Volume 17, Issue 2 (4-2025)
jorar 2025, 17(2): 116-127 |
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Sibevei A, Ebrahimi S, Khazaei M. Addressing Barriers to Open Data Networks in Healthcare: A Systems Approach to Pandemic Response. jorar 2025; 17 (2) :116-127
URL: http://jorar.ir/article-1-1019-en.html
URL: http://jorar.ir/article-1-1019-en.html
Assistant professor, Department of Agricultural Economics, University of Torbat Heydarieh, Razavi Khorasan, Iran
Abstract: (251 Views)
INTRODUCTION: The COVID-19 pandemic has revealed major shortcomings in healthcare data systems worldwide, particularly the need for accessible and transparent data sharing. In Iran, these shortcomings were particularly visible due to the lack of a structured open data network in the healthcare sector. Hence, this study addresses the barriers to open data networks in healthcare.
METHODS: This study used Interpretive Structural Modeling (ISM) supported by MICMAC analysis to examine and prioritize barriers to the establishment and use of open data platforms in the Iranian healthcare system. Data were collected through expert consultations with eight experts in the field of health information and policy.
FINDINGS: The analysis revealed significant barriers to implementation, including lack of government coordination, high startup costs, and inadequate technology infrastructure. For use, the most prominent barriers included the lack of data standards, poor data management, and uncontrolled growth of unstructured data. Many of these barriers were interrelated, and some acted as root causes that hindered systemic progress.
CONCLUSION: Addressing these challenges requires coordinated strategic efforts focused on increasing ICT competencies, upgrading infrastructure, and strengthening institutional support. Establishing a functional open data network is essential to improve public health outcomes and enable faster responses to future health crises in Iran.
METHODS: This study used Interpretive Structural Modeling (ISM) supported by MICMAC analysis to examine and prioritize barriers to the establishment and use of open data platforms in the Iranian healthcare system. Data were collected through expert consultations with eight experts in the field of health information and policy.
FINDINGS: The analysis revealed significant barriers to implementation, including lack of government coordination, high startup costs, and inadequate technology infrastructure. For use, the most prominent barriers included the lack of data standards, poor data management, and uncontrolled growth of unstructured data. Many of these barriers were interrelated, and some acted as root causes that hindered systemic progress.
CONCLUSION: Addressing these challenges requires coordinated strategic efforts focused on increasing ICT competencies, upgrading infrastructure, and strengthening institutional support. Establishing a functional open data network is essential to improve public health outcomes and enable faster responses to future health crises in Iran.
Keywords: Open data, Interpretive Structural Modeling, Healthcare systems, Covid-19, Coronavirus pandemic.
Article Type: Research article |
Subject:
Epidemic in Crisis
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