Research Focus

Acute deterioration prediction: Developing predictive models to help analyze electronic health records to better serve hospitalized patients at risk for sepsis or other critical deterioration.

Precision population health: Developing data infrastructure, predictive models and care models to help improve outcomes for behavioral/mental health and asthma by collecting data, identifying at-risk individuals and implementing proactive interventions to prevent emergency department visits and hospitalizations.

Clinician and health system efficiency: Developing HIPAA-compliant infrastructure; regulatory framework for AI models, notifications and education; and AI-based triaging based on patient/family communications to increase efficiency within hospitals and across care networks.

Objectives

To achieve these objectives, we will:

• Create frameworks for ethical AI that promote transparency, equity and fairness.
• Build data infrastructure for internal and external users.
• Incorporate innovative engineering and science into designing new
  AI solutions for clinical and patient-facing settings.
• Demonstrate the capability of using AI in the short-term to improve outcomes for children in and out of the hospital with acute or chronic conditions, clinician and health system efficiency, and prevention and wellness efforts.
• Scale successful strategies across Children’s and beyond through academic and industry collaborations.