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    Area of Science:

    • Healthcare innovation
    • Health informatics
    • Artificial intelligence in medicine

    Background:

    • The US healthcare system faces critical challenges in quality, affordability, and staffing.
    • Artificial intelligence (AI) presents a transformative opportunity to enhance healthcare safety, effectiveness, and patient-centeredness.
    • Significant investment in healthcare AI underscores its potential to address industry strains.

    Purpose of the Study:

    • To compare the current AI era in healthcare with the electronic health record (EHR) implementation era.
    • To identify key differences and extract actionable lessons from EHR adoption for AI implementation.
    • To guide healthcare organizations in leveraging AI to improve patient outcomes and transform care delivery.

    Main Methods:

    • Comparative analysis of the EHR and AI eras in healthcare.
    • Review of literature and expert experience regarding EHR and AI implementation.
    • Identification of critical success factors for AI adoption in healthcare.

    Main Results:

    • The AI era is distinguished by a different regulatory landscape, workforce capacity limitations, and a faster pace of change compared to the EHR era.
    • Five critical lessons from EHR implementation are crucial for AI deployment: prioritize the human element, establish robust governance, adapt leadership and culture, prepare the workforce, and plan for the long term.
    • Unintended consequences of EHR adoption, such as clinician burnout, offer valuable insights for mitigating risks in AI implementation.

    Conclusions:

    • Applying lessons learned from EHR implementation can help organizations successfully integrate AI.
    • Strategic planning and a focus on human factors are essential for realizing AI's potential in healthcare.
    • Organizational leaders can harness AI to significantly improve patient outcomes and revolutionize healthcare services.