Implementing an Evidence-Based Practice Change for Alcohol Implementing an Evidence-Based Practice Change for Alcohol Withdrawal in an Acute Care Hospital Withdrawal in an Acute Care Hospital

Practice Problem: Alcohol Use Disorders (AUD) affects a significant portion of the population in the United States. When AUD is either unrecognized or inadequately treated in the acute care setting it can lead to medical complications, increased length or stay (LOS), increased healthcare expense, and increased patient mortality. PICOT: In a population of adult patients admitted to an acute care hospital progressive care unit (P), how does applying an initial evidence-based screening tool to detect risk for moderate to severe alcohol withdrawal, the PAWSS (I), compare to no standard screening or assessment for potential alcohol withdrawal symptoms (C) affect the occurrence of patient deterioration for acute alcohol withdrawal symptoms (O) within an eight week timeframe (T)? Evidence: The primary research articles included resulted in Level II grade criteria according to the Johns Hopkins EBP Model rating hierarchy. The PAWSS tool was supported as both a reliable and valid predictive measure of risk for developing AWS in the acute care setting. Intervention : The PAWSS tool was utilized to screen all patients admitted to the progressive care unit. Patients identified at moderate to severe risk by a score of ≥4 were treated according to the standard facility practice with included CIWA-Ar monitoring and medication management with benzodiazepine medication. Outcome: The project was able to demonstrate a significant decrease in the mean LOS for those patients identified at risk and treated for AWS, with an average decrease of 50 hours in length of stay for those patients treated during the project implementation. Conclusion: Early recognition of patients at risk for AWS is an important component of effective management and treatment. Further study is needed into best practices for treatment of patients at risk, and internal compliance measures within the organization.

medical condition (Sukhenko, 2015). Complications of withdrawal can include mild symptoms like nausea, vomiting, or increased blood pressure (Trevisan et al., 1998). Up to 20% of patients with acute alcohol withdrawal develop severe symptoms associated with complicated AWS, including delirium tremens and withdrawal seizures (Maldonado et al., 2010) or Wernicke encephalopathy (Ostrovsky, 2018). For those patients that do experience these more severe complications, up to 20% may ultimately die from these complications compounded by other medical comorbidities (Campos et al., 2011).

PICOT Question
The PICOT question addresses the relationship between a population (P), intervention (I), comparison (C), outcome (O) and time (T). This project poses the question in a population of adult patients admitted to an acute care hospital progressive care unit (P), how does applying an initial evidence-based screening tool to detect risk for moderate to severe alcohol withdrawal, the PAWSS (I), compare to no standard screening or assessment for potential alcohol withdrawal symptoms (C) affect the occurrence of patient deterioration for acute alcohol withdrawal symptoms (O) within an eight week timeframe (T)? The population will include all adult patients admitted to the progressive care unit in an acute care hospital. The intervention would be the application of the PAWSS at the time of admission to the unit. The comparison would be the current practice of no standard assessment at the time of admission for risk for alcohol withdrawal. The outcome of patient deterioration would be defined by indicators of severe alcohol withdrawal including the need for rapid response, CIWA-Ar scores increased over mild range indicated by a score of greater than 15, "code green" security personnel response for patient behavioral deterioration, or symptoms requiring a higher level of care or ICU transfer.
The timeframe would be eight weeks.

Evidence-Based Practice Framework & Change Theory
This project utilized the John's Hopkins Evidence-Based Practice (EBP) model to describe the practice change. The John Hopkins Nursing EBP model follows a three-step process for practice change: practice question, evidence, translation (Dang & Dearholt, 2018). The goal of this model is to incorporate the latest research findings and best practices into patient care in a manner that efficient and appropriate (Dang & Dearholt, 2018). This model starts with generating a practice question, then evaluates evidence to support practice change, and finally translates the evidence into practice change.
This change project utilized Lewin's Change Theory to facilitate practice change.
According to Lewin's Change Theory as it is applied to nursing practice, "change occurs in three stages: unfreezing, moving and refreezing" (Lee, 2006, p. 489). Specific strategies are needed at each stage of change to continue to facilitate the change process. This theory was a good fit to address practice change for implementing an alcohol withdrawal assessment tool because it was easily applied to evidence-based practice change (Manchester et al., 2014). In the initial unfreezing stage, evidence-based practice change was identified and stakeholder buy-in helped drive momentum for the practice change. The project manager helped develop an awareness of the significance of the practice problem and the gaps within the current organizational practices.
Additionally, the project manager helped to identify nurse champions to keep change momentum progressing during the "moving" stage. During the moving phase, the project manager, along with key stakeholders in the process, implemented the change in practice. Throughout this "moving" stage, staff were educated and encouraged to implement the change to drive better patient outcomes. During the "refreezing" stage, the change continues to be reinforced. This included organizational and environmental changes to sustain the change, and recommendations to include other techniques like periodic auditing to cement the practice change.

Evidence Search Strategy
To gain an initial understanding of the scope and depth of the problem, as well as to become more familiar with some of the current literature trends and key relevant search terms, initial literature searches were conducted via databases such as the Cochrane Database of Systemic Reviews (CDSR), PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL) complete and APA PsychINFO database search as well as Google Scholar website.
Some of the terms searched in different combinations included inclusion criteria of the terms: alcohol withdrawal syndrome, AWS, alcohol withdrawal assessment, and acute care, clinical assessment, and acute alcohol withdrawal. Articles that were systematic reviews or metaanalyses were identified within the literature review were also examined to help further define search criteria and potential exclusions. Medical Subject Headings (MeSh) terms of "Alcohol Withdrawal Syndrome" and "Clinical Assessment" were utilized also as inclusion criteria. These searches resulted in over 2200 articles from the 2010 to 2021 timeframe.
Exclusion criteria were developed to attempt to eliminate research content related to emergency department or ICU care setting, mental health settings, studies primarily focused on medication management strategies, pediatric population, articles focused on animal populations, and articles not written in the English language. The abstracts were reviewed for exclusion criteria and those articles that met the inclusion criteria for relevance to the PICOT question were full-text reviewed. A total of 48 articles were included for full-text review and 26 articles were included in the final synthesis of the literature.

Evidence Search Results
To understand and define AUD, understand AWS, and evaluate the evidence-based recommendations for assessment and treatment of the disorder, a comprehensive literature review was conducted. Initial search results were outlined according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model as discussed above (See Figure 1 for PRISMA model). Of these, four articles were identified as primary research related to the PICOT question (see Appendix A) and four were identified from systematic reviews as relevant to the PICOT question (See Appendix B).
Those articles included as primary research were graded according to the Johns Hopkins EBP Model rating hierarchy for the level of research evidence (Dang & Dearholt, 2018). All of the primary research articles included resulted in Level II grade criteria of research (Griessbach et al., 2019;Mahabir et al., 2020;Maldonado et al., 2015;Padron, 2019). One study was a retrospective analysis design (Griessbach et al., 2019), while the others were quasi-experimental designs. Due to the nature of AUD and AWS, there were no primary research studies identified in the literature that would allow for randomized control studies (RCTs) which would allow for Level I research evidence.
Those articles included in the systematic review and meta-analysis were narrowed down to represent those related to predicting the development of AWS and Severe Alcohol Withdrawal Syndrome (SAWS). There were two articles with Level I quality data (Hlleck, Merchant & Gunderson, 2019;Pribek et al., 2021) and another two with Level II quality data (Goodson, Clark & Douglas, 2014;Maldonado et al., 2014) according to the John Hopkins EBP Model rating hierarchy for the level of research evidence (Dang & Dearholt, 2018). (See Appendix B).
These systematic reviews related directly to the assessment of risk for developing AWS or SAWS.

Understanding the Population
Multiple studies have shown that patients who have an Alcohol Use Disorder (AUD) diagnosis, pre-existing their admission to an acute care hospital, are at increased risk for developing complications from AWS, including potentially death (Maldonado et al., 2014, Shu et al., 2015Sukhenko, 2015). Chernyavsky et al. (2020) estimate in the United States that more than 20% of patients admitted to the acute care hospital setting meet the criteria for AUD and that more than 2 million patients experience withdrawal each year. The percentage of patients admitted for medical issues other than AUD who experience alcohol withdrawal can be as high as 42% in veteran populations (Shu, Lin & Chang, 2015). Maldonado, et al. (2014), estimated only 7% of physicians correctly identify patients at risk for complicated AWS in the acute care setting. Additionally, studies support that no one factor is predictive of severe alcohol withdrawal (Goodson, Clark, & Douglas, 2014;Burkhardt et al., 2020;Rosoff et al., 2020).

Evidence-Based Treatment for Patients at Risk for Acute Alcohol Withdrawal Syndrome
Most assessment and treatment strategies for AWS are based on the understanding of the physiology of withdrawal (Maldonado et al., 2014). The ingestion of alcohol has an inhibitory effect on the N-methyl-D-aspartate (NMDA) receptors in the central nervous system and an agonistic effect on gamma-aminobutyric acid type-A (GABAA) receptors within the central nervous system (Dixit et al., 2016;Haass-Koffler, Cannella & Ciccocioppo, 2020). Over time, the continued exposure to alcohol leads to tolerance in the central nervous system, reflected in the NMDA receptors being up-regulated while the GABAA receptors are down-regulated (McKeon, Frye, & Delanty, 2008). When the alcohol exposure in the central nervous system is abruptly decreased or eliminated, these roles are reversed, leading to "dopaminergic dysregulation" and producing the signs and symptoms of AWS (Sykhenko, 2015). Generally, AWS is made up of a cluster of symptoms resulting from this dopaminergic dysregulation. Table 1 for the stages of AWS) usually develop within 24-48 hours after last use including tachycardia, diaphoresis, tremors, irritability, agitation, hypertension, seizures, and sometimes delirium or hallucinations in the later stages (Holt et al., 2016;Ostrovsky, 2018). Not all patients experience all stages, nor do they progress sequentially through them, making recognition of the potential for AWS and ongoing monitoring for those at risk essential in the appropriate management of alcohol withdrawal (Lindsay et al., 2020;Maldonado et al., 2014;Monte et al., 2010).

Evidence-Based Screening for Risk Assessment
While assessment for alcohol withdrawal was previously relegated to those patients displaying risk factors of alcohol abuse, more evidence has shown that routine screening of patients for risk for alcohol withdrawal is more effective than just relying on clinical judgment (Keys, 2011;Maldonado et al., 2014;Sutton & Jutel, 2016). The Clinical Institute Withdrawal Assessment for Alcohol-Revised (CIWA-Ar) has been the most widely researched tool; however, despite its prevalent use, the CIWA-Ar has shown to be inconsistent in its ability to correctly identify patients needing intervention for withdrawal (Hecksel et al, 2008;Holleck et al., 2019;Pribék et al., 2021) In fact, Hecksel et al. (2008) reported that as many as 64% of patients on CIWA-Ar monitoring may be incorrectly identified as needing medication. One of the major critiques of this tool has been its moderate performance at predicting severe alcohol withdrawal (Eloma et al., 2018) presumably because it was designed as a monitoring tool, not a predictive assessment tool.
The Alcohol Withdrawal Triage Tool (AWTT) (Mahabir et al., 2020) is comprised of a complicated set of independent predictors of risk for AWS that need to be gathered from the medical record. The AWTT showed some potential to identify patients at risk for severe AWS, but the validation dataset "resulted in a c-statistic pf 0.786" which was not sufficient to support this as a stand-alone tool (Mahabir et al., 2020, p. 5). The Alcohol Withdrawal Assessment Tool (AWAT) was developed as a shorter version of the CIWA-Ar that took into account some of the physiological symptoms of alcohol withdrawal and had 4 questions related to pulse/blood pressure, agitation/tremors, confusion/hallucination, and diaphoresis (Davis et al., 2018). While the AWAT is one of only a few tools that was tested in the acute care setting, the AWAT was only tested with a small sample size (n=51), and the predictability was compared against the CIWA-Ar which as discussed was not designed to predict risk for alcohol withdrawal. The Luebeck Alcohol withdrawal Risk Scale (LARS) was also developed to predict the severity of withdrawal and has been validated in psychiatric settings (Wetterline et al., 2006). However, there was no evidence of validation for the tool within the acute care hospital setting.
The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) has been both studied and validated in the acute care hospital setting (Maldonado et. al., 2014) for its predictive validity in identifying patients at risk for severe alcohol withdrawal (Maldonado et al., 2015;Padron & Salzman, 2019). See Appendix A. The PAWSS tool research was graded according to the Johns Hopkins EBP Model rating hierarchy for the level of research evidence (Dang & Dearholt, 2018) and was graded Level II primary research, due to strong predictive validity (Maldonado et al, 2015) with a Positive Predictive Validity of 93.1% (95%CI), and Negative Predictive Validity of 99.5% (95%CI). The PAWSS lacks randomized controlled trials, which was anticipated given the nature of the tool. The PAWSS is an open-source tool that does not require permission to use. The application of this assessment tool has not been shown to have any potential risks for the patient population as it can be incorporated into the standard admission assessment.

Practice Recommendation
The evidence-based practice recommendation for patients admitted to an acute care hospital unit was to utilize the PAWSS to screen and predict risk for severe alcohol withdrawal.
By standardizing the risk assessment tool, the facility would be able to more accurately identify those patients at risk for moderate to severe AWS. Those patients identified at moderate to severe risk by a score of ≥4 would be treated according to the standard facility practice which included utilizing the CIWA-Ar (see Appendix H) to monitor for current alcohol withdrawal symptoms and to treat the patient according to the current standard practice which included symptom-based benzodiazepine medication or fixed-dose dependent on the physician orders.
The early and accurate identification of those patients at risk for AWS would support appropriate treatment recommendations to minimize the risk for patient deterioration from unrecognized AWS.

Setting, Stakeholders, and Systems Change
The Southern California setting identified for this evidence-based change project was in an acute care hospital on a progressive care unit. The mission of the organization was to improve the health of those served with a commitment to excellence in all that the organization does. The organizational goal was to offer quality care and services that set community standards, exceed patients' expectations, and provide care that is convenient, cost-effective, and accessible. The organization has 524 licensed beds, with services ranging from Emergency Room Care, Medical and Surgical ICU, Progressive Care, Oncology, Women's Health, Physical Rehab, and Behavioral Health Services. The healthcare facility has a Magnet Designation for Nursing Excellence®. The hospital Emergency Department (ED) serves a diverse population and demographic area that stretches from the coast of Southern California to the Arizona border and was in the top five percent of the nation in Emergency Room patient volume.
A SWOT analysis was conducted as part of the organization gap analysis. There was an identified opportunity to improve patient outcomes with early recognition and treatment of AUD The CNO and the Director of PCU were already aware of instances of adverse patient outcomes and patient deterioration attributed to AWS within the acute care setting. The cooperation of the interdisciplinary team was also essential to the successful implementation of the evidence-based practice change. The front-line nursing staff were responsible for the implementation of the PAWSS screening tool and communicating with the admitting physician the need for implementation of the alcohol withdrawal order set protocol for those patients who screened positive for risk. The CNS as well as the project manager were involved to support the education around the new tool and to refresh the nurses' knowledge on the general treatment of alcohol withdrawal. The social workers were also involved for those patients that needed connection to ongoing treatment options once they were medically stable to discharge from the acute care setting.
The change project impacted individual patients on the micro-level by providing early recognition and treatment of AWS within the acute care treatment setting. Providing routine screening may have also decreased the potential patient stigma and potential bedside RN negative bias associated with Substance Use Disorders (SUDs). This effect was not specifically measured during this project but is a focus of ongoing assessment at the organization. On the meso level, the families of patients receiving treatment are affected by the patient's SUD and may have received greater support once the disorder was acknowledged, and may have sought help to improve their own social supports (Church et al., 2018). On the macro level, providing early recognition and interventions for a chronically undertreated or marginalized medical condition such as SUDs has been shown to improve the overall health of the community.
Additionally, providing proactive treatment may continue to draw larger attention and support for the treatment of marginalized disorders and increase awareness of the mental health parity laws.

Implementation Plan with Timeline and Budget
The construction of a PICOT question, extensive literature review, and practice recommendation formed the foundation for the evidence-based practice change proposal. This foundation was based on the John Hopkins Nursing EBP model that follows a three-step process for practice change: practice question, evidence, and translation. The implementation plan for the EBP change proposal included identifying key project objectives, outlining SMART goals, and finally the application of the change model. These goals had to be accomplished within a reasonable timeline for the project to be successful (see Appendix I for Project Goals and Implementation Strategy, and Appendix D for the Project Timeline).
The first objective of the project proposal was to complete a SWOT analysis of the organization and identify an area for practice change within the first four weeks of the NUR7801 class (see Appendix C SWOT Analysis). This formed the foundation for generating a PICOT question and completing a literature review with practice recommendations. The next objective was to identify key stakeholders and gain buy-in for the project. This goal is measured by the stakeholders identified. The goal was accomplished by demonstrated buy-in of the unit and staff, and key stakeholders identified including the hospital CNO, the Director of PCU, the Unit Manager, the CNS, and hospital informaticist within the first 12 weeks of NUR7801. Once the PICOT question was refined, the literature review was completed, and a practice recommendation was generated. The practice change recommendation was to implement the PAWSS predictive risk assessment at the time of admission to the acute care PCU (see Appendix G for the PAWSS tool). A potential budget was also developed at this stage as it is part of the final approval process for the healthcare organization. The only associated expenses for the project were under $50 for paper supplies to provide the PAWSS assessment tool to the unit, which was incorporated into the existing unit budget, and the CNS/nurse educator time (approximately 10 hours X $60/hr = $600) to help with unit staff training and audits for compliance during the eight weeks of implementation. Since these tasks were incorporated into the CNS regular unit responsibilities, it did not end up creating any additional cost to the organization. The project had the potential to decrease costs to the organization due to decreasing length of stay or need for a higher level of care, however, these specific monetary calculations were outside of the scope of this project. The department manager reviewed and approved the potential budget. The work done to define the problem and propose an evidence-based change aligns with the "unfreezing" stage of Lewin's change theory. It was essential to develop an awareness of the problem and foster motivation for change.
The next portion of the change project timeline consisted of components in Lewin's Change Theory's second stage; the "moving" stage. Primary objectives during this stage included gaining approval for the project implementation from the DNP program and healthcare organization within the first three weeks of NUR7802 (see Appendix D). The University's approval was gained through the EBP Project Review Council (EPRC) process. The facility approval was gained by first submitting the project proposal to the entity-based IRB representative as a project proposal, then once it was determined that the project proposal was EBP not research then the proposal was submitted to the Innovations and Professional Excellence Committee for final approval. A primary reviewer was assigned and requested that an additional flyer detailing the project information to be generated and provided to patients agreeing to be screened with the PAWSS tool. Additionally, the request for data from the EMR needed to be submitted to the organization informatics department, rather than allowing the project manager to collect data directly. This was to ensure that all patient identifiers were removed prior to data analysis.
After gaining both university and facility approval for EBP project, the next stage was the implementation of the educational/training for the unit staff on the PAWSS tool and reinforcing the healthcare facility protocol for the monitoring and treatment of alcohol withdrawal (See Appendix I for Project Goals and Implementation Strategy). The final component of the "moving" stage of change was the implementation of the screening tool on the unit for the eight week intervention period and monitoring for compliance.
During the final stage of Lewin's Change Theory, the "refreezing" stage the focus was on sustaining the practice change. During this stage, the project data was analyzed while impact and practice implications were shared with the key stakeholders. During the "refreezing" stage, the key stakeholders including the Unit Manager, front-line nurses, and the interdisciplinary team took over ownership of sustaining the change process. During the "refreezing" stage, the unit CNS took on the responsibility to continue to perform chart audits to identify any reverting to prior behaviors of not completing the PAWSS screening. Re-education and reinforcement of the application of the PAWSS will be offered by the CNS to sustain practice change. There was a recommendation to "hardwire" the assessment into the electronic medical record however the organization is in the process of changing EMR systems. There was reluctance to spend money to incorporate this assessment tool in the old EMR; however, there is an opportunity to incorporate this practice change in the design of the new EMR, which would support "refreezing" of this practice change.
The EBP change project implementation required a project manager to keep the project on track. This role required the DNP student project manager to be clear and consistent in setting project goals. The project manager was also responsible for gaining key stakeholder buy-in and delegating tasks like nursing education to the unit clinical nurse specialists. The project manager also developed key partnerships with information technology staff to assist with data collection and validation during and following the project implementation. The project manager was responsible for the analysis of the project results and disseminating the findings, clinical significance and future practice considerations to the key stakeholders.

Results
In the PICOT question, the outcome reflecting patient deterioration were defined by indicators of the need for rapid response, CIWA-Ar scores increased over mild range, code green security personnel response for behavioral deterioration, or symptoms requiring a higher level of care or ICU transfer. The literature demonstrates the absence of effective AWS recognition and treatment has resulted in the need for rapid response, transfers to a higher level of care, and longer length of stay (Holt et al., 2016;Maldonado et al., 2014;Muzyk et al., 2017) which supports face validity for these measures. Pinkhasov et al. (2020) report AWS was associated with a 13-fold increase in the risk of a behavioral disturbance (95% CI, 8 to 22-fold). The need for security to respond to behavioral disturbances has been correlated with adverse patient outcomes and increased risk for staff and patient injury, which was identified as an important area of concern by key stakeholders including the unit manager and CNO. The project also measured the time interval between when the patient was admitted to the unit and when the alcohol withdrawal order set was implemented based on the time the CIWA-Ar was initiated.
The significance of this measure was to identify when treatment may have been delayed due to failure to recognize the risk or AWS at the time of admission.
Once EBP Project Review Council (EPRC) at the university and the entity-based IRB committees approved the project proposal, data was collected for the eight weeks before the implementation of the project and then again during the eight week project implementation (see Appendix E &F for data description and data collection sheet). Paper copies of the completed PAWSS tool were collected and stored in the nursing lead office on the unit, in a folder, not visible to the public, and stored according to HIPAA guidelines the same as patient medical records on the unit. They were collected by the project manager weekly for analysis, and even though they contained no patient information, the paper copies were stored on-site, double-locked, and not visible to the public until the completion of the project. Upon completion of the project, the PAWSS tools were disposed of according to hospital practice for patient data utilizing the secure document shredding procedure. All data from the EMR was validated in conjunction with the organization's information technology staff, and all patient identifiers were removed prior to inputting the data points into Microsoft Excel© for statistical analysis.
Statistical analysis was completed on a hospital computer with hospital software.
During the project implementation period, the admitting nurse attempted to screen each patient admitted using the PAWSS tool (See Appendix G). A total of 242 patients were screened with the PAWSS tool. Patients who were non-verbal, whose level of consciousness did not allow them to participate in the screening, and patients who refused to participate with the PAWSS assessment were excluded. A PAWSS assessment was completed on 60% of all patients admitted to the unit. For patients screened, 41 patients met threshold criteria and 30 patients had a score ≥4, indicating High Risk for developing moderate to severe AWS.
During the initial eight week pre-project implementation period, a total of 38 patients admitted to the progressive care unit required treatment for AWS, while a total of 46 patients required treatment for AWS in the eight week project implementation period (N = 84). The primary outcomes compared between these groups were; (1) the time interval in minutes between admittance and ordering treatment for AWS; (2) the number of rapid response team (RRT) incidents per patient; (3) the highest recorded scores on the CIWA-Ar instrument; (4) the patient's length of stay in hours; and (5) code green events. Each of these outcomes were compared using a between subjects t-test with unequal variances assumed.
The average interval between admittance and beginning treatment for AWS was not  Maldonado et al., 2015). The results for this EBP project demonstrated a statistically significant reduction in hospital length of stay, which decreased overall healthcare costs. By providing a timely and evidence-based practice intervention for early risk assessment, this healthcare organization will be better positioned to provide timely quality care to its patients, while at the same time decreasing the overall length of stay.
The implementation of this project generated interest within hospital leadership to more closely examine how the organization provides care to patients with substance use disorders. The hospital leadership initiated an internal committee to further explore the established treatment pathways, patient outcome results and to develop evidence-based practice change to address areas of deficit. Additionally, at the unit level, the CNS is continuing to explore staff confidence and competency in treating alcohol withdrawal following the project implementation. That study is ongoing at this time, but shows a deepened organizational interest in both evidence-based practices as well as treating the AUD population.

Limitations
The relatively low sample size and the short period of data collection may have made it difficult to detect any statistical significance in the data points for the number of rapid response events. The average interval between admittance and beginning treatment for AWS did slightly increase in the implementation period. Although this finding is not statistically significant, it may suggest that the PAWSS tool would be most helpful if implemented in the emergency department prior to arrival to the unit. By waiting until the time of admission, the time interval may have been increased. Alternatively, this slight increase in the time interval could also suggest that patients were identified as being at risk who may have otherwise been missed. Due to limitations in how the code green data was able to be accessed at the organization, this data point could not be correlated for any association with patients at risk for alcohol withdrawal. The increase in the overall number of responses may be important to continue to investigate from an organizational perspective. For future implementation, the organization may be able to explore if this data could be captured in a different way that could include the appropriate patient identifier to correlate the findings.
Although this project was in its design phase before the impact of the COVID-19 pandemic could be anticipated, it was also implemented at a particularly challenging time for the population. Early research suggests that the pandemic has increased alcohol consumption in the United States (Attonito, Villalba & Fontal, 2021;Wardell et al., 2020). This increase in consumption put more patients at risk for complications of AWS during their hospital stay. At the same time that the project was implemented, there was a nationwide shortage in chlordiazepoxide medication, one of the main medications used by this organization in its standard treatment for alcohol withdrawal (American Society of Health System Pharmacists, 2021). The lack of availability of this medication may have negatively impacted patient care outcomes during this project. Additionally, the COVID-19 pandemic has also impacted nursing training and staffing needs. It was difficult to ensure that all nurses working on the unit were equally educated about the intervention because of the increased use of float staff and travelers.
Nurse fatigue and burnout may also impact the enthusiasm for the adoption of practice and culture change.

Dissemination Plan
The results of the project were first shared with the project manager's preceptor and presented to the manager and staff of the acute care unit via the staff daily huddle meeting and a poster presentation on the unit. Next, results were disseminated within the system's Innovation and Professional Excellence Committee for review and feedback. Additionally, the results from the project were shared with hospital leadership, including the CNO, and department managers during the monthly leadership forum.
Outside of the hospital organization, the results of the DNP project will be published in the Scholarship and Open Access Repository (SOAR@USA) collection as a DNP student project manuscript. Additionally, the results could be considered for submission for publication through peer-reviewed journals such as the Clinical Nurse Specialist: The Journal for Advanced Nursing Practice and will be submitted for consideration to present at a professional nursing conference.
The results will be relevant to a large audience, including hospital administrators as well as frontline acute care staff. This practice change could help decrease the length of stay in this patient population, increase patient safety, increase nurse satisfaction, increase nursing knowledge about assessing and treating acute alcohol withdrawal and provide better community population health practices for decreasing stigma associated with AUD treatment.

Conclusion
Early recognition of the presence of an AUD in acute hospital care settings and  Table 1 The Stages of Alcohol Withdrawal Syndrome (Ostrovsky, 2018) Stage of AWS Timeframe Common Symptoms A retrospective analysis collected data from the medical record from CIWA-Ar measures along with physiological data to complete Wetterling scale (11-item combination of CIWA-Ar measures with physiological markers). The results from the first three days of the stay along with presence of diagnosis of AWS were grouped into "mild"(<6) "moderate" (6-9) or "severe" (>9) and then correlated with hospital outcome data STROBE guidelines (STrengthening the Reporting of OBservational studies in Epidemiology) The authors analyzed potential associations of the maximum AWS score with worse clinical outcomes, i.e., increased LOS and inhospital mortality, using multivariable linear and logistic regression, respectively According to the authors "Higher maximum AWS scores are associated with increased Legnth of Stay (LOS) and inhospital mortality.
Determination of the maximum AWS score within 3 days after the first assessment appears to be sufficient and may predict increased LOS and in-hospital mortality.
This may help health care providers to anticipate AWS progression and in properly preparing short-, medium-, and long-term care." Mahabir, C. A., Anderson, M., Cimino, J., Lyden, E., Siahpush, M., & Shiffermiller, J. Level II with a retrospective analysis. Patients were randomly divided into two cohorts: the "Derivation cohort" and the "Validation cohort. Within the "derivation cohort" 908 patients were analyses and in the "Validation cohort" 461 patients were analyzed relationship between the 8 identified predictors of Severe Alcohol Withdrawal Syndrome (SAWS).
predict SAWS with high sensitivity. The makers were identified individually, but not studied as individual predictors of SAWS.
The reliance on ICD-10 codes for a number of predictive factors is problematic for reliability.
The AWTT could be useful as part of a standardized admission protocol, but not as a stand-alone tool for the prediction of risk for SAWS Maldonado, J. R., Sher, Y., Das, S., Hills-Evans, K., Frenklach, A., Lolak, S., Talley

EBP Project Implementation
Begin education of the staff with roll out of education materials "elevator speech" at the daily huddle on the unit with Charge Nurse and Nurse Manager • The DNP student will attend daily huddles with a short "elevator speech" to outline the EBP project Within the 5 th week of NUR7802 DNP Student, CNS educators, Charge Nurse • The Charge nurse will communicate the practice change in the shift report to all on-coming staff EBP Project Implementation Unit CNS educating floor staff on the PAWSS • The CNS educators will highlight the practice change within their "poster presentation area" on the unit where they normally highlight best practices for the unit. The CNS will also work with each staff on the unit approximately 5 minutes to review the PAWSS tool and see a return demonstration of the staff administering the tool Within the 5 th week of NUR 7802 CNS Nurse Educator

EBP Project Implementation
Provide staff access to written education materials outlining the PAWSS and current practices for treatment of AWS for reference • The unit staff will be provided with an electronic link to a recorded educational presentation on the administration of the PAWSS tool and a general overview of AWS management. This will be prepared by the DNP student with input from the CNS • The paper tool will be completed by the bedside RN with each patient admitted to the unit. • Nursing staff will be directed to record positive scores (≥4) in the nursing interdisciplinary note in the EMR and to notify the attending physician according to the standard process when there is a need for additional orders • The completed PAWSS tool will be collected in a folder in the charge nurse office, not viewable to the public, and stored in accordance with all HIPAA compliance measures to protect health information COVID restrictions

Staffing challenges
Lack of appropriate medications (nation wide Librium shortage)

EBP Project Implementation
Collect pre-intervention data for a period of 8 weeks prior to implementation • The EMR will be accessed for data collection as well as the security safety logs for the unit for the 8 weeks prior to project implementation.
After gaining facility IRB approval for EMR access DNP Student

EBP Project Implementation
Supervise Observation Audits with the CNS nurse educators to assure completion of tool and fidelity to the tool.
• The CNS will complete a minimum of 7 audits per week during the first week, each of different staff, to ensure compliance and fidelity to the tool • Audits will be repeated at the third week and then at the 5 th week, to Week 6-14 of NUR7802. These audits will happen weekly CNS Nurse Educator verify compliance and fidelity to the tool. • The audit will consist of the CNS verifying that for a random patient admission that the PAWSS tool was completed and there is documentation of physician notification in the EMR for scores ≥4. This information will be recorded on a checklist on the front inside cover of the PAWSS collection folder in the nursing office EBP Project Implementation Collect intervention data for PAWSS and outcome measures for eight week intervention period (see data sheet in Appendix F) Week 6-14 of NUR7802.

DNP Student
Time management.

Post-Intervention
Compile results of data collected and perform statistical analysis • Using the results collected in the date sheet, the data will be entered into the Intellectus Statistic software for data analysis NUR7803 (see timeline in Appendix D) DNP Student Delays in data pull from IT department due to conflicting priorities

Post-Intervention
Analyze findings and complete write up of the analysis and outcomes from the practice change with feedback from USA faculty advisor and DNP preceptor NUR7803 (see timeline in Appendix D) DNP Student, USA faculty and DNP preceptor

Post-Intervention
Finally, the results and clinical significance will be synthesized and disseminated to the key stakeholders and the larger healthcare community • Results and significance will be reported back to the unit manager and staff during the daily huddle NUR7803 (see timeline in Appendix D) DNP student • Results and significance will be presented to the USA faculty of course NUR7803 • Results and significance will be reported back to the facility Innovations, Inquiry and Professional Excellence Committee • Summary of results and significant will be presented to hospital leadership including the CEO and CNO with recommendation for future sustainability practices • Final results and significance will be published in a scholarly journal to further nursing EBP