Evaluation of an Automated, Pharmacist-Driven, Antimicrobial Patient Acuity Scoring System for Hospitalized Bacteremic Patients

HOSPITAL PHARMACY(2024)

引用 0|浏览5
暂无评分
摘要
Purpose: The implementation of an automated, pharmacist-driven, scoring system within the EMR has been shown to improve patient care in patients with Staphylococcus aureus bacteremia by increasing the adherence to disease specific quality-of-care measures. However, there are a lack of studies evaluating the incorporation of blood culture review into standard, non-antimicrobial stewardship pharmacist workflow. Our institution implemented an automated, pharmacist-driven, antimicrobial scoring system in the electronic medical record (EMR) on August 6, 2019. Methods: This was a retrospective, single-center, quasi-experimental study of hospitalized, non-critically ill adult (18-89 years of age) patients with bacteremia between July 6, 2018 and July 5, 2019 (pre-implementation group) and September 6, 2019 and September 5, 2020 (post-implementation group). The primary outcome was time to directed antibiotic therapy in patients with positive blood cultures. Secondary outcomes included hospital length-of-stay, days of therapy (DOT) while inpatient, time to effective therapy, 30-day all-cause mortality, and rates of Clostridioides difficile infections documented within 3 months of positive culture results. Results: Implementation of the antimicrobial scoring system did not result in a significant change in time to directed antibiotic therapy (32.5 hours vs 37.4 hours; P = .757). There was also no difference found for time to effective antibiotic therapy (-12.6 hours vs -14.2 hours; P =.905) and no difference found for all other secondary outcomes. Conclusion: The implementation of the antimicrobial scoring system did not lead to an improvement in clinical outcomes. Further research is needed to better define a patient population that may benefit from this system.
更多
查看译文
关键词
anti-infectives,information systems and technology,infectious diseases
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要