Keywords
healthcare management model
data driven
How to Cite
Abstract
Objective To study the driving role of the deployment and application of the national infectious disease monitoring and early warning front-end software in enhancing the management mode and efficiency of medical institutions, and to provide more theoretical support and practical reference for the establishment of digital medical management system in the new era. Methods To analyze the technical principles and core functions of the National Infectious Disease Surveillance and Early Warning Front-end Software, and to reveal the driving role of the National Infectious Disease Surveillance and Early Warning Front-end Software related technologies in the medical management mode of organizational structure, workflow, information management, personnel responsibilities, and resource allocation through the analysis of systematic literature and theoretical retrospective causes. Results The deployment and application of national infectious disease surveillance and early warning precursor software will drive the transformation of healthcare organizations' management model to data intelligence, but the change may face challenges such as technological integration, organizational inertia, and ethical risks. Conclusion The hospital management model driven by the NIDSS is transforming from “experience-driven” to “data-intelligence-driven”, and the change of its management model is essentially a self-adaptive revolution of healthcare organizations into the era of digital healthcare, which requires the use of intelligent algorithmic modeling technology, organizational inertia, and ethical risks. It is necessary to make breakthroughs in intelligent algorithmic modeling technology, digital literacy competence certification and data regulatory innovation system to build a modern medical management system that combines “level and urgency”.
References
[1] Gao GF. From "A" IV to "Z" IKV: attacks from emerging and re-emerging pathogens. *Cell*. 2018;172(6):1157-1159.doi:10.1016/j.cell.2018.02.025.
[2] Hu H, Lu JH. Responding to emerging and re-emerging infectious diseases based on the One Health strategy [published online in Chinese]. *J Sun Yat-sen Univ (Med Sci)* 2022;43(5):705-711.
[3] Deng Y, Ren X, Huang S, et al. Main research and application progress of big data in infectious disease surveillance and early warning [published online in Chinese]. *Dis Surveill*. 2022;37(8):1003-1009. doi:10.3784/jbjc.202206100255.
[4] Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. *Appl Geogr*. 2020; 118:102202. doi: 10.1016/j.apgeog.2020.102202.
[5] Yang WZ, Lan YJ, Lyu W, et al. Establishment of a multi-point trigger mechanism and multi-channel surveillance and early warning mechanism for intelligent infectious disease early warning in China [published online in Chinese]. *Chin J Epidemiol*. 2020;41(11):1753-1757.
[6] Ma JQ, Wang SW, Guo Q, et al. Overall design and implementation of the national pre-software for intelligent infectious disease surveillance and early warning [published online in Chinese]. *China Digit Med*. 2025;20(3):89-95.doi:10.3969/j.issn.1673-7571.2025.03.013
[7] National Disease Control and Prevention Administration. The National Disease Control and Prevention Administration held a special press conference on "Progress and Effectiveness in Promoting the High-Quality Development of Disease Control" [published online in Chinese]. National Disease Control and Prevention Administration website. Published December 27, 2024. Accessed March 22, 2025. https://www.ndcpa.gov.cn/jbkzzx/XWFBF/common/content/content_1873896628558614528.html
[8] Cozzoli N, Salvatore FP, Faccilongo N, Milone M. How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. *BMC Health Serv Res*. 2022;22(1):809. doi:10.1186/s12913-022-08167-z
[9] Undavia JN, Patel AM. Big data analytics in healthcare: applications and challenges. *Int J Big Data Anal Healthc*. 2020;5(1):19-27. doi:10.4018/IJBDAH.2020010102
[10] Li X, Shu Q, Kong C, et al. An intelligent system for classifying patient complaints using machine learning and natural language processing: development and validation study. *J Med Internet Res*. 2025;27:e55721. doi:10.2196/55721
[11] Bani-Salameh H, Al-Qawaqneh M, Taamneh S. Investigating the adoption of big data management in healthcare in Jordan. *Data*. 2021;6(2):16. doi:10.3390/data6020016
[12] Isakov TM, Laatikainen T, Kaila M, et al. From challenges to opportunities: digital transformation in hospital-at-home care. *Int J Med Inform*. 2024;192:105644. doi:10.1016/j.ijmedinf.2024.105644
[13] Ma YY, Shen SG. The path and countermeasures to promote the modernization of the medical and health governance system and governance capacity: based on the perspective of long-term response to public health emergencies [published online in Chinese]. *Humanit J*. 2020;(6):104-111. doi:10.15895/j.cnki.rwzz.2020.06.012
[14] Hügle T, Grek V. Digital transformation of an academic hospital department: a case study on strategic planning using the balanced scorecard. *PLOS Digit Health*. 2023;2(11):e0000385. doi:10.1371/journal.pdig.0000385
[15] Wang SQ, Hou ZG, Chen ZM, et al. Design of a new hospital information system [published online in Chinese]. *Chin Med Equip J*. 2022;43(3):38-45. doi:10.19745/j.1003-8868.2022053
[16] Nagarajan R, Lee H, Asress M, et al. Economics and equity of large language models: health care perspective. *J Med Internet Res*. 2024;26:e64226. doi:10.2196/64226
[17] Li XJ, Tang WB, Li SH. Practice and thinking on the integrated treatment mode for critically severe burns [published online in Chinese]. *Chin J Burns Wounds*. 2024;40(12):1101-1106. doi:10.3760/cma.j.cn501225-20240827-00317
[18] Deng L, Cheng F, Gao X, et al. Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology. *Sci Rep*. 2023;13(1):299. doi:10.1038/s41598-022-24221-6
[19] Chen SB, Zhao M, Zhang L, Ke JL, Niu LC. Research on a blockchain-based data sharing model for medical consortia [published online in Chinese]. *Chin J Health Inform Manag*. 2023;20(1):116-121. doi:10.3969/j.issn.1672-5166.2023.01.021
[20] Xue J, Li Z, Zhang S. Multi-resource constrained elective surgical scheduling with Nash equilibrium toward smart hospitals. *Sci Rep*. 2025;15(1):3946. doi:10.1038/s41598-025-87867-y
[21] Akinluyi EA, Stell D, Perera N, Sibley-Allen C. Developing the COVID-19 intensive care medical equipment distribution platform: outcomes and lessons learned. *BMJ Open Qual*. 2021;10(2):e001383. doi:10.1136/bmjoq-2021-001383
[22] China Hospital Information Management Association. Construction and application of a human resources intelligent management platform based on talent data operation [published online in Chinese]. China Hospital Information Management Association website. Published May 5, 2023. Accessed March 22, 2025. https://www.chima.org.cn/Html/News/Articles/16108.html
[23] Hallinan CM, Ward R, Hart GK, et al. Seamless EMR data access: integrated governance, digital health and the OMOP-CDM. *BMJ Health Care Inform*. 2024;31(1):e100953. doi:10.1136/bmjhci-2023-100953
[24] Yu WP. *Organizational Change: The Guarantee for Realizing Strategic ERP Value* [published online in Chinese]. Tsinghua University Press; 2004.
[25] Taddeo M, Floridi L. How AI can be a force for good. *Science*.2018;361(6404):751-752. doi:10.1126/science.aat5991
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