A Data-Driven Approach to Child Health Monitoring and Medical Leave Automation
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Schools play a vital role in student health but often lack real-time medical updates. Traditional parental reporting of student illnesses is slow and prone to errors. This paper proposes integrating hospital Electronic Health Records (EHRs) with the Educational Management Information System (EMIS) for real-time health monitoring and automated medical leave certification. The system uses FHIR/HL7-compliant APIs to securely send medical updates and leave certificates from hospitals to schools. Role-based access control (RBAC) and encryption ensure compliance with data protection laws. When a student is diagnosed, hospitals update the EHR, generating a digital medical leave certificate that is instantly sent to EMIS, allowing schools to update attendance and support remote learning. This integration improves emergency response, automates leave tracking, prevents fraud, and enhances communication between schools, parents, and healthcare providers. Anonymized data can also help government agencies track disease outbreaks. Challenges like data privacy, interoperability, and adoption resistance are addressed through encryption, standardized protocols, and pilot testing. Future research will explore AI-driven health risk prediction and blockchain-based medical leave verification. By connecting healthcare and education, this system enhances student safety, reduces administrative burdens, and improves communication among stakeholders.
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Fionna Ananth, P.ELDIN RINO (2025). A Data-Driven Approach to Child Health Monitoring and Medical Leave Automation. International Journal of Technology & Emerging Research (IJTER), 1(7), 87-94. https://doi.org/10.64823/ijter.2507011
BibTeX
@article{ijter2025212511105321,
author = {Fionna Ananth and P.ELDIN RINO},
title = {A Data-Driven Approach to Child Health Monitoring and Medical Leave Automation},
journal = {International Journal of Technology & Emerging Research },
year = {2025},
volume = {1},
number = {7},
pages = {87-94},
doi = {10.64823/ijter.2507011},
issn = {3068-109X},
url = {https://www.ijter.org/article/212511105321/a-data-driven-approach-to-child-health-monitoring-and-medical-leave-automation},
abstract = {Schools play a vital role in student health but often lack real-time medical updates. Traditional parental reporting of student illnesses is slow and prone to errors. This paper proposes integrating hospital Electronic Health Records (EHRs) with the Educational Management Information System (EMIS) for real-time health monitoring and automated medical leave certification. The system uses FHIR/HL7-compliant APIs to securely send medical updates and leave certificates from hospitals to schools. Role-based access control (RBAC) and encryption ensure compliance with data protection laws.
When a student is diagnosed, hospitals update the EHR, generating a digital medical leave certificate that is instantly sent to EMIS, allowing schools to update attendance and support remote learning. This integration improves emergency response, automates leave tracking, prevents fraud, and enhances communication between schools, parents, and healthcare providers. Anonymized data can also help government agencies track disease outbreaks.
Challenges like data privacy, interoperability, and adoption resistance are addressed through encryption, standardized protocols, and pilot testing. Future research will explore AI-driven health risk prediction and blockchain-based medical leave verification. By connecting healthcare and education, this system enhances student safety, reduces administrative burdens, and improves communication among stakeholders.},
keywords = {EMIS, EHR, child health monitoring, medical leave certification, HL7, FHIR, real-time health data, school emergency response, data privacy. },
month = {Nov},
}
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Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.