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01.12.2023 | Original Paper

Factors Influencing the Effectiveness of Adopting Electronic Medical Record-Based Reporting Systems for Notifiable Disease Surveillance: A Quantitative Analysis

verfasst von: Li-Hui Lee, Jen-Hsiang Chuang, Yu-Cih Wu, Wan-Nin Chen, Jiunn-Shyan Wu, Chi-Ming Chang, Ean-Wen Huang, Ding-Ping Liu

Erschienen in: Journal of Medical Systems | Ausgabe 1/2023

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Abstract

The coronavirus disease 2019 (COVID-19) pandemic has led to greater attention being given to infectious disease surveillance systems and their notification functionalities. Although numerous studies have explored the benefits of integrating functionalities with electronic medical record (EMR) systems, empirical studies on the topic are rare. The current study assessed which factors influence the effectiveness of EMR-based reporting systems (EMR-RSs) for notifiable disease surveillance. This study interviewed staff from hospitals with a coverage that represented 51.39% of the notifiable disease reporting volume in Taiwan. Exact logistic regression was employed to determine which factors influenced the effectiveness of Taiwan’s EMR-RS. The results revealed that the influential factors included hospitals’ early participation in the EMR-RS project, frequent consultation with the information technology (IT) provider of the Taiwan Centers for Disease Control (TWCDC), and retrieval of data from at least one internal database. They also revealed that using an EMR-RS resulted in more timely, accurate, and convenient reporting in hospitals. In addition, developing by an internal IT unit instead of outsourcing EMR-RS development led to more accurate and convenient reporting. Automatically loading the required data enhanced the convenience, and designing input fields that may be unavailable in current databases to enable physicians to add data to legacy databases also boosted effectiveness of the reporting system.

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Titel: Factors Influencing the Effectiveness of Adopting Electronic Medical Record-Based Reporting Systems for Notifiable Disease Surveillance: A Quantitative Analysis
verfasst von: Li-Hui Lee
Jen-Hsiang Chuang
Yu-Cih Wu
Wan-Nin Chen
Jiunn-Shyan Wu
Chi-Ming Chang
Ean-Wen Huang
Ding-Ping Liu
Publikationsdatum: 01.12.2023
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 1/2023
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-023-01971-y

Springer Medizin

Abstract

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