Volume 17, Issue 2 (4-2025)                   jorar 2025, 17(2): 116-127 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

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
Assistant professor, Department of Agricultural Economics, University of Torbat Heydarieh, Razavi Khorasan, Iran
Abstract:   (252 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.

 
Full-Text [PDF 754 kb]   (35 Downloads)    
Article Type: Research article | Subject: Epidemic in Crisis

References
1. Al-Metwali BZ, Al-Jumaili AA, Al-Alag ZA, Sorofman B. Exploring the acceptance of COVID-19 vaccine among healthcare workers and general population using health belief model. J Eval Clin Pract. 2021;27(5):1112-1122. [DOI:10.1111/jep.13581]
2. Moynihan R, Sanders S, Michaleff ZA, Scott AM, Clark J, To EJ, et al. Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review. BMJ Open. 2021;11(3):e045343. [DOI:10.1136/bmjopen-2020-045343]
3. Pakpour A, Alijanzadeh M, Yahaghi R, Rahmani J, Yazdi N et al. Large-scale dataset on health literacy, sleep hygiene behaviors, and mental well-being in the general population of Qazvin, Iran. Data in Brief. 2023; 48: 109072. [DOI:10.1016/j.dib.2023.109072]
4. Alamo T, Reina D.G, Mammarella M, Abella A. Covid-19: open-data resources for monitoring, modeling, and forecasting the epidemic. Electronics 2020;9: 827 [DOI:10.3390/electronics9050827]
5. Vollset, Stein Emil et al. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. The Lancet, 2020; 396 (10258): 1285-1306 [DOI:10.1016/S0140-6736(20)30677-2]
6. Park S, Ramon G.J. Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap. Government Information Quarterly. 2021; 39(1): 101456 [DOI:10.1016/j.giq.2020.101456]
7. Masoumi H, Farahani B., Shams Aliee F. Systematic and ontology-based approach to interoperable cross-domain open government data services. Transforming Government: People, Process and Policy, 2022; 16(1): 110-127 [DOI:10.1108/TG-08-2021-0132]
8. Yaqoob, I., Salah, K., Jayaraman, R. and Al-Hammadi, Y. Blockchain for healthcare data management: opportunities, challenges, and future recommendations, Neural Computing and Applications, 2020: 1-16
9. Antunes R. S., André da Costa C., Küderle A., Yari I. A., Eskofier B. Federated learning for healthcare: systematic review and architecture proposal, ACM Transactions on Intelligent Systems and Technology (TIST). 2022:13(4): 1-23. [DOI:10.1145/3501813]
10. Krishnamoorthi R., Joshi S., Almarzouki H. Z., et al. A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques. Journal of Healthcare Engineering . 2022;2022:10. [DOI:10.1155/2022/1684017]
11. Tayefi, M. Challenges and Opportunities beyond Structured Data in Analysis of Electronic Health Records. Wiley Interdisciplinary Reviews: Computational Statistics, 2021; 13(6): e1549. [DOI:10.1002/wics.1549]
12. Tagde P. Blockchain and artificial intelligence technology in e-Health, Environmental Science and Pollution Research.2021;28:52810-52831. [DOI:10.1007/s11356-021-16223-0]
13. Rayens E., Norris K. A. Prevalence and healthcare burden of fungal infections in the United States, 2018, in Open forum infectious diseases, Oxford University Press US, 2022;9(1): 593. [DOI:10.1093/ofid/ofab593]
14. Yehudi Y., Hughes-Noehrer L., Goble C., Jay C. COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic, Data & Policy. 2025;7: e4 [DOI:10.1017/dap.2024.79]
15. Santos S. The SARS-CoV-2 test scale-up in the USA: an analysis of the number of tests produced and used over time and their modelled impact on the COVID-19 pandemic, The Lancet Public Health. 2025; 10(1):e47-e57. [DOI:10.1016/S2468-2667(24)00279-2]
16. Copeland K. A., Amsterdam A., Gerker H., Bennett D et al. Why is ECE enrollment so complicated? An analysis of barriers and co-created solutions from the frontlines, Early Childhood Research Quarterly. 2025; 71:12-25 [DOI:10.1016/j.ecresq.2024.11.007]
17. Guan A. Combining mixed methods and community-based participatory research approaches to identify neighborhood-level needs during the COVID-19 pandemic, Journal of Mixed Methods Research, 2025; 19(1):103-117. [DOI:10.1177/15586898231222037]
18. Mian N. D, Glutting J. H. Leaks in the workforce pipeline: understanding barriers to pursuing mental health careers among undergraduate psychology students, Teaching of Psychology. 2025;52(1):59-68. [DOI:10.1177/00986283221141370]
19. United Nation. Guidelines on Open Goverment Data for Citizen Engagement Retrieved. 2013 [Internet] available from:https://publicadministration.desa.un.org/sites/default/files/old-site/OGDCE%20Toolkit%20v1_13-Feb2013.pdf
20. Tauberer J. Open Government Data Definition: The 8 Principles of Open Government Data. Second Edition: 2014. [Internet] Available from:https://opengovdata.org/
21. Gurin J. Open Data now: the secret to hot startups, smart investing, Savvy Marketing, and Fast Innovation-January.2014;7: 2014
22. Zuiderwijk A. Janssen M. The negative effects of open government data-investigating the dark side of open data, in Proceedings of the 15th Annual International Conference on Digital Government Research, 2014: 147-152. [DOI:10.1145/2612733.2612761]
23. Zuiderwijk A, Janssen M. A coordination theory perspective to improve the use of open data in policy-making, in International Conference on Electronic Government, Springer. 2013:38-49. [DOI:10.1007/978-3-642-40358-3_4]
24. Ubaldi B. Open government data: towards empirical analysis of open government data initiatives, 2013.
25. Schubart J. R. Einbinder J. S. Evaluation of a data warehouse in an academic health sciences center, International Journal of Medical Informatics. 2000; 60(3): 319-333 [DOI:10.1016/S1386-5056(00)00126-X]
26. Khan, S. I., Hoque A. S. M. L. Development of national health data warehouse for data mining, Database Systems Journal. 2015;6(1):3-13.
27. Choi I. Y. Development of prostate cancer research database with the clinical data warehouse technology for direct linkage with electronic medical record system, Prostate international, 2013; 1(2):59-64 [DOI:10.12954/PI.12015]
28. Mirani N.,Ayatollahi H., Haghani H. [Examining the obstacles to creating and implementing electronic health records in Iran (Persian)]. Health Management. 2012; 15(50):65-75
29. Abdolhossenzadeh M, Sanayi M., Zolfagharzadeh S. M., [The concept of open Government data policy and explain the advantages and benefits of the different policy fields (Persian)]. Strategic Studies of public policy.2017;7(22):55-74 Available from: http://sspp.iranjournals.ir /article_26097 _071e687ed03883551cb32795ab65407f.pdf.
30. Asadi F., Mastaneh Z. [Challenges of using information technology in hospitals affiliated to shaheed beheshti university of medical sciences (Persian)]. Iranian Journal of Surgery 2012;20(1): Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=262195
31. Alamo T, Reina DG, Mammarella M, and Abella A. Open data resources for fighting covid-19, Electronics 2020; 9 (5): 827 [DOI:10.3390/electronics9050827]
32. Reichman O. J., Jones M. B., Schildhauer M. P. Challenges and opportunities of open data in ecology, Science. 2011; 331(6018): 703-705 [DOI:10.1126/science.1197962]
33. Keen J., Calinescu R., Paige R., Rooksby J. Big data+ politics= open data: The case of health care data in England, Policy & Internet. 2013; 5(2):228-243. [DOI:10.1002/1944-2866.POI330]
34. Hartung C., Lerer A., Anokwa Y., Tseng C., Brunette W., Borriello G. Open data kit: tools to build information services for developing regions, in Proceedings of the 4th ACM/IEEE international conference on information and communication technologies and development. 2010:1-12. [DOI:10.1145/2369220.2369236]
35. Lane J., Gimeno E., Levitskaya E., Zhang Z., Zigoni A. Data inventories for the modern age? Using data science to open government data. Harvard Data Science Review. 2022; 4(2). [DOI:10.1162/99608f92.8a3f2336]
36. Sivarajah U., Kamal M. M., Irani Z., Weerakkody V. Critical analysis of Big Data challenges and analytical methods, Journal of Business Research. 2017;70:263-286 [DOI:10.1016/j.jbusres.2016.08.001]
37. Zuiderwijk A., Janssen M., Choenni S., Meijer R. Design principles for improving the process of publishing open data, Transforming Government: People, Process and Policy, 2014. [DOI:10.1108/TG-07-2013-0024]
38. Armbrust M. A view of cloud computing, Communications of the ACM, 2010;53(4):50-58 [DOI:10.1145/1721654.1721672]
39. Patil DJ. Building data science teams. O'Reilly Media, Inc., First edition. 2011.
40. Mahajan H. B. Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems, Applied Nanoscience 2022: 1-14. [DOI:10.1007/s13204-024-03007-4]
41. Goldbaum S., Mihaly A., Ellison T., Barr E. T., Marron M. High Assurance Software for Financial Regulation and Business Platforms, in International Conference on Verification, Model Checking, and Abstract Interpretation, Springer. 2022: 108-126. [DOI:10.1007/978-3-030-94583-16]
42. Kushnir N., Yatskevich E., Trishkin E., Bobina N. Cloud storage and information protection, The Scientific Heritage. 2022; 83(1):59-61
43. Gupta P., Mehra R. Modeling drivers of machine learning in health care using interpretive structural modeling approach, in Modeling, Simulation and Optimization: Springer. 2021: 453-464. [DOI:10.1007/978-981-15-9829-6_35]
44. Ali C, Abdelsalam A.M. Investigating the drivers and barriers of reverse logistics practices in the pharmaceuticals supply chain: interpretive structural modeling (ISM) approach. Logistics and Supply Chain Management in the Globalized Business Era. IGI Global Scientific Publishing. First Edition. 2022:169-206 [DOI:10.4018/978-1-7998-8709-6.ch008]
45. Gholian-Jouybari F. Developing environmental, social and governance (ESG) strategies on evaluation of municipal waste disposal centers: A case of Mexico, Chemosphere. 2024; 364:142961 [DOI:10.1016/j.chemosphere.2024.142961]
46. Khazaei M, Gholian-Jouybari F, Dolatabadi M.D, Alamdari A.P et al. Renewable energy portfolio in Mexico for Industry 5.0 and SDGs: Hydrogen, wind, or solar. Renewable and Sustainable Energy Reviews. 2025; 213:115420 [DOI:10.1016/j.rser.2025.115420]
47. Ghaedi M., Foukolaei P. Z., Asari F. A., Khazaei M. Pricing electricity from blue hydrogen to mitigate the energy rebound effect: a case study in agriculture and livestock, International Journal of Hydrogen Energy. 2024; 84: 993-1003. [DOI:10.1016/j.ijhydene.2024.08.241]

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 CC BY-NC 4.0 | http://www.journalsystem.ir/demo5

Designed & Developed by : Yektaweb