IFAM Webinar: Integrating Clinical Decision Support into Everyday Care

Introduction

• Speaker: Dr. Ethan Molich Howe
• Hosted by: Ron Ackerman, IFAM Director and Senior Associate Dean for Public Health
• Occasion: 25th anniversary of the Public Health Program at the Feinberg School of Medicine
• Title: Integrating Clinical Decision Support into Everyday Care

Speaker Background

• Dr. Ethan Molich Howe
  • Graduate of the Public Health Program in 2008
  • Assistant Professor of Medicine, University of Chicago
  • Lead hospitalist for COVID-19 care
  • Focus: Electronic medical records, health information technology, direct patient care, patient-physician interaction, and quality improvement

Key Topics

Challenges in Clinical Decision Making

• Uncertainty and numerous variables in medicine
• Costs and risks associated with testing
• Limitations of human cognitive capacity
• Impact of biases and heuristics on decision-making:
  • Availability bias
  • Representative bias
  • Anchoring
  • Value-induced bias
• Environmental factors affecting decision-making:
  • High stress and fatigue
  • Limited ability to multitask
  • Distractions and interruptions

Clinical Decision Support Systems (CDSS)

• Definition: Active knowledge systems that help clinicians make better decisions
• Goals:
  • Bridge gap between science and practice
  • Improve decision-making and reduce errors
• Types:
  • Diagnostic vs. Interventional
  • Passive vs. Active
  • Consulting vs. Critiquing
• Knowledge-based systems:
  • Use rules and probabilistic associations
  • Integrate with patient data for case-specific advice

Practical Applications

Implementations at the University of Chicago

• Vanderbilt's ICU Clinical Support: Alerts for sedation targets
• Brigham Women's Longitudinal Medical Record: Focus on medication safety

Case Studies

• Insulin Pen Prescriptions: Improved order accuracy and reduced pharmacy callbacks
• Correctional Insulin Dosing: Changed from formula-based to table-based orders
• Physical Therapy Consultations: Utilized mobility scores to guide consultations
• Cardiac Arrest Prediction (eCART): Uses multiple variables to predict patient deterioration
• Clinical Pathways for COVID-19: Integrated structured care paths for updated guidelines

Strategies for Effective Implementation

• Aligning CDSS with workflow
• Automating processes
• Involving local stakeholders
• Providing training and education
• Addressing alert fatigue by limiting number of alerts

Challenges and Considerations

• Time lag from research to practice (average 17 years)
• Overcoming alert fatigue
• Prioritizing IT development for CDSS tools
• Educating clinicians on new practices and tools

Conclusion

• Clinical Decision Support Systems can enhance patient care and physician decision-making.
• Importance of ongoing updates, education, and managing information overload.

Q&A

• Discussions on strategies for integrating CDSS tools effectively
• Addressing challenges in implementation and education

Closing Remarks

• Appreciation for the presentation and contributions by Dr. Ethan Molich Howe
• Emphasis on continuous improvement and adaptation in clinical practices.