| Description |
Background: Delirium tremens (DTs) is a life-threatening complication of alcohol withdrawal, occurring in 5-12% of cases. Without timely treatment, DTs can lead to seizures, cardiac instability, respiratory failure, and mortality rates of 15-40%. Its symptoms often overlap with psychosis and other psychiatric disorders, increasing the risk of misdiagnosis or delayed recognition. Early diagnosis and treatment are critical to reducing morbidity and mortality. Local Problem: In a psychiatric inpatient hospital in Salt Lake City, Utah, inconsistent recognition and assessment of DTs were identified as key challenges. Contributing factors included limited nursing education on alcohol withdrawal, reliance on subjective screening tools, and time constraints that hindered accurate diagnosis. Although the Clinical Institute Withdrawal Assessment for Alcohol (CIWA-Ar) is well validated, it has limitations in detecting DTs in patients with altered mental status or poor self-reporting. Methods: This quality improvement project aimed to enhance nurses' confidence, knowledge, and accuracy in assessing DTs through evidence-based interventions. A pre-survey (n=11) evaluated baseline knowledge, confidence, and practices related to DTs screening. An evidence-based DTs recognition algorithm was developed alongside nursing education sessions, algorithm training, and resource allocation. After a 12-week intervention, a post-survey assessed algorithm usability, feasibility, and satisfaction. Quantitative data were analyzed using descriptive statistics, while qualitative responses were reviewed for emergent themes. Interventions: A structured DTs identification algorithm was introduced during nursing department meetings and supplemented with educational materials and ongoing staff support. Nurses were trained to integrate the algorithm into their evaluation processes to facilitate early identification of patients at risk for DTs. Results: Pre-intervention data showed that 55% (n=6) of nurses rated their confidence in assessing DTs as moderate. Post-implementation results indicated improved confidence levels, with 73% (n=8) of participants reporting increased confidence in recognizing DTs. Additionally, 91% (n=10) of nurses reported greater familiarity with DT symptoms, and 82% (n=9) found the algorithm helpful in guiding assessments. Despite positive feedback, time constraints remained a significant barrier to consistent implementation; 82% of participants cited time limitations as an obstacle. Conclusion: The implementation of a structured DTs Recognition Algorithm (DTRA) improved nurses' confidence, knowledge, and assessment accuracy within psychiatric inpatient care settings. While the intervention was well received and deemed feasible, challenges such as time constraints must be addressed to ensure sustained integration into clinical workflows. Future steps include embedding the algorithm into electronic health records (EHRs), expanding education initiatives, and evaluating long-term sustainability in psychiatric settings. |
