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Area of Science:

  • Oncology
  • Health Services Research
  • Artificial Intelligence in Medicine

Background:

  • Avoidable emergency department (ED) visits and hospitalizations in cancer patients represent a significant burden on patients and healthcare systems.
  • Quality improvement (QI) initiatives are crucial for optimizing cancer care delivery and resource allocation.
  • Reducing acute care utilization (ACU) is a key goal for improving efficiency in oncology practices.

Purpose of the Study:

  • To leverage patient risk-based prescriptive analytics to reduce avoidable acute care use (ACU) in a community oncology practice.
  • To implement and evaluate an augmented intelligence (AI) tool for predicting and preventing harm in cancer patients.
  • To decrease the incidence of cancer-related emergency department visits and hospitalizations managed inappropriately in outpatient settings.

Main Methods:

  • Utilized the Plan-Do-Study-Act (PDSA) methodology for a quality improvement project.
  • Implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool within an Oncology Care Model (OCM) practice.
  • Applied continuous machine learning to predict the risk of preventable harm (avoidable ACU) and generated patient-specific recommendations for nurse implementation.

Main Results:

  • Monthly ED visits per 100 OCM patients decreased by 18% (from 13.7 to 11.5).
  • Quarterly admissions per 100 OCM patients decreased by 13% (from 19.5 to 17.1).
  • The practice achieved potential annual savings of $2.8 million USD by reducing avoidable ACU.

Conclusions:

  • The AI tool empowered nurse case managers to proactively identify and resolve clinical issues, effectively reducing avoidable ACU.
  • Targeting short-term interventions to high-risk patients can lead to improved long-term care and patient outcomes.
  • QI projects integrating predictive modeling, prescriptive analytics, and nurse outreach show promise in reducing acute care utilization in oncology.