Application of Automatic Data Matching Method in Assessing the Radioimmunoassay Data in a Medical Center

• Main Authors: Hui-Chu Chiou, 邱彗株
• Other Authors: Chu-Ching Wang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/99718274141460841385

碩士 === 淡江大學 === 全球華商經營管理數位學習碩士在職專班 === 99 === The aim of this study is to investigate the data quality problems existing in the Laboratory Information System (LIS) and the Departmental Registration System (DRS), which is one of the subsystem of the Hospital Information System (HIS).We start from literature review regarding the interfaces among LIS, HIS and DRS, data quality problems, and solutions to solve data quality problems. We use a customized analysis tool, which is developed in-house using Microsoft Visual Basic on the platform of EXCEL software, to solve the data heterogeneity between LIS and DRS and to perform data matching automatically. By using this tool, we are able to perform data matching between LIS and DRS at a rate of 300 groups of data within 30 seconds, with each group of data comprising patient name, history number, accessing number, and an average of 3 medical items. Besides, the tool also provides automatic summarization of the detected data errors on an independent EXCEL data sheet. According to our study case, we found data heterogeneity between LIS and DRS, making manual data matching difficult and erroneous and automatic data matching impossible. Our data analysis tool integrated the heterogeneous data into uniform data to allow automatic data matching, which was proven capable to detect intra-system data repeatability and inter-system data inconsistency. Automatic data matching provides in-time detection of data quality problems, avoids health insurance declaration problems, cost waste problems, and medical care quality problems, etc. From the viewpoint of management, automatic data matching provides the administrator a rapid, in-time, accurate tool for detecting data quality problem, making up the data quality problem hiding behind the data heterogeneity between different systems.