Optimizing the electronic health records through big data analytics: A knowledge-based view

Caifeng Zhang, Rui Ma, Shiwei Sun*, Yujie Li, Yichuan Wang, Zhijun Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Many hospitals are suffering from ineffective use of big data analytics with electronic health records (EHRs) to generate high quality insights for their clinical practices. Organizational learning has been a key role in improving the use of big data analytics with EHRs. Drawing on the knowledge-based view and big data lifecycle, we investigate how the three modes of knowledge can achieve meaningful use of big data analytics with EHRs. To test the associations in the proposed research model, we surveyed 580 nurses of a large hospital in China in 2019. Structural equation modelling was used to examine relationships between knowledge mode of EHRs and meaningful use of EHRs. The results reveal that know-what about EHRs utilization, know-how EHRs storage and utilization, and know-why storage and utilization can improve nurses' meaningful use of big data analytics with EHRs. This study contributes to the existing digital health and big data literature by exploring the proper adaptation of analytical tools to EHRs from the different knowledge mode in order to shape meaningful use of big data analytics with EHRs.

Original languageEnglish
Article number8824131
Pages (from-to)136223-136231
Number of pages9
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Big data analytics
  • Electronic health records and impacts
  • Knowledge-based view

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