Summary
Documentation technology is the foundation of modern healthcare delivery. Convoluted, redundant, and excessive documentation is a pervasive problem that causes inefficiency in all aspects of the industry. At IncludedHealth, we are developing an AI-assisted documentation that summarizes and documents conversations between patients and their care providers. A care provider can push one button and have their entire patient encounter captured in a succinct and standardized format. Upon a pilot launch, the results were staggering. Within 6 months, we demonstrated a 64% reduction in time per encounter! However, despite our promising results, there still remain challenges specific to the demands of the healthcare domain. As our team continues to develop solutions to meet these challenges, we gain even more clarity on what it takes to design a human-backed, AI-powered healthcare system. Takeaways From this session, you can expect to learn the following: Developing AI design in healthcare requires close collaboration between end users and your data science team Piloting GenAI solutions may be more effective than traditional prototyping Trading accuracy for efficiency is a barrier to adopting GenAI tools in healthcare GenAI design in healthcare requires establishing critical boundaries as well as a good understanding of cognitive processing Other factors to consider when designing AI solutions for service-based industries are understanding how training might be impacted, the importance of standardization vs. personalization of data output and the need for more autonomy and control elements due to consequences of unpredictable output errors
Key Insights
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Generative AI can significantly reduce documentation time in healthcare settings.
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Collaboration among designers, data scientists, and clinicians is essential for successful AI tool development.
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Initial excitement about AI tools can fade if the tools don't meet users' evolving expectations.
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It’s crucial to balance efficiency and accuracy when designing AI solutions.
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AI is not a one-size-fits-all solution; its applications must be tailored to specific use cases.
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Understanding user needs is vital for effective AI integration into workflows.
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Feedback mechanisms, such as measuring edit rates, help assess AI tool performance.
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Documentation quality evaluation metrics may need reevaluation when introducing AI tools to users.
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Human oversight is necessary to maintain quality and manage cognitive biases toward AI outputs.
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AI tools should be positioned as augmentative rather than fully autonomous solutions.
Notable Quotes
"AI is a unique design material that designers must ensure is used correctly."
"Our company combines virtual care, clinician-led care navigation, and patient advocacy to enhance healthcare access."
"Healthcare documentation is rife with inefficiencies; a physician can spend up to two hours documenting for every one hour with a patient."
"We've seen documentation time reduced by 64% since implementing our AI tool."
"AI is perfect for addressing documentation as it is tedious and mentally taxing for humans."
"Our design process changed to accommodate the unpredictable nature of AI outputs."
"The highest rated combinations of model parameters were chosen based on human evaluation of AI-generated notes."
"Traditional prototyping methods were ineffective; we learned through incremental pilots instead."
"We're discovering that user sentiment towards AI tools can shift over time, influenced by performance expectations."
"It's critical to reinforce users' understanding that AI tools are augmentative rather than standalone solutions."
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