Over the last decade, there has been a growing interest in patient satisfaction. In a free enterprise and a competitive healthcare system like the United States, patient satisfaction has been referred to as the indispensable outcome. More often than not, operational problems are a significant contributor to the overall satisfaction of patients (Storm-Versloot, 2014). With the large percentage of patients being admitted from the emergency department, achieving satisfaction is a primary part of the equation. Surgical services need ease in the admission process, communication, education, and discharge. Also, the inpatient units need effective communication strategies and knowledge. However, many nursing leaders face the challenge of improving the flow of patients and reducing the time that patients stay at these departments (Storm-Versloot, 2014). It is paramount for nursing leaders to increasingly focus on enhancing the experiences of patients when they interact with different parts of the healthcare system. While a majority of the nursing leaders indicate that improving the experience of patients is one of their priorities, few of the leaders report that they have seen improvements in their facilities amidst specific practices having proven that when implemented they improve patient satisfaction. Likewise, waiting time has been cited as a factor that significantly impacts patient satisfaction. Waiting time is the time that patients remain in hospital units before they are attended to by nurses and other clinical staff. As stated by Storm-Versloot (2014), a patient's waiting time is a significant indication of the quality offered by hospitals. Hence, this document elaborates on the issue of patients satisfaction and waiting time by presenting two articles that develop on the impact of waiting time on patient satisfaction. The paper will also focus on interventions that can address waiting time in order to improve patient satisfaction in my current clinical setting and statistic of nurse leader to address the outcomes of the responses.
There is a tremendous need for interventions in hospital units to improve the service delivery part to achieve patient satisfaction. However, nursing leaders are unable to mitigate operational challenges in emergency departments, surgical services, and inpatient units, resulting in low patient satisfaction (Storm-Versloot, 2014). Even though nursing leaders have implemented a variety of strategies, the outcome has been minimal. Several studies have been carried out regarding the quality of service and patient satisfaction (Dale & Howanitz, 2015). Nonetheless, there is no clear meaning of the dimensions that patients rate their satisfaction. Most studies have collected data on background variables such as sex, age, ethnicity, social status, the seriousness of illnesses and how nurses respond to patients within these variables. Most of these studies have reported that age and ethnic background have a 20% relation with satisfaction. However, waiting time and the responsiveness of nurses are strongly linked to patient satisfaction at 67% (Wright et al., 2013). Inclusion and exclusion had some variations in the studies. While the severity of a sickness increases, the patients expectation towards satisfaction decreases. Furthermore an investigation carried out by Morgan among the residents of Sheffield in comparing the academic and community response to emergency department waiting time, he reported that it is the most significant challenge that impacts patient satisfaction representing 82% (Wright et al., 2013). Therefore, there is a need for interventions that can help reduce patient waiting time in hospitals units.
Literature support aims in offering evidence regarding a problem and helps researchers to emphasize the importance of an issue, the focus of critical points as well as compare concepts and methods of work. To assess and improve patient satisfaction, it is essential to understand the context of health care (Wright et al., 2013). Achieving patient satisfaction starts with the time patients spend in the waiting room. Long waiting time is attributed to inefficient operational services. There has been a tremendous increase in the number of studies done to investigate the influence of waiting time to patients satisfaction.
A cross-sectional study was conducted at the Gracemed medical facility in 2015. 135 participants were involved in the research, including patients seeking therapy in the emergency department and inpatient administration (Patel & Patel, 2017). The patients were randomly selected following both the inclusion and exclusion method. Regarding inclusion criteria, both female and males were included in emergency and inpatient administration while the exclusion criteria involved the patients seeking surgical and pediatric services. The clinical data of each patient participating in the study was recorded. These data included their name, age sex, and address. The hospital unit they visited was registered as well as the time they spent in the waiting room before they were served as well. Also, the participants were asked whether they were satisfied with the hospital staff. The outcome of the investigation demonstrated that the majority of the patients who were between 20 to 40 years representing 97% indicated that they were dissatisfied with the services offered by nurses (Patel & Patel, 2017). Consequently, 87.41% attributed their dissatisfaction to the fact that they spend most of the time in the waiting room (Patel & Patel, 2017). Even though 10% of the patients were satisfied with the medical services offered, the satisfaction was as a result of the treatment cost (Patel & Patel, 2017).
Further, research indicates that most inpatient and emergency departments have its patients wait for a longer time to get examined by a doctor. Wong et al. (2015) undertook an investigation concerning hospital waiting time for patients visiting the emergency department, X-ray room, and surgical services. The study gathered 112 participants randomly from the three departments on Monday and Friday and issued them with questionnaires. The outcome of the investigation indicated that patients spend a minimum time of 346 minutes in the waiting room. That is, they invested only 5% the time with the nursing specialist and 95% of this time in the waiting area (Wong et al., 2015). The most extended waiting time was at the surgical services with a minimum of 123 minutes, X-ray room a minimum of 104 minutes and at the inpatient administration 65 minutes. These figures represent 23%, 31% and 14% respectively (Wong et al., 2015). The average time, however, was shorter for patients who arrived at the facility around before 8 am yet at the same time longer for those patients who visited the hospital after 11 am. Average waiting time was longer for patients seeking medical services on a Friday as compared to Monday (Wong et al., 2015). From both studies, patients spend a long time in waiting rooms, and as a result, nursing leaders are faced with the responsibility of finding ways to reduce the time spend in waiting rooms to achieve patient satisfaction.
A series of interventions will be discussed to mitigate long waiting time and enhance patient satisfaction in the current place of work in the ED, inpatient, and surgical departments. They include changes in procedure, supplying of side changes as well as demand side changes (Michael et al., 2013). Procedure changes entail simplifying schedules of appointments, allotting patients with more precise estimation of time interim for counsel in light of the standard examination and assigning private discussion rooms to each on-duty nurse. It also includes setting up an assistance work area to re-plan the patients who arrive the ones who need assistance (Michael et al., 2013). Supply-side change involves checking on the attendance of nurses who are off-duty as well as giving financial penalties to those who arrive late. Also, on-duty nurses will be informed in advance through their phones regarding the number of patients to be expected. Also, the number of nurses in the emergency department and inpatient administration will also be increased. Demand-side changes will entail reminding all the visiting surgical patients their time of consultations through sending of texts, and increasing the number of providers in ED and inpatient services as well (sun et al., 2017)
The statistical analysis utilizes the average length of waiting time and the corresponding patient satisfaction to evaluate the trajectories in waiting time spend at ED and inpatient services towards consulting nurses and other medical providers over time to assess the results of the intervention. The time series data will be analyzed using the segmented linear progression model with statistical software which will help to determine the shifts in trends of waiting for time reduction intervention for emergency care, inpatient admission and surgical services. Interfered time series factual programming can control for auto-corresponded mistakes, and will likewise be used to change the potential serial relationship of information. I will regard six months after the implementation of the interventions as the intercession time points for decreasing waiting time in the three departments.
Fragmented linear regression will separate the time arrangement into a pre-and post-intervention time of implementing the interventions.
The graph below is an example of a segmented linear regression.
Graph 1.1(sun et al., 2017)
The outcome of the segmented regression analysis will be able to demonstrate whether the interventions would have made any significant changes after the implementation. For example, Gijo, and Antony (2014) indicates that a segmented regression analysis will be able to demonstrate whether after application there would be an immediate increase in average length of waiting time or there would be a decrease. Likewise, it is essential to compare the adjustments in patterns and levels of waiting time before and after the implementation of waiting for time reduction proposed interventions (Bleustein et al., 2014). Additionally, regression evaluation for the involved departments patient satisfaction will score towards the attending nurses will be undertaken.
Pearson correlation analysis will also be used as it helps to show the quality of the relationship between waiting time and separate patient fulfillments with the services rendered (Meyer et al., 2016). For example, the analysis outcome will demonstrate the strength or the negative relationship the time spend in waiting rooms before being attended to and patient satisfaction score in ED is more grounded than that of the waiting time at the inpatient scores towards patient satisfaction.
The evidence presented by two articles indicates that there is a rising concern for patient satisfaction due to long waiting hours in ED, inpatient admission, and surgical services. Therefore, interventions aimed at reducing the waiting time and raising the level of patient satisfaction in my current place of work is presented. These interventions include changes in procedure, supplying of side changes as well as demand-side changes. Statistical analysis of the interventions is intended to utilize the average length of waiting time and the corresponding patient satisfaction to evaluate the trajectories in waiting time spend at ED and inpatient services towards consulting nurses and other medical providers over time to assess the results of the intervention. This information alongside segmented linear regression and Pearson correlation analysis will be able to offer the imp...
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