Sensitive personal health information, public trust, and biases in algorithmic decision-making: public health concerns in digital wellness

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. 1The 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. 5Addressing 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

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Publication Details

Journal
Journal of Public Health
Published
2024-10-26
DOI
https://doi.org/10.1093/pubmed/fdae164
Primary Topic
Behavioral Health and Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

Sensitive personal health information, public trust, and biases in algorithmic decision-making: public health concerns in digital wellness

Raup Padillah
Journal of Public Health
Behavioral Health and Interventions
article

Sensitive personal health information, public trust, and biases in algorithmic decision-making: public health concerns in digital wellness

Raup Padillah
article en

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. 1The 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. 5Addressing 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

Journal of Public HealthVol. 47(1)
Universitas Pgri Banyuwangi (ID)
Peace, Justice and strong institutions
Openalex Percentile: Top 78%
Behavioral Health and Interventions
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Sensitive personal health information, public trust, and biases in algorithmic decision-making: public health concerns in digital wellness — Raup Padillah · Journal of Public Health (2024) | TGRS Research Map | TGRS