# Hospital adaptation and resiliency for infected and uninfected patients during respiratory viral surge events: from seasonal influenza to COVID-19

> **NIH NIH K23** · UNIVERSITY OF PENNSYLVANIA · 2022 · $164,313

## Abstract

Project Summary / Abstract
My long-term career goal is to become a leading independent investigator developing and evaluating
surveillance, preparedness, and operations response strategies to combat the public health burdens from
respiratory viral surge events. Respiratory viral surge events, in which hospitals face capacity strain from an
influx of infected patients, range from annual respiratory viral seasons dominated by seasonal influenza to
rarer and more severe epidemics such as due to novel influenzas (e.g., H1N1) and coronaviruses (e.g.,
COVID-19, SARS, MERS). Optimizing outcomes for both infected patients and uninfected patients admitted
during viral surges (i.e., “bystander patients”), requires that hospitals display: (1) adaptation—the ability to
improve care and outcomes for infected patients by implementing new care processes based on accumulated
experience, and (2) resiliency—the ability to continue to deliver high quality care to uninfected patients despite
the presence of a surge event. However, it is unknown what enables hospitals to display adaptation and
resiliency, thereby threatening care quality for all patients during viral surges. I am an Instructor of Medicine in
the Division of Pulmonary, Allergy, and Critical Care at the University of Pennsylvania Perelman School of
Medicine. My preparations for this career path include masters degrees in clinical epidemiology and biomedical
ethics, mentored research training resulting in high-impact first-author publications serving as preliminary data,
national invited talks at universities and academic conferences, and clinical work as a pulmonologist and
medical intensivist at a major academic referral center. This grant application seeks to combine my and my
mentorship team’s experience in defining and studying healthcare capacity strain with purposefully selected
career development activities to achieve my complementary training and research goals including
methodologic training in advanced statistical modeling, qualitative research methods, implementation science,
and cost-effectiveness analysis. The specific aims of this grant are to: (1) Quantify adaptation by determining
how hospitals’ cumulative seasonal experiences with influenza affect processes of care and clinical outcomes
among high acuity patients with influenza. (2) Measure resiliency by determining how hospitals’ daily capacity
strain and cumulative experience during respiratory viral surges affect processes of care and clinical outcomes
among bystander patients (i.e., without infection) at risk for acute respiratory failure. (3) Identify organizational
characteristics that may influence how hospitals achieve, or struggle to achieve, adaptation and resiliency in
the face of a respiratory viral surge event. At the end of the proposed K23 award, I expect to understand how
care delivery and outcomes change over the course of a respiratory viral surge event and what organizational
factors may account for observed differen...

## Key facts

- **NIH application ID:** 10348997
- **Project number:** 1K23HL161353-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** George L Anesi
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $164,313
- **Award type:** 1
- **Project period:** 2021-12-15 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10348997

## Citation

> US National Institutes of Health, RePORTER application 10348997, Hospital adaptation and resiliency for infected and uninfected patients during respiratory viral surge events: from seasonal influenza to COVID-19 (1K23HL161353-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10348997. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*