AI in Healthcare: Building Trust for Adoption
Artificial intelligence (AI) has the power to revolutionize healthcare, but only if it can gain the trust of providers, patients, and the public. To accelerate adoption and reduce disruption, innovators must change the narrative about the purpose of AI, pay attention to implementation, and assure patients and the public that AI serves their needs.
1. Changing the narrative: AI should be designed to complement human decision-making, not replace it. By relieving providers of rote tasks and enabling them to focus on patients and higher-order tasks, AI can enhance the patient-provider relationship. Some providers are even using AI to improve communication with patients.
2. Paying attention to implementation: Before implementing AI applications, they must be proven to improve outcomes and provide better experiences. Common understanding between payers, health systems, and providers is essential to determine when and how AI should be used, and to identify and mitigate potential side effects. AI-driven programs must be curated by physicians to reduce risks and biases.
3. Assuring patients and the public: Frameworks such as the European Commission’s Ethics Guidelines for Trustworthy AI can guide the development of AI applications that are safe, effective, unbiased, and promote equitable healthcare outcomes. Patients should be informed when automated systems are used and have the ability to opt out when necessary.
Lessons from the past: The slow adoption of electronic health records (EHRs) compared to the rapid acceptance of minimally invasive gallbladder surgery highlights the importance of minimizing switchover disruptions. The implementation of EHRs was costly and required significant changes to workflows, leading to resistance and burnout among providers. On the other hand, the new surgical technique had fewer disruptions and was more readily accepted.
Applying AI in healthcare: Some AI applications, like predicting fall risks in hospitals, can be easily integrated into existing workflows. However, the use of large language models (LLMs) for automated decision-making comes with significant disruptions that threaten to devalue human expertise and eliminate jobs. Hallucinations and the added workload of checking for them create further resistance to change.
In conclusion, building trust in AI is crucial for its adoption in healthcare. By focusing on complementing human decision-making, ensuring proper implementation, and assuring patients and the public of their rights, AI can enhance healthcare outcomes and experiences. How do you feel about AI in healthcare? Share your thoughts and comment below!
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