Ethical considerations in the use of patient medical records for research

https://doi.org/10.53730/ijhs.v7nS1.15415

Authors

  • Salem Mohammad Raea KSA, National Guard Health Affairs
  • Khaled Minawir Almotairi KSA, National Guard Health Affairs
  • Awadh Mutab Alharbi KSA, National Guard Health Affairs
  • Ghalib Talal Almutairi KSA, National Guard Health Affairs
  • Abdulaziz Mohammed Alhassun KSA, National Guard Health Affairs
  • Khalid Rashad A Binselm KSA, National Guard Health Affairs
  • Reef Ibrahim Mohammed Alruqaie KSA, National Guard Health Affairs
  • Basim Menwer Albalawi KSA, National Guard Health Affairs
  • Abdullah Mohammed Abdullah Alyamani KSA, National Guard Health Affairs
  • Badr Jaza Alamri KSA, National Guard Health Affairs
  • Majed Ayidh Alharbi KSA, National Guard Health Affairs
  • Ahmad Nafal Mohmmed Alsulami KSA, National Guard Health Affairs
  • Mohammed Hassan Albather KSA, National Guard Health Affairs
  • Ibrahim Saleh A Alfawzan KSA, National Guard Health Affairs

Keywords:

Data sharing, communication, patient’s identity, consent, data sovereignty, medical ethics, research compliance

Abstract

Background: The exchange of data allows major advancements in the medical field and contributes to growth in data research in general. However, these practices also raiseethical concerns such as patient’s privacy rights, patients’ self-ownership, and reporting and ownership of data. Aim: The custodianship of data; whether it should be one’s best to share or collaborate depends on the benefits to be accrued from the research, against the rights of the patients. Methods: A literature review of current literature and major ethical codes was also employed to identify the main hurdles to data sharing and cooperation. Results: The study also revealed explicit threats regarding the dangers which privacy and data integrity of patients may face, especially for de-identified data. It also brought into focus issues on consent procedures, conflicts in ownership particularly where collaborations involve several institutions, and procedural lapses on the part and parity of ensuring compliance to ethics and the law. Conclusion: Ethical issues arising from data-sharing and collaboration call for still stronger measures for protective governance as well as equitable share and open policies for patients’ protection as research is enhanced.

Downloads

Download data is not yet available.

References

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. DOI: https://doi.org/10.1038/s41467-019-10933-3

Keyes, K. M., & Westreich, D. (2019). UK Biobank, big data, and the consequences of non-representativeness. Lancet, 393(10178), 1297. DOI: https://doi.org/10.1016/S0140-6736(18)33067-8

Wachter, R. M., & Cassel, C. K. (2020). Sharing health care data with digital giants: Overcoming obstacles and reaping benefits while protecting patients. JAMA: The Journal of the American Medical Association, 323(6), 507-508. DOI: https://doi.org/10.1001/jama.2019.21215

Ballantyne, A. (2019). Adjusting the focus: A public health ethics approach to data research. Bioethics, 33(3), 357-366. DOI: https://doi.org/10.1111/bioe.12551

Sadowski, J., Viljoen, S., & Whittaker, M. (2021). Everyone should decide how their digital data are used, not just tech companies. Nature, 595(7866), 169-171. DOI: https://doi.org/10.1038/d41586-021-01812-3

Sideri, K., Cockbain, J., Van Biesen, W., De Hert, M., Decruyenaere, J., & Sterckx, S. (2022). Digital pills for the remote monitoring of medication intake: A stakeholder analysis and assessment of marketing approval and patent granting policies. Journal of Law and Biosciences, 9(2), Isac029.

Richter, G., Borzikowsky, C., Hoyer, B. F., Laudes, M., & Krawczak, M. (2022). Secondary research use of personal medical data: Patient attitudes towards data donation. BMC Medical Ethics. DOI: https://doi.org/10.1186/s12910-021-00728-x

Hawkins, J., Wellsted, D., Corps, C., Fluck, R., Gair, R., Hall, N., et al. (2022). Measuring patients' experience with renal services in the UK: Development and validation of the Kidney PREM. Nephrology, Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association-European Renal Association, 37(8), 1507-1519. DOI: https://doi.org/10.1093/ndt/gfac030

Morzywolek, P., Steen, J., Vansteelandt, S., Decruyenaere, J., Sterckx, S., & Van Biesen, W. (2022). Timing of dialysis in acute kidney injury using routinely collected data and dynamic treatment regimes. *Crit Care, 26*(1), 365. DOI: https://doi.org/10.1186/s13054-022-04252-1

Fu, E. L., Evans, M., Carrero, J. J., Putter, H., Clase, C. M., Caskey, F. J., et al. (2021). Timing of dialysis initiation to reduce mortality and cardiovascular events in advanced chronic kidney disease: Nationwide cohort study. *BMJ, 375, e066306. DOI: https://doi.org/10.1136/bmj-2021-066306

Van Biesen, W., Van Der Sträeten, C., Sterckx, S., Steen, J., Diependaele, L., & Decruyenaere, J. (2021). The concept of justifiable healthcare and how big data can help us to achieve it. *BMC Medical Informatics and Decision Making, 21(1), 87. DOI: https://doi.org/10.1186/s12911-021-01444-7

McCord, K. A., Al-Shahi Salman, R., Treweek, S., Gardner, H., Strech, D., Whiteley, W., et al. (2018). Routinely collected data for randomized trials: Promises, barriers, and implications. Trials, 19(1), 29. DOI: https://doi.org/10.1186/s13063-017-2394-5

Van Acker, P., Van Biesen, W., Nagler, E. V., Koobasi, M., Veys, N., & Vanmassenhove, J. (2021). Risk prediction models for acute kidney injury in adults: An overview of systematic reviews. PLoS One, 16(4), e0248899. DOI: https://doi.org/10.1371/journal.pone.0248899

Vanmassenhove, J., Steen, J., Vansteelandt, S., Morzywolek, P., Hoste, E., Decruyenaere, J., et al. (2021). The importance of the urinary output criterion for the detection and prognostic meaning of AKI. Scientific Reports, 11*(1), 11089. DOI: https://doi.org/10.1038/s41598-021-90646-0

Sawhney, S., Bell, S., Black, C., Christiansen, C. F., Heide-Jorgensen, U., Jensen, S. K., et al. (2022). Harmonization of epidemiology of acute kidney injury and acute kidney disease produces comparable findings across four geographic populations. Kidney International, 101(6), 1271-1281. DOI: https://doi.org/10.1016/j.kint.2022.02.033

Dehrlein, E. M., Graff, J. S., Harris, J., & Perfetto, E. M. (2019). Patient-community perspectives on real-world evidence: Enhancing engagement, understanding, and trust. atient, 12(4), 375-381. DOI: https://doi.org/10.1007/s40271-019-00356-z

Fiske, A., Degelsegger-Marquez, A., Marsteurer, B., & Prainsack, B. (2022). Value-creation in the domain: A typology of what health data help us do. Biosocieties, 1-25. DOI: https://doi.org/10.1057/s41292-022-00276-6

Prainsack, B., El-Sayed, S., Forgo, N., Szoszkiewicz, L., & Baumer, P. (2022). Data solidarity: Governing health futures. Lancet Digital Health, 4*(11), e773-e774. DOI: https://doi.org/10.1016/S2589-7500(22)00189-3

Hernán, M. A., Hsu, J., & Healy, B. (2019). A second chance to get causal inference right: A classification of data science tasks. CHANCE, 32(1), 42-49. DOI: https://doi.org/10.1080/09332480.2019.1579578

Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., et al. (2020). Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 368, m689. DOI: https://doi.org/10.1136/bmj.m689

Van Biesen, W., Vanmassenhove, J., & Decruyenaere, J. (2020). Prediction of acute kidney injury using artificial intelligence: Are we there yet? DOI: https://doi.org/10.1093/ndt/gfz226

Nephrology, Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association-European Renal Association, 35(2), 204-205.

Naik, N., Hameed, B. M., Shetty, D. K., et al. (2022). Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322 DOI: https://doi.org/10.3389/fsurg.2022.862322

Fletcher, R. R., Nakeshimana, A., & Olubeko, O. (2020). Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health. Frontiers in Artificial Intelligence, 3, 561802. https://doi.org/10.3389/frai.2020.561802 DOI: https://doi.org/10.3389/frai.2020.561802

Chen, Y., Clayton, E. W., Novak, L. L., Anders, S., & Malin, B. (2023). Human-centered design to address biases in artificial intelligence. Journal of Medical Internet Research, 25*, 0. https://doi.org/10.2196/43251 DOI: https://doi.org/10.2196/43251

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford), 2020, 0. https://doi.org/10.1093/database/baaa010 DOI: https://doi.org/10.1093/database/baaa010

Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in healthcare: Accountability and safety. Bulletin of the World Health Organization, 98, 251-256. https://doi.org/10.2471/BLT.19.237487 DOI: https://doi.org/10.2471/BLT.19.237487

1. Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1-9. https://doi.org/10.1186/s12911-020-01332-6 DOI: https://doi.org/10.1186/s12911-020-01332-6

2. Brown, E. (2020). A healthy mistrust: Curbing biometric data misuse in the workplace. Stanford Technology Law Review, 23(1), 252-305.

McBride S. Tietze M. Nursing informatics for the advanced practice nurse: patient safety, quality, outcomes, and interprofessionalism. Springer Publishing Company; 2022. DOI: https://doi.org/10.1891/9780826185266

Published

15-01-2023

How to Cite

Raea, S. M., Almotairi, K. M., Alharbi, A. M., Almutairi, G. T., Alhassun, A. M., Binselm, K. R. A., Alruqaie, R. I. M., Albalawi, B. M., Alyamani, A. M. A., Alamri, B. J., Alharbi, M. A., Alsulami, A. N. M., Albather, M. H., & Alfawzan, I. S. A. (2023). Ethical considerations in the use of patient medical records for research. International Journal of Health Sciences, 7(S1), 3829–3841. https://doi.org/10.53730/ijhs.v7nS1.15415

Issue

Vol. 7 No. S1 (2023)

Section

Peer Review Articles
Copyright & Licensing

Copyright (c) 2023 International journal of health sciences

Creative Commons License

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

Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.

Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)

<< < 1 2