Explore Data Quality Challenges Based on Data Structure of Electronic Health Records

被引:0
作者
Liu, Caihua [1 ]
Peng, Guochao [2 ]
Lan, Chaowang [1 ]
Kong, Shufeng [2 ]
机构
[1] Guilin Univ Elect Technol, Guilin 541004, Guangxi, Peoples R China
[2] Sun Yat Sen Univ, Guangzhou 510275, Guangdong, Peoples R China
来源
HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION, HIMI 2023, PT I | 2023年 / 14015卷
关键词
Data quality; Electronic health records; Data structure; PRIMARY-CARE;
D O I
10.1007/978-3-031-35132-7_17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the adoption of electronic health records (EHR) in primary care, ensuring high-quality data used is the premise of the quality of decision making and quality of care. Prior literature on EHR data quality has addressed dimensions and methods of data quality assessment for reuse, however, the challenges of data quality in EHR during the process of primary care from the perspective of EHR data structure have received limited attention. Looking at the EHR data structure helps improve the understanding of data quality challenges from the information pathway. Such a study assists in better designing and developing EHR systems and achieving high-quality data when using EHR. This paper thus aims at exploring challenges of data quality from the perspective of EHR data structure. For this to happen, the present study firstly investigates five main practices of primary care and describes a use case diagram of EHR systems based on these practices. Referring to the EHR systems' functions described in the use case diagram, the study then conceptualizes the EHR data structure used in primary care, including a conceptual data model, a data flow diagram and a database schema, to better understand the data elements contained in EHR, and analyzes the changes of data elements in EHR when the practices of primary care are carried out and possible challenges of data quality in EHR. Finally, this study proposes several strategies addressing these challenges to help practitioners achieve high-quality data. Future research directions are also discussed.
引用
收藏
页码:236 / 247
页数:12
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