Big Data and the Smallest People: Leveraging Informatics and Machine Learning to Improve Your Clinical and Research Practice

Friday, April 28, 2023

3:45 PM – 5:15 PM ET

Location: Convention Center: 101

CE: Live CME: 1.50

Leader(s)

Zachary Vesoulis, MD MSCI (he/him/his)

Assistant Professor
Pediatrics
Washington University School of Medicine
St. Louis, MO, United States

Co-Leader(s)

Zachary Vesoulis, MD MSCI (he/him/his)

Assistant Professor
Pediatrics
Washington University School of Medicine
St. Louis, MO, United States
Brynne Sullivan, MD, MSCR (she/her/hers)

Assistant Professor
Pediatrics
University of Virginia School of Medicine
KESWICK, Virginia, United States
Ameena Husain, DO (she/her/hers)

Assistant Professor
University of Utah School of Medicine
Salt Lake City, Utah, United States
Kristyn Beam, MD MPH (she/her/hers)

Neonatologist
Beth Israel Deaconess Medical Center
Boston, Massachusetts, United States
AB

Andrew Beam, PhD (he/him/his)

Harvard
Boston, Massachusetts, United States

Disclosure(s):

Zachary A. Vesoulis, MD MSCI: Edwards LifeSciences (Ongoing) (Products/Services: Research Support including clinical trials & the principal or named investigator); Medtronic (Ongoing) (Products/Services: Consultant, Research Support including clinical trials & the principal or named investigator)

Brynne A. Sullivan, MD, MSCR: I do not have any relevant financial / non-financial relationships with any proprietary interests.

Ameena N. Husain, DO: No financial relationships to disclose

Kristyn Beam, MD MPH: I do not have any relevant financial / non-financial relationships with any proprietary interests.

Andrew Beam, PhD: Generate Biomedicines (Ongoing) (Products/Services: Consultant, Intellectual Property including patent beneficiary and royalties, Ownership Interest including individual stocks, stock options, excluding diversified mutual funds)

Workshop

Description: As NICUs grow, the volume of clinical data expands exponentially, straining providers. Research is revealing complex patterns in vital sign, clinical, and demographic data which alter the risk profile of an infant but are not readily apparent.

Technology is responsible for this problem, but it also can be a solution. In this workshop we will examine emerging technologies to aid in the detection of sepsis and cardiorespiratory decompensation. Topics will include vital sign analytics, EMR-based tools, and machine learning. The target audience for this workshop includes researchers looking to incorporate Big Data and machine learning into projects and clinical providers interested in leveraging the same tools for QI projects, measuring quality and safety metrics, and improving outcomes through clinical decision support tools.

Workshop leaders will not only describe the technology but also provide practical advice on cost effective, real-world implementation. Considering the 2022 decision by FDA to re-classify clinical decision support tools as regulated medical devices, a thorough examination of the implications will be conducted.

The entire workshop will feature interactive components including poll-based discussion and live demonstrations. This workshop brings a novel approach to attendee engagement; prior to the session, all registered attendees will be sent an electronic “welcome packet” containing instructions on how to obtain software needed to perform data analysis (focusing on free and open-source tools) and a sample data set. All attendees are encouraged to bring this laptop to the session so that they can experience hands on learning, rather than merely watching a demonstration.

Learning Objectives:

Understand the scope of “data overload” issues in the NICU
Examine the applications of vital sign analytics, EMR-based tools, and machine learning to clinical and research contexts
See data in action through sample data analytics and interactive case scenarios