By the end of this unit you should:
Work alone. Sketch your own ethical framework based on this course, your reading and your background. This framework should show how you link or separate different ideas. It may be the same as an existing framework, it may combine different frameworks, or for those creative individuals, it may be entirely novel.
Be prepared to explain your framework to a partner.
Assessment criteria
Read. Hover over the terms in yellow to see more details.
Health information ethics is an interdisciplinary field that examines the ethical implications associated with the collection, storage, and dissemination of health-related data. Given the sensitive nature of medical information, safeguarding patient privacy becomes a paramount concern. This is often guided by legal frameworks like HIPAA and ethical guidelines such as the Belmont Report. Concepts like autonomy and Informed consent are paramount, ensuring that individuals have a say in how their information is used. Various ethical theories, including Kantian deontological ethics and Utilitarianism, can offer different perspectives on these complex issues. The principle of justice, often interpreted through Rawlsian theories, prompts us to consider equitable access to healthcare services.
Listen to your tutor describe his work as a medical consultant to a group of hospitals located in different countries in Asia. Should you have a question, immediately raise the question. Prior to answering, your tutor may encourage you to discuss your views. These incidents mostly relate to ethics in general and not information ethics.
Here are some of the topics that your tutor will discuss.
Work in pairs and discuss the content of this slideshow.
Work in groups. Discuss each ethical dilemma in turn.
One of the key ethical concerns in health informatics is the balance between patient privacy and the benefits of data sharing for research purposes. The utilization of anonymized patient data has the potential to make significant advancements in medical research. However, even when anonymized, there is an inherent risk of patient re-identification, thereby compromising privacy. Additionally, measures to secure this data often conflict with system usability. For instance, robust security protocols like multi-factor authentication safeguard patient information but can render healthcare systems less user-friendly.
As machine learning models increasingly assist in diagnosis and treatment, another ethical quandary arises: the tension between clinical autonomy and algorithmic decision-making. The potential for algorithms to err begs the question of who bears the responsibility in such cases—the healthcare provider or the algorithm's developers. Moreover, proprietary algorithms commonly used in healthcare raise issues of transparency versus intellectual property rights.
Advancements in health informatics can also exacerbate existing healthcare disparities. The adoption of sophisticated informatics tools can increase the cost of healthcare, thereby raising ethical questions about the balance between quality of care and cost efficiency. Similarly, there's a dilemma between technological advancement and equality of access. For example, rural areas and disadvantaged communities may lack the infrastructure to support advanced informatics tools, widening the healthcare gap between different socio-economic groups.
In an increasingly interconnected world, the ethical dimensions of health informatics extend beyond national borders. The sharing of health data across nations can lead to invaluable advancements in global health. However, this global data sharing can conflict with national or regional regulations concerning data protection and patient privacy. Furthermore, as data storage methods evolve over time, issues related to the long-term integrity and usability of healthcare data come into play.
Watch the demonstation. Then, take part in the debate.
Can you:
If you can not, make sure that you do before your next class.
Running count: 80 of 80 concepts covered so far.