Emergency Department (ED) overcrowding is a universal
health issue that impairs the public access to emergency care.
The medical society has long understood the root cause of
ED overcrowding, i.e., the prolonged occupation of ED beds
by patients who are admitted and waiting to be transferred
to inpatient beds. This phenomenon is also referred to as ED
blocking. One initiative to alleviate ED blocking is to put in a
request for inpatient beds at triage for patients who are very
likely to be admitted, even before the patients are seen by
physicians. Inpatient beds are scarce and expensive resources.
Hence, such decisions are contingent on the prediction
accuracy of the admission decisions.
In this case study, Dr Zhankun Sun, Assistant Professor in
the Department of Management Sciences and co-authors,
build a classifier to predict the disposition of patients using
manually typed nurse notes collected during triage in a large
teaching hospital.
"Our data analysis on 600,000 electronic health records
shows that the triage notes contain strong predictive
information towards classifying the disposition of patients for
certain medical complaints, such as altered consciousness or
stroke," says Sun.
This improvement could be clinically impactful for certain
patients, especially when the scale of hospital patients is
large. Furthermore, the generated word-topic vectors provide
a bi-clustering interpretation under each topic due to the
orthogonal formulation, which can be beneficial for hospitals
in better understanding the symptoms and reasons behind
patients' visits.
These predictions can potentially be incorporated to early
bed coordination and fast track streaming strategies to
reduce waiting times and alleviate overcrowding in the
ED. However, these triage notes involve high dimensional,
noisy, and sparse text data, which make model-fitting and
interpretation difficult. To address this issue, they propose
a novel semi-orthogonal non-negative matrix factorization
for both continuous and binary predictors to reduce the
dimensionality and derive word topics. The triage notes can
then be interpreted as a non-subtractive linear combination
of orthogonal basis topic vectors.
