Epidemic Surge Model Use to Improve Patient, Staff, and System Safety and Resiliency

Project Summary
Broad agreement exists that future epidemics will occur, better preparedness is needed for managing surges,
and much should be learned from the COVID-19 pandemic. The SARS-CoV-2 virus to-date has caused over
46 million infections, 3.25 million hospitalizations, and 745,000 deaths in the U.S. alone, with regional surges of
varied timing, magnitude, and duration profoundly straining healthcare capacity and impacting patient, staff,
and system safety. As with other epidemics, local outbreaks and surges continuously change, often resulting in
crisis management, makeshift rooming, sub-standard personal protection equipment, and rationing of limited
resources. Among other needs, better real-time methods are needed to anticipate hospital, equipment, and
staff capacities and shortages to allow earlier preemptive mitigation (gap).
While analytic models are increasingly used, most are at the more macro policy level rather than facility-spe-
cific operational level (gap), in the latter case with little known about their use in practice, accuracy, decision-
making workflow, adoption, utility, and impact on operations, outcomes, and safety. In our own work, we devel-
oped and widely deployed integrated models that predict facility-specific and unit-specific demand, adapt to
real-time changes in these, and estimate 4-week ahead daily capacity, demand, and shortages (rooms, equip-
ment, staff) within any given facility, downloaded by systems in all 50 states and 91 countries. While use of
systems engineering models is well-accepted in other settings, their use, utility, and impact is significantly un-
der-studied in this important context and healthcare more generally, with potentially important lessons for the
future (gap).
This project thus will take a multi-methods approach to (Aim 1) conduct modeling research to further refine re-
sults to-date, optimize accuracy, and address identified technical needs, (Aim 2) evaluate impacts and accu-
racy of the developed models on improved hospital capacity and safety under a wide range of simulated future
and past surge scenarios, and (Aim 3) maximize future utility by studying how our models were used in prac-
tice during COVID-19, the model adoption process, types of resulting actions, barriers to use, and user percep-
tions of utility, accuracy, and model-based decision-making. The project will be led by an experienced interdis-
ciplinary healthcare modeling team, working closely with varied hospital data sites and an advisory committee
with expertise in patient safety, epidemic response, hospital surges, and modeling. Anticipated results include
(1) validated robust models for preemptively anticipating and responding to care surges, (2) reduced unsafe
hospital crisis management conditions during future epidemics, and (3) improved understanding of how to best
use systems engineering models to address epidemic surges and other important public health and care deliv-
ery problems.

Grant Number: 5R01HS028458-04
NIH Institute/Center: AHRQ
Principal Investigator: JAMES BENNEYAN