Bridging the trust gap: technology and healthcare

"Healthcare will not benefit from technology until it learns to trust it as much as we trust human expertise. Both are fallible, but our bar is irrationally higher for machines." Dr. Sven Jungmann

Our healthcare systems are facing an existential challenge: the integration of innovative technologies into healthcare is more about trust than the pace of technological progress itself. I've said it before and I'll say it again: healthcare will not benefit from technology until it learns to trust it as much as we trust human expertise. Both are fallible, but our bar is irrationally higher for machines.

Understanding the trust gap

Humans are inherently imperfect, yet we implicitly understand and accept this. When doctors misdiagnose or miss a crucial detail, we generally acknowledge their human limitations. Machines, however, are held to almost irrational standards of accuracy, as if they were immune to error. This difference in expectations creates a trust gap that hinders the adoption of technology in healthcare.

Facing the reality of fallibility

Technology, particularly artificial intelligence (AI) and machine learning, can empower healthcare professionals to deliver more accurate and effective care. However, scepticism remains widespread due to a mix of factors, including fear of job displacement, privacy concerns, and historical biases in algorithms. Both humans and machines make mistakes, and acknowledging this fact should lead us to focus on how they can complement each other. By setting realistic standards for both, we can build a more productive partnership.

Strengths and weaknesses of both

Humans excel at empathy, communication and nuanced decision making informed by social context. Machines, on the other hand, excel at quickly processing large amounts of data, identifying patterns, and making probabilistic recommendations. For example, AI can analyse a digital 'twin' of a patient to detect early signs of disease that doctors might miss due to information overload or cognitive bias. Therefore, combining human expertise with machine efficiency can lead to better diagnostic accuracy and treatment outcomes.

Towards rational trust

Instead of fearing technological advances, we need to embrace and regulate them, recalibrating our trust to be both realistic and fair. Here's a roadmap for how this can happen:

• Education and training: Healthcare professionals need to be educated about the benefits and limitations of technology. Familiarity can help dispel misconceptions and reduce fear.
• Transparent algorithms: AI systems should be transparent and explainable, providing an understandable rationale for their conclusions so that physicians can confidently incorporate them into their practices.
• Ethical oversight: Robust ethical guidelines must ensure that AI doesn't exacerbate existing biases or inequalities in care.
• Gradual integration: Start small by integrating the technology into non-critical or administrative aspects of care, before gradually expanding its role.

Conclusion

The integration of technology into healthcare requires a recalibrated trust - one that recognises the fallibility of both humans and machines, while harnessing the strengths of both. By harnessing combined intelligence, we can improve patient outcomes while freeing healthcare professionals to focus on what matters most: patient care.

If you're intrigued by these ideas, my book explores how AI and digitalisation can make healthcare more humane. It's available in English from Penguin RandomHouse.