Urgent, Don’t Let AI Hijack You! 3 Takeaways to Build a Workforce Skilled in AI

strategic health leadership (SHELDR) applying futuring to create better health, wealth, and resilience for individuals, families, and communities in the future

Strategic Health Leader (SHELDR) Thought Leadership Series

Harnessing Artificial Intelligence or AI in Healthcare Management: Building a Futuristic Workforce Skilled in AI

Key Takeaways

  1. AI technologies, especially clinical decision support algorithms, will help doctors diagnose and treat patients.
  2. To comprehend and act on AI predictions, physicians must be proficient in probability and risk analysis.
  3. To overcome change resistance, medical education must emphasize probabilistic skills, algorithmic prediction evaluation, and AI in patient care.

Introduction to AI

AI in healthcare administration could revolutionize patient care, diagnosis, and therapy. As AI technologies like clinical decision support algorithms become part of medical practice, healthcare personnel must be trained to use them successfully. The strategic health leader must drive this transformative path, overcome barriers, and uncover AI’s benefits for individuals, families, communities, patients, providers, and payers.

healthcare professionals thinking about the future of health and healthcare and AI

Discussion on AI

Clinical decision support (CDS) algorithms can help doctors decide on antibiotics and intricate operations using AI. The achievement of AI’s potential depends on physicians’ ability to understand and act on algorithm forecasts. A New England Journal of Medicine perspective notes that many healthcare practitioners need more skills to comprehend AI’s risk forecasts.

CDS approaches span from risk calculators to AI algorithms that predict clinical uncertainty. These algorithms can forecast untreated infection-related sepsis risk or find life-saving heart disease remedies.

Doctors need more probability and risk adjustment expertise to help AI technology in clinical contexts.

Medical education and clinical training on AI must change to close this gap

  1. Mastering Probability Skills: Medical students should be explicitly taught probabilistic reasoning, uncertainty, and visualization to improve probability thinking. To appropriately assess algorithm and test performance, conduct sensitivity and specificity interpretation.
  2. Critical Analysis of Algorithmic Output: Physicians should be able to assess and use CDS predictions in decision-making critically. Understand the algorithm’s background, limits, and patient-specific factors that may affect projections.
  3. CDS-Applied Learning: Medical education and training should include CDS prediction in real patient scenarios. Medical students and professionals should test algorithms to see how they affect patient care.

Achieve Buy-In on AI 

  1. Awareness: Create awareness of AI’s benefits and offer thorough training programs to equip healthcare personnel with the essential abilities.
  2. Collaboration: Work with AI experts and healthcare professionals to integrate AI technologies into clinical workflows. Work with educational institutions, healthcare providers, and AI experts to create customized training modules.
  3. Demonstrate: To show the benefits of AI in healthcare management, highlight success stories. Show reports and results emphasize strategic health leaders’ importance in driving AI use in healthcare management. 
  4. Seek Skillful Transformation: Promote comprehensive AI training for healthcare workers.
  5. Establish Benchmarks: Set AI proficiency benchmarks for healthcare workers and track progress.

Conclusion

Integrating AI and healthcare administration could improve patient care, diagnostics, and efficiency. A skilled workforce that can interpret and act on AI forecasts can only realize this potential. Strategic health leaders must promote probabilistic reasoning, AI algorithm evaluation, and practical application in medical education.

Addressing change resistance and fostering AI integration will help the healthcare industry achieve an ideal future where AI improves human capabilities and patient outcomes.

Discussion Questions

  1. How can healthcare companies work with AI experts to create professional training programs?
  2. How can AI training be smoothly integrated into medical education and clinical training without disturbing curricula?
  3. How will AI adoption change the strategic health leader’s position, and what skills will they need to lead in this AI-driven healthcare landscape?

About the Author: I am passionate about making health a national strategic imperative, transforming and integrating health and human services sectors to be more responsive, and leveraging the social drivers and determinants of health (SDOH) to create healthier, wealthier, and resilient individuals, families, and communities. I specialize in coaching managers and leaders on initial development, continuously improving, or sustaining their Strategic Health Leadership (SHELDR) competencies to thrive in an era to solve wicked health problems and artificial intelligence (AI).

Visit https://SHELDR.COM or contact me for more BLIP-ZIP SHELDR advice, coaching, and consulting. Check out my publications: Health Systems Thinking:  A Primer and Systems Thinking for Health Organizations, Leadership, and Policy: Think Globally, Act Locally. You can follow his thoughts on LinkedIn and X Twitter: @Doug_Anderson57 and Flipboard E-Mag: Strategic Health Leadership (SHELDR)

References: Goodman, K. E., et al. (2023) Preparing Physicians for the Clinical Algorithm Era. New England Journal of Medicine. doi.org/10.1056/NEJMp2304839.

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