Clinical trials remain a critical ethical issue as the shared data is highly confidential yet required to be gathered for medicine and healthcare industries development. I agree with you that modern nurses’ strategy to ensure their patients about the processes, materials, concerns, and advantages of participating is beneficial. The approach eliminates the risk of information abuse and addresses clients’ ethical rights. Policies of storing the data also changed in today’s clinical trials management and raised new concerns regarding the records (Demirsoy et al., 2021). Indeed, you stated that “technology and systems can have intended and unintended effects, there is a need to regulate access to this information responsibly,” and ethical aspects influenced. Consequently, the alternative perspective can be applied in clinical trials is machine learning for selecting and securing data stored.
Technological development allows the research outcomes and samples’ personal information to be automatically analyzed and registered. Char et al. (2020) state that “ethical evaluations of machine learning algorithms implemented will need to structure the overall problem of evaluating these technologies, especially for a diverse group of stakeholders” (p. 8). Aside from the research, I have experience working on complicated computing systems: in my facility, a technical support professional implemented the algorithm that uses machine learning to detect sensitive data and timely encrypt it. The benefit of such a system is that no individuals access the clinical trials’ data to sort and encrypt it. This strategy decreases the human intervention, and therefore ethical concerns for doctors and nurses switch to the disclosing data aspect (Demirsoy et al., 2021). The evidence based on the clinical trial practices from nursing technology and medical codes perspectives suggests that ethics require encryption and advanced technical support for confidentiality. Although liabilities still exist concerning ethical rights, modern technologies are the perspective direction for further improvement.
Privacy infringement you studied and observed during your practice at a healthcare facility is an urgent issue that must be addressed in the contemporary world of novelties for information storing and gathering. The range of individuals capable of getting access to the patients’ personal and sensitive information broadened as hospitals integrated digital strategies for analyses (Ayatollahi & Shagerdi, 2017). Consequently, the authorization rights providence became rather an ethical concern than a question of personnel’s responsibilities. I agree with your statement that “the unauthorized access cases indicate that the organization has failed to secure the information contained in EHRs.” Applying EHRs in various facilities might also provoke leakages or data theft, even though the networks only have internal access. You were right by mentioning the fines, dismissals, and licenses nullification as the regulative measures to prevent unauthorized access to the patients’ data.
However, recent researches suggest that alternative perspectives can be applied to regulate privacy and address the ethical aspect of data processing. Limited access to the clients’ records exists to address the moral principles of autonomy and beneficence, yet it inhibits the healthcare research that might result in discoveries critical for patients’ health (Williams & Pigeot, 2017). I have experience asking clients to sign an agreement of non-disclosing their data with the range of privacy explained. This documentation can be reviewed by involving the anonymization of records and providing patients with information that accessing some info is beneficial and will be done within limits of non-maleficence. The suggestion based on the evidence, experiences, codes of medical associations, and concerns about privacy infringement is to revise data classification strategies. For instance, information that cannot be anonymized or was protected by a client’s demand can be stored in the limited-access databases. The broader range of records might decrease privacy and be accessed for healthcare research development.
References
Ayatollahi, H., & Shagerdi, G. (2017). Information security risk assessment in hospitals. The Open Medical Informatics Journal, 11, 37-43.
Char, D. S., AbrĂ moff, M. D., & Feudtner, C. (2020). Identifying ethical considerations for machine learning healthcare applications.The American Journal of Bioethics, 20(11), 7-17.
Demirsoy, N., Ă–ztĂĽrk, H., & ErgĂĽn Acar, N. (2021). A cross-sectional study: Patient privacy according to doctors and nurses. Nursing Science Quarterly, 34(2), 114-122.
Williams, G., & Pigeot, I. (2017). Consent and confidentiality in the light of recent demands for data sharing. Biometrical Journal, 59(2), 240-250.