Sensitive personal health information, public trust, and biases in algorithmic decision-making: public health concerns in digital wellness
Year: 2024
Type: article
Abstract: Latest correspondence article entitled ‘Gearing up for the era of digital wellness: challenges and opportunities’ explain using AI requires the collection and analysis of vast datasets, which frequently contain sensitive personal health information.1 The sensitivity of health information means that any lapse in security can have severe consequences, not only for individual privacy but also for public trust in health systems. Ensuring that robust data protection mechanisms are in place is critical to maintaining the integrity and confidentiality of personal health information. Public trust is a foundational element of effective digital wellness strategies.2 Without trust, patients may be reluctant to share vital health information, thereby undermining the quality and comprehensiveness of health data. This trust is built on transparency, security, and the ethical use of health information. Healthcare providers and technology companies must demonstrate their commitment to protecting patient data and using it responsibly. Failure to do so can lead to a breakdown in trust, which can have far-reaching implications for public health initiatives and digital wellness programs. Algorithmic decision-making, a cornerstone of modern digital health solutions, introduces another layer of complexity. These algorithms, which can assist in diagnosing conditions, recommending treatments, and predicting health trends, must be designed and implemented with great care. One significant concern is the potential for biases within these algorithms,3,4 which can lead to disparities in health outcomes. Biases can stem from the data used to train the algorithms or from the way the algorithms are designed.5 Addressing these biases is crucial to ensuring equitable and accurate health decisions. The intersection of sensitive health information, public trust, and algorithmic biases presents a unique set of challenges for public health in the digital wellness era. Policymakers, healthcare providers, and technology developers must work collaboratively to address these issues. This involves not only improving data security and transparency but also rigorously testing and validating algorithms to eliminate biases. By doing so, they can help build a more trustworthy and effective digital health ecosystem. In conclusion, as digital wellness continues to evolve, it is imperative to address the public health concerns related to sensitive personal health information, public trust, and algorithmic decision-making biases. Protecting patient data, fostering trust, and ensuring unbiased health algorithms are essential steps toward a healthier, more equitable future. Stakeholders across the health and technology sectors must prioritize these concerns to harness the full potential of digital wellness while safeguarding public health interests. Our gratitude goes to Alfrina Mewengkang and Tellma Mona Tiwa from Universitas Negeri Manado for their assistance in preparing this manuscript. The authors declare no conflict of interest in this paper. The authors declared no funding was received for this paper.
Source: Journal of Public Health
Author: Raup Padillah
Institution: Universitas Pgri Banyuwangi
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4403789614