“Solving consumer engagement is worth a trillion dollars for our organization,” a health insurance executive told me one day. While he clearly made some sensationalist remarks about the size of the numbers, this has been a common theme of strategic dialogue across healthcare for quite some time. In short, much of the high cost, waste, and poor clinical outcomes of our healthcare are to blame. The woes of the system stem from the lack of individual involvement in the system, or, conversely, the inability of healthcare organizations to engage with patients and members at scale and in a cost-effective manner.
And consumer engagement isn’t the only thing that needs solving. Our nation’s severe clinician burnout, coupled with high overall turnover, is making it difficult for healthcare providers to provide core services. A study shows that the average hospital has turned over 100.5% of its employees over the past five years.
In this context, when it comes to generative AI, healthcare is the industry we see the most likely to have a visible and measurable impact. Traditional methods of training and importing human labor will not be enough to meet the shortage of millions of health workers in the next few years while at the same time increasing the utilization of the already existing health workers. is not. We are excited to help Hippocratic AI apply and execute generative AI for this set of opportunities.
All patients, health care providers and administrative staff interact with responsive, fully situational, and fully competent and charismatic interlocutors to enable each individual to choose the right path or Imagine a world where we can help people do their jobs better (in the form of “always-on triage”, as we’ve discussed in the past). Imagine if the marginal cost of engaging with a patient on an empathetic phone call was around $0.10 per hour, compared to $50+ today. The very nature of generative AI, which is conversational, scalable, and accessible to non-technical users, solves the shortcomings of previous generations of rule-based chatbots and other similar products in bringing these concepts to life. It has potential.
But AI applications in healthcare pose the greatest risks of any industry. AI skeptics may point out that many companies in this space do not focus on responsibility, safety and regulatory compliance. Not to mention the challenge of assembling a multidisciplinary team with deep expertise in LLM development, healthcare delivery, and healthcare management to build a working AI product.
The name Hippocratic AI alone embodies its safety-first ethos (refers to the Hippocratic Oath to which physicians pledge, whose core principles are “do no harm” to patients and the confidentiality of patient medical information). ). They include professional-level certification, his RLHF (Reinforcement Learning from Human Feedback) by a panel of medical experts, and the ability to incorporate “bedside manners” into a non-diagnostic, patient-facing, conversational LLM. I built my own framework. Passing a medical board exam isn’t enough to make sure your model is ready to be deployed in a real-world environment.
We have known CEO Munjal Shah since he invested in his last company in 2017 (this was his third after previously selling an AI company to Google). . The intersection of AI and healthcare. He most recently ran a Medicare brokerage business that included a nationwide call center providing personalized recommendations to seniors based on an individual’s billing history. There he overcame the operational challenges of scaling an empathetic and efficient engagement platform for consumers in a regulated healthcare environment. These competencies enable him and his founding team (consisting of individuals with clinical, LLM development, and healthcare operations experience) to be impactful, accountable, and secure in bringing generative AI products to market. We believe that we can give you an edge in understanding what you need. It’s an honor to support him again.
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