Issue

Vol. 1 No. 2 (2024)

Date Log

Submitted
June 27, 2026
Published
December 31, 2024

Copyright (c) 2024 Abdu Bakri Hassan Sumayli, Ahmed Mohammed Nasser Someli, Mohammed Ibrahim Ali Hakami, Fatimah Mohammed Ibrahim Hakami, Aisha Ali Ail Hakamy, Moudhi Alsaif, Mashael Waslallah Al-Otibi, Fadwa Mahmoud Kamel Jawda, Tahani Suliman Alzeed, Sara Hamdan Al Fahhad

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Patient-Centered Data Governance and Consent Management: Interdisciplinary Perspectives from Health Informatics and Health Care Security

https://doi.org/10.64483/202412728
Abdu Bakri Hassan Sumayli
Al-Tawal General Hospital,Ministry of Health, Saudi Arabia
Ahmed Mohammed Nasser Someli
Umm Al-Shanoun Primary Health Care Center, Samtah Sector, Jazan Region,Ministry of Health , Saudi Arabia
Mohammed Ibrahim Ali Hakami
Erada Hospital for Mental Health in Jazan, Ministry of Health, Saudi Arabia
Fatimah Mohammed Ibrahim Hakami
Al-Qa'ariyah Primary Health Care Center - Jazan Health Cluster, Ministry of Health, Saudi Arabia
Aisha Ali Ail Hakamy
Jazan - Al-Ardah Hospital, Ministry of Health, Saudi Arabia
Moudhi Alsaif
King Salman Hospital, Riyadh, Ministry of Health, Saudi Arabia
Mashael Waslallah Al-Otibi
Al-Ardah Complex for Mental Health and Addiction – Riyadh, The Third Health,Ministry of Health , Saudi Arabia
Fadwa Mahmoud Kamel Jawda
Erada Complex for Mental Health, Riyadh, Ministry of Health, Saudi Arabia
Tahani Suliman Alzeed
Erada Complex for Mental Health, Ministry of Health, Saudi Arabia
Sara Hamdan Al Fahhad
Eradah Complex for Mental Health, Riyadh,Ministry of Health , Saudi Arabia

Corresponding Author(s) : Abdu Bakri Hassan Sumayli

absumayli@moh.gov.sa

Saudi Journal of Medicine and Public Health, Vol. 1 No. 2 (2024)

Abstract

Background: The digitization of health care has enabled unprecedented collection and sharing of patient data, yet traditional governance models remain institution-centric, often excluding patients from meaningful control over their own information. This challenge extends across all health disciplines, including dentistry, where electronic dental records (EDRs), digital imaging, and intraoral scanning generate vast patient data requiring robust governance frameworks. Aim: This narrative review synthesizes interdisciplinary evidence from health informatics and health care security (2015–2024) to examine patient-centered data governance and consent management, with specific application to dentistry and oral health settings. Methods: A systematic literature search was conducted in PubMed, IEEE Xplore, ACM Digital Library, Scopus, and Web of Science, identifying 85 peer-reviewed articles, of which 40 met inclusion criteria for narrative synthesis. Dentistry-specific literature was additionally sourced from dental informatics databases. Results: Key findings reveal that patient-centered governance requires dynamic, granular consent models supported by blockchain, attribute-based encryption, and user-centric identity management. Security challenges include data breach risks, insider threats, and interoperability gaps. In dentistry, unique considerations include integration of EDRs with general health records, consent for radiographic and photographic data, and patient-controlled sharing with dental laboratories and insurers. Effective frameworks integrate FAIR principles, GDPR compliance, and patient-reported outcome measures. Conclusion: Achieving patient-centered data governance demands interdisciplinary co-design, scalable technical architectures, and regulatory alignment that prioritizes patient autonomy without compromising security. Dentistry-specific implementations must address the bidirectional flow of oral-systemic health data and the unique consent needs of dental practice environments.

Keywords

patient-centered data governance, consent management, health informatics, health care security, patient autonomy, dental informatics, electronic dental records, oral health data governance.
Sumayli, A. B. H., Ahmed Mohammed Nasser Someli, Mohammed Ibrahim Ali Hakami, Fatimah Mohammed Ibrahim Hakami, Aisha Ali Ail Hakamy, Moudhi Alsaif, … Sara Hamdan Al Fahhad. (2024). Patient-Centered Data Governance and Consent Management: Interdisciplinary Perspectives from Health Informatics and Health Care Security. Saudi Journal of Medicine and Public Health, 1(2), 2507–2515. https://doi.org/10.64483/202412728
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Ahamed, J., & Chishti, M. A. (2021). Ontology based semantic interoperability approach in the internet of things for healthcare domain. Journal of Discrete Mathematical Sciences and Cryptography, 24(6), 1727-1738.
  2. Andreotta, A. J., Kirkham, N., & Rizzi, M. (2022). AI, big data, and the future of consent. Ai & Society, 37(4), 1715-1728.
  3. Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016, August). Medrec: Using blockchain for medical data access and permission management. In 2016 2nd international conference on open and big data (OBD) (pp. 25-30). IEEE.
  4. Benchoufi, M., Porcher, R., & Ravaud, P. (2018). Blockchain protocols in clinical trials: Transparency and traceability of consent. F1000Research, 6, 66.
  5. Benchoufi, M., & Ravaud, P. (2017). Blockchain technology for improving clinical research quality. Trials, 18(1), 1-5.
  6. Bialke, M., Geidel, L., Hampf, C., Blumentritt, A., Penndorf, P., Schuldt, R., ... & Hoffmann, W. (2022). A FHIR has been lit on gICS: facilitating the standardised exchange of informed consent in a large network of university medicine. BMC Medical Informatics and Decision Making, 22(1), 335.
  7. Bogaert, P., Verschuuren, M., Van Oyen, H., & Van Oers, H. (2021). Identifying common enablers and barriers in European health information systems. Health Policy, 125(12), 1517-1526.
  8. Boppiniti, S. T. (2023). Data ethics in ai: Addressing challenges in machine learning and data governance for responsible data science. International Scientific Journal for Research, 5(5), 1-29.
  9. Carroll, S. R., Herczog, E., Hudson, M., Russell, K., & Stall, S. (2021). Operationalizing the CARE and FAIR Principles for Indigenous data futures. Scientific data, 8(1), 108.
  10. Carroll, S. R., Rodriguez-Lonebear, D., & Martinez, A. (2019). Indigenous data governance: strategies from United States native nations. Data science journal, 18, 31.
  11. Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. Information and privacy commissioner of Ontario, Canada, 5(2009), 12.
  12. Eom, J., Lee, D. H., & Lee, K. (2016). Patient-controlled attribute-based encryption for secure electronic health records system. Journal of medical systems, 40(12), 253.
  13. Favaretto, M., Shaw, D., De Clercq, E., Joda, T., & Elger, B. S. (2020). Big data and digitalization in dentistry: a systematic review of the ethical issues. International journal of environmental research and public health, 17(7), 2495.
  14. Fricton, J., Chen, H., Shaefer, J. R., Mackman, J., Okeson, J. P., Ohrbach, R., ... & Heir, G. (2022). New curriculum standards for teaching temporomandibular disorders in dental schools: a commentary. The Journal of the American Dental Association, 153(5), 395-398.
  15. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of management annals, 14(2), 627-660.
  16. Greenhalgh, T., Thorne, S., & Malterud, K. (2018). Time to challenge the spurious hierarchy of systematic over narrative reviews?. European journal of clinical investigation, 48(6), e12931.
  17. Hu, V.C., Ferraiolo, D.F., Kuhn, R., Schnitzer, A., Sandlin, K., Miller, R., & Scarfone, K. (2014). Guide to Attribute Based Access Control (ABAC) Definition and Considerations.
  18. Ivanova, J., Grando, A., Murcko, A., Saks, M., Whitfield, M. J., Dye, C., & Chern, D. (2020). Mental health professionals’ perceptions on patients control of data sharing. Health informatics journal, 26(3), 2011-2029.
  19. Javed, I. T., Alharbi, F., Bellaj, B., Margaria, T., Crespi, N., & Qureshi, K. N. (2021, June). Health-ID: A blockchain-based decentralized identity management for remote healthcare. In Healthcare (Vol. 9, No. 6, p. 712). MDPI.
  20. Jha, S., Sural, S., Atluri, V., & Vaidysa, J. (2019). Security Analysis of ABAC under an Administrative Model. IET information security, 13(2), 96–103.
  21. John, M. M., Olsson, H. H., & Bosch, J. (2021). Towards MLOps: A Framework and Maturity Model. Proceedings - 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021, 334–341.
  22. Kalkman, S., Mostert, M., Gerlinger, C., van Delden, J. J., & van Thiel, G. J. (2019). Responsible data sharing in international health research: a systematic review of principles and norms. BMC medical ethics, 20(1), 21.
  23. Kassam, I., Ilkina, D., Kemp, J., Roble, H., Carter-Langford, A., & Shen, N. (2023). Patient perspectives and preferences for consent in the digital health context: state-of-the-art literature review. Journal of medical Internet research, 25, e42507.
  24. Kaye, J. (2012). The tension between data sharing and the protection of privacy in genomics research. Annual review of genomics and human genetics, 13(1), 415-431.
  25. Kurteva, A., Chhetri, T. R., Pandit, H. J., & Fensel, A. (2024). Consent through the lens of semantics: State of the art survey and best practices. Semantic Web, 15(3), 647-673.
  26. Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019). Why digital medicine depends on interoperability. NPJ digital medicine, 2(1), 79.
  27. Mandl, K. D., & Kohane, I. S. (2016). Time for a patient-driven health information economy?. New England Journal of Medicine, 374(3), 205-208.
  28. Mascalzoni, D., Melotti, R., Pattaro, C., Pramstaller, P. P., Gögele, M., De Grandi, A., & Biasiotto, R. (2022). Ten years of dynamic consent in the CHRIS study: informed consent as a dynamic process. European Journal of Human Genetics, 30(12), 1391-1397.
  29. Meslin, E. M., & Schwartz, P. H. (2015). How bioethics principles can aid design of electronic health records to accommodate patient granular control. Journal of general internal medicine, 30(Suppl 1), 3-6.
  30. Pradeep Ghantasala, G. S., Reddy, A. R., & Mohan Krishna Ayyappa, R. (2022). Protecting Patient Data with 2F‐Authentication. Cognitive Intelligence and Big Data in Healthcare, 169-195.
  31. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature medicine, 25(1), 37-43.
  32. Rahman, M., Hasan, M., Rahman, M., & Momotaj, M. (2024). A framework for patient-centric consent management using blockchain smart contracts in pre-dictive analysis for healthcare in-dustry. Int. J. Health Syst. Med. Sci, 3(3), 45-59.
  33. Rocher, L., Hendrickx, J. M., & De Montjoye, Y. A. (2019). Estimating the success of re-identifications in incomplete datasets using generative models. Nature communications, 10(1), 3069.
  34. Schuler Scott, A., Goldsmith, M., & Teare, H. (2018). Wider research applications of dynamic consent. IFIP International Summer School on Privacy and Identity Management, 114-120.
  35. Schwendicke, F. A., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of dental research, 99(7), 769-774.
  36. Schwendicke, F., & Krois, J. (2022). Data dentistry: how data are changing clinical care and research. Journal of dental research, 101(1), 21-29.
  37. Terry, N. P. (2017). Regulatory disruption and arbitrage in health-care data protection. Yale Journal of Health Policy, Law, and Ethics, 17(1), 143–208.
  38. Tokas, S., & Owe, O. (2020, June). A formal framework for consent management. In International Conference on Formal Techniques for Distributed Objects, Components, and Systems (pp. 169-186). Cham: Springer International Publishing.
  39. Tonetti, M. S., Greenwell, H., & Kornman, K. S. (2018). Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. Journal of periodontology, 89, S159-S172.
  40. Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: a systematic literature review. BMC medical informatics and decision making, 23(1), 18.
  41. Wang, M., Zhang, H., Wu, H., Li, G., & Gai, K. (2022, May). Blockchain-based secure medical data management and disease prediction. In Proceedings of the fourth ACM international symposium on blockchain and secure critical infrastructure (pp. 71-82).
  42. Wilbanks, J. T., & Topol, E. J. (2016). Stop the privatization of health data. Nature, 535(7612), 345-348.
  43. Yudkin, J. S. (2023). Advancing patient-centered care: moving from outcome-based to risk factor-based models using the big four risk factors. Revista Panamericana de Salud Pública, 46, e162.
Read More
Author biographies is not available.
Download
PDF
Statistic
Read Counter : 7 Download : 42