Snowflake: Transforming patient healthcare with Gen AI

Jesse Cugliotta, Healthcare & Life Science at Snowflake, discusses revolutionising healthcare efficiency and improving patient care with Gen AI

Jesse Cugliotta, Global Industry GTM Lead, Healthcare & Life Science at Snowflake spoke exclusively to Healthcare Digital to discuss Gen AI, predictive management and the future of the healthcare sector. 


What are the key focuses for early Gen AI / LLM returns in the healthcare industry? 

“Patients and healthcare regulators will always require a trained and qualified human involved in treating patients. As a result, the early gen AI & large language models (LLMs) actual returns will focus on optimising staff’s time and improving human efficiency. Gen AI / LLMs can be used for more administrative tasks, such as identifying coding gaps, writing up a discharge summary, or suggesting the next best action. Patients will also be able to use chatbots and other AI interfaces to boost efficiency. The impact of these technologies is expected to make a difference in 2024, however, the fundamental patient-healthcare provider experience is not changing.”


Why are healthcare companies bringing Gen AI/LLM models in-house?

“Life sciences and healthcare companies are encouraged to bring gen AI & LLMs in-house, especially with concerns around HIPAA compliance. Although not all use cases need to be HIPAA compliant, the need to protect PII information will lead to investments in data engineering teams and tech platforms, which allow models to be trained on their own data. A deeper ROI will be rewarded for those who pursue this strategy, benefiting from advancing research, precision medicine, improving staff efficiency, and delivering an excellent patient experience.”


How will the shift from reactive to predictive management impact healthcare systems?

“As healthcare systems reach data and analytical maturity, the system will shift from reactive to predictive management. Whilst hospitals are battling inflation with their suppliers and facing a staffing crisis, it’s essential for healthcare systems to become more effective in managing inventory, forecasting demand and staffing their hospitals. As an increasing number of hospitals adopt technology and processes for near real-time retrieval and sharing across their organisation and supplier networks, management will embrace a more predictive management philosophy in order to maintain their operating margins.”


Why is federated learning gaining importance in life sciences and healthcare in 2024?

“Companies often simply do not have enough data as they look to train AI models and leverage predictive analytics. With patients spread across different health systems and countries, it poses challenges for healthcare organisations to make advancements in treating rare diseases. In order to tackle this, life science companies will gravitate towards tech platforms to train models from global data without physically copying and moving data around the globe.”


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Other magazines that may be of interest - Manufacturing Magazine.


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