NLP & Generative AI: Practical applications for clinicians

In the past year, Generative AI has gained prominence in healthcare, sparking interest in its potential application to enhance operations and patient care. Despite the hype, many healthcare organisations are taking a cautious approach, observing its evolution before considering it a healthcare solution. Most discussions about Generative AI in healthcare emphasise its promise rather than tangible results.

Generative AI involves two steps: identifying relevant historical data through natural language processing and generating summaries from that data. While the former is proven and valuable for clinicians, the latter, creating new content, remains unproven and generates much of the excitement. However, helping clinicians access relevant content is more crucial, offering context for informed decision-making.

Proven impacts of content-gathering tools in clinical settings include identifying behavioural and social gaps, understanding patient history, spotting risks, and recognising care quality issues. These practical use cases surpass the prospective but unproven benefits of Generative AI, such as drafting patient notes, automating chatbot interactions, and suggesting clinical care plans.

Should the healthcare industry shift its focus toward actionable content identification rather than getting caught up in the hype of creating new content with Generative AI? While Generative AI has become part of the culture, delivering tangible value today should take precedence.