Patient Satisfaction With Noise

Practice Problem: Noise on hospital units can interfere with patient rest, timely recovery, and statisfacton with care. Improvements in the reduction of hospital noise levels lead to improved patient satisfaction and improved rest. Patients were surveyed on their satisfaction with noise during the day and at night. PICOT: In Acute Care Patients (P), does the implementation of a noise reduction program (I) compared to no noise reduction program (C), affect patient satisfaction with noise (O) over a sixweek period (T)? Evidence: Studies show that many hospitals have noise levels that exceed the World Health Organization’s recommended standards for noise levels. Evidence showed that implementing a noise reduction program that included quiet times and sleep menus produced an increase in patient satisfaction with noise. Intervention: Implementation of a noise reduction program and establishing a two-hour quiet time during the day. The program also established a sleep menu to identify and support patient bedtime rituals. Outcome: The implementation of a noise reduction program showed a statistical decrease in measurable noise levels. The project produced a clinically significant increase in patient satisfaction during the day and a clinically significant improvement in patient satisfaction with sleep quality and quantity. Conclusion: The goal of the noise reduction project was to improve the patients’ overall satisfaction with hospital noise during the day and overnight. This project showed that a noise reduction program could decrease noise levels and improve patient satisfaction with noise. PATIENT SATISFACTION WITH NOISE 4 Patient Satisfaction With Noise Improvements in the reduction of hospital noise levels lead to improved patient satisfaction as well as improved rest. Patients are surveyed on their satisfaction with noise at night. These survey scores are public information. Low satisfaction scores can lead new and existing patients to seek other healthcare facilities for their healthcare needs. This paper describes a Doctor of Nursing Practice (DNP) project for a noise reduction program to improve satisfaction with noise for acute care patients. The project started with a discussion of the significance of the problem and the PICOT question. The change theory and framework were discussed, a review of the literature was conducted, as well as an overview of the theme(s) identified in the literature. From the theme(s), a practice recommendation was developed. The project setting and plan were described to include the method, evaluation, and sustainability of the project. Significance of the Practice Problem Florence Nightingale (1860) stated that erratic noise is harmful to the patient. Her studies showed that rest was essential to patient recovery, and noise interrupted the patient’s rest. She advocated for hospital wards to be as quiet as possible (Nightingale, 1860). The Center for Medicare and Medicaid Services (CMS) has developed a method of grading the performance of each healthcare facility. This method is called the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). Each inpatient is surveyed, and one of the questions addresses satisfaction with noise at night. The results of these surveys are published as part of the HCAHPS scores. The scores are also used in the calculation of hospital reimbursement rates from CMS. This means lower satisfaction scores may lead to lower reimbursement rates (CMS, n.d.-a). PATIENT SATISFACTION WITH NOISE 5 The hospital’s scores for patient satisfaction of noise levels at night are lower than other items scored in the survey. While the hospital’s noise satisfaction levels are on par with the national averages, these scores are below the state of Nebraska’s average. The facility has a noise satisfaction rating of 61%, while state satisfaction with noise levels is 70%, and the national average is 62% (CMS, n.d.-b). The World Health Organization (WHO) has published a report on the negative effects of noise in European countries. This report showed that noise levels above 45 decibels (dB) had caused sleep interruption and decreased rest (WHO, 2009). To bring this into perspective, libraries and quiet offices have noise levels of 40 dB (Center for Hearing and Communication, n.d.). In comparison, a normal conversation produces noise levels at 60 dB (Center for Disease Control and Prevention, 2019). Using Hospital Compare and HCAHPS scores, patients and families can research each hospital (Centers for Medicare and Medicaid Services, n.d.-b). This research provides a mechanism for patients and families to shop for a preferred hospital. With these choices, patient satisfaction scores become important for continued return patients as well as developing new patient relationships. A decrease in patient population will cause a reduction in hospital revenue. The reduction in revenue, combined with a decrease in CMS reimbursement, will have a negative impact on the facility’s financial health.

effective monitoring of the patient . A 2-hour quiet time was implemented from 1 p.m. to 3 p.m. daily. During this quiet-time, lighting levels were reduced in patients' rooms, and room doors were closed. Staff limited patient interactions to only those specific interactions needed to provide safe and effective patient care . Attempts were made to schedule all medications, lab sampling, and testing outside of the quiet time.
Nurses, when possible, scheduled routine assessments and rounds outside of quiet time. Patient families were educated about quiet-time and the need for patient rest. Families were requested to limit patient visits and interactions during this time . When patient interactions were unavoidable, they were limited to only what was necessary for safe and effective patient care.
The comparison was normal day time activities with no special attempts to reduce sound levels. Normal daily activities meant that lights were maintained at normal daytime levels, and patient doors may have remained open throughout the day. Patient interactions with staff occurred as scheduled or when new orders were received. All testings were performed when the order was received and could be scheduled with the appropriate service. Nursing assessments were performed as scheduled and when convenient for the nurse. Provider rounds occurred when the provider found it convenient to perform this duty. Alarm and monitor volumes were maintained at the level that was preset with no decrease in volume (Adatia et al., 2014).
PATIENT SATISFACTION WITH NOISE 7 A survey tool was used to measure patient satisfaction with noise . This project was conducted over six weeks. Patient surveys were conducted during discharge. This allowed enough time to collect noise and survey data that was significant to detect overall and sustained change. The sound data and surveys were reviewed weekly at the performance improvement meeting.

Quality Improvement Framework and Change Theory
This project used the knowledge-to-action (KTA) process framework. Quality assurance and the understanding of evidence-based practice has been around for many years. There was still a gap between the knowledge and the application in practice. The KTA process was developed to help minimize this gap and create a tool to transfer the knowledge into actions (Graham et al., 2006).
The KTA process is highly adaptable for individuals, teams, and organizations. This process is an eight-phase process designed to work well in healthcare organizations. The first phase is identifying the problem. Once the problem is identified, the next phase is to identify and review the knowledge relevant to the problem. After the knowledge has been identified and reviewed, the third phase is to adapt this knowledge to the local context. The fourth phase is to look for possible barriers to the use of the identified knowledge. The next phase is to develop and implement interventions to use this knowledge. The last three phases are to monitor the use of the knowledge, evaluate the outcome, and then sustain the use of the knowledge (Graham et al., 2006). See (Figure 1) for the Knowledge to action process diagram.
Kurt Lewin's theory of change was used as the change theory for this project. The theory provided a simple change structure with which many individuals are familiar. There were three phases in this change theory. The first phase was to unfreeze the current situation. This could be PATIENT SATISFACTION WITH NOISE 8 accomplished by increasing the driving force of the change or decreasing the resistance to the change. This was accomplished by the education of the noise problem and its effect on patient satisfaction as well as causes of excessive noise. The second phase was moving or changing in which the institution moved to a new equilibrium. This phase was the implementation of the noise reduction program and monitoring the change. Refreezing was the final phase and was accomplished with maintaining the change and applying it to other inpatient settings within the organization. This was the point where the change was sustained within the institution (Lewin, 1957, as cited in White, 2016).

Evidence Search Strategy
A search of databases was performed that included: Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and ProQuest. Searches were conducted to find literature specific to the following PICOT question. In Acute Care Patients (P), does the implementation of a noise reduction program (I) compared to no noise reduction program (C), affect patient satisfaction with noise (O) over a six-week period (T)? The headings and keywords for the initial search used the terms: noise, patient, satisfaction, and hospital. These terms were placed in a Boolean argument of "noise AND patient AND satisfaction AND hospital." To ensure current literature was referenced, the search was limited to articles published in 2015 through 2020. The searches were limited to peer reviewed articles in academic journals with a subject of noise and written in English. A second set of searches was performed using the terms: sleep, disruption, noise, and hospital. These terms were then placed in a Boolean argument of "sleep AND disruption AND noise AND hospital." As with the first search, this search was limited to articles published in 2015 through 2020, as well as articles in academic journals that were peer-reviewed with a subject of noise and written in English. A review of the titles and PATIENT SATISFACTION WITH NOISE 9 abstracts was performed using the following inclusion criteria: noise reduction, acute care setting, and review of patient satisfaction surveys. All studies that met the inclusion criteria were retrieved and evaluated to determine final eligibility. This final evaluation was performed to ensure the articles used were research articles.

Evidence Search Results and Evaluation
A search of databases was performed that included: Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and ProQuest. The headings and keywords for the initial search used the terms: noise, patient, satisfaction, and hospital. These terms were placed in a Boolean argument of "noise AND patient AND satisfaction AND hospital." To ensure current literature is referenced, the search was limited to articles published in 2015 through 2020. The searches were limited to peer reviewed articles in academic journals with a subject of noise and written in English. A second set of searches was performed using the terms: sleep, disruption, noise, and hospital. These terms were then placed in a Boolean argument of "sleep AND disruption AND noise AND hospital." As with the first search, this search was limited to articles published in 2015 through 2020, as well as articles in academic journals that were peer-reviewed with a subject of noise and written in English.
The initial database search produced 70 articles. The second search produced 78 articles.
The combined database searches produced 124 individual articles. A review of the titles and abstracts was performed using the following inclusion criteria: noise reduction, acute care setting, and review of patient satisfaction surveys. The inclusion criteria review produced 45 articles. All studies that met the inclusion criteria were retrieved and evaluated to determine final eligibility. This final evaluation was performed to ensure the articles used were research articles.
PATIENT SATISFACTION WITH NOISE 10 This produced a total of 16 research articles for further evaluation. See Figure 2 for the Prisma flow diagram The level and quality of the evidence was evaluated using the Johns Hopkins Nursing Evidence Based Practice Evidence Level and Quality Guide (Dang & Dearholt, 2018). Table 1 describes the criteria.

Themes from the Evidence
The issue of excessive noise in the hospital was documented in several studies. These studies noted that nighttime sound levels could regularly exceed 50 dB. Daytime noise levels could average as high as 75 dB with spikes as high as 90 dB Ryan et al., 2016;Zamani et al., 2018). Six of the studies, that were rated of good quality or better using Johns Hopkins Nursing Evidence-Based Practice Evidence Level and Quality Guide, measuring noise levels. Of these six, four were in an intensive care unit (ICU), and two were in MSUs. All six studies reported excessive noise levels MacKay et al., 2019;Ryan et al., 2016;Zamani et al., 2018).
These sound levels exceeded the WHO's recommendations for healthcare settings to maintain nighttime sound levels below 30 dB and daytime sound levels below 35 dB .
When a noise reduction program was implemented, patients perceived a reduction in noise levels. The program also correlated to an improvement in the patients' overall satisfaction with noise. Three of the studies reviewed showed that patient satisfaction improved when there was a perception by the patient of noise reduction. The three studies on patient perception of noise were rated at good quality Johns Hopkins Nursing Evidence-Based Practice Evidence Level and Quality Guide, two were in an ICU, and one was in an MSU Mutair et al., 2019;Younis et al., 2020). One of the studies showed that during the implementation of a noise reduction program that included daytime quiet time, the perception of noise reduction occurred despite no actual reduction in measurable noise levels .
The causes of sleep disturbances were from four sources. These sources were staff interventions, noise, light, and room temperature. Staff interventions were the highest cause of sleep disturbances followed by noise Gulam et al., 2020;Mutair et al., 2019;Stickland et al., 2016;Stremler et al., 2015;Younis et al., 2020).  noted in their study that the expectation of both staff and patients was for the patient to experience reduced levels of sleep and rest while in the hospital.
Noise reduction strategies included staff education, implementation of quiet-time, staff behavior modification, and identification and mitigation of equipment noise sources Kaur et al., 2016;Stickland et al., 2016). Primary sources of noise were staff conversations, monitors and alarms, noise from other patients, and families Christofel et al., 2016;Ryan et al., 2016;Stremler et al., 2015).
The implementation of a daytime quiet time improved patients' perception of noise as well as their quality of rest. The quiet time was set as 1 to 2 hours in the afternoon with reduced noise. This time was also used to limit staff interventions to promote rest Tabas et al., 2019;Waller-Wise & Mad, 2019).
Processes to improve sleep at night included noise reduction and the wearing of eye masks and earplugs Tabas et al., 2019). In addition to eye masks and earplugs, Clark and Mills (2017)

Practice Recommendations
Excessive noise was a leading theme in the research Ryan et al., 2016;Zamani et al., 2018). From the beginning of nursing as a formalized training, Florence Nightingale stated that excessive noise was detrimental to the health and recovery of the patient (Nightingale, 1860). Poor rest in hospitals can lead to increased problems with hypertension, hyperglycemia, delirium, and slower recovery. The studies showed that sleep had a positive effect on patient recovery and the speed of recovery Zhang et al., 2020).
A formal noise reduction protocol was implemented Kaur et al., 2016;Stickland et al., 2016). Part of the program was the establishment of a 2-hour daytime quiet period running from 1 p.m. to 3 p.m. This quiet period included the dimming of lights and closing of patient doors. Staff interactions with patients were minimized to those items that were necessary for patient health and safety. All non-critical activities were scheduled outside of this quiet-time Tabas et al., 2019; Waller-Wise & Mad, 2019).
Education was needed to assist the staff in understanding what excess noise is and the causes of excess noise. The causes of excessive noise could include staff conversations, monitors and alarms, noise from other patients, and families Christofel et al., 2016;Ryan et al., 2016;Stremler et al., 2015). The education also needed to include why noise control was important and how the staff could actively participate in the elimination of noise to provide a restful environment for the patients Kaur et al., 2016;Stickland et al., 2016).
PATIENT SATISFACTION WITH NOISE 14 Patient preferences for a restful night were noted and provided to the best of the staff's ability. These preferences could include such items as door closure, light brightness, warm blankets, bedtime snacks, eye masks, and earplugs Tabas et al., 2019). To facilitate the communication of the patient's bedtime ritual requests, a sleep menu that lists the patient's individual preferences was used as a communication tool .

Project Setting
This project was implemented in the MSU of a physician owned acute care hospital in central Nebraska. The MSU was a 23-bed facility with all beds in private rooms. The unit was laid out in a "T" shape with the nurses' station at the intersection of the "T." The MSU supported both medical and surgical patients that needed hospitalization but did not need the advanced care of a post-critical unit (PCU) or ICU.
The organization had a mission to improve the health of the population served, with a commitment to excellence. The organization's vision was to provide advanced medical care, in a network, that was physician guided. This care needed to be inspired by the community and be compassionate, personal, and innovative.
A vertical organizational structure was used. The chief executive officer had senior executives that reported to him. Each executive was responsible for one or more departments with department directors reporting to that executive. This allowed for faster decision making as well as increased accountability at all levels. The disadvantage of this type of structure was the possible isolation between branches. This could create reduced inter-department communications and collaboration (Society for Human Resource Management, 2015).
PATIENT SATISFACTION WITH NOISE 15 A culture of innovation was promoted at all levels of the organization. Members were encouraged to bring innovative ideas to management for consideration and possible implementation. All departments in the organization were actively looking to change and improve the care provided. Change could be small or large. The important part of the change must be focused on patient safety and satisfaction.
The CMS collects quality data on all hospitals receiving Medicare or Medicaid payments for reimbursement. This information is collected and recorded in the HCAHPS (CMS, n.d.-a).
This data is public information and reported on Hospital Compare. Currently, the organization had a rating of 61% for the quiet at night question. The state average for the same question was 70%, and the local competing hospital had a score of 60% (CMS, n.d.-b). This, as well as observations by leadership at all levels, identified noise in the hospital as an issue.
All personnel and groups that provided services or interacted with the patients in the MSU were stakeholders in the project. This included the leadership for these groups. The stakeholders were medical staff, nursing staff, radiology staff, surgery staff, respiratory staff, laboratory staff, dietary staff, rehabilitation services staff, pharmacy staff, housekeeping staff, and facilities staff. Along with these staff members, the patients and their families were also stakeholders in this project. Support for this project was shown from the start. The chief nursing officer determined that noise was an issue that needed to be addressed and requested that a project be developed to reduce noise and improve patient satisfaction with noise. Once the noise reduction program was implemented within the MSU, management continued to reinforce the need for noise reduction and continued use of the tools implemented for the project. Upon completion and evaluation of the noise reduction program within the MSU, this same project was implemented in other inpatient units throughout the facility.
The organization promoted interprofessional collaboration and communication. This collaboration was accomplished through regular meetings with many disciplines represented.
One example of this was the Clinical Leadership Team (CLT). The CLT met weekly to discuss any current issues and trends. All individuals had equal input and all input was valued. As action items were identified, they were assigned to the appropriate area of the organization with due dates for completion of report. The results of these meetings ensured that all parts of the organization were communicating to prevent isolation.
The strengths, weaknesses, opportunities, threats (SWOT) analysis (see Appendix C) showed that the organization was forward-thinking and readily embraces needed change. There was a limitation on the needed space, which was being addressed with an expansion project. The major threat to the organization was competing facilities, both locally and regionally.

Project Overview
The mission of this project was to provide a quiet and restful environment for the patients and their families to promote healing. The vision of the project was to contribute to the compassionate care of the patient. These aligned with the organization's mission of promoting community health and the vision of providing personal, compassionate, and innovative care.
There were two short-term objectives for the noise reduction project. The first objective was to reduce hospital noise in the MSU. The other objective was to improve patient and family satisfaction with noise. The long-term objective was to expand the project throughout the facility with the ultimate goal of improving the HCAHPS score for noise at night.

PATIENT SATISFACTION WITH NOISE 17
The risks and unintended consequences included the possibility that noise levels did not decrease. There was the possibility of missed medication during quiet times. Decreased alarm volumes could lead to longer response times for alarms. Ancillary departments may have had a risk of conflict in performing their needed duties such as room and floor cleaning.

Project Plan (Method)
This project used the KTA process, which was developed as a tool to transfer knowledge into action. The KTA process was highly adaptable for individuals, teams, and organizations.
The first phase was identifying the problem. Once the problem was identified, the next phase was to identify and review the knowledge relevant to the problem. After the knowledge had been identified and reviewed, the third phase was to adapt this knowledge to the local context. The fourth phase was to look for possible barriers to the use of the identified knowledge. The next phase was to develop and implement interventions to use this knowledge. The last three phases were to monitor the use of the knowledge, evaluate the outcome, and then sustain the use of the knowledge. The KTA model was a continuous process that loops back to identifying the problem and then researching, developing, and implementing a solution (Graham et al., 2006).

Identify a Problem That Needs Addressing
According to Hospital Compare, 61% of patients stated that the area outside their room was quiet at night. This compared to an average of 70% for the state (CMS, n.d.-b). The hospital decided to address the overall noise in the facility. showed that the implementation of a noise reduction program, along with the use of sleep menus, increases the patient's satisfaction with noise in the hospital Mutair et al., 2019;Younis et al., 2020).

Adapt the Identified Knowledge or Research to the Local Context
The research identified in the literature review was conducted in medical-surgical, postcritical, intensive care, and maternal units. Results from research in the non-MSUs were similar to the MSU. The research showed the use of noise reduction education, monitoring, quiet-times, and sleep menus were effective in reducing noise and improving patient satisfaction with noise Kaur et al., 2016;Stickland et al., 2016;Tabas et al., 2019). All results could easily be adapted to the local context.

Assess Barriers to Using the Knowledge
Staff might have been resistant to change. Medical and ancillary services might have resisted rescheduling of non-emergent tasks to hours outside of "Quiet-Time." Visitors might not have cooperated with noise reduction initiatives. Staff might not have been motivated or feel that the change was important. The education of all staff and supervisors was performed by the project manager. The staff that was trained included but was not limited to medical staff, nursing staff, radiology staff, surgery staff, respiratory staff, laboratory staff, dietary staff, rehabilitation services staff, pharmacy staff, housekeeping staff, and facilities staff. This education included noise sources, ways to mitigate the noise, and the impact of noise on patient rest, healing, and satisfaction (see Appendix D). All staff monitored perceived noise levels and reported any sources of perceived excessive noise. Staff also worked to minimize excessive noise by lower alarm volumes, closing patient doors, and minimizing loud conversations.

Select, Tailor, and Implement Interventions to Promote the Use of Knowledge
Six-weeks prior to the implementation of the quiet-time hour, patients started receiving the Patient Survey on Noise During Hospital Stay at their discharge (see Appendix E). The survey used a paper form. This survey was developed for  and was used with the permission of the developer (see Appendix F)(D. Applebaum, personal communications, July 1, 2020). The surveys continued to be used throughout the entire project.
Prior to implementation, the sound meter was installed. The training of supervisors and staff was completed, and the pre-implementation surveys were completed. The project intervention ran for six weeks before the final evaluation was performed, and the data compiled for reporting. The timeline is shown in (Appendix G). The projected budget for the project is shown in (Appendix H).

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During the project, a sleep menu was implemented. The options on the sleep menu were developed with input for the MSU staff and leadership. This sleep menu was part of the patient communication board in each room. This sleep menu was used to communicate patient preferences for nighttime rituals that promoted rest (see Appendix I). The unit aides discussed with the patient and entered sleep menu preferences onto the patient communications board. In the evening, the unit aides ensured that patient preferences were implemented.

Monitor Knowledge Use
Sound meter readings were used to measure quantitative noise levels. The readings were used to trend noise levels during the project. All patients received the Patient Survey on Noise During Hospital Stay during their discharge education. All surveys were reviewed for trends and patient feedback. The hospital's leadership rounded daily to determine if quiet time was being implemented and recorded the results for analysis and final reporting.
The project manager compiled and analyzed all data from the surveys and sound monitoring equipment. This data was used to provide ongoing feedback to staff and leadership.
All surveys were reviewed for trends and patient feedback. The results of this analysis were provided to the staff and leadership. The results were presented at the leadership and staff meetings. The project manager facilitated discussion of possible improvements or identifying and resolving any concerns of the staff and leadership.

Results
This section will review the results of the project. An analysis of the project was performed to determine if the there was a statistical or clinical significance to the project.

Recruitment and Selection of Participants
All patients discharged from the MSU were asked to complete the Patient Survey on Noise During Hospital Stay. The patient had the option to refuse participation. Patients with known hearing impairments were excluded from the survey data.

Data Collection
Sound meter readings were used to measure the quantitative change in noise levels. Noise level satisfaction was determined by surveys given to the patients during their discharge education. Survey data was collected 6 weeks before the start of the project to provide baseline patient satisfaction levels. Baseline sound level data was collected for 6 weeks before the implementation of the project. The baseline data and discharge surveys were used to determine the outcome of the project.
Noise satisfaction scores were obtained with discharge surveys and used to measure patient satisfaction with noise levels. The tool used was the Patient Survey on Noise During Hospital Stay, as seen in Appendix E. This tool consisted of four items on a five-point Likert scale. The scale was: strongly agree, agree, neither, disagree, and strongly disagree. These four questions measured the patient's satisfaction with noise in the hospital. An additional five items using the same Likert scale were used to assess the quiet-time implementation's efficacy. There were five questions used to help identify sources and times of noises that disturb the patients.
Additional questions included sex, age, and length of stay. No personal identifying information was collected.
Noise levels were continuously monitored using a permanently placed sound monitor using a REED Instruments R8070SD Sound Level Meter. This ensured that noise monitoring was valid and reliable.

Data Collection, Analysis, and Storage
Discharge nurses provided all patients being discharged with a copy of the Patient Survey on Noise During Hospital Stay during their discharge education. The patients were requested to complete the survey before leaving the hospital. The completed survey forms were stored in the unit manager's office and then collected by the project manager. The survey forms were then scanned electronically and stored in encrypted cloud storage. The original forms were then shredded.
Sound data were collected using a REED Instruments R8070SD Sound Level Meter. This meter had internal storage for sound data collected. The project manager downloaded the data from the sound meter weekly.
The project manager collected and compiled all data and performed the analysis. The data were stored in cloud storage. The files were encrypted using Advanced Encryption Standard (AES) 256-bit encryption (Nechvatal et al., 2001). This ensured redundancy as well as the security of the data.

Data Source Integrity
Patient satisfaction data were collected using the Patient Survey on Noise During Hospital Stay, as seen in Appendix E. This survey was developed for  and was used with the developer's permission (D. Applebaum, personal communications, July 1, 2020). The survey was used throughout the entire project.
The survey was administered using a paper form. Sound levels were recorded using a REED Instruments R8070SD Sound Level Meter. This meter had internal storage for data.

Missing Data and Data Storage
Missing data were most likely missing at random (MAR). Pairwise deletion was the least biased way of treating MAR data and was used for missing data (Kang, 2013). All collected data were stored in cloud storage. The files were encrypted using Advanced Encryption Standard (AES) 256-bit encryption (Nechvatal et al., 2001). The encryption and cloud storage ensured redundancy as well as the security of the data.

Evaluation Design
Because sampling was by the convenience of the patients being discharged, there was no random sampling ability. The comparison group used primary data collected during the 6-weeks before implementing the noise reduction program. Descriptive statistics were used to describe the results of the project. Sound levels were analyzed, comparing baseline data with postintervention data using an independent t-test. Patient satisfaction with noise levels was analyzed using baseline and post-intervention data with an independent t-test. Statistical analysis of the data was performed using Intellectus Statistics.

Categories of Measures
Outcome measures were used to show the final results of the project. This included the satisfaction survey results and sound level measurements at the end of the 6-week implementation period. The process measure for this project was sound levels in the MSU. The balancing measures included ensuring that medication errors did not increase or an increase in missed monitor or IV pump alarms. The financial measure was ensuring that the project stays within budget. The sustainability measures will be implementing the intervention in other departments throughout the organization after the initial six weeks in the MSU.

WHO (2009) provided recommendations for noise levels in hospitals.
These recommendations were used for setting the noise level benchmarks. The organization stives to achieve the highest satisfaction level. This led to the benchmarks for all satisfaction survey questions. These benchmarks are presented in Table 2.

Tools
Baseline noise levels were obtained before the intervention. Historical satisfaction scores with hospital noise were obtained from discharge surveys collected for 6 weeks before implementing the change project. Noise levels were continuously monitored using a permanently placed sound monitor using a calibrated REED Instruments R8070SD Sound Level Meter. This ensured that noise monitoring was valid and reliable. This data was continuous.

PATIENT SATISFACTION WITH NOISE 25
Noise satisfaction scores were obtained with discharge surveys and indicated the patients' satisfaction with noise levels. The tool used was the Patient Survey on Noise During Hospital Stay, as seen in (Appendix E). This tool consisted of four items in a five-point Likert scale. The scale is: strongly agree, agree, neither, disagree, and strongly disagree. These four questions measured the patients' satisfaction with noise in the hospital. An additional five items using the same Likert scale were used to assess the quiet-time implementation's efficacy. Five questions were used to help identify sources and times of noises that disturb the patients. Additional questions included sex, age, and length of stay. No personal identifying information was collected. Reliability for the tool was determined by Cronbach α coefficient calculation and resulted in a coefficient of 0.60. The Patient Survey on Noise During Hospital Stay was administered using a paper form. The survey data was ordinal.

Control for Extraneous Influences
The MSU supervisor or house supervisor conducted daily reviews and walkthroughs to ensure that the noise reduction plan was implemented. Deviations from the plan will be corrected using just in time education of the staff and the reimplementation of the missing processes. This will also be reported to the project manager.

Patient Demographics
The most frequently observed category of Gender was Female (n = 69, 64%).
Frequencies and percentages are presented in Table 3.

Awakened at Night by Sounds
A two-tailed independent samples t-test was conducted to examine whether the mean of awakened at night by sounds was significantly different between the pre and post intervention categories. The result of the two-tailed independent samples t-test was not significant based on an alpha value of 0.05, t(103) = 0.75, p = 0.452, indicating the null hypothesis cannot be rejected. This finding suggested the mean of awakened at night by sounds was not significantly different between the pre and post intervention categories. The results are presented in Table 5.

Rest During the Day Disturbed by Sound
A two-tailed independent samples t-test was conducted to examine whether the mean of rest during the day was disturbed by sounds was significantly different between the Pre and Post Intervention categories. The result of the two-tailed independent samples t-test was not significant based on an alpha value of 0.05, t(103) = -0.67, p = 0.501, indicating the null hypothesis cannot be rejected. This finding suggests the mean of rest during the day was disturbed by sounds was not significantly different between the pre and post intervention categories. The results are presented in Table 6.

Acceptable Sleep Quality
A two-tailed independent samples t-test was conducted to examine whether the mean of acceptable sleep quality was significantly different between the pre and post intervention categories. The result of the two-tailed independent samples t-test was not significant based on an alpha value of 0.05, t(104) = -0.29, p = 0.776, indicating the null hypothesis cannot be rejected. This finding suggests the mean of acceptable sleep quality was not significantly different between the pre and post intervention categories. The results are presented in Table 7.

Acceptable Sleep Quality
A two-tailed independent samples t-test was conducted to examine whether the mean of acceptable sleep quality was significantly different between the pre and post intervention categories. The result of the two-tailed independent samples t-test was not significant based on an alpha value of 0.05, t(102) = -0.54, p = 0.591, indicating the null hypothesis cannot be rejected. This finding suggests the mean of acceptable sleep quality was not significantly different between the Pre and Post Intervention categories. The results are presented in Table 8. Note. N = 104. Degrees of Freedom for the t-statistic = 102. d represents Cohen's d.

Sound Analysis
A two-tailed paired samples t-test was conducted to examine whether the mean difference of pre and post-intervention average noise levels was significantly different from zero. The result of the two-tailed paired samples t-test was significant based on an alpha value of 0.05, t(143) = 3.03, p = 0.003, indicating the null hypothesis can be rejected. This finding suggests the difference in the mean of pre and the mean of post-intervention average noise levels was significantly different from zero. The mean of pre-intervention average noise was significantly higher than the mean of post intervention average noise. The results are presented in Table 9.

Statistical and Clinical Significance
During the intervention, a statistical decrease in measurable noise levels was expected.
Any notable decrease in sound levels or increased patient satisfaction with noise will be of clinical significance.
The analysis showed a statistical decrease in average noise levels. The four questions dealing with patient satisfaction with noise did not show a statistical change. Sleep disturbances caused by noise at night showed a decrease in patient satisfaction. Patient rest during the day, as well as overall sleep quality and quantity, did show improvements. Because of the increase in patient satisfaction with noise in three areas and the statistical decrease in average noise levels, this project showed clinical significance.

Human Rights and Privacy
This project proposal was submitted to the University of St. Augustine for Health Sciences Nursing Evidence-Based Practice Project Review Council for review and approval.
Once this approval was obtained, the project proposal was forwarded to the facility's chief nursing officer for final approval. This project had minimal risk to the patients as well as minimal risk of subject identification. The patients were surveyed with no patient identification information. The data collected was the patient's perception of noise and satisfaction with the noise levels. All data was stored in encrypted cloud storage. There were no conflicts of interest.
There was no incentive given for participating in this project.

Impact
This project brought an awareness of noise to the facility staff and how it can impact patient satisfaction. Staff is aware of the noise that they generate and are cognizant of how they can help to reduce excess noise. Digital sound level displays were left in place for the staff to PATIENT SATISFACTION WITH NOISE 30 self-monitor the noise levels in the MSU. The visual feedback sound monitor remains installed.
This gives management and staff a visual tool to help reenforce the need for noise reduction.
HCAHPS scores will be reviewed periodically by management to measure progress and sustainability. These tools will help to make this a part of the everyday work culture in the MSU.
To monitor the continued effectiveness of the project, HCAHPS scores for the patients' satisfaction with noise will need to be monitored for any changes.

Plans for Dissemination
Due to the COVID-19 pandemic, the project results were presented through virtual meetings utilizing GoToMeeting with a PowerPoint. The results of this project were presented to the CLT. PATIENT There are two organizations in Nebraska where the findings of this project will be presented. The Nebraska Hospital Association sponsors a quality improvement conference twice a year. One is the eastern part of the state, and the other is in the western region. This presentation will be submitted for presentation at both conferences. The other organization is the Nebraska Association of Healthcare Quality, Risk, and Safety. This organization meets every other month, and the project will be submitted for presentation at one of these meetings.
An abstract will be submitted to the National Association of Healthcare Quality (NAHQ) for consideration as a presenter for their annual conference. If the presentation is not accepted, then a separate application will be submitted for a poster presentation at the same conference.
NAHQ was chosen because it is the leading professional organization for healthcare quality.
Publication is planned in the Journal of Healthcare Quality. This journal is the official journal of NAHQ. This will provide for the broadest dissemination among the healthcare quality community. The journal is peer-reviewed and has an internal peer-review process.
This evidence-based project will be submitted to the Scholarship and Open Access Repository website at the University of St. Augustine for Health Sciences (SOAR@USA). To contribute to wider dissemination, the project will also be submitted to ProQuest.

Conclusion
The goal of the noise reduction project was to improve the patients' overall satisfaction with hospital noise during the day and overnight. The ultimate goal was to improve the HCAHPS score for noise outside of the patients' room at night. This score is reported by CMS and is readily accessible by the public.    Christofel et al., 2016;Ryan et al., 2016;Stremler et al., 2015) Noise

Monitoring Tools
Patient Survey Sound Level Meter REED Instruments, n.d.) Questions?

15.
The two hour of quiet time (1 p.m. -3 p.m.) greatly helped to decrease the level of noise in my room. Strongly Agree☐ Agree ☐ Neither ☐ Disagree ☐ Strongly Disagree ☐

16.
The implementation of quiet time for 2 hour facilitated a quieter, more restful environment for the rest of the day. Strongly Agree☐ Agree ☐ Neither ☐ Disagree ☐ Strongly Disagree ☐

17.
Please rank (from 1-5) the top 5 sources of noises that disturbed you during your hospital stay, with 1 being the most bothersome, and 5 being the least.