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Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision

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Machine learning (ML) effectively reduced clinical decision support (CDS) alerts for shingles vaccination without impacting order rates. This smart system optimizes alert relevance, improving clinician interaction with essential best practices.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Clinical decision support (CDS) alerts are vital for best practices but often ineffective due to alert fatigue from excessive, inappropriate notifications.
  • Electronic health record (EHR)-integrated machine learning (ML) offers a solution to enhance the signal-to-noise ratio of CDS alerts.

Purpose of the Study:

  • To develop and implement an ML-based system (SmartCDS) to optimize CDS alerts by reducing low-value herpes zoster (shingles) vaccination alerts.
  • To improve the signal of critical alerts by decreasing the volume of less relevant ones.

Main Methods:

  • Developed and deployed the SmartCDS system using personalized user activity profiles to suppress non-interactive shingles vaccination alerts.
  • Extracted EHR data from January 2017 to March 2019, including 327,737 encounters, 780 providers, and 144,438 patients.

Main Results:

  • During a 6-week pilot, SmartCDS suppressed 43.67% of potential shingles alerts.
  • Weekly shingles vaccination orders and user-alert interactions remained stable, with no statistically significant differences compared to the control group (P=.38 and P=.20, respectively).

Conclusions:

  • An automated, ML-based alert suppression system is feasible and effective without negatively impacting overall order rates.
  • This pioneering work demonstrates the potential of ML for encounter-level customization of alert display to maximize CDS effectiveness.