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Artificial intelligence (AI) has rapidly transformed healthcare, introducing advanced tools that optimize workflows, enhance diagnostic accuracy, and support clinical decision-making. While numerous studies have highlighted the potential benefits of AI in improving healthcare delivery, there remains a growing need to understand its impact on healthcare professionals’ job satisfaction and work quality. This study aimed to explore the positive and negative impacts of AI systems on the job quality and satisfaction of healthcare professionals, focusing on both individual well-being and organizational effectiveness. A systematic review was conducted following PRISMA guidelines, with searches performed in PubMed, Scopus, and Web of Science from 1997 to January 2025. The inclusion criteria encompassed empirical studies evaluating AI's impact on job satisfaction and work quality among healthcare professionals, without restrictions on study design. Articles focused solely on technical aspects of AI or unrelated to healthcare were excluded. Of 184 records initially identified, 9 studies met the eligibility criteria, representing a total of 1425 healthcare workers across diverse professions. Positive outcomes included improved diagnostic accuracy, reduced administrative burden, and enhanced work-life balance, particularly through workflow optimization and cognitive support. For instance, documentation time was reduced by up to 40% in complex cases. However, challenges such as deskilling risks, documentation errors, and interoperability issues were identified. Concerns regarding ethical implications, including privacy and data security, were also highlighted. AI systems demonstrate significant potential to enhance job satisfaction and work quality by alleviating administrative burdens and improving clinical decision-making. However, addressing challenges like training gaps, ethical concerns, and system interoperability is crucial for maximizing AI's benefits. Future research should prioritize the inclusion of diverse healthcare settings, particularly in resource-limited contexts, to ensure equitable and effective implementation.

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The Impact of AI Implementation on Healthcare Workers’ Satisfaction and Performance

  • 会议论文

  • 发表日期：

    2026年

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  • Iván Claudio Suazo Galdames,
  • Alain Manuel Chaple Gil

摘要

Artificial intelligence (AI) has rapidly transformed healthcare, introducing advanced tools that optimize workflows, enhance diagnostic accuracy, and support clinical decision-making. While numerous studies have highlighted the potential benefits of AI in improving healthcare delivery, there remains a growing need to understand its impact on healthcare professionals’ job satisfaction and work quality. This study aimed to explore the positive and negative impacts of AI systems on the job quality and satisfaction of healthcare professionals, focusing on both individual well-being and organizational effectiveness. A systematic review was conducted following PRISMA guidelines, with searches performed in PubMed, Scopus, and Web of Science from 1997 to January 2025. The inclusion criteria encompassed empirical studies evaluating AI's impact on job satisfaction and work quality among healthcare professionals, without restrictions on study design. Articles focused solely on technical aspects of AI or unrelated to healthcare were excluded. Of 184 records initially identified, 9 studies met the eligibility criteria, representing a total of 1425 healthcare workers across diverse professions. Positive outcomes included improved diagnostic accuracy, reduced administrative burden, and enhanced work-life balance, particularly through workflow optimization and cognitive support. For instance, documentation time was reduced by up to 40% in complex cases. However, challenges such as deskilling risks, documentation errors, and interoperability issues were identified. Concerns regarding ethical implications, including privacy and data security, were also highlighted. AI systems demonstrate significant potential to enhance job satisfaction and work quality by alleviating administrative burdens and improving clinical decision-making. However, addressing challenges like training gaps, ethical concerns, and system interoperability is crucial for maximizing AI's benefits. Future research should prioritize the inclusion of diverse healthcare settings, particularly in resource-limited contexts, to ensure equitable and effective implementation.