Gary E. Weissman

About

Gary E. Weissman, MD, MSHP, is an Assistant Professor of Medicine in the Department of Medicine at the University of Pennsylvania's Perelman School of Medicine. His clinical expertise includes critical care medicine, pulmonary diseases, and general internal medicine. His research interests encompass population health, health policy, and health services research, with a focus on clinical informatics, natural language processing, machine learning, and social network analysis. Dr. Weissman is a senior fellow at the Leonard Davis Institute of Health Economics and the Institute for Biomedical Informatics, and he is a core faculty member at the Palliative and Advanced Illness Research Center. His work involves evaluating and regulating artificial intelligence medical devices for clinical decision support, as well as exploring the application of AI in healthcare settings.

Research topics

• Medicine
• Intensive care medicine
• Emergency medicine
• Computer science
• Internal medicine

Selected publications

• Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

  Annals of Internal Medicine · 2020-04-07 · 319 citations

  article1st author

  BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. OBJECTIVE: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. DESIGN: Monte Carlo simulation instantiation o…

  PublisherDOI

• Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models

  American Journal of Respiratory and Critical Care Medicine · 2021-03-22 · 101 citations

  articleOpen access

  Abstract Rationale Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white pati…

  PublisherOA PDFDOI

• Measuring Goal-Concordant Care Using Electronic Clinical Notes

  JAMA Network Open · 2025-07-03 · 2 citations

  articleOpen access

  Importance: Goal-concordant care (GCC) is recognized as the highest quality of care and most important outcome measure for serious illness research, yet practical methods for measuring it are lacking. Objective: To measure GCC using clinical notes in patients' medical records. Design, Setting, and Participants: This longitudinal cohort study involved a retrospective medical record review in 3 urban hospitals in a single health system. Participants included adults with a hospital encounter of 3 o…

  PublisherOA PDFDOI

• Two Physiologic Latent Classes of Acute Hypoxemic Respiratory Failure in Sepsis Are Distinguished by Lung Mechanics and Gas Exchange

  Critical Care Explorations · 2025-09-22 · 1 citations

  articleOpen access

  OBJECTIVES: Physiologic subtypes of acute hypoxemic respiratory failure (AHRF) may confer a differential response to treatments, particularly therapeutic strategies that are specific to pulmonary organ failure. We sought to identify physiologic latent classes of sepsis-associated AHRF defined by respiratory mechanics, oxygenation, ventilation, and radiographic patterns of lung injury, and to determine the association between class membership and 30-day mortality. DESIGN: We performed latent clas…

  PublisherDOI

• A Normal Forced Vital Capacity Does Not Reliably or Equitably Exclude Restriction

  Annals of the American Thoracic Society · 2025-06-12 · 1 citations

  letterOpen accessSenior author
  PublisherOA PDFDOI

Recent grants

• Using natural language processing and machine learning to identify potentially preventable hospital admissions among outpatients with chronic lung diseases

  NIH · $933k · 2018–2023

Frequent coauthors

• Scott D. Halpern

  University of Pennsylvania

  79 shared

• Rachel Kohn

  University of Pennsylvania

  38 shared

• George L. Anesi

  University of Pennsylvania

  35 shared

• Rebecca A. Hubbard

  32 shared

• Meeta Prasad Kerlin

  University of Pennsylvania

  28 shared

• Jennifer C. Ginestra

  Hospital of the University of Pennsylvania

  23 shared

• Michael O. Harhay

  20 shared

• Katrina Armstrong

  Columbia University

  20 shared

Labs

• Gary E. Weissman LabPI

Similar researchers at University of Pennsylvania

• John Q. Trojanowskih-296
• Craig B. Thompsonh-277
• Daniel J Raderh-236