Development of digital health management systems in longitudinal study: The Malaysian cohort experience.

Autor:	Abdullah N; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Husin NF; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Goh YX; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Kamaruddin MA; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Abdullah MS; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Yusri AF; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Kamalul Arifin AS; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia., Jamal R; UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Jazyk:	angličtina
Zdroj:	Digital health [Digit Health] 2024 Sep 12; Vol. 10, pp. 20552076241277481. Date of Electronic Publication: 2024 Sep 12 (Print Publication: 2024).
DOI:	10.1177/20552076241277481
Abstrakt:	Background: The management of extensive longitudinal data in cohort studies presents significant challenges, particularly in middle-income countries like Malaysia where technological resources may be limited. These challenges include ensuring data integrity, security, and scalability of storage solutions over extended periods. Objective: This article outlines innovative methods developed and implemented by The Malaysian Cohort project to effectively manage and maintain large-scale databases from project inception through the follow-up phase, ensuring robust data privacy and security. Methods: We describe the comprehensive strategies employed to develop and sustain the database infrastructure necessary for handling large volumes of data collected during the study. This includes the integration of advanced information management systems and adherence to stringent data security protocols. Outcomes: Key achievements include the establishment of a scalable database architecture and an effective data privacy framework that together support the dynamic requirements of longitudinal healthcare research. The solutions implemented serve as a model for similar cohort studies in resource-limited settings. The article also explores the broader implications of these methodologies for public health and personalized medicine, addressing both the challenges posed by big data in healthcare and the opportunities it offers for enhancing disease prevention and management strategies. Conclusion: By sharing these insights, we aim to contribute to the global discourse on improving data management practices in cohort studies and to assist other researchers in overcoming the complexities associated with longitudinal health data. Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© The Author(s) 2024.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu