Method and system for dynamically monitoring task progress of medicine collection and collection in hospital based on R language
By using automated analysis scripts in R language in hospitals to monitor drug task progress, the problem of insufficient performance and flexibility of traditional drug procurement management in large-scale data processing is solved, and efficient and accurate drug task monitoring and early warning notification are achieved , improves the efficiency and rationality of drug procurement management.
Patent Information
- Application Number
- CN202410170727.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-02-06
- Publication Date
- 2024-12-20
AI Technical Summary
Traditional drug procurement management methods have performance and functional limitations when processing large-scale medical data, making it difficult to achieve efficient and accurate drug task progress monitoring and analysis, resulting in high operating costs and inflexible drug management.
Automatic processing and analysis scripts based on R language are adopted to obtain centralized drug procurement information, count tasks and completion progress, build data visualization charts, conduct personalized data analysis and early warning notifications, and optimize drug procurement and resource allocation.
It has realized dynamic monitoring and early warning of hospital drug task progress, improved the efficiency and rationality of drug procurement, reduced operating costs, and promoted the informatization and intelligent development of medical institutions.
Smart Images
Figure CN119170293A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of medical data processing, and in particular to a method and system for dynamically monitoring the progress of a hospital's centralized drug procurement task based on R language. Background Art
[0002] In recent years, with the rapid development of the medical system, hospitals have continuously improved their requirements for their own medical service levels. Ensuring clinical drug safety and rational drug use is the primary task, and high-quality pharmaceutical management is the key. At present, due to the complexity of the drug supply chain and the rapid rise in drug costs, traditional drug procurement management methods are facing severe challenges. Drug supply issues not only directly affect the management and operation of hospitals, but also affect patients' drug expenditures and drug use experience. In order to achieve economic benefits and efficient management in drug procurement, and to maximize the satisfaction of patients' drug needs and ensure the rational use of medical resources, the state organizes the supply model of centralized drug procurement (centralized procurement). Centralized procurement can promote the healthy development of the pharmaceutical industry, help medical institutions better manage drug supply and standardize drug use processes, thereby effectively reducing procurement costs and ensuring rational drug use.
[0003] However, with the increasing degree of informatization in the medical industry, the amount of data involved in pharmaceutical management is becoming increasingly large. How to accurately and quickly analyze information that is of substantive significance to hospital pharmaceutical management from a large amount of data has become an urgent problem to be solved.
[0004] At present, the dynamic monitoring and management of the centralized drug procurement tasks of medical institutions are mainly completed through Power BI and Excel. Compared with the R language, these two software are subscription paid services and have operating costs. Moreover, the statistical analysis and data processing capabilities of PowerBI and Excel are relatively limited. When processing large-scale data and performing complex data conversion and sorting, they may encounter performance and functional limitations. In addition, although PowerBI and Excel provide some visualization and report design functions, compared with the R language, their programming capabilities, flexibility and diversity are poor, and they may be subject to functional limitations when carrying out personalized data processing and analysis tasks. Summary of the invention
[0005] Based on this, the embodiment of the present application provides a method and system for dynamically monitoring the progress of hospital centralized drug procurement tasks based on R language, develops automated processing and analysis scripts for hospital centralized drug usage data based on R language, comprehensively, efficiently and accurately conducts dynamic monitoring of the progress of centralized procurement tasks, optimizes drug procurement and resource allocation, and promotes rational drug use and operational efficiency of medical institutions.
[0006] In the first aspect, a method for dynamically monitoring the progress of a hospital centralized drug procurement task based on R language is provided, the method comprising:
[0007] Obtain the information table of all the selected drugs in the centralized procurement batches and build the dictionary information of the centralized procurement drugs;
[0008] Extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks;
[0009] Analyze the task progress of centralized procurement drugs based on the monthly task requirements and centralized procurement completion progress of each centralized procurement batch;
[0010] Based on the analysis results, further statistics are collected on the completion status of each department for the drugs that did not meet the target in the current month's centralized procurement batch;
[0011] For departments that have not completed their tasks, an R markdown report document with early warning of drug progress that has not met the task standards is output.
[0012] Optionally, the method further comprises:
[0013] Conduct custom data analysis based on the R language extension package; data analysis specifically includes studying the impact of centralized procurement policies on the frequency of drug use and costs in hospitals.
[0014] Optionally, obtain an information table of the selected centralized procurement drugs of all centralized procurement batches, wherein the information table includes at least the project number, drug code, drug name, manufacturer specification, packaging unit and number of tasks; and use the geom_treemap function in the R extension package treemapify to construct a tree diagram of the currently executed centralized procurement batches and the proportion of their selected drug task quantities.
[0015] Optionally, extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, calculate the completion progress of the centralized procurement task, and also include:
[0016] Calculate the completion rate based on actual usage and task volume, issue warnings for batches that are lower than the preset completion rate, and focus on the drugs and departments in the warning batches;
[0017] The geom_bar function in the ggplot extension package of the R language is used to construct a bar chart based on the completion rate data of each batch to intuitively display the completion progress of each batch.
[0018] Optionally, after extracting the centralized procurement drugs and usage data, counting the task volume and actual usage of each batch, and calculating the completion progress of the centralized procurement task, it also includes:
[0019] Perform data cleaning and standardization, including removing duplicate, erroneous or incomplete data and converting data of different dimensions into a unified scale;
[0020] Further judge the rationality of the task; among them, compare the task volume of each batch and the historical procurement data to judge whether the goal is reasonable; analyze the task volume and actual usage of each department to judge whether there is unreasonable task allocation.
[0021] Optionally, analyze the task progress of the centralized procurement drugs based on the monthly task requirements of each centralized procurement batch and the centralized procurement completion progress, including:
[0022] The R language script is used to automatically organize the data information of the current total usage, department usage, usage ratio and task completion progress of each batch of centralized procurement drugs, count the completion rate of task drugs, and use the geom_line function to construct a line chart of drug completion progress.
[0023] Optionally, based on the analysis results, further statistics are collected on the completion status of the drugs that do not meet the target in the current month's task of the centralized procurement batch in each department, including:
[0024] Use the geom_col function to complete a stacked bar chart of the target drug task ratio and completion rate for each department.
[0025] Optionally, for departments that have not completed their tasks, an Rmarkdown report document with an early warning of drug progress that has not met the task target is output, including:
[0026] According to the analysis result data of the departments that have not completed the task, the drug information that has not met the task requirements is screened out; the drug information at least includes the drug name, department completion ratio, actual completion ratio of the whole hospital, the ratio of the whole hospital's task for the month, and time;
[0027] Select an appropriate R Markdown document template and insert the screened drug list into the document;
[0028] Use a table to display the progress information of each drug.
[0029] In the second aspect, a hospital centralized drug procurement task progress dynamic monitoring system based on R language is provided, which includes:
[0030] The collection unit is used to obtain the information table of the selected centralized procurement drugs of all centralized procurement batches and build the dictionary information of the centralized procurement drugs;
[0031] The progress calculation unit is used to extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks;
[0032] The first analysis unit is used to analyze the task progress of the centralized procurement drugs according to the monthly task requirements of each centralized procurement batch and the centralized procurement completion progress;
[0033] The second analysis unit further counts the completion status of each department for the drugs that did not meet the target in the current month's task of the centralized procurement batch based on the analysis results;
[0034] The output unit is used to output an Rmarkdown report document of drug progress warning that does not meet the task target for departments that have not completed the task.
[0035] Optionally, the system further comprises:
[0036] The extension unit is used to conduct customized data analysis based on the R language extension package; the data analysis specifically includes studying the impact of centralized procurement policies on the frequency of use and cost of hospital drugs.
[0037] The present invention is based on R language to write personalized analysis scripts, comprehensively monitor the monthly usage of all drugs in the current centralized procurement batches in this hospital, automatically statistically analyze the completion progress of task drugs and departments, visualize data, and generate notifications of non-compliant drugs in departments, so as to realize dynamic supervision of the use of centralized procurement drugs, efficiently discover problems and communicate and feedback with departments in a timely manner, ensure the smooth completion of centralized procurement tasks, and provide overall and refined management for national drug centralized procurement work, so that centralized procurement work can be improved from post-feedback to in-process supervision to pre-prompt improvement. In addition, due to the ecology of R language, a variety of custom data analysis can be carried out according to individual needs, such as ITS research.
[0038] Statistical analysis based on R language improves work efficiency while ensuring accuracy, reduces data maintenance and calculation costs, and plays an important role and significance in ensuring the smooth implementation of drug procurement. In addition, by introducing automated data analysis methods, it will further promote the development of the medical industry towards informatization and intelligence, and improve the overall level of medical services. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.
[0040] Figure 1 A flowchart of the centralized drug procurement data analysis based on R language provided in the embodiment of the present application;
[0041] Figure 2This is a summary chart of the procurement batches and the proportion of the selected drugs in this application;
[0042] Figure 3 Schematic diagram of the completion status of the selected drugs in each centralized procurement batch;
[0043] Figure 4 Schematic diagram of the completion status of the drugs selected for renewal in the second and fourth centralized procurement batches;
[0044] Figure 5 Schematic diagram of the completion status of the selected drug acarbose in different departments;
[0045] Figure 6 Schematic diagram of Rmarkdown reproducible document for drugs that fail to meet the standards in departmental centralized procurement tasks;
[0046] Figure 7 Schematic diagram of the impact of the eighth batch of centralized procurement on commonly used drugs for cardiovascular diseases based on ITS analysis. DETAILED DESCRIPTION
[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0048] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two. The terms "first" and "second" in the specification and claims of the present invention and the above-mentioned drawings are intended to distinguish the objects referred to. For schemes with a sequential flow, this terminology does not have to be understood as describing a specific order or sequence. For schemes with device structures, this terminology does not distinguish between importance, positional relationships, etc.
[0049] In addition, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that have been explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units that are added based on further optimization schemes conceived by the present invention.
[0050] This application develops an automated processing and analysis script for hospital centralized procurement drug usage data based on R language, comprehensively, efficiently and accurately conducts dynamic monitoring of the progress of centralized procurement tasks, optimizes drug procurement and resource allocation, and promotes the rational use of drugs and operational efficiency of medical institutions. In addition, combined with R's rich dependency packages, in-depth data analysis research can be expanded, such as the impact of centralized procurement policies on the frequency of use and cost of hospital drugs based on ITS, which can provide more comprehensive data analysis to support decision-making and optimization of centralized procurement work.
[0051] Use the HIS system to build dictionary information for centralized procurement drugs, retrieve and extract the usage data of centralized procurement drugs for the current month, import all data files into the working directory, use R language for data pre-processing and standardization, build R language scripts for personalized data analysis, and automatically perform statistical analysis on the number of drugs in each centralized procurement batch, the monthly task volume, the drug usage of each department, and the completion rate of each batch and department task volume. For departments that have not completed their tasks, output R markdown report documents for progress warnings. In addition, custom data analysis can be carried out according to individual needs, such as studying the impact of centralized procurement policies on the frequency of use and cost of hospital drugs based on ITS. The specific related processes of the entire data analysis are as follows: Figure 1 As shown, the specific process includes the following:
[0052] 1. Information collection on centralized drug procurement tasks
[0053] This step achieves obtaining the information table of the selected drugs in all centralized procurement batches and constructing the dictionary information of the centralized procurement drugs. Specifically: collect and organize the information table of the selected drugs in all centralized procurement batches, mainly including project number, drug code, drug name, manufacturer specification, packaging unit and number of tasks, etc., to establish standardized statistical task information for subsequent data analysis. In order to more intuitively reflect the key drugs in each batch and their proportion, the geom_treemap function in the R extension package treemapify is used to construct a tree diagram of the current centralized procurement batch and its selected drug task volume ratio. The specific results are as follows: Figure 2 As shown. Different grayscales represent different centralized procurement batches currently being implemented, and the area occupied by the drug represents its task proportion in the batch. It can be seen that tropisetron, cefotaxime, kallikrein, felodipine, amoxicillin, acarbose, metoprolol, lixaban and Xiaojinwan are the drugs with a relatively large proportion in the current centralized procurement batches.
[0054] 2. Progress of completion of centralized procurement batch tasks
[0055] This step realizes the extraction of usage data of centralized procurement drugs, statistics of the task volume and actual usage of each batch, and calculation of the completion progress of centralized procurement tasks. Specifically: According to the information table of selected drugs in the centralized procurement batches, statistics of usage data for the month are compiled, and statistics of task information for each batch each month are compiled, and warnings are issued for drugs and departments that fail to complete the task progress on time. In addition, due to the different execution times of each centralized procurement batch, there are also differences in the task progress of the month. In order to intuitively warn of centralized procurement batches that fail to complete the task progress on time, statistics are issued on the proportion of all drugs in each batch that have reached the standard, and the completion ratio threshold is set to 50% as the warning red line for each batch, with a focus on batches that do not meet the standard. The geom_bar function in the ggplot extension package is used to construct a bar chart of the completion progress of each centralized procurement batch. The specific results are as follows: Figure 3 As shown, different gray levels correspond to different batches, among which only 45% of the drugs in the second and fourth renewal batches of the national centralized procurement meet the standards, and need to be paid special attention below the set threshold red line.
[0056] In an optional embodiment of the present application, data cleaning and standardization are also included; wherein duplicate, erroneous or incomplete data are removed, and data of different dimensions are converted into a unified scale;
[0057] Further judge the rationality of the task; among them, compare the task volume of each batch and the historical procurement data to judge whether the goal is reasonable; analyze the task volume and actual usage of each department to judge whether there is unreasonable task allocation.
[0058] 3. Analysis of the progress of the centralized drug procurement task
[0059] This step realizes the analysis of the task progress of the centralized procurement drugs according to the monthly task requirements and the centralized procurement completion progress of each centralized procurement batch. Specifically, according to the monthly task requirements of each centralized procurement batch, it is convenient for the R language script to automatically organize the data information of the current total usage, department usage, usage ratio and task completion progress of each batch of centralized procurement drugs, and count the completion rate information of the task drugs. Taking the second and fourth national centralized procurement renewal batches as an example, the task start time of this batch is July 31, 2023, and the completion progress should reach about 20% of the task volume by September 31. In order to intuitively display the data analysis results, the geom_line function is used to construct a line graph of the drug completion progress, in which the data points represent different drugs, and the red dotted line is the task progress threshold of the batch. The specific situation is shown in the attached. Figure 4 As shown in the figure, there are 31 drugs in this batch, and 17 drugs failed to meet the standards. Metronidazole has the lowest task completion rate, followed by parecoxib, abiraterone, valsartan and amlodipine.
[0060] 4. Completion status of centralized drug procurement in task departments
[0061] This step further counts the completion status of each department for drugs that do not meet the standards in the current month's tasks for the centralized procurement batch based on the analysis results. Specifically, for drugs that do not meet the standards in the current month's tasks for the centralized procurement batch, it is necessary to further count their completion status in each department. Currently, there are 17 types of drugs that do not meet the standards in the second and fourth national centralized procurement renewal batches. This section takes the non-compliant drug acarbose as an example. According to the results of the R language data analysis, the amount of acarbose used, the amount of tasks, the proportion of tasks, and the completion rate of each department are sorted out, focusing on the departments with the highest task proportion. In order to facilitate the comparison of the completion status of each department, the data is normalized, and the geom_col function is used to complete the stacked bar chart of the acarbose task proportion and completion rate of each department. Details are as follows. Figure 5 As shown, the dotted line is the threshold of the task progress for that month. The Department of Endocrinology had the largest proportion of acarbose tasks, followed by the Department of Cardiovascular Medicine, the Department of Neurology, and the Department of Geriatrics / International Medical Center. However, only the Department of Endocrinology met the target for task completion that month, while the other three departments did not.
[0062] 5. Early warning notification of the progress of the task of centralized procurement of drugs that do not meet the standards
[0063] This step realizes the output of Rmarkdown report documents for the progress warning of drugs that fail to meet the task targets for departments that have not completed the task. Specifically, according to the ideas described above, R language scripts are used to automatically process the usage of all selected drugs in the current centralized procurement batches, determine whether the monthly tasks of the selected drugs in each batch have met the targets, and sort out and summarize the responsible departments for the drugs that failed to meet the targets. After aggregating the data, use the Rmarkdown extension package to write an R markdown progress warning document for drugs that fail to meet the task targets for each department. The specific format of the document is as follows: Figure 6 As shown in the figure, batch output can be used for notification. R markdown reproducible documents can effectively reduce the time of writing written notifications and improve work efficiency. Combined with R language scripts for high-throughput statistical analysis of centralized procurement data, the use of centralized procurement drugs can be communicated to the task department in a timely manner, encouraging doctors to give priority to the use of centralized procurement drugs, and ensuring the smooth completion of the centralized procurement drug task.
[0064] In an optional embodiment of the present application, based on the analysis result data of the departments that have not completed the task, the drug information that has not met the task requirements is screened out; wherein the drug information at least includes the drug name, the department's completion ratio, the actual completion ratio of the entire hospital, the hospital's task ratio for the month, and the time; select an appropriate RMarkdown document template, and insert the screened drug list into the document; use a table to display the progress information of each drug.
[0065] 6. Conduct ITS data analysis based on R language
[0066] This step implements custom data analysis based on the R language extension package; data analysis specifically includes studying the impact of centralized procurement policies on the frequency of use and costs of hospital drugs. Specifically, R language is an open source statistical computing and data visualization programming language. In addition to being used to build automated data analysis processes and high-quality personalized statistical charts, its large number of extension packages can also be used to implement a variety of statistical analyses. Users can expand the functionality of R by writing their own functions and packages to meet various task requirements in data analysis work. This example takes the use of the eighth batch of national centralized procurement drugs in 2023 as an example, and studies the impact of the eighth batch of national centralized procurement tasks on the frequency of use of commonly used drugs for cardiovascular diseases and patients' medication costs based on ITS. The results are as follows Figure 7 It can be seen that after the implementation of the eighth batch of national centralized procurement tasks, the monthly usage frequency (DDDs) of levamlodipine besylate, amlodipine atorvastatin calcium and losartan potassium hydrochlorothiazide did not change significantly, among which the DDDs of felodipine showed a downward trend, while the DDDs of levocarnitine increased slightly; the cost of each drug was significantly reduced.
[0067] The present application also provides a system for dynamically monitoring the progress of hospital drug procurement tasks based on R language. The system includes:
[0068] The collection unit is used to obtain the information table of the selected centralized procurement drugs of all centralized procurement batches and build the dictionary information of the centralized procurement drugs;
[0069] The progress calculation unit is used to extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks;
[0070] The first analysis unit is used to analyze the task progress of the centralized procurement drugs according to the monthly task requirements of each centralized procurement batch and the centralized procurement completion progress;
[0071] The second analysis unit further counts the completion status of each department for the drugs that did not meet the target in the centralized procurement batch for the current month based on the analysis results;
[0072] The output unit is used to output an Rmarkdown report document of drug progress warning that does not meet the task target for departments that have not completed the task.
[0073] In an optional embodiment of the present application, the system further includes:
[0074] The extension unit is used to conduct customized data analysis based on the R language extension package; the data analysis specifically includes studying the impact of centralized procurement policies on the frequency of use and cost of hospital drugs.
[0075] The R language-based dynamic monitoring system for the progress of hospital drug procurement tasks provided in the embodiment of the present application is used to implement the above-mentioned R language-based dynamic monitoring method for the progress of hospital drug procurement tasks. For the specific limitations of the R language-based dynamic monitoring system for the progress of hospital drug procurement tasks, please refer to the limitations of the R language-based dynamic monitoring method for the progress of hospital drug procurement tasks, which will not be repeated here. Each part of the above-mentioned R language-based dynamic monitoring system for the progress of hospital drug procurement tasks can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the device in the form of hardware, or can be stored in the memory in the device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0076] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0077] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.
Claims
1. A dynamic monitoring method for the progress of hospital drug procurement tasks based on R language. It is characterized in that The method comprises: Obtain the information table of all the selected drugs in the centralized procurement batches and build the dictionary information of the centralized procurement drugs; Extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks; Analyze the task progress of centralized procurement drugs based on the monthly task requirements and centralized procurement completion progress of each centralized procurement batch; Based on the analysis results, further statistics are collected on the completion status of each department for the drugs that did not meet the target in the current month's centralized procurement batch; For departments that have not completed their tasks, an R markdown report document with early warning of drug progress that has not met the task standards is output.
2. The method according to claim 1, It is characterized in that The method further comprises: Conduct custom data analysis based on the R language extension package; data analysis specifically includes studying the impact of centralized procurement policies on the frequency of drug use and costs in hospitals.
3. The method according to claim 1, It is characterized in that Obtain an information table of the selected drugs for all centralized procurement batches, where the information table includes at least the project number, drug code, drug name, manufacturer specification, packaging unit, and number of tasks; and use the geom_treemap function in the R extension package treemapify to construct a tree diagram of the currently executing centralized procurement batches and the proportion of their selected drug task quantities.
4. The method according to claim 1, It is characterized in that Extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, calculate the completion progress of the centralized procurement tasks, and also include: Calculate the completion rate based on actual usage and task volume, issue warnings for batches that are lower than the preset completion rate, and focus on the drugs and departments in the warning batches; The geom_bar function in the ggplot extension package of the R language is used to construct a bar chart based on the completion rate data of each batch to intuitively display the completion progress of each batch.
5. The method according to claim 1, It is characterized in that Extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks, and also include: Perform data cleaning and standardization, including removing duplicate, erroneous or incomplete data and converting data of different dimensions into a unified scale; Further judge the rationality of the task; among them, compare the task volume of each batch and the historical procurement data to judge whether the goal is reasonable; analyze the task volume and actual usage of each department to judge whether there is unreasonable task allocation.
6. The method according to claim 1, It is characterized in that Analyze the task progress of the centralized procurement drugs according to the monthly task requirements and the completion progress of the centralized procurement of each batch, including: The R language script is used to automatically organize the data information of the current total usage, department usage, usage ratio and task completion progress of each batch of centralized procurement drugs, count the completion rate of task drugs, and use the geom_line function to construct a line chart of drug completion progress.
7. The method according to claim 1, It is characterized in that Based on the analysis results, the completion status of the drugs that did not meet the target in the centralized procurement batch for the month was further counted in each department, including: Use the geom_col function to complete a stacked bar chart of the target drug task ratio and completion rate for each department.
8. The method according to claim 1, It is characterized in that For departments that have not completed their tasks, an R markdown report document is output for early warning of drug progress that has not met the task target, including: According to the analysis result data of the departments that have not completed the task, the drug information that has not met the task requirements is screened out; the drug information at least includes the drug name, department completion ratio, actual completion ratio of the whole hospital, the task ratio of the whole hospital for the month, and time; Select an appropriate R Markdown document template and insert the screened drug list into the document; Use a table to display the progress information of each drug.
9. A dynamic monitoring system for the progress of hospital drug procurement tasks based on R language. It is characterized in that The system comprises: The collection unit is used to obtain the information table of the selected centralized procurement drugs of all centralized procurement batches and build the dictionary information of the centralized procurement drugs; The progress calculation unit is used to extract the usage data of the centralized procurement drugs, count the task volume and actual usage of each batch, and calculate the completion progress of the centralized procurement tasks; The first analysis unit is used to analyze the task progress of the centralized procurement drugs according to the monthly task requirements of each centralized procurement batch and the centralized procurement completion progress; The second analysis unit further counts the completion status of each department for the drugs that did not meet the target in the current month's task of the centralized procurement batch based on the analysis results; The output unit is used to output an R markdown report document of drug progress warning that does not meet the task target for departments that have not completed the task.
10. The system according to claim 9, It is characterized in that The system further comprises: The extension unit is used to conduct customized data analysis based on the R language extension package; the data analysis specifically includes studying the impact of centralized procurement policies on the frequency of use and cost of hospital drugs.