Prepare for Patient Surges with AI/ML Models

With the worst still to come, hospitals across the country are gearing up for a surge in patient admissions with the Coronavirus (COVID-19) infection curve ahead of testing and quarantine measures.

How can healthcare providers quickly spin up AI/ML models to prepare? Use cases that could be of particular interest in these times include: 

  1. Medical Supply Inventory: Automated restocking with predictive real-time models 
  2. Patient Admissions: Streamline health insurance card scan, paper medical information and consent forms with document processing
  3. Patient Scheduling: Align communications with urgent care facilities and medical offices arriving by email or fax with document processing

According to the Annals of Internal Medicine as of March 11, 2020

covid- 19 Approximately 95 000 critical care beds, including surgical and specialty unit beds, are available in U.S. hospitals today. Conservative estimates suggest that we may need almost twice this amount should the COVID-19 pandemic resemble the influenza pandemics of 1957 or 1968, especially if it is sustained . Because some patients will be critically ill and need scarce resources, such as extracorporeal membrane oxygenation and ventilators , hospitals must prepare now for how they will triage patients, allocate resources, and staff wards. The Table lists the essential elements of a hospital’s planning process.

The Annals of Internal Medicine and the National Academy of Medicine recommend coming up with a operational task force, communication, staffing, and supply strategy. In addition to these critical management levers, AI/ML models can be quickly spun up with your Information Management team to support the planning efforts. 

Solution Architecture for Provider Healthcare Analytics on GCP

Google Cloud has built in AI/ML models for each use case mentioned above and provides the modern infrastructure to integrate data from various systems into an analytics data warehouse like BigQuery. Once data is integrated, advanced analytics and machine learning can be built and insights and dashboards exposed to business users.  Here’s the high level architecture diagram that shows how the various components in Google Cloud fit together to build a provider analytics solution.

Google Cloud ArchitectureSpringML can quickly spin up a model for each use case but speed to deploy also depends on the data available. 

These times depend on rapid reaction times to save lives. Investing in AI/ML to have the data and insights available for quick decisions is vital. Contact us us today to find out how you can leverage these models for increased automation so healthcare workers and management can focus on care for patients.  

To understand how Patient 360 use cases can transform the patient experience and improve operational efficiency watch the video