Healthcare Security Breaches in the United States: Insights and their
Socio-Technical Implications
- URL: http://arxiv.org/abs/2311.03664v1
- Date: Tue, 7 Nov 2023 02:20:31 GMT
- Title: Healthcare Security Breaches in the United States: Insights and their
Socio-Technical Implications
- Authors: Megha M. Moncy and Sadia Afreen and Saptarshi Purkayastha
- Abstract summary: This research examines the pivotal role of human behavior in the realm of healthcare data management.
An in-depth analysis of security breaches in the United States from 2009 to the present elucidates the dominance of human-induced security breaches.
- Score: 1.0704308511937222
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research examines the pivotal role of human behavior in the realm of
healthcare data management, situated at the confluence of technological
advancements and human conduct. An in-depth analysis of security breaches in
the United States from 2009 to the present elucidates the dominance of
human-induced security breaches. While technological weak points are certainly
a concern, our study highlights that a significant proportion of breaches are
precipitated by human errors and practices, thus pinpointing a conspicuous
deficiency in training, awareness, and organizational architecture. In spite of
stringent federal mandates, such as the Health Insurance Portability and
Accountability Act (HIPAA) and the Health Information Technology for Economic
and Clinical Health (HITECH) Act, breaches persist, emphasizing the
indispensable role of human factors within this domain. Such oversights not
only jeopardize patient data confidentiality but also undermine the
foundational trust inherent in the healthcare infrastructure. By probing the
socio-technical facets of healthcare security infringements, this article
advocates for an integrated, dynamic, and holistic approach to healthcare data
security. The findings underscore the imperative of augmenting technological
defenses while concurrently elevating human conduct and institutional ethos,
thereby cultivating a robust and impervious healthcare data management
environment.
Related papers
- Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
Mental illness is a widespread and debilitating condition with substantial societal and personal costs.
Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.
Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
arXiv Detail & Related papers (2025-02-01T15:10:02Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.
In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - Implications of Artificial Intelligence on Health Data Privacy and Confidentiality [0.0]
The rapid integration of artificial intelligence in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency.
However, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance.
This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information.
arXiv Detail & Related papers (2025-01-03T05:17:23Z) - SoK: Security and Privacy Risks of Medical AI [14.592921477833848]
The integration of technology and healthcare has ushered in a new era where software systems, powered by artificial intelligence and machine learning, have become essential components of medical products and services.
This paper explores the security and privacy threats posed by AI/ML applications in healthcare.
arXiv Detail & Related papers (2024-09-11T16:59:58Z) - Securing The Future Of Healthcare: Building A Resilient Defense System For Patient Data Protection [0.0]
The study predicts the severity of healthcare data breaches using a gradientboosting machine learning model.
The findings revealed that hacking and IT incidents are the most common type of breaches in the healthcare industry.
The model evaluation showed that the gradient boosting algorithm performs well.
arXiv Detail & Related papers (2024-07-23T04:25:35Z) - Healthcare Data Governance, Privacy, and Security - A Conceptual Framework [0.4972323953932129]
The abundance of data has transformed the world in every aspect.
Despite all these advances, privacy and security remain critical concerns of the healthcare industry.
We propose a conceptual privacy and security driven healthcare data governance framework.
arXiv Detail & Related papers (2024-03-26T12:29:56Z) - Progression and Challenges of IoT in Healthcare: A Short Review [0.0]
The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future.
Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19.
The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy.
arXiv Detail & Related papers (2023-11-11T08:38:04Z) - FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI.
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
arXiv Detail & Related papers (2023-08-11T10:49:05Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Physical Adversarial Attack meets Computer Vision: A Decade Survey [55.38113802311365]
This paper presents a comprehensive overview of physical adversarial attacks.
We take the first step to systematically evaluate the performance of physical adversarial attacks.
Our proposed evaluation metric, hiPAA, comprises six perspectives.
arXiv Detail & Related papers (2022-09-30T01:59:53Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.