of the cost-effectiveness of interventions to improve seamless care focusing on medication | |
| McDonell et al. | Estimating the real world daily usage and cost for exenatide twice daily and liraglutide in Germany, the Netherlands, and the UK based on volumes dispensed by pharmacies | |
| Da Conceição et al. | Issues in healthcare supply chain management: from literature to practice | |
| US20150371338A1 (en) | Pharmacy contribution management system and method | |
| Shiau | A drug association based inventory control system for ambulatory care | |
| US11232176B1 (en) | Collection, arrangement, linkage and allocation of longitudinal cost, reimbursement and clinical data at the patient encounter level |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |