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
