Alcohol Withdrawal Syndrome (AWS) is a potentially life-threatening condition that can arise when individuals abruptly stop or reduce their alcohol consumption following heavy drinking (Bahji et al., 2022). It is marked by a variety of symptoms, including tremors, anxiety, sweating, nausea, and seizures. The length of hospital stays (LOS) for patients with AWS can vary significantly, influencing healthcare resource utilization and patient experiences (Dixit et al., 2016; Keys, 2011; Melkonian et al., 2019; Melson et al., 2014; Unlu et al., 2024). Factors that impact LOS in AWS include the severity of withdrawal symptoms, comorbid conditions, and patient demographics (Dixit et al., 2016; Keys, 2011; Melkonian et al., 2019; Melson et al., 2014; Unlu et al., 2024). The burden on healthcare systems from AWS is growing. According to data from the Nationwide Inpatient Sample, the incidence of AWS was 3405.6 per million adult hospitalizations in 2019, an increase from 2671.8 in 2010 (Laswi et al., 2022). This trend highlights the urgent need for effective management strategies. Understanding these factors is vital for predicting resource utilization and optimizing patient care.

Effective management of AWS is crucial for preventing complications and improving patient outcomes (Blackburn et al., 2024; Hoffman & Weinhouse, 2025). While various treatment approaches have been suggested for AWS, the optimal strategy remains unclear. Traditional fixed-dose regimens of benzodiazepines can be effective, but they carry significant risks such as oversedation and more extended hospital stays (Day & Daly, 2022). Alternatively, symptom-triggered protocols offer a more personalized treatment approach (Melkonian et al., 2019; Sen et al., 2017; Sullivan et al., 1989). These protocols aim to minimize medication exposure while maximizing patient comfort, thereby improving outcomes (Knight & Lappalainen, 2017; Melkonian et al., 2019; Sen et al., 2017). Research shows that symptom-triggered protocols significantly reduce the need for medications and LOS compared to fixed-dose regimens (Schmidt et al., 2016; Sen et al., 2017).

However, a critical gap exists in the literature regarding the empirical validation of the clinical and operational impact of formalized nurse autonomy within these pathways. Existing studies often lack granular analysis of how key nursing process measures—such as the time elapsed before the initial AWS assessment—directly influence system-level outcomes, such as LOS. This single-center, retrospective study addresses this gap by quantifying the efficiency gains achieved by a unique nurse-managed pathway and contrasting its metrics with outcomes from traditional physician-led or fixed-dose models. The ultimate goal is to generate evidence supporting enhanced nurse autonomy, thereby improving patient safety and optimizing inpatient operational efficiency. Although this study is a single-center retrospective analysis, it provides unique granular data on nurse-led operational metrics that physician-led models often overlook.

This study's theoretical foundation is grounded in Bandura's (1977) self-efficacy theory. We posit that a standardized, stepwise protocol provides the structural mastery necessary to increase a nurse's clinical confidence. In this model, the protocol serves as a tool for efficacy, enabling standardized scoring to yield predictable, high-performance outcomes.

The purpose of this study is to evaluate the clinical and operational effectiveness of a standardized, nurse-managed, stepwise symptom-triggered treatment protocol for AWS. The specific objectives of this study include:1.

Evaluate the operational efficiency of the nurse-managed protocol, specifically measuring the association between time from admission to first AWS assessment and total LOS.

2.

Using hierarchical regression to identify which demographic and clinical factors (age, gender, initial AWS score) independently predict LOS.

3.

Determining if final symptom severity predicts LOS once the acute withdrawal phase is managed.