journal article
Open Access
Jan 18, 2022
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Abstract
The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of diverse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.
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Metrics
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Citations
284
References
Details
- Published
- Jan 18, 2022
- Vol/Issue
- 55(2)
- Pages
- 1-40
- License
- View
Authors
Funding
Telstra Health and the Digital Health Cooperative Research Centre
Australian Government’s Department of Industry, Science, Energy and Resources
Cite This Article
Tabinda Sarwar, Sattar Seifollahi, Jeffrey Chan, et al. (2022). The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges. ACM Computing Surveys, 55(2), 1-40. https://doi.org/10.1145/3490234
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