Innovation of Public Health Management Model in the Big Data Environment
DOI:
https://doi.org/10.54097/56t2ga19Keywords:
Big data; Public health; Data driven; Management innovation.Abstract
In the era of Big Data (BD), PHM is facing unprecedented changes and opportunities. This article discusses the innovation of Public Health Management (PHM) mode under the BD environment, points out the challenges faced in this process, and puts forward corresponding solutions. Through the introduction of innovative models such as data-driven decision support system, inter-departmental collaboration and information sharing, and precise public health intervention, the potential of BD technology in improving PHM efficiency, optimizing resource allocation, and strengthening disease prevention and control is demonstrated. This innovation process also faces challenges such as data privacy and security, data quality and accuracy, shortage of technology and talents, cross-departmental coordination problems and lagging laws and regulations. Therefore, this article puts forward a series of targeted countermeasures in order to provide guidance for the future development of PHM and promote the sustainable progress of global public health undertakings.
Downloads
References
Zamelski P. Principles of forest management in the context of concern for the common welfare and public health in Poland[J]. Sylwan, 2018, 162(8):658-663.
Parikh S R, Campbell H, Mandal S, et al. Primary meningococcal conjunctivitis: Summary of evidence for the clinical and public health management of cases and close contacts[J]. Journal of Infection, 2019, 79(6):490-494.
Bradshaw D, Mbisa J L, Geretti A M, et al. Consensus recommendations for resistance testing in the management of chronic hepatitis C virus infection: Public Health England HCV Resistance Group[J]. Journal of Infection, 2019, 79(6):503-512.
Nawaz A, Khan H Z A, Shah S A R, et al. COVID-19 epidemic spread and the impact on public health & safety policy: An analysis of the adoption of preventive measures and effective management: Evidence from Pakistan[J]. Revista Argentina de Clinica Psicologica, 2020, 29(4):722-736.
Fenta T G. Human resources for public health supply chain management in Ethiopia: Competency mapping and training needs[J]. Ethiopian Journal of Health Development, 2018, 31(4):266-275.
Burger R, Christian C S, Gerdtham U G, et al. Use of simulated patients to assess hypertension case management at public healthcare facilities in South Africa[J]. Journal of Hypertension, 2020, 38(2):362-367.
Chen B. A meta-modeling framework in public health emergency management[J]. Simulation, 2018, 94(5):401-419.
Gupta A, Singh E J. Arsenic-Iron Relationships in Aquifers of North East India: Implications for Public Health and the Environment[J]. Environmental Management, 2019, 63(4):437-443.
Costa J P, Grobelnik M, Fuart F, et al. Meaningful Big Data Integration for a Global COVID-19 Strategy[J]. IEEE Computational Intelligence Magazine, 2020, 15(4):51-61.
Wang Y, Zeng D. Development of sports industry under the influence of COVID-19 epidemic situation based on big data[J]. Journal of Intelligent and Fuzzy Systems, 2020, 39(6):8867-8875.
Yan H. Microbial control of river pollution during COVID-19 pandemic based on big data analysis[J]. Journal of Intelligent and Fuzzy Systems, 2020, 39(1):1-6.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
