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What should healthcare executives and healthcare administration students know about AI?

The sick care workforce, including medical students, trainees, clinicians, front line workers and executives and healthcare administration students are adapting to artificial intelligence and upskilling themselves.

While there are overlapping competencies, some are particularly relevant to health service organization leaders and executives. They are:

  1. Education and awareness
  2. Adoption and Dissemination
  3. Implementation to solve Finanancial, operational and clinical problems
  4. Leading innovators, not managing innovation systems
  5. 3 steps to digital/AI transformation
  6. Gradually increasing their organizational AI maturity models
  7. Leading change
  8. Hiring, developing, rewarding and promoting AI intrapreneurs
  9. Vetting vendors and integrating solutions
  10. Measuring results
  11. Overcoming the barriers to AI dissemination and implementation
  12. Creating a culture of responsible AI

The four areas where health systems are implementing AI solutions right now include: clinician documentation and messaging (100%), point-of-care clinical aids (97%), clinical data analytics and predictions (97%), and business analytics (96%).

Healthcare executives might have an AI strategy but are not deploying them at scale. Most are applying AI to solving operational (staffing) and financial (revenue cycle management) problems, instead of clinical ones because their clinician staff and patients don’t yet trust or have full confidence in AI.

As that trust is earned, maturity models will increase. If they don’t then we will lose the 5th industrial revolution.

Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs on Substack and Editor of Digital Health Entrepreneurship.

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Dr. Anthony Chang’s book is available on Amazon now! 

Intelligence-Based Cardiology and Cardiac Surgery Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (Intelligence-Based Medicine: Subspecialty Series) Data Science, Artificial

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