PROJECT SUMMARY/ABSTRACT
Candidate: Sriram Ramgopal, MD is a pediatric emergency medicine physician and early career clinical
investigator focused on improving the diagnosis of community-acquired pneumonia (CAP) among children in
the acute care setting. Dr. Ramgopal’s long-term career goal is to become an independent physician-
investigator focused on improving the health outcomes of children with common infectious emergencies using
predictive analytics, informatics, and implementation science approaches. Research Context/Objective: CAP
is the second most common reason for hospitalization and the second most costly pediatric condition to treat
among children in the United States. The diagnosis of CAP is complicated by a lack of data driven tools and
overlapping presenting features with other common respiratory conditions, resulting in overuse, and
occasionally underuse, of chest radiographs (CXR) and antibiotics. There are a paucity of validated tools to
guide the diagnosis of children with suspected CAP. To address this knowledge and practice gap, the
objectives of this proposal are to develop a clinical prediction model for CAP using electronic health record
(EHR) data, validate a prediction model for CAP, and develop a prototype clinical decision support (CDS)
system for CAP. Specific Aims: 1) Derive and internally validate a prediction model for the diagnosis of CAP
using an EHR registry; 2) determine how a prediction model for pediatric CAP will be implemented as a CDS
system in routine clinical practice using implementation science and human-centered design methods; and 3)
prospectively evaluate the performance of clinical and EHR-based prediction models for CAP. Research Plan:
Dr. Ramgopal will perform a retrospective cohort study using data from a harmonized, high-resolution pediatric
emergency department registry to develop a prediction model to identify the risk of CAP in children. He will
lead co-design sessions to develop a CDS system prototype using implementation science approaches, and
externally validate three clinical prediction rules for CAP within a pediatric emergency department. Career
Development Plan/Environment: Through a combination of local and national didactic activities, experiential
learning, workshops, seminars, professional activities, and coursework, Dr. Ramgopal will acquire expertise in
clinical informatics, predictive analytics, and implementation science methodology. Dr. Ramgopal is supported
by an experienced, multidisciplinary team of NIH-funded mentors, advisors, and collaborators with expertise in
lower respiratory tract infections, prediction modeling, machine learning, and implementation science. Dr.
Ramgopal will meet regularly with his mentors individually, and quarterly as a full committee, to ensure
completion of all research and training benchmarks. The institutional commitment to researchers at
Northwestern University and Lurie Children’s Hospital provide an outstanding infrastructure to enable a
successful award and ensure Dr. Ramgopal’s development into an independent clinician-scientist.