Clinical Guides: Data Literacy: Ethics

Informed consent refers to the process of providing patients with comprehensive information about how their data will be used and obtaining their explicit agreement. This is particularly important when data is collected for purposes beyond direct care, such as research, public health monitoring, or data sharing across different institutions.

Elements of Informed Consent:
- Transparency: Patients must be fully informed about what data is collected, how it will be used, who will have access to it, and the risks and benefits involved.
- Voluntary Participation: Consent must be given freely without coercion, and patients should be allowed to withdraw their consent at any time.
- Understanding: Healthcare providers must ensure that patients understand the implications of data use, particularly for sensitive information like genetic data or mental health records.

Challenges:

Complex data-sharing ecosystems can make it difficult to ensure patients fully understand how their data might be used. Additionally, data-sharing agreements may involve multiple third parties, complicating the consent process.

Resources:

Office of the Australian Information Commissioner: Health Information
Graves, A., et al. (2019). Consent to data linkage in a large online epidemiological survey of 18-23 year old Australian women in 2012-13. BMC medical research methodology, 19(1), 235. https://doi.org/10.1186/s12874-019-0880-z
Lutomski, J. E., & Manders, P. (2024). From opt-out to opt-in consent for secondary use of medical data and residual biomaterial: An evaluation using the RE-AIM framework. PloS one, 19(3), e0299430. https://doi.org/10.1371/journal.pone.0299430

Confidentiality ensures that patient data is protected from unauthorised access and disclosure. Healthcare providers are ethically and legally obligated to keep patient information private and share it only with authorised individuals or organisations who need it for patient care or legitimate purposes, such as billing or regulatory compliance.

Confidentiality Breaches: This can occur due to unauthorised access (e.g., hacking), improper sharing (e.g., discussing patient details in public), or errors in handling data (e.g., misdirected emails).
Data Safeguards: Measures like encryption, secure access controls, and anonymisation of data are commonly used to protect patient confidentiality. Healthcare organisations must have strong cybersecurity practices and policies in place to prevent data breaches.

Resources:

Latte Course - Privacy Training
Australian Government Department of Health: About health data in Australia
Bonomi, L., et al. (2022). Sharing personal ECG time-series data privately. Journal of the American Medical Informatics Association, 29(7), 1152–1160. https://doi.org/10.1093/jamia/ocac047

Data ownership refers to the legal and ethical issues around who owns and controls healthcare data. Traditionally, healthcare providers or institutions have maintained control over patient data, but there is growing recognition that patients have a right to access and manage their own health information.

Patient Rights: Patients often have the right to view, correct, and request copies of their health data. Laws such as the Australian Privacy Act and My Health Records Act give patients control over access to their digital health records.
Healthcare Providers: While patients have rights to their data, healthcare providers also have responsibilities to ensure the integrity, accuracy, and confidentiality of that data.

Challenges:

In cases where multiple parties are involved in care, or in collaborative research environments, determining who controls data can be complex.
Patients may also face barriers to accessing their data, such as technical challenges or lack of knowledge about their rights.

Resources:

My Health Record: Managing Access
Australian Digital Health Agency: Health Records
Joseph, C. L. M., et al. (2022). Demographic differences in willingness to share electronic health records in the All of Us Research Program. Journal of the American Medical Informatics Association, 29(7), 1271–1278. https://doi.org/10.1093/jamia/ocac055

Bias and equity concerns arise when data collection, analysis, or interpretation leads to unequal healthcare outcomes for certain populations. In healthcare, it is critical that data is used ethically to ensure fairness, avoid perpetuating disparities, and provide equitable access to care for all patients.

Sources of Bias:
- Data Collection: If data is collected only from certain populations, or if certain groups are underrepresented, it can lead to biased analyses. For example, a lack of data from minority populations can result in less effective treatments for those groups.
- Algorithmic Bias: Healthcare algorithms that are trained on biased data may lead to biased outcomes, such as misdiagnosing or under-treating patients from certain demographic groups.
Addressing Bias:
- To promote equity, it is important to ensure diverse data representation and involve stakeholders from all affected communities in research and healthcare data initiatives.
- Transparency in algorithmic design and auditing for biases is essential in ensuring fair outcomes in data-driven healthcare systems.

Resources:

Juhn, Y. J., et al. (2022). Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index. Journal of the American Medical Informatics Association, 29(7), 1142–1151. https://doi.org/10.1093/jamia/ocac052
Saint James Aquino Y. (2023). Making decisions: Bias in artificial intelligence and data‑driven diagnostic tools. Australian Journal of General Practice, 52(7), 439–442. https://doi.org/10.31128/AJGP-12-22-6630