Spark NLP in Action: Improving Patient Flow Forecasting

Kaiser Permanente Case Study

Kaiser Permanente is one of the USA’s largest health plans, serving 12.3 million members via 39 hospitals and over 217,000 employees.

This case study shows how the company leveraged John Snow Labs’ AI Platform (for model training, deployment, and monitoring) and Spark NLP (for extracting key features from EMR notes) to optimize hospital patient flow models.

The solution enabled real-time decision-making and strategic planning, by predicting:

  • Bed demand
  • Safe staffing levels
  • Hospital gridlock
About Kaiser Permanente

Kaiser Permanente exists to provide high-quality, affordable health care services and to improve the health of our members and the communities they serve.

They are trusted partners in total health, collaborating with people to help them thrive and creating communities that are among the healthiest in the nation.

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Hear from Kaiser Permanente

Kaiser Permanente uses Spark NLP to integrate domain-specific NLP as part of a scalable, performant, measurable, and reproducible ML pipeline and improve the accuracy of forecasting the demand for hospital beds.

Executive Director, Analytics FoundationKaiser Permanente