药品质量管理风险分析与评估数据仓库系统的设计与实现

发布时间：2018-05-17 05:21

本文选题：药企品质保证 + 数据仓库　；参考：《天津大学》2014年硕士论文

【摘要】：药品安全是关乎百姓生命安全及社会经济发展的重大问题,随着我国新版《GMP》、《中国药典》与《国家药品安全“十二五”规划》等政策的实施,对我国药品质量管理方面提出更高的要求,同时也强调了风险的预判与评估在药品生产过程中的重要性。在药品生产企业组织生产过程中,会产生大量具有重要意义的生产数据与偏差数据,包括产品名称、产品批号、产品规格与偏差情况描述等重要信息,品质保证部通过总结数据之间相互作用的内在因素生成的相关数据与数据之间的关系图形,根据决策者需求进行生产情况回顾性分析,以及车间偏差情况回顾性分析,然后根据生产情况与偏差发生情况进行风险发生预判与评估工作,为决策者的正确决策提供数据保障。同时品质保证人员处理实验室药品化验过程产生的实验数据,包括含量数据与pH值数据。按照一定方法将这些数据划分区间,寻找数据频繁出现的数据汇集区域,为精准分析生产情况提供实验数据支持。本文在品质保证部汇总生产数据与实验数据基础上,将数据仓库与数据挖掘技术引入到药品风险分析与评估的工作当中。利用数据仓库中OLAP技术进行生产数据图形汇总,分析偏差信息与生产数据之间相互作用的机制,满足用户分析的需求。利用数据挖掘中的聚类技术对实验数据进行科学的分类,并与现行梯度法进行比较,有效地发现药品检验中含量数据与pH值数据的数据汇集区,了解参数产生变化的原因。该功能模块根据品质保证部需要结合近几年车间生产信息与实验室药物化验分析结果,总结规律。使决策者可以通过风险分析评估报告查询到具体情况信息集合,进行及时的对生产车间的操作指导。该系统很好的解决了统计数据量大、回馈时间长等问题。该系统可以对车间改造与风险处理提供分析依据、提高效率,从而准确为决策者提供优质的数据支持,在品质保证部风险管理工作中具有重要的意义。
[Abstract]:Drug safety is a major issue related to the safety of people's lives and social and economic development. With the implementation of the new edition of China's "GMP", the "Chinese Pharmacopoeia" and the "Twelfth Five-Year Plan for National Drug Safety", etc. Higher requirements for drug quality management in China are put forward, and the importance of risk prediction and evaluation in the process of drug production is also emphasized. In the process of organizing production, a large number of important production data and deviation data, including product name, product lot number, product specification and deviation description and so on, will be produced. The quality Assurance Department carries out retrospective production analysis according to the needs of the decision makers, and the workshop deviation by summarizing the relationship graph between the related data and the data generated by the internal factors of the interaction between the data, as well as the retrospective analysis of the workshop deviation. Then, according to the production situation and the deviation occurrence situation, the risk occurrence prejudgment and the appraisal work are carried out, which provides the data guarantee for the decision maker to make the correct decision. At the same time, the quality assurance personnel process the laboratory drug test process produced experimental data, including content data and pH data. According to a certain method, these data are divided into regions, and the data gathering regions are found frequently, which can provide experimental data support for accurate analysis of production situation. Based on the production data and experimental data collected by quality assurance department, data warehouse and data mining technology are introduced into drug risk analysis and evaluation. The OLAP technology in data warehouse is used to collect production data graph and analyze the mechanism of interaction between deviation information and production data to meet the needs of user analysis. By using the clustering technique in data mining, the experimental data are scientifically classified and compared with the current gradient method. The data collection area of the content data and the pH value data in drug testing is found effectively, and the reasons for the change of the parameters are understood. According to the requirement of the quality assurance department, the function module summarizes the rules by combining the production information of workshop and the results of laboratory drug analysis in recent years. Through the risk analysis and evaluation report, the decision maker can inquire into the information set of the concrete situation, and guide the operation of the production workshop in a timely manner. The system solves the problems of large amount of statistical data and long time of feedback. The system can provide analysis basis for workshop transformation and risk processing, improve efficiency, and provide accurate data support for decision makers. It is of great significance in risk management of quality assurance department.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R95;TP311.13

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