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Running Head: EVALUATING PHYSICIANS AND NURSES
PERSPECTIVE ON FACTORS CONTRIBUTING TO
READMISSION OF OPHTHALMIC DISCHARGED PATIENTS
AND POTENTIAL ONLINE FOLLOW-UP STRATEGIES TO
REDUCE THEIR READMISSION IN A GOVERNMENT
HOSPITAL OF RIYADH
[Type text] [Type text] [Type text]
EVALUATING PHYSICIANS AND NURSES PERSPECTIVE
ON FACTORS CONTRIBUTING TO READMISSION OF
OPHTHALMIC DISCHARGED PATIENTS AND POTENTIAL
ONLINE FOLLOW-UP STRATEGIES TO REDUCE THEIR

READMISSION IN A GOVERNMENT HOSPITAL OF
RIYADH
Evaluating Physicians and Nurses Perspective on Factors
Contributing to Readmission of Ophthalmic Discharged Patients
and Potential Online Follow-Up Strategies to Reduce Their
Readmission in a Government Hospital of Riyadh
CHAPTER I
Introduction
Annually, unplanned readmissions cost 15-20 billion dollars,
and preventing such readmission will potentially improve the
quality of life for patients and decrease the financial pain of
health care systems, (Alper,2017). The existing precedence of
many healthcare facilities is to reduce readmissions using post-
discharge follow-up practices. The definitive objective of health
care providers is to deliver high-quality health care services to
patients using transitional care models. The methodologies
encourage the use of appropriate outpatient follow-up
appointments implemented through Medicare incentives to
promote the reduction in hospital readmissions (Adib-
Hajbaghery, Maghaminejad & Abbasi, 2013). Comment by
Editor: Setting of margins required
Many of the researchers analyzing the outcome of follow-up on
outpatients in reducing hospital readmissions majors on
particular illnesses in a state; hence it is shallow. The review
indicates that outpatient follow-ups decrease sickle cell anemia,
pediatric asthma, and heart failure patient readmission (Hasan et
al., 2010; Alper, 2017). However, there is an indication of
mixed results obtained from examinations on hospitalized
individuals. One study carried out by the Medicare Payment
Advisory Commission demonstrated that there is no relationship
between the timing of outpatient follow-up, and 30-day
readmission rate in discharged patients from medical facilities
(Ferrandino et al., 2017).
Statement of the Problem
According to Ferrandino et al. (2017), the readmission of

roughly a half of the Medicaid receivers countrywide is within
30 days of discharge, and they fail to get a follow-up on
outpatient before readmission. Equally, patients readmitted for
chronic illnesses recorded a decrease in outpatient follow-up. It
implies that the use of timely follow-ups to decrease the rates of
readmissions is extensive. According to Jencks et al. (2009),
13,065,937 patients from the Medicare program “fee-for-
service” are enrolled in total and from October 1st, 2003 to
September 30th, 2004; 4926 were discharged. Within 30 days
19.6% of the patients were readmitted, 34.0% within 90 days,
and 56.1% within 365 days.The analysis of information aims to
draw the relationship between immediate outpatient follow-up
through possibly an online-based system and probability of
readmission. As discussed by Adib-Hajbaghery et al. (2013),
only one study used an online-based post-discharge follow-up
method. This web-based system enabled patients to
communicate with nurses and record parameters related to their
conditions. Using such a system appeared to be more effective
in reducing the readmission rate; in the stated intervention
group, only 8.3% were readmitted whereas 41.6% were admitted
in other groups using the conventional follow-up method.
This present work aims to establish the true benefit of using an
online-based follow-up method to be able to develop the online
system in the future to reduce readmission rates. For the
purpose of this research, hospital readmission is defined as the
total number of patients who were discharged from the hospital
and readmitted within 365 days from the initial discharge.
An Internet-based follow-up approach provides an innovative
revenue to provide patients' direct contact to their healthcare
providers. Such systems can offer a framework for chronic
medical management that simplifies patient-physician
communication, personalization, and education. A software-
based system can be easily developed and adapted along with
the appropriate workflow guideline, and it only requires access
to the Internet, (Kashem et al., 2008)

CHAPTER II
Review of Literature
Definition
Hospital readmission is defined as the return of the same patient
to the same hospital for the same condition within a 30-day
period. Unplanned readmissions are indicative of inadequate
health care outcomes; factors that precipitate high readmission
rates emanate partly from offhand practices that exist within
healthcare facilities (Gruneir et al., 2017). Hospital
readmissions after 30 days have proven to be costly not only to
patients but also to the state, and as such, it has attracted public
policy attention. In concurrence with Alper, O'Malley, and
Greenwald (2017) on the cost of readmission, Zuckerman et al.,
(2016) assert that hospital readmissions that occur within 30
days after discharge consume about 17 billion of the entire
affordable care act expenditure.
History
Alper, O'Malley, and Greenwald (2017) also observed that
among Medicare beneficiaries, about 20% of those discharged
from hospitals were readmitted within thirty days. The level of
readmissions as observed thereof impact significantly on the
cost of health-care (Medicare Payment Advisory Commission,
2007). Alper, O'Malley, and Greenwald (2017) also indicate that
the cost of unplanned readmission alone ranges between 15-20
million US dollars annually. To demonstrate how costly the rate
of readmission can get, Adib-Hajbaghery, Maghaminejad,
Abbasi (2013) indicate that in the year 2003, the cost of
readmission for patients who presented with heart failure in Iraq
alone was about 400 billion Rials. Readmissions, therefore,
weigh significantly on health financial allocation in various
countries and constitute a considerable amount of the entire
Medicare budget in the US, (Alper, O'Malley & Greenwald,

2017).
Alper, O’Malley & Greenwald (2017) also observe that
readmissions sometimes arise when patients are discharged
excessively early in cases where continued hospitalization are
necessary. In such instances, healthcare professionals have
often demonstrated a failure by putting little considerations on
the severity of the patients’ conditions and ailments (Ferrandino
et al., 2017). Discharging patients without proper considerations
to the seriousness of their conditions as well as discharging
patients into environments that hardly support their recovery
process invariably set the ground for readmission.
In concurrence with Alper, O'Malley & Greenwald (2017) on
the factors that precipitate the rate of hospital readmissions,
Adib-Hajbaghery, Ahmadinejad and Abbasi (2013) opine that
when the input of caregivers, pharmacists, and insurers are not
sought at the time of discharge, the success of continual care
outside the precincts of healthcare facilities may be impeded.
According to Alper, O'Malley, and Greenwald (2017), some of
the individuals whose input are critical when preparing a
discharge plan for a patient include the insurer, social worker,
physician, occupational therapists among others.
Since the formulation of the hospital readmission reduction
program, the rate of readmission has reduced remarkably.
Zuckerman et al., (2016) indicate that readmission rates for
targeted and no targeted conditions have decreased significantly
from 2012 to 2016. According to Alper, O'Malley, and
Greenwald (2017), hospital readmission risk factors
encapsulates clinical, logistical and demographic factors such as
low health literacy, race, and discharge against medical advice.
Besides, lack of adequate training tools, a factor that can be
classified as a logistical hitch, makes it impossible for
healthcare professionals to isolate patients who would likely be
readmitted in the future so that they could be subjected to
rigorous treatment that could reduce readmission. Lack of
practical training tools, therefore exemplifies a failure, from the
hospital end, that potentially precipitates readmission rate,

(Alper, O'Malley & Greenwald,2017).
Other key contributors to readmissions include complications
from the initial hospital's stay such as infections from initial
surgical procedures, chronic conditions that create frequent
acute events, poorly managed post-acute care one of which is
medication non-compliance, taking the wrong dosage, not
taking the prescribed regimen (Auerbach et al., 2016). Poor
communication between hospital clinicians and primary care
providers often result in poor care coordination and of course,
increases the chances of patients' care readmission. It explains
why the key goal of chronic care management is managing
communication between provider to provider to patients to
improve compliance and reduce the rate of readmission.
The rates of readmissions have, in part, been exacerbated by the
way patient discharge into homes has been handled. For
instance, research has shown that, in some cases, relatively
stable patients have been discharged with little considerations
to the patients’ ability to perform self-care activities, (Alper,
O’Malley & Greenwald, 2017). Failure to explore questions as
to whether patients can or cannot maintain proper diet after
discharge and whether they can maintain follow up calls with
designated providers have heightened the likelihood of
readmissions, (Alper, O’Malley & Greenwald,2017). Similarly,
discharge from one facility to another has, in some cases, been a
recipe for readmission. Often times, a mismatch arises between
a patient’s needs and the services offered in facilities where
they are enrolled subsequent to their discharge from previous
facilities. Failure to reconcile such mismatch invariably leads to
hospital readmissions.
Further, lack of clear discharge summaries that are meant to
provide roadmaps for aftercare providers on ways of continuing
care after a patient is discharged. Failure to review discharge
information by family/ patient caregivers leads to the challenge
of readmission (Alper, O'Malley & Greenwald, 2017). Others
include therapeutic errors, i.e., patients sent home without the
requisite regimen for their complications or ailments.

Significance of the Problem Worldwide
Van Walraven, Bennett, Jennings, Austin, & Forster (2011)
explain that hospital readmission has, for a while, been used as
a measure of the quality of care. Much as this measure is
accurate, it has, nonetheless, been used on a limited scale
because it is only applicable in cases where the proportions of
readmissions that are avoidable are known (Alper, O'Malley &
Greenwald, 2017). Of note, even if readmission can be used as a
metric for gauging the quality of care offered by health
facilities, studies indicate that the process of establishing
avoidable readmissions reliably is yet to be achieved.
Clinician resources investigate and facilitate interventions that
improving healthcare discharge; some of such interventions take
the form of project boost and the care transition programs. The
interventions mentioned thereof provide roadmaps for continued
care even after discharge. The lack of such programs
contributes towards soaring hospital readmission rates
especially if other interventions explored in place of project
boost and the care transition programs prove to be ineffective.
According to Adib-Hajbaghery, Maghaminejad, Abbasi (2013),
the efficiency of nursing care after discharge contributes
significantly in controlling and reducing the rate of readmission
for patients who have suffered from heart failure at a point in
their lives.
Since reimbursement has transitioned from pay per service to
value-based care, chronic care professionals have reworked
their priorities. The Center for Medicare and Medicaid Services
announced that almost all Medicare payment would be value-
based. The announcement presented an enormous challenge for
readmissions (Alper, O'Malley & Greenwald, 2017). The
hospital readmission reduction program anchored in the
affordable care act seeks to control the pronounced readmission
rates for specified conditions some of which include total knee
or hip replacement, heart failure, myocardial infarction among
others.
Interventions explored by the state has presented healthcare

facilities with a different reality; in some cases, patients who
would ordinarily be readmitted have, in some cases, been kept
in observation units and denied readmission, (Zuckerman et
al.,2016). Readmission trends post the ACA have, therefore,
been somewhat consistent with incentives that have been
created to reduce readmissions.
Until recent years, the quality of care derived from health
facilities was pegged on evidence-based clinical care. Boulding
et al. (2011) indicate that patients' perceptions and overall
satisfaction scores of the overall discharge process have been
found to correlate negatively with the hospitals 30-day
readmission rates. Much as the drivers of hospital readmission
are complex, the findings of Boulding et al., 2011 reveal that
patients' perspective on inpatient care and discharge are shaped
significantly by hospital performances, (Boulding et al., 2011).
Patients overall satisfaction was also found to be dependent on
factors such as interactions between hospitals’ staff and the
patients. Some of the answers to the quality of care received by
a patient has been determined by answering questions such as;
how often nurses communicated well with patients? The issues
could also seek to unearth whether patients receive help quickly
from hospital staff when they ask for assistance.
Administrators at the hospitals have learned that higher
readmission rates are consistent with low patient satisfaction.
To curb the challenge of patient satisfaction; hospitals have
instituted a raft of, measures to reduce patient readmission.
Bradley et. al. (2012) report on efforts adopted by healthcare
facilities. Among the techniques that have particularly made a
list regarding reducing patient readmission rates are patient
education, follow-up telephone calls, proper coordination with
outpatient providers and home visits, (Bradley et al., 2012).
Some hospitals also employ medication management practices
that are geared towards achieving medication reconciliation and
reducing the chance of wrong prescriptions. Some of the areas
that health care providers have slacked in include; failure to
provide patients with home health services the contacts for

specific inpatient physicians in case they had inquiries, and lack
of means to alert outpatient physicians to the discharge within
48 hours, (Bradley et al., 2012). Some hospitals also failed to
make discharge summaries. In cases where patients were
transferred from one hospital to another nurse-nurse report were
not always conducted.
According to Jack et al., (2009), reengineered hospital
discharge (RED) decreased readmissions and department visits
within 30 days of discharge Jack et al., (2009) observe that the
RED intervention explored comprehensive discharge planning,
post-discharge reinforcement and patient center education. The
intervention thereof translated into a reduction in instances of
adverse drug events and by extension the rate of readmissions.
Patient education was also found to be integral because other
than equipping patients with knowledge on the prescribed
regimen, they also generally helped patients to hone
comprehension skills needed for patients self-care.
While healthcare facilities might have benefited from patients’
readmissions in the past, the affordable care act (ACA) sought
to discourage readmissions rates for target conditions by
imposing penalties on health care facilities whose readmission
rates surpassed allowable readmission rates. In compliance with
the Affordable care act’s readmission rates, healthcare facilities
have explored a host of pre and post-discharge interventions
(Hasan et al., 2010). Some of the interventions include patient
education, pre-discharge planning, home visits and follow-up
calls (Alper, O’Malley & Greenwald, 2017). Some health
facilities have even coopted telemedicine in their day-to-day
practice to help bridge the distance between patients,
pharmacists, doctors, and nurses.
In healthcare facilities where telemedicine has been explored,
the interactions between caregivers and the patients improved.
Kashem et. al. (2008) explain that interactions in an isolated
case involving patients with heart failure (HF) and providers
stood at 3774 while the messages that emanated from the
telemedicine patients were 1887. According to a study

conducted by Kashem, Droogan, Santamore, Wald, Bove (2008),
the application of telemedicine for patients who had a history of
heart failure diminished hospitalization and readmission rates.
Telemedicine eased communication between providers and
providers over secure internet system.
Significance of the Problem in Saudi Arabia
Literature outlines that a 30-Day Readmission Rate is
considered as an indicator of the quality of inpatient care.
However, some early readmissions are thought to be avoidable.
A randomized trial done prospectively showed that from all
readmission that 12% to 75% could be prevented through
patient education, pre-discharge assessment, and timely
aftercare. Patient’s demographics, co-morbidities, preoperative
care, length of stay, and post-discharge care are variable
contributing to the readmission after discharge within 30 days.
In addition, shorter length of stay and early discharge are linked
with higher risk of readmission after discharge immediately
afterward, (Azza et al., 2012).
CHAPTER III
Objectives
Purpose of the Study
The purpose and the primary objective of this study is to assess
the opinions of professional health care providers (physicians &
nurses) and to understand the reasons leading to unplanned
hospital readmission after discharge.
The secondary objective focused on the interventions that might
be helpful in developing an online tool as a follow-up
mechanism after discharge for patients to communicate with
their healthcare providers in King Khaled Eye Specialist
Hospital to decrease unplanned hospital readmissions rates and
facilitates coordination of transition and continuum of care.
The approach aims at improving attempts that assist patients in
acquiring and adhering to the treatment regimen and outpatient
appointments.
CHAPTER IV

Methodology
Setting, Participants, and Sample size & Selection
The setting took place at King Khaled Eye Specialist Hospital
in Riyadh, Kingdom of Saudi Arabia, focusing on the transition
and continuum of care. This also cares for high-risk patients by
improving the quality of care and offering support. The
participants involved were physicians and nurses working at
King Khaled Eye Specialist Hospital to assess their feedback on
strategies to prevent readmission, & possible online follow-ups
after discharge interventions to reduce readmission.
The targeted population was physicians and nurses working at
King Khaled Eye Specialist Hospital will be included in the
research for purposes of the study analysis.
The physicians and nurses participated in responding and filling
the questionnaire survey during the study. The questionnaire is
a paper-based survey, and questions consist of a mixture of both
open and closed-ended questions. A convenience sampling
selection technique was the method of choice for the research
purpose.
The sample size is 180 subjects drawn out of a total population
of 300, with a confidence level of 95%, the margin error being
about 5.0 %
Eligibility Criteria
The Purpose of Eligibility Criteria is to define the sample
characteristics required for meeting the study objectives.
Inclusion Criteria
Type of studies:
· Studies from any geographical location.
Rationale: This assessment study did not have the resources
necessary to evaluate and accommodate non-English writing
publications.
Location:
· Riyadh, Kingdom of Saudi Arabia.
Rationale: This study did not have the resources necessary to
evaluate data from outside of Riyadh.
Setting:

· King Khaled Eye Specialist Hospital.
Rationale: Resources necessary to evaluate from outside from
this particular setting are not available.
Participants:
· Physicians and nurses dealing directly with patients working
in outpatient and inpatient departments at King Khaled Eye
Specialist Hospital.
Rationale: The professional feedback of those selected
individuals is the key focus of this study because they are in
direct contact with their patients and are responsible for the
smooth discharge transition.
Exclusion Criteria
Participants:
· Other HCP working at King Khaled Eye Specialist Hospital.
Rationale: Not all health care providers are involved with the
discharge process, and they do not work closely and directly
with patients at the time of discharge and post-discharge.
Information Sources
The information gathered from physicians and nurses in the
inpatient and outpatient units at King Khaled Eye Specialist
Hospital to provide their perspective regarding reasons for
readmission and interventions that might be helpful in
developing an online tool for healthcare staff that might reduce
hospital readmission.
Study Design
A cross-section descriptive study design was used. The
investigation will focus on the professional point of view of
physicians and nurses working at KKESH on the strategies to
prevent readmission and practicability of online follow-up
interventions post-discharge to reduce their hospital
readmission. Readmission is the return to the hospital to seek
medical attention after getting permission from the physician to
go home. There is no difference between deliberate and
unintended readmission. Also, the clinical relationship between
the first admissions and the readmission is indistinguishable.

Pilot Study
A published questionnaire has been adapted and used in the
research for which the validity and liability have already been
checked (Herzig et al., 2016).
Methods of data collection
Gathering of information was through the primary methods of
data collection. Physicians and nurses participated in
responding to the questionnaires. In addition, reports from the
journal and different books and articles provided the basis for
comparing the primary and secondary data.
Ethical Considerations
It is essential to safeguard the personal details of patients to
protect their identity and privacy. The research was conducted
with a high standard of quality and integrity; all participants
contributed voluntarily, and informed consent were obtained; all
data collected were treated with the utmost autonomy and
confidentiality, and a non-maleficence approach was followed
throughout the entire research process. Additionally, there was
no forcing of respondents to provide information in subjects
they felt uncomfortable to share.
Statistical Data Analysis
The frequency with which physicians and nurses selected each
of the pre-specified factors that in their opinion contributed to
re-admission ranked from low to high. The frequency of each
category subject presented. For the strategies to prevent
readmission, we report the frequency with which the doctors
and nurses reported anything other than “no probability” for
each of the potential preventive strategies (slightly probable,
slightly less than 50/50 and slightly probable). We choose this
because we are interested in any degree of preventability. The
percentage of slightly more than 50/50 and strongly probable
indicate a possible way to prevent readmission.
CHAPTER V
Results & Discussion
Presentation and Discussion of the Results of the Field Study:
This section deals with the analysis of the results of the field

study by presenting and analyzing the demographic data of the
study sample, as well as presenting the responses of the sample
members to the questions of the study (paragraphs), and
processing it statistically by using descriptive statistics
concepts and statistical methods to reach the results. To address
the study data, frequencies and percentages were used to
identify the demographic data of the study sample and to
determine their responses to the terms of the study instrument
used. The results of the field study are presented below:
First: Demographic Data Results:
1- Profession Variable for the Study Sample:
To identify the Demographic Information, the frequencies and
percentage of the profession were calculated, and the results
were as follows:
Table (1):
The Distribution of the Sample of the Study According to the
(Profession) Variable
profession
Frequency
Percent
Doctor
30
16.9
Nurse
148
83.1
Total
178
100.0
The previous table, on the distribution of the study sample
according to the variable (profession), shows that (148) of the
study sample work in the profession of (Nurse) and their
percentage was (83.1%), while (30) of the study sample work as
(Doctor) and their percentage was (16.9%). The following
figure illustrates this:

Figure (1):
Explains Profession Variable
2-The Sub-Specialty Variable of Doctors:
To specify the doctors' sub-specialty, the frequencies and the
percentage of the sub-specialty variable were calculated, and
the results were as follows:
Table (2):
The Distribution of the Sample of the Study According to the
Variable of (The Doctors' Sub-Specialty)
the Doctors' Sub-Specialty
Frequency
Percent
Cornea
1
3.3
Glaucoma
6
20.0
Anterior segment
6
20.0
Oculoplastic
3
10.0
Pediatric
1
3.3
Resident
5
16.6
Retina
8
26.6

Total
30
100.0
The previous table shows the distribution of the study sample
according to (the doctors' sub-specialty) that (8) of the study
sample (Retina) with their percentage (26.6%), then (6) of the
study sample (Glaucoma) and their percentage (20.0%), and (6)
of the study sample (Anterior Segment) with their percentage
(20.0%), and then (5) of the study sample (Resident) with their
percentage (16.6%), and (3) out of the study sample
(Oculoplastic) with their percentage (10.0%), then (1) out of the
study sample (Cornea) with their percentage (3.3%), and (1) out
of the study sample (Pediatric) with their percentage (3.3%).
The figure mentioned below shows that:
Figure (2):
Shows Doctors' Sub-Specialty
Second: Results for Answering the Questionnaire:
To achieve the objectives of the study and the analysis of the
collected data, many appropriate statistical methods were used
using (Statistical Package for Social Sciences) which is
abbreviated as SPSS, after coding and inputting data to the
computer.
The Following Statistical Measures Were Then Calculated:
· Frequencies and percentages to identify the responses of the
sample of the study towards the terms of the main axes
contained in the study instrument.
· The arithmetic "Mean" to find out how high or low responses
of the study sample on the main axes (Mean phrases), knowing
that it is useful in the order of axes by the highest arithmetic
mean.
· The standard deviation to identify the extent of deviation of
the study sample responses for each of the terms of the study
variables, and each axis of the main axes of the mean
arithmetic. It is noted that the standard deviation shows the

dispersion in the responses of the members of the study sample
for each of the terms of the study variables. The more the value
of the standard deviation is close to zero, the responses become
more focused, and fragmentation decreased between the scales.
1 - Factors Contributing to the Return of Discharged Patients:
In order to identify the factors contributing to the return of
discharged patients, this part of the scale was given grades of
(1, 2, 3, 4, 5). These figures correspond to the following:
- Number (1) contribution to the degree (none).
- Number (2) contribution to the degree (low).
- Number (3) contribution to the degree (medium).
- Number (4) contribution to the degree (high).
- Number (5) contribution to the degree (very high).
To determine the length of the five-meter cells (minimum and
upper limits) used to identify (factors contributing to the return
of discharged patients), the range (5-1 = 4) was calculated then
divided by the number of cells of the scale to obtain the correct
cell length, i.e. (4/5 = 0.80). This value was then added to the
lowest value in the scale to determine the upper limit of this
cell; thus the cell length became as follows:
· From 1.00 to 1.79 represents (non-existent) towards each
statement according to the axis to be measured.
· From 1.80 to 2.59 (low) towards each statement according to
the axis to be measured.
· From 2.60 to 3.39 (medium) towards each statement according
to the axis to be measured.
· From 3.40 to 4.19 (high) towards each statement according to
the axis to be measured.
· From 4.20 to 5 represents (very high) towards each statement
according to the axis to be measured.
Table (3):
The Views of the Study Sample on the Statements of the Axis
(Factors Contributing to the Return of Discharged Patients)
No
Statement
Acceptance degree

Mean
standard deviation
Order
Non
Low
Medium
High
Very high
F
P
F
P
F
P
F
P
F
P
PATIENT UNDERSTANDING AND ABILITY TO SELF-
MANAGE
1
Patient or caregiver lack of understanding of the post-discharge
plan
4
2.2
7

3.9
22
12.4
64
36.0
81
45.5
4.19
0.95
1
2
Patient or caregiver inability to manage his/her medications
2
1.1
8
4.5
31
17.4
79
44.4
58
32.6
4.03
0.89
3
3
Patient or caregiver inability to manage his/her symptoms
7
3.9
8
4.5
31
17.4
76
42.7
56

31.5
3.93
1.01
4
4
Patient inability to otherwise care for him/herself or caregiver's
inability to otherwise provide care Insufficient or ineffective
patient or caregiver education
5
2.8
7
3.9
33
18.5
65
36.5
68
38.2
4.03
0.99
2
General arithmetic mean
4.04
CONTINUITY OF CARE AND PROVIDER
COMMUNICATION
1
Failure to involve you sufficiently in the development of the
post-discharge plan
10
5.6
10
5.6
28
15.7
52
29.2

78
43.8
4.00
1.15
3
2
Discharge summary unavailable in a timely manner
11
6.2
7
3.9
24
13.5
74
41.6
62
34.8
3.95
1.10
5
3
Discharge summary is poorly written or with missing or
erroneous information
9
5.1
8
4.5
30
16.9
66
37.1
65
36.5
3.96
1.08
4

4
Lack of verbal communication with you re follow-up plans
6
3.4
6
3.4
33
18.5
60
33.7
73
42.0
4.06
1.02
2
5
Failure to obtain an appropriately timed follow-up appointment
or follow-up studies
2
1.1
11
6.2
29
16.3
69
38.8
67
37.6
4.06
0.94
1
6
The inability of the patient to keep the follow-up appointment
or follow-up studies
1
6

8
4.5
35
19.7
90
50.6
44
24.7
3.94
0.82
6
7
Insufficient monitoring of the patient's condition(s) after
discharge
5
2.8
6
3.4
42
23.6
69
38.8
56
31.5
3.93
0.97
7
3.98
SOCIAL SUPPORTS
1
Inadequate support for non-clinical issues (such as food, heat,
transportation, or ability to afford medications)
50
28.1
19

10.7
70
39.3
17
9.6
22
12.4
2.67
1.31
2
2
Inadequate home services or equipment after discharge
16
9.0
21
11.8
70
39.3
39
21.9
32
18.0
3.28
1.16
1
2.98
PROBLEMS WITH INITIAL ADMISSION
1
Misdiagnosis made during the initial admission
12
6.7
10
5.6
33
18.5

78
43.8
45
25.3
3.75
1.10
2
2
Inappropriate/inadequate treatment of the patient during the
initial admission
7
3.9
10
5.6
43
24.2
79
44.4
39
21.9
3.75
0.99
1
3
Discharged from the hospital too soon after initial admission
7
3.9
16
9.0
51
28.7
74
41.6
30
16.9
3.58

1.00
3
4
Absent, erroneous, or incomplete medication reconciliation
11
6.2
25
14.0
44
24.7
49
27.5
49
27.5
3.56
1.21
4
3.66
The total arithmetic mean of the axis
3.66
The table above shows the views of the study sample on the
terms of the axis (factors contributing to the return of
discharged patients), the general arithmetic mean for this aspect
(3.66) which means that the sample of the study agrees on the
axis degree (high) in general. Given the arithmetical averages of
the dimensions discussed in this aspect, we find out that
dimension (PATIENT UNDERSTANDING AND ABILITY TO
SELF-MANAGE) obtained an average of 4.04, which means that
the sample of the study agree with the degree (high), according
to the gradual five-dimensional scale, the highest dimensions of
the contribution to the return of discharged patients of other
dimensions. While the lowest after a contribution to the return
of discharged patients from the point of view of the sample of
the study is (SOCIAL SUPPORTS), which obtain an average of

(2.98), which corresponds to the degree (medium) according to
the gradual five-dimensional scale.
The most important factors contributing to the return of
discharged patients can be summarized from the point of view
of the study sample in all dimensions in the following:
· Patient or caregiver lack of understanding of the post-
discharge plan.
· Failure to obtain an appropriately timed follow-up
appointment or follow-up studies.
· Inadequate home services or equipment after discharge.
· Inappropriate/inadequate treatment of the patient during the
initial admission.
2- The Sample of The Study Investigate That the Other Reason
That Might Contribute to Unplanned Readmission:
· No clear communication.
· Early discharge.
· No outpatient follow-ups.
· Discharge against medical advice.
· Unplanned discharge.
· Patients do not report adverse events.
· No instruction post-discharge.
· Doctors do not communicate properly with patients.
· Unclear medication education.
· Patient does not understand instructions.
· Family discharge patient against medical advice.
· The patient is unaware of their serious conditions.
· Poor patient-doctor communication.
· Doctors discharge patient too early.
· Patients are not taking care of themselves.
· The patient does not want to get treatment.
· Doctors do not listen to patient concerns.
3-Factors Contributing to Reducing the Return of Discharged
Patients (How Probable Do You Think Each of These Potential
Types of Interventions Might Have Been Contributing in
Preventing Readmission):
In order to identify factors contributing to the reduction of

discharged patients' return, this part of the scale was given a
number of responses:
· No probability.
· Slightly probable.
· Slightly less than 50-50.
· Slightly more than 50-50.
· Strongly probable.
· Nearly certain.
To determine the length of the hexagrams (minimum and upper
limits) used to identify the factors contributing to the reduction
of discharged patients' return, the range (6-1 = 5) was
calculated and then divided by the number of cells of the scale
to obtain the correct cell length (5/6 = 0.83) Then this value
was added to the lowest value in the scale to determine the
upper limit of this cell, thus the cell length became as follows:
• From 1.00 to 1.83 represents (No probability) towards each
• From 1.83 to 2.66 (Slightly probable) towards each statement
• From 2.66 to 3.49 (Slightly less than 50) towards each
• From 3.49 to 4.32 (Slightly more than 50) towards each
• From 4.32 to 5.15 (Strongly probable) towards each statement
• From 5.15 to 6.00 represents (Almost certain) towards each
Table (4):
The Views of the Study Sample on the Statements of the Axis
(Factors Contributing to Reducing the Return of Discharged
Patients)
No
Statement
Acceptance degree
Mean

standard deviation
Order
No probability
Slightly probable
Slightly less than 50-50
Slightly more than 50-50
Strongly probable
Nearly certain
F
P
F
P
F
P
F
P
F
P
F
P
How probable do you think each of these potential types of
interventions might have been contributing to preventing
readmission?
1
Complete communication of information (e.g., tests or
appointments to be completed after discharge)

2
1.1
5
2.8
6
3.4
13
7.3
71
39.9
81
45.5
5.19
1.03
5
2
Improved clarity, timeliness or availability of information
provided at the discharge
-
-
1
.6
12
6.7
11
6.2
75
42.1
79
44.4
5.23
0.88
4
3
Improved self-management plan at discharge (e.g., patient-
centered discharge instructions, transition coaches)

2
1.1
4
2.2
5
2.8
13
7.3
53
29.8
101
56.7
5.33
1.02
2
4
Provision of resources to manage care and symptoms after
discharge (e.g., the online monitoring system of complication &
condition prognosis)
3
1.7
7
3.9
8
4.5
15
8.4
57
32.0
88
49.4
5.13
1.17
6
5
Improved discharge planning (e.g., appointments scheduled in

advance)
2
1.1
3
1.7
6
3.4
7
3.9
65
36.5
95
53.4
5.33
0.97
1
6
Improved discharge education (e.g., training on the new online
system)
5
2.8
2
1.1
6
3.4
8
4.5
59
33.1
98
55.1
5.29
1.10
3
5.25

How probable do you think each of these potential types of
interventions after discharge might have been contributing to
preventing readmission?
1
Improved communication of care between the patient &
healthcare provider
1
.6
2
1.1
7
3.9
2
1.1
60
33.7
106
59.6
5.45
0.87
1
2
Improved attention to medication safety (e.g., medication
reconciliation)
-
-
4
2.2
2
1.1
10
5.6
66
37.1
96
53.9

5.39
0.83
3
3
Increased awareness of personal hygiene and sanitation in
reducing the probability of acquiring infections through detailed
education from healthcare providers
1
.6
7
3.9
1
.6
9
5.1
56
31.5
104
58.4
5.38
0.98
4
4
Lifestyle adjustment to increase the chance of remission
through detailed education from healthcare providers
1
.6
5
2.8
7
3.9
8
4.5
67
37.6
90

50.6
5.28
0.99
6
5
Improved patient transition to the outpatient follow-up process
1
.6
8
4.5
4
2.2
9
5.1
52
29.2
104
58.4
5.33
1.06
5
6
Increasing awareness to declaring adverse events by patients
after discharge
-
-
4
2.2
8
4.5
7
3.9
49
27.5
110
61.8

5.42
0.93
2
5.37
The total arithmetic mean of the axis
5.31
The above table shows the views of the study sample on the
statements of the axis (factors contributing to reducing the
return of discharged patients), the overall arithmetic mean for
this aspect (5.31), which means that the sample of the study
believe that the factors mentioned in this axis can contribute to
reducing the return of discharged patients degree (nearly
certain) in general, this average is in the sixth category of the
scale ranging from 5.15 to 6.00.
Given the arithmetical averages of the dimensions discussed in
this aspect, we find that dimension (How probable do you think
each of these potential types of interventions might have been
contributing in preventing readmission?), obtained an average
of (5.25), which means that the sample of the study sees this
contribution as (nearly certain).
Also, the dimension (How probable do you think each of these
potential types of interventions after discharge might have been
contributing in preventing readmission?), obtained an average
of (5.37), which means that the sample of the study sees its
contribution to the degree (almost certain).
The most important factors contributing to reducing the return
of discharged patients can be summarized as follows:
· Improved discharge planning (e.g., appointments scheduled in
advance).
· Improved self-management plan at discharge (e.g., patient-
centered discharge instructions, transition coaches).
· Improved communication of care between patient & healthcare
provider.
· Increasing awareness of declaring adverse events by patients

after discharge.
4- The Sample of the Study Tries to Illustrate Other Things
Related to the Contributions to Unplanned Readmissions:
· No outpatient follow-up.
· No Clear communication.
· Poor communication.
· Discharge against medical advice.
· Cultural and personal barriers.
· The patient will not listen to doctors.
· The patient does not understand how to takes their medication.
· Patient refuse treatment.
· Doctors are not engaged with their patient after discharge.
The most important findings of the previous chapter (results &
discussion), with a review of some of the views in previous
studies related to these results. The most important results
reached are as follows:
1- The results showed that most of the study sample which is
(148) study samples are working as "Nurse" (83.1%). The
researcher believes that this ratio gives the study greater
credibility, as Nurses are the most categories of contact with
patients, who are primarily responsible for providing health care
and follow-up and supervise patients.
2 - The results of the study showed that the most important
factors contributing to the return of discharged patients (In your
opinion, rank according to seriousness which of the following
factors may have contributed to the readmission?), are as
follows:
-Patient or caregiver lack of understanding of the post-discharge
plan. This result indicates the importance of ensuring that the
patient reaches an appropriate degree of recovery, allowing
him/her to leave the hospital and continue his/his life, this
requires a review of the patient's health status and specific
guidance for continued recovery.

-Failure to obtain an appropriately timed follow-up appointment
or follow-up studies. This result indicates the importance of
timely access to patients for review, follow-up of their
condition and health developments. Because of this procedure
reduces their re-hospitalization.
3- The results of the study showed that the most important
factors contributing to reducing the return of discharged
patients (How probable do you think each of these potential
types of interventions might have been contributing in
preventing readmission) are as follows:
- Improved discharge planning (e.g., appointments scheduled in
advance). This result indicates the importance of planning and
organizing the times of patient review of the hospital, timing,
and interviews of the patient's condition and the amount of
medication given to him.
CHAPTER VI
Conclusion (including limitation) Comment by Nada A.
Alabdan: In conclusion do not give others references
they can be given in discussion to make it more strong and in
depth, this will also increase the paras of discussion.
In conclusion give your own findings do not quote others even
if findings are similar,
You can change the language and give it as your findings
/study inferences
Use sentences like:
It has been found in the present study,
Observed in the present work, reported in the undertaken study,
and so on
Recommendations Comment by Editor: Use specific sentences
for every parameter or criterion or observation like :
It is recommended that , it is suggested that
Can be re drafted as per style indicated in comment

References Comment by Editor: Style of writing all
references must be same do not change the format keep them
constant
For Ex
Name of Journal
Adib-Hajbaghery, M., Maghaminejad, F., & Abbasi, A. (2013).
The Role of Continuous Care in Reducing Readmission for
Patients with Heart Failure. Journal of caring sciences, 2(4),
255.
Alper, E., O’Malley, T., & Greenwald, J. (2017). Hospital
Discharge and Readmission. UpToDate. Retrieved March 2018,
from https://www.uptodate.com/contents/hospital-discharge-
and-readmission
Auerbach, A. D., Kripalani, S., Vasilevskis, E. E., Sehgal, N.,
Lindenauer, P. K., Metlay, J. P., & Williams, M. V. (2016).
Preventability and Causes of Readmissions in a National Cohort
of General Medicine Patients. JAMA Internal Medicine, 176(4),
484-493.
Azza A. El. Mahalli; Al-Nujaidi, Heba Y.; Al-Turaiyef, Nourah
A.; Al-Rashed, Sara
S.; Al- Asiri, Salha F(2012). 30-Day Readmission Rate as an

Indicator of the Quality of Elective Surgical Inpatient Care at
one of the Eastern Province's Hospitals, Kingdom of Saudi
Arabia. Journal of King Abdulaziz University: Medical Sciences
. 2012, Vol. 19 Issue 2, p29-43. 15p. Comment by Editor:
Match this with
Boulding, W., Glickman, S. W., Manary, M. P., Schulman, K.
A., & Staelin, R. (2011). Relationship between patient
satisfaction with inpatient care and hospital readmission within
30 days. The American journal of managed care, 17(1), 41-48.
Comment by Editor: Match this with below
Bradley, E. H., Curry, L., Horwitz, L. I., Sipsma, H.,
Thompson, J. W., Elma, M. A., … Krumholz, H. M. (2012).
Contemporary Evidence about Hospital Strategies for Reducing
30-Day Readmissions: A National Study. Journal of the
American College of Cardiology, 60(7), 607–614. Comment by
Editor: Letters are capitalComment by Editor: No year
Comment by Editor: No p here
Epstein, A. (2009). Revisiting Readmissions — Changing the
Incentives for Shared Accountability. The New England Journal
of Medicine, N Engl J Med 2009; 360:1457-1459. Comment by
Editor: Year is here
Ferrandino, R., Roof, S., Ma, Y., Chan, L., Poojary, P., Saha,
A., Teng, M. S. (2017). Unplanned Thirty-day Readmissions
after Parathyroidectomy in Patients with Chronic Kidney
Disease: A Nationwide Analysis. Otolaryngology-Head and
Neck Surgery :Official Journal of American Academy of
Otolaryngology-Head and Neck Surgery, 157(6), 955–965.
Gruneir, A., Dhalla, I. A., van Walraven, C., Fischer, H. D.,
Camacho, X., Rochon, P. A., & Anderson, G. M. (2011).
Unplanned Readmissions after Hospital Discharge Among
Patients Identified as being at High Risk for Readmission Using
a Validated Predictive Algorithm. Open Medicine, 5(2), e104–
e111.
Hasan, O., Meltzer, D. O., Shaykevich, S. A., Bell, C. M.,
Kaboli, P. J., Auerbach, A. D., … Schnipper, J. L. (2010).
Hospital Readmission in General Medicine Patients: A

Prediction Model. Journal of General Internal Medicine, 25(3),
211–219.
Herzig, S. J., Schnipper, J. L., Doctoroff, L., Kim, C. S.,
Flanders, S. A., Robinson, E. J.,
Auerbach, A. D. (2016). Physician Perspectives on Factors
Contributing to Readmissions and Potential Prevention
Strategies: A Multicenter Survey. Journal of General Internal
Medicine, 31(11), 1287–1293. http://doi.org/10.1007/s11606-
016-3764-5
Hesselink, G., Zegers, M., Vernooij-Dassen, M., Barach, P.,
Kalkman, C., Flink, M., on behalf of the European HANDOVER
Research Collaborative. (2014). Improving patient discharge
and reducing hospital readmissions by using Intervention
Mapping. BMC Health Services Research, 14, 389.
Jack, B. W., Chetty, V. K., Anthony, D., Greenwald, J. L.,
Sanchez, G. M., Johnson, A. E., … Culpepper, L. (2009). A
Reengineered Hospital Discharge Program to Decrease
Rehospitalization: A Randomized Trial. Annals of Internal
Medicine, 150(3), 178–187.
Jencks SF, Williams MV, Coleman EA. Rehospitalizations
among patients in the Medicare fee-for-service program. N Engl
J Med 2009; 360:1418.
Kashem, A., Droogan, M. T., Santamore, W. P., Wald, J. W., &
Bove, A. A. (2008). Managing heart failure care using an
Internet-based telemedicine system. Journal of cardiac failure,
14(2), 121-126.
McIlvennan, C. K., Eapen, Z. J., & Allen, L. A. (2015).
Hospital Readmissions Reduction Program. Circulation,
131(20), 1796–1803.
MedPAC (Medicare Payment Advisory Commission). (2007,
June). Report to the Congress: Promoting Greater Efficiency in
Medicare. Retrieved from: http://medpac.gov/docs/default-
source/reports/Jun07_EntireReport.pdf
Van Walraven, C., Bennett, C., Jennings, A., Austin, P. C., &
Forster, A. J. (2011). Proportion of Hospital Readmissions
Deemed Avoidable: A Systematic Review. CMAJ : Canadian

Medical Association Journal, 183(7), E391–E402.
Zuckerman, R. B., Sheingold, S. H., Orav, E. J., Ruhter, J., &
Epstein, A. M. (2016). Readmissions, observation, and the
hospital readmissions reduction program. New England Journal
of Medicine, 374(16), 1543-1551.
Profession
profession Doctor Nurse 16.899999999999999
83.1
Doctors' sub-specialty Cornea Glaucoma Interior
segment Oculophstic Pediatric Resident Retina 3.3 20
20 10 3.3 16.600000000000001 26.6
1
1
You are Alex, the director of external affairs for a national not-
for-profit medical research center that
does not research on diseases related to aging. The center's
work depends on funding from multiple
sources, including the general public, individual estates, and
grants from corporations, foundations,
and the federal government.
Your department prepares an annual report of the center's
accomplishments and financial status for
the board of directors. It is mostly text with a few charts and
tables, all black and white, with a simple
cover. It is voluminous and pretty dry reading. It is inexpensive
to produce other than the effort to pull
together the content, which requires time to request and

expedite information from the centers other
departments.
At the last board meeting, the board members suggested the
annual report be 'upscaled' into a
document that could be used for marketing and promotional
purposes. They want you to mail the
next annual report to the centers various stakeholders, past
donors, and targets high-potential future
donors. The board feels that such a document is needed to get
there center 'in the same league'
with other large not-for-profit organizations with which it feels
it competes for donations and funds.
The board feels that the annual report could be used to inform
these stakeholders about the
advances the center is making in it's research efforts and it's
strong fiscal management for effectively
using the funding and donations it receives.
You will need to produce a shorter, simpler, easy to read annual
report that shows the benefit of the
centers research and the impact on peoples lives. You will
include pictures from various hospitals,
clinical, and long-term care facilities that are sing the results of
the centers research. You also will
include testimonials from patients and families who have
benefited from the centers research. The
report must be eye catching. It needs to be multicolor, contain a
lot of pictures, and easy-to
understand graphics, and be written in a style that can be
understood by the average adult potential
donor. This is a significant undertaking for your department,
which include three other staff
members. You will have to contract out some of the activities
and may have to travel to several
medical facilities around the country to take photos and get

testimonials. You will also need to put
the design, printing, and distribution out to bid to various
contractors ti submit proposals and prices
to you. You estimate that approximately 5 million copies need
to be printed and mailed.
It is now April 1. the bard asks you to come to its next meeting
on May 15 to present a detailed plan,
schedule, and budget for how you will complete the project. The
board wants the annual report 'in
the mail' by November 15, so potential donors will receive it
around the holiday season when they
may be in a giving mood. The centers fiscal year ends
September 30, and its financial statements
should be available by October 15. However, the non financial
information for the report can start to
be pulled together right after May 15 board meeting.
Fortunately, you are taking a project management course in the
evening at a local university and see
this as an opportunity to apply what you have been learning.
You know that this is a big project and
that the board has high expectations. You want to be sure you
meet their expectations, and get them
to approve the budget that you will need for this project.
However, they will only do that if they are
confident that you have a detailed plan for how you will get it
all done. You and your staff have 6
weeks to prepare a plan to present to the board on May 15. If
approved, you will have 6 months from
May 15 to November 15, to implement the plan and complete
the project.
Your staff consists of Grace, a marketing specialist; Levi, a
writer/editor, and Lakysha, a staff
assistant whose hobby is photography (she is going to college

part-time in the evening to earn a
degree in photojournalism and has won several local
photography contests).

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