written assignment

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

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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
General arithmetic mean	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
General arithmetic mean	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
General arithmetic mean	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 statement according to the axis to be measured.

• From 1.83 to 2.66 (Slightly probable) towards each statement according to the axis to be measured.

• From 2.66 to 3.49 (Slightly less than 50) towards each statement according to the axis to be measured.

• From 3.49 to 4.32 (Slightly more than 50) towards each statement according to the axis to be measured.

• From 4.32 to 5.15 (Strongly probable) towards each statement according to the axis to be measured.

• From 5.15 to 6.00 represents (Almost certain) towards each statement according to the axis to be measured.

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
General arithmetic mean	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
General arithmetic mean	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:

· Unplanned discharge.

· No outpatient follow-up.

· No Clear communication.

· Poor communication.

· Unplanned discharge.

· 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.

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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 capital Comment 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.

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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

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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

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