Welcome to the 2020 ACR® Virtual Annual Conference on Quality and Safety, a robust and interactive learning program where you will discover how better communication can improve safety and optimize patient care in radiology practices!
Access the conference platform here.
*All times listed in the agenda are Eastern Time.
Day 1 | Thursday, October 22
Opening Session
- Welcome and Introductions — Marta Heilbrun, MD & Alexander Towbin, MD
- Radiology Response to Tragedy — Shlomit Goldberg-Stein, MD
10:50–11 AM — Break
11 AM–12 PM
Session 1: Leveraging Electronic Communication Tools
- Using Virtual Collaboration to Co-Produce Change — Hanna Zafar, MD
- Radiology Communication Across Health Systems — Starla Longfellow
- Communicating Studies to Radiologists: Radiologist Workflow Optimization — Ben Strong, MD
- Q&A/Panel Discussion
12–12:15 PM
Break
12:15–12:50 PM
Online Small Group Lunches (moderated)
1–2 PM
Session 2: Interdisciplinary Communication
- Standardizing Care Across Sites — Pam Johnson, MD, FACR
- Communicating Non-critical Findings to Providers and Critical Findings to Radiologists via AI — Melissa Davis, MD
- Structured Reporting to Providers/Benefits of CDEs — Victoria Chernyak, MD, MS
- Q&A/Panel Discussion
2–2:45 PM
Scientific Sessions/Abstract Presentations (concurrent sessions)
- General Improvement Stories/Peer Learning — Moderator: Nelly Tran, MD
- Improving Communication/Patient Experience — Moderator: Nadja Kadom, MD
- Improving Workflow — Moderator: Nina Kottler, MD
- General Improvement Stories/Pandemic Response — Moderator: Matt Davenport, MD
2:45–3 PM
Break
3–4:30 PM
Breakout Sessions (concurrent)
- Getting Started in QI: Boot Camp — Neville Irani, MD & Marta Heilbrun, MD
- Peer Learning — Jenn Broder, MD & Ryan Lee, MBA, MD
- Engaging Technologists in QI — Regan City, PA-C, CPHQ, Susan Reich, MHA, RDMS, CLSSBB & Dot Cordova
- Error Disclosure and Apology — Stephen Brown, MD & Michael Bruno, MD, FACR
4:30–5 PM
Break
5–5:45 PM
Online Cocktail Party
Day 2 | Friday, October 23
Online Small Group Breakfast (moderated)
10–11 AM
Session 3: Communication Within Radiology
- Creating Effective Teams — Bettina Siewert, MD, FACR
- Improving Employee Engagement Through Communication — Paulo Castaneda, CNMT
- Everyday Communication Strategies at Work — Arun Krishnaraj, MD, MPH
- Q&A/Panel Discussion
11 AM–12:00 PM
Session 4: Communication with Patients
- Providing Equitable Patient Care in a Time of Crisis — Lucy Spalluto, MD
- Inviting Our Patients to Receive Care — Jackie P. Hunter, DC, MHA
- Using Electronic Communication Tools to Improve Care for Underserved Populations — Efren Flores, MD
- Q&A/Panel Discussion
12–12:45 PM
Online Small Group Lunches (moderated)
12:45–1 PM
Break
1–2:30 PM
Breakout Sessions
- Optimizing Radiologist/Technologist Interactions — Mindy Gadke & Rachel Smith, MS
- Using Safety Event Reporting to Drive Improvement — Ben Wandtke, MD & Susan Sotardi, MD, MS
- Communication Strategies for Leaders — Hannah Zafar, MD & Matt Davenport, MD
- Meet the ACR Q&S Commission Leader/Talk — Mythreyi Chatfield, PhD & David Larson, MD, MBA
2:30–2:40 PM — Break
2:40–3:10 PM
Closing Session
- Vision for the ACR Q&S Commission — David Larson, MD, MBA
- Closing Remarks and Awards — Marta Heilbrun, MD & Alexander Towbin, MD
Accreditation and Designation Statements
The American College of Radiology® (ACR®) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American College of Radiology designates this live activity for a maximum of 8.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The American College of Radiology (ACR) is approved by the American Registry of Radiologic Technologists (ARRT) as a Recognized Continuing Education Evaluation Mechanism (RCEEM) to sponsor and/or review Continuing Education programs for Radiologic Technologists and Radiation Therapists.
The American College of Radiology (ACR) designates this educational activity as meeting the criteria for up to 9.0 Category A credit hours of the ARRT.
For information about the accreditation of this program, please contact the ACR at [email protected].
TBD
KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams
Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.
Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)
SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion
Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.
- Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
- How to begin using AI
- Looking for increased efficiency in operations and incorporating AI into workflows
Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester
Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School
KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams
Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.
Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)
SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion
Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.
- Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
- How to begin using AI
- Looking for increased efficiency in operations and incorporating AI into workflows
Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester
Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School
KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams
Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.
Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)
SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion
Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.
- Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
- How to begin using AI
- Looking for increased efficiency in operations and incorporating AI into workflows
Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester
Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School
KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams
Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.
Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)
SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion
Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.
- Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
- How to begin using AI
- Looking for increased efficiency in operations and incorporating AI into workflows
Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester
Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School
KEYNOTE SESSION: Challenges and Opportunities in Machine Reading of Radiology Exams
Machine learning methods are enabling computers to interpret unstructured document content and perform real-world tasks. Our keynote will highlight the latest trends in deep learning models that utilize imaging, free text, and structured data to advance early diagnosis, treatment, and disease prevention.
Speaker: Regina Barzilay, PhD, Professor, Department of Electrical Engineering and Computer Science and Member, Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT)
SESSION 1: Current state of AI in Practice: Diverse Perspectives and Panel Discussion
Artificial intelligence in the health care industry is gaining traction. Hear from those with experience using AI in clinical care and hospital operations about the issues that will impact you most.
- Insight from the perspective of the radiologist, informatics/IT department, business leader and patient.
- How to begin using AI
- Looking for increased efficiency in operations and incorporating AI into workflows
Moderator: Amy Kotsenas, MD, ACR DSI Council Steering Committee Liaison and Associate Professor of Radiology at Mayo Clinic, Rochester
Presenters:
Melissa A. Davis, MD, MBA, Section Chief, Emergency Radiology, Yale University
Daniel Blezek, PhD, Associate Consultant, Department of Radiology, Mayo Clinic
David Andrews, Patient Advocate
Christoph Wald, MD, PhD, MBA, FACR, Chairman, Department of Radiology, Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School