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From compute to care: Lessons learned from deploying an early warning system into clinical practice.

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Summary

Deploying an AI early warning system (CHARTwatch) improved patient monitoring in hospitals. Key factors for success included robust infrastructure, silent testing, and end-user engagement for predicting clinical deterioration.

Keywords:
clinical pathwaydeploymentearly warning systemhealthcaremachine learning

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Area of Science:

  • Healthcare AI
  • Clinical Decision Support Systems
  • Machine Learning in Medicine

Background:

  • Significant gap exists between developing high-performing machine learning (ML) models for healthcare and their actual clinical deployment.
  • Artificial intelligence (AI) holds promise for improving healthcare, necessitating effective deployment strategies.
  • CHARTwatch, an AI-based early warning system, was developed to predict patient risk of clinical deterioration.

Purpose of the Study:

  • To describe the end-to-end infrastructure and process for deploying the CHARTwatch AI system.
  • To identify and address challenges encountered during the deployment of a clinical AI system.
  • To evaluate the success of the deployment using metrics such as model performance, workflow adherence, and infrastructure uptime.

Main Methods:

  • Developed an end-to-end infrastructure for real-time data extraction and risk score communication.
  • Documented technical, process-related, and pandemic-related challenges during deployment.
  • Quantified deployment success through model performance, workflow adherence, and infrastructure uptime.
  • Assessed adherence to Good Machine Learning Practice (GMLP) principles and identified gaps.

Main Results:

  • CHARTwatch demonstrated consistent real-time performance (AUC 0.76) and strong performance on heldout test data (AUC 0.79).
  • The deployment infrastructure maintained >99% uptime in the first year.
  • Deployment adhered to all 10 GMLP guiding principles.
  • Several crucial deployment steps, like silent testing and end-user engagement, require more detailed guidance within GMLP.

Conclusions:

  • Successful deployment of the AI-based early warning system (CHARTwatch) for predicting clinical deterioration in hospitals was achieved.
  • Critical success factors included meticulous data infrastructure, a silent testing phase, continuous monitoring, and strong end-user collaboration.
  • Further evaluation of clinical outcomes and protocol adherence is ongoing.