NSF CHOT IUCRC PROGRESS REPORT – PROJECT # 2
Identifying Emergency Department Efficiency Frontiers and the Factors Associated
with their Efficiency Performance
Research team
Hyojung Kang, Chris Deflitch, Harriet
Nembhard
Description
To improve the efficiency of care, hospitals
have collected performance measures of
emergency department (ED) processes and
developed initiatives that focus on reducing
waiting times. However, using disaggregated
measures independently imposes several
limitations. In particular, a simple comparison
of the performance metrics between different
systems can lead to biased conclusions.
Data Envelopment Analysis (DEA) can be an
effective tool for overcoming the limitations of
using a single outcome measure to evaluate
ED efficiency among a set of peer groups. Also,
the analysis allows hospitals to identify the
frontier EDs with an efficient system and
benchmark against them.
How is this different than related research?
Many studies have used time intervals (e.g.,
door to doctor, door to bed, and length of stay)
to measure efficiency of EDs. However, the set
of information reflects limited parts of an entire
system. Also, a simple comparison of the
numbers can lead to inaccurate conclusions
when the definitions of the metrics are not the
same and when other significant factors
affecting the efficiency are not considered. By
using a DEA and statistical methods, this study
developed an aggregated ED performance
measure
that
incorporates
multifaceted
aspects of the care system.
Milestones achieved to date
This study developed DEA models that include
three inputs and four outputs. Using the
models and a large dataset including over 300
EDs across the nation, we analyzed scale and
technical efficiencies of the EDs.
The results showed that many EDs operated at
less the optimal level. The decomposition of
between
scale
efficiency
and
technical
efficiency indicated that many EDs may need
to focus their efforts on re-engineering their
processes to utilize key inputs more efficiently
rather than modifying the size of their
operations to improve overall efficiency. Also,
the DEA results pointed out that patient
volume was closely associated with scale and
technical efficiencies.
In the second stage of the study, we
investigated the significant exogenous factors
associated with EDs’ technical efficiency. Using
a multivariate logit model, we identified that
several variables, associated with hospital and
ED characteristics, had a significant influence
on the performance of ED technical efficiency.
This analysis provided insights into effective ED
benchmarking.
*Manuscript under review for publication in
European Journal of Operational Research.
Next Steps
A future study will link the findings from the
current DEA models to quality measures in
order to investigate the relationship between
efficiency and quality.
Potential member benefits
Siemens, as a NSF-CHOT partner, has identified the strategic priority around proliferating best
practices in emergency departments. This project will contribute to increasing knowledge of various
factors contributing to efficiency levels. It will also provide effective strategies for ED managers
and external healthcare organizations to find comparable ED benchmarking and to design EDs with
respect to crucial resources.