Public Health

Infectious Diseases

Hospital-based Medicine

Epidemiology & Health Services Pathway

Digital Therapeutics Pathway

Community Pediatrics

Telemedicine/EHR/Medical Informatics

Health Services Research

Health Equity/Social Determinants of Health

General Pediatrics

Emergency Medicine

Cross-Disciplinary Pathway

Critical Care

Clinical Research Pathway

Advocacy Pathway

Academic and Research Skills

Using Moneyball Approaches to Address Equity in Child Health

Chair(s)

• 

  Jeffrey Yaeger, MD, MPH

  Assistant Professor
  Pediatrics
  University of Rochester School of Medicine and Dentistry
  Rochester, New York, United States

Seasoned baseball scouts are experts at identifying talented prospects just as experienced clinicians are experts at diagnosing and managing patients’ medical conditions. Although both scouts and clinicians may be correct most of the time, mistakes can be costly, leading to millions of dollars of losses for baseball franchises, and potentially preventable morbidity in the health care setting. Over the last 15 years, baseball executives have sought to guard against such mistakes by largely embracing the use of advanced analytic tools to identify those baseball prospects with the most potential for success. Termed “Moneyball,” and immortalized in a film of the same name, advanced analytics have revolutionized the sport as athletes are now throwing harder and hitting more home runs than ever before.

Health care, on the other hand, has lagged behind. Although we know that health outcomes are largely influenced by behaviors, social circumstances, and physical surroundings, this information is rarely collected and used in diagnosis or medical decision-making. Tools to collect and methods to analyze this important information have not yet been robustly developed, nor adequately applied in healthcare settings to improve outcomes. These skills are needed now more than ever, as pediatricians strive to provide care that is equitable and support patients and families affected by systemic racism, other social injustices, and the COVID-19 pandemic.

But this is about to change. With the use of advanced technologies such as geospatial analysis, machine learning, and natural language processing, we are now developing tools to not only improve the identification of clinical, but also social and environmental risk factors, and to incorporate this information into clinical decision-making at the point-of-care. Such methods are simultaneously helping to build situational awareness capabilities, critical in the midst of the COVID-19 pandemic. These methods, when fully implemented, represent important strategies of enhancing delivery of high-value, equity-driven care. Indeed, clinicians, researchers, communities, and health systems that can harness the vast quantities of clinical, community, education, and government data will be well-positioned to improve child health outcomes, mitigate disparities, optimize decisions, and enhance the value of care. With its focus on utilizing cutting-edge technologies to develop data-driven improvement in outcomes across multiple disciplines, this presentation aligns well with the PAS Meeting’s Strategic Plan.

The speakers will describe examples of research programs and quality improvement initiatives that have capitalized on each of these three advanced analytic methods. They will discuss how such approaches can be applied across disciplines and sub-specialties, using the fields of public health, general pediatrics, emergency medicine, and hospital medicine as examples. PAS attendees across a multitude of disciplines will be interested in this presentation as it is relevant for those interested in the Digital Therapeutics, Clinical Research, and Advocacy Pathways.

Using the revolution of baseball analytics as an analogy, the first speaker will set the stage, describing the gap between current analytic capabilities and how pediatricians currently make clinical decisions. All speakers will then focus their discussion on a description of a particular methodology (geospatial analysis, machine learning, and natural language processing), their specific motivation, challenges encountered, how barriers were overcome, and outcomes achieved. They will also discuss how principles can be applied across disciplines and future implications of their work, including challenges and opportunities, specifically as related to equity in care and development of population health situational awareness capabilities. If able, the speakers plan to use a polling system throughout the presentations to more fully inform the depth of each presentation and engage participants. A 20-minute Q&A session will follow the three presentations, allowing audience members to interact with the speakers. Audience members will leave the session with a clear understanding of the capabilities of these technologies and how they might be able to implement them with their own work at their institution.

Presentations:

• 1:00 PM - 1:15 PM US CT

  What is Moneyball and What Does It Have To Do With Kids?
  

  Presenter: Jeffrey P. Yaeger, MD, MPH – University of Rochester School of Medicine and Dentistry
• 1:15 PM - 1:40 PM US CT

  Using Geospatial Analytics to Inform Equity-Oriented Community Health Initiatives and Build Population Situational Awareness
  

  Presenter: Andrew F. Beck, MD, MPH – Cincinnati Children's Hospital Medical Center
• 1:40 PM - 2:05 PM US CT

  Use of Machine Learning Techniques to Identify Low-Risk Patients in the Emergency Department
  

  Presenter: Sriram Ramgopal, MD – Ann and Robert H Lurie Children's Hospital of Chicago
• 2:05 PM - 2:30 PM US CT

  Use of Natural Language Processing to Enhance Child Health Research
  

  Presenter: Jeffrey P. Yaeger, MD, MPH – University of Rochester School of Medicine and Dentistry
• 2:30 PM - 3:30 PM US CT

  Break
  

  Presenter: Belinda Thomas
• 3:30 PM - 4:00 PM US CT

  Click for Live Q&A: Using Moneyball Approaches to Address Equity in Child Health
  

  Presenter: Belinda Thomas