Munjal Shah Advocates for AI-Powered ‘Super-Staffing’ to Address Healthcare Worker Shortages
At the recent HLTH 2023 conference, Munjal Shah, CEO of Hippocratic AI, proposed an intriguing solution for alleviating healthcare staffing troubles: AI-powered “understaffing.”
The annual HLTH event in Las Vegas brings together leaders in healthcare innovation to discuss the latest trends and technologies. This year’s hot topic was how to harness the rapid progress in generative AI to improve healthcare delivery. During the “There’s No ‘AI’ in Team” panel, Munjal Shah highlighted an issue he sees as crucial for applying these new AI capabilities: shortages in nondiagnostic medical staff across nursing, administration, dietetics, and more.
Shah explained that while diagnostic use cases for large language models (LLMs) remain concerning due to the risk of hallucinated information, there are many nondiagnostic applications where AI could safely fill pressing staffing gaps. Hippocratic AI, the company Shah founded, aims to deploy LLMs to provide services like chronic care nursing, appointment coordination, and explaining billing – areas that don’t require diagnosis but are chronically understaffed worldwide.
The panelists agreed that AI alone can’t solve all healthcare’s woes. However, if managed carefully, AI can partner with human medical teams in targeted ways that enhance care and access. For Munjal Shah, staffing shortages represent one such opportunity.
Shah believes this “understaffing” approach can reach patients at a scale impossible for human-only staffing. Since AI virtual assistants don’t get burnt out and can be deployed instantly at a massive scale, combining them with human overseeing could help meet more unmet needs more affordable than ever before. Shah explained, “You can’t call every patient two days after they start every new medication. But at this cost structure, maybe you can.”
To develop safe and reliable healthcare LLMs, Hippocratic AI has employed thousands of medical professionals to train the models and validate their responses. This “centaur” strategy ensures the AI mimics proven best practices. Shah stressed that successful LLM design requires substantial reinforcement learning from human feedback. With each mistake identified, the system learns not to repeat errors.
Vetted through this expert human process, LLMs can be scaled up infinitely cheaply. Early research shows AI often matches or exceeds human capabilities on quality measures and empathy for patient communications. Generative AI’s strengths in conversational reasoning make it ideal for patient-facing interactions.
At HLTH 2023, generative AI proved to be a hot topic. But Shah stood out by grounding the discussion in the real-world impacts LLMs could have right now through virtual understaffing. With the WHO projecting a 10 million healthcare workers shortage by 2030, AI augmentation represents a timely solution. Shah writes, “Generative AI [is] the key to closing this staffing gap and ensuring that more people can receive high-quality, comprehensive care.”
Rather than replace human jobs, understaffing allows AI and people to work together to fill needs at a pace previously impossible. More patients can receive personal chronic care support, clear explanations of billing, prompt answers to questions pre- and post-operation, and specialty services like genetic counseling. Superstaffing aims to democratize access to comprehensive, individualized care.
At the HLTH 2023 conference, Munjal Shah presented a pragmatic vision for generative AI’s potential in healthcare. Through human-AI collaboration and creative approaches like virtual understaffing, leaders like Shah are driving real progress in addressing staff shortages and barriers to care. The future looks bright for AI and humans teaming up to improve health outcomes worldwide.
