Barrier/health care administration issue

Conduct a literature review. Evaluate qualitative or quantitative articles and find six to eight peer-reviewed primary research
articles that provide critical support for the specific health care administration problem or barrier(Improving Emergency
Department (ED) Patient Flow and Minimizing Patient Wait Times) you want to address in your Business Research Paper.
Use the “Literature Review: Table of Evidence” template to document your detailed assessment of each primary study. This
document helps break down the components of each article to ensure the article meets the requirements for a professional
health care paper. Ensure that each of your articles provides information that meets each criterion listed. Provide a detailed
and clear explanation when describing how each article supports your business research topic (between three and five
sentences).
This assignment uses a “Literature Review: Table of Evidence Grading Form” that corresponds to the “Literature Review:
Table of Evidence” template. Instructors will be using the grading form to assess the assignment and provide useful feedback;
therefore, students should review the grading form prior to beginning the assignment to become familiar with the assignment
criteria and expectations for successful completion of the assignment.

Literature Review: Table of Evidence

Student Name:

2
Describe the barrier/health care administration issue addressed in your Business Research Paper (two or three sentences):

Barrier/Problem: Overcrowding and long wait times at the ED department
Description:
Emergency Hospital Departments are often experiencing prolonged wait times due to overcrowding. This problem acts as a barrier to
better healthcare service delivery. As a result, quality of healthcare has deteriorated. This business research paper reviews literature on
Improving Emergency Department (ED) Patient Flow and Minimizing Patient Wait Times.

3

Criteria Article 1 Article 2 Article 3 Article 4 Article 5
Author,
Journal
(Peer-
Reviewed),
and
Permalink or
Working
Link to
Access Article

Omar M. Ashour&Gül
E. Okudan Kremer
Health Care Manag
Science Journa

Burström, L., Starrin, B.,
Engström, M., &Thulesius,

H.
BMC Health Services
Journal

Kuo, Y., Rado, O.,
Lupia, B., Leung, J. M.,
Y., & Graham
Flexible Services and
Manufacturing Journal,
28(1-2)

Qiu, S., Chinnam, R.
B., Murat, A.,
Batarse, B.,
Neemuchwala, H., &
Jordan, W
. Health Care Journal
Management Science
http://dx.doi.org/10.1
007/s10729-014-
9283-1

Bard, J. F., Shu, Z.,
Morrice, D. J.,
Wang, D. (.Poursani,
R., &Leykum, L.
Health Care
Management
Science Journal

Article Title
and Year
Published

Dynamic patient
grouping and
prioritization: a new
approach
to emergency
department flow
improvement
2016

Waiting management at the
emergency department – a
grounded theory study
2013

Improving the
efficiency of a hospital
emergency department:
A simulation study with
indirectly imputed
service-time
distributions
2016

A cost sensitive
inpatient bed
reservation approach
to reduce emergency
department boarding
times.
2015

Improving patient
flow at a family
health clinic.
2016

Research
Questions
(Qualitative)/
Hypothesis
(Quantitative
), and
Purposes/Aim
of Study

In this paper, the group
technology
(GT) concept is applied
to the triage process to
develop a
dynamic grouping and
prioritization (DGP)
algorithm. This
algorithm identifies
most appropriate
patient groups and

This study was aimed at
exploring what was actually
going on at
an ED.

H0: There is no
difference between the
average net times from
triage to
consultation for
category 4 patients in
2009 and 2010.
H1: The average net
time from triage to
consultation for
category 4 patients in

The objective of this
work is to answer two
specific questions to
streamline the ED
patient flow: (1)What
is the optimal
admission probability
threshold for a patient
beyond which a
ward-bed reservation

The objective of the
study was to obtain a
better understanding
of patient flow
through the clinic
and to investigate
changes to current
scheduling
rules and operating
procedures.

4

prioritizes
them according to
patient- and system-
related information..

2010 is less than the
one in 2009.

should be made? (2)
If
the decision is to
make a ward-bed
reservation for a
patient,
then what is the
optimal reservation
time slot?

Design
(Quantitative,
Qualitative,
or Other)

Qualitative, empirical
design

Quantitative and qualitative Experimental/simulatio
n

experimental Experimental design

Setting/Sampl
e

Clinic/Hospital Mainly quantitative data
from two other
EDs at hospitals with
91.000 yearly visits and a
catchment
population of 600.000,
called ED2 and ED3 with
65.000 yearly visits and a
catchment population of
430.000 was collected from
251,000 people

Clinic/Hospital Not specified Clinic/Hospital

Methods:
Intervention/I
nstruments

Clinical interviews Survey/questionnaire 100 replications of
simulations for
34 days with a warm-
up period of 3 days,
which is equivalent to
100 simulations of
31 days starting from
non-empty systems.

Survey/Questionnaire survey

Analysis An ED is a critical part
of the healthcare
system. Crowding in

To manage non-acceptable
waiting is a driving force
behind much of the staff

The paper analyzed
patient flows in a
hospital ED in Hong

The difficulty in
achieving greater
savings in many

The paper proposed
a novel variant of a
newsvendor

5

an ED is a significant
problem in the U.S. as
well as globally.
Its adverse effects also
impact the other parts
of the healthcare
system. To help remedy
this problem, EDs often
utilize the
ESI triage algorithm to
regulate patients flow
by sorting and
grouping the patients
into distinct and
clinically meaningful
groups. Unfortunately,
the algorithm lacks in
many ways, as
reviewed earlier. This
paper has investigated
these problems
and shortcomings, and
proposed a solution
towards resolving
them.

behavior at an ED.
Waiting management is
done either by increasing
throughput of patient flow
or by changing the waiting
experience.

Kong. It analyzed the
enhancements of the
system changes after
the relocation of the
ED in October 2010.
We also developed a
simulation tool for the
ED to evaluate the
impacts on patient
flows with different
scenarios

of the case study test
settings stems from
the
inability of the ED
LOS models to more
accurately estimate
patient LOS and
should be the target
for future research.
Instead of the
uniform interval
length discretization
for age
and ED arrival time
adopted by our case
study experiments, a
future study could
consider alternative
approaches to
determine
the intervals.

modeling framework
that integrates
patient admission
probability
prediction within a
cost-sensitive bed
reservation system
to improve the
effectiveness of bed
coordination efforts
and
reduce the boarding
times of ED patients
along with the
resulting costs

Key Findings To help remedy this
problem, EDs often
utilize the
ESI triage algorithm to
regulate patients flow
by sorting and
grouping the patients
into distinct and
clinically meaningful
groups. Unfortunately,
the algorithm lacks in

The main driver of the ED
staff in this study was to
reduce non-acceptable
waiting. Signs of non-
acceptable
waiting are physical
densification, contact
seeking, and the emergence
of critical situations. The
staff
reacts with frustration,

Computational results
show that our proposed
solution methodology
is
effective in producing
good estimates of
parameters. With a
good estimate of
parameters, we did a
series of simulation
runs to evaluate

Timely
and accurate
predictions coupled
with a reservation
management
system can help
reduce the ED
boarding times,
improve
patient flows, and
reduces

The results showed
that up to an 8.5 %
reduction in patient
length of stay is
achievable without
noticeably affecting
the
other metrics by
carefully adjusting
appointment times.
Another major

6

many ways, as
reviewed earlier.

shame, and eventually
resignation when they
cannot reduce non-
acceptable waiting.
Waiting management
resolves the problems and
is done either by reducing
actual waiting time by
increasing
throughput of patient flow
through structure pushing
and shuffling around
patients, or by changing the
experience
of waiting by calming
patients and feinting
maneuvers to cover up.

different possible
scenarios.

overcrowding.. finding was that the
providers are the
limiting
factor in improving
patient flow.

Recommenda
tions

The proposed and
tested solution, as
evidenced in the
results, has
contributions to the
dynamic grouping in
general,
and patient
prioritization in applied
triage settings.

To manage non-acceptable
waiting was a driving force
behind much of the staff
behavior at a Swedish ED.
Increasing
throughput of patient flow
and changing the
experience
of waiting were the two
main ways of waiting
management that emerged
from this study.

The simulation model
could
be used by the
operations manager in
the ED to evaluate
many other possible
changes
in the system, such as
layout, capacities and
resources, which can
also throw some light
on key issues of
decision making for the
operations manager.

The paper
recommends a
framework for
streamlining ED
patient flow by
proposing a
cost sensitive ward-
bed reservation
policy based on the
admission
likelihood prediction
of the ED patients.
The policy
identifies an
admission probability
as the threshold for
making
the reservation
decision. It also

With an average
utilization
rate above 90 %
there is little
prospect in
shortening the total
patient time in the
clinic without
reducing the
providers’
average assessment
time.

7

recommends an
optimal bed
reservation time slot
based on a modified
news-vender model
to minimize the cost
of patient waiting and
bed wastage..

Explanation
of How the
Article
Supports
Your
Identified
Barrier or
Issue in
Health Care

It proposes a solution to
the identified barrier-
DGP algorithm based
system

It explores waiting
management at the
emergency department and
further proposes a solution

Proposes ways of
improving the
efficiency of a hospital
emergency department

It proposes a cost
sensitive inpatient
bed reservation
approach to reduce
emergency
department boarding
times.

Proposes a novel
variant of a
newsvendor
Modeling
framework for
Improving patient
flow at a family
health clinic.

Criteria Article 6 Article 7 Article 8
Author,
Journal
(Peer-
Reviewed),
and
Permalink or
Working
Link to
Access Article

Pan, C., Zhang, D.,
Kon, A. W., Mei, Wai,
C. S., Lea, &Ang, W.
B.
Health Care
Management Science J
http://dx.doi.org/10.100
7/s10729-014-9291-1

Article Title
and Year
Published

Patient flow
improvement for an
ophthalmic specialist
outpatient clinic with
aid of discrete event
simulation and design
of experiment.

8

2015

Research
Questions
(Qualitative)/
Hypothesis
(Quantitative
), and
Purposes/Aim
of Study

Propose a discrete
event simulation
(DES) model to
represent the patient
and information
flow in an ophthalmic
SOC system in the
Singapore
National Eye Centre
(SNEC). Different
improvement strategies
to reduce the
turnaround time for
patients in the SOC
were proposed and
evaluated with the aid
of the DES model
and the Design of
Experiment (DOE).

Design
(Quantitative,
Qualitative,
or other)

Quantitative/Experimen
tation

Setting/Sampl
e

2,322 patients

Methods:
Intervention/I
nstruments

Data collection sheet

Analysis Based on the data
collected, the proposed
DES framework was
shown to have the

9

capability of
representing the
performance of the
current SOC system.
A set of improvement
strategies were
proposed and
assessed using the DES
model
Key Findings A significant reduction

in
patient turnaround time
and improvement in
performance
can be achieved by
regulating patient
arrival
Patterns through
scheduling system
changes.

Recommenda
tions

The paper contributes
to healthcare
management and
validates the
application of DES and
DOE in evaluating
the effect of
improvement strategies
on performance.

Explanation
of How the
Article
Supports
Your
Identified
Barrier or

The solutions proposed
in this
study might be
applicable to similar
problems faced by
other ophthalmic
clinics

10

Issue in
Health Care

11

REFERENCES

Ashour, O. M., &Okudan Kremer, G.,E. (2016). Dynamic patient grouping

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