In many parts of the world, healthcare is an inefficient, top-heavy industry. In the United States, roughly 35% of the close to $4 trillion spent on healthcare goes to administration. Think about that: Around a trillion dollars is spent on healthcare administration in the United States every year. Large contributors to this broken system are the fragmentation of healthcare providers and the closed, proprietary nature of many applications used in the clinical and financial sides. Furthermore, incentives are aligned in a way that causes poor interoperability and data mobility across systems, institutions, and companies. This results in human workers acting as information routers, manually moving data between systems by selecting, copying, and pasting. This robotic work is tedious and unsatisfying, and it also has a high error rate because people lose their focus and interest when they do monotonous jobs.
Esteban joined NetApp to build a Healthcare AI practice leveraging our full portfolio to help create ML-based solutions that improve patient care, and reduce provider burnout. Esteban has been in the Healthcare IT industry for 15 years, having gone from a being storage geek at various startups to spending 12 years as a healthcare-storage geek at FUJIFILM Medical Systems. He's a visible participant in the AI-in-Healthcare conversation, speaking and writing at length on the subject. He is particularly interested in the translation of Machine Learning research into clinical practice, and the integration of AI tools into existing workflows. He is a competitive powerlifter in the USAPL federation so he will try to sneak early-morning training in wherever he's traveling.
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