Smith Terri, Philmon Carla L, Johnson Gregory D, Ward William S, Rivers LaToya L, Williamson Sharon A, Goodman Edward L
Clinical Pharmacy Specialist, Department of Pharmacy, Texas Health Presbyterian Hospital of Dallas , Dallas, Texas.
Clinical Pharmacy Manager, Department of Pharmacy, Texas Health Presbyterian Hospital of Dallas , Dallas, Texas.
Hosp Pharm. 2014 Oct;49(9):839-46. doi: 10.1310/hpj4909-839.
Antibiotic stewardship has been proposed as an important way to reduce or prevent antibiotic resistance. In 2001, a community hospital implemented an antimicrobial management program. It was successful in reducing antimicrobial utilization and expenditure. In 2011, with the implementation of a data-mining tool, the program was expanded and its focus transitioned from control of antimicrobial use to guiding judicious antimicrobial prescribing.
To test the hypothesis that adding a data-mining tool to an existing antimicrobial stewardship program will further increase appropriate use of antimicrobials.
Interventional study with historical comparison.
Rules and alerts were built into the data-mining tool to aid in identifying inappropriate antibiotic utilization. Decentralized pharmacists acted on alerts for intravenous (IV) to oral conversion, perioperative antibiotic duration, and restricted antimicrobials. An Infectious Diseases (ID) Pharmacist and ID Physician/Hospital Epidemiologist focused on all other identified alert types such as antibiotic de-escalation, bug-drug mismatch, and double coverage. Electronic chart notes and phone calls to physicians were utilized to make recommendations.
During 2012, 2,003 antimicrobial interventions were made with a 90% acceptance rate. Targeted broad-spectrum antimicrobial use decreased by 15% in 2012 compared to 2010, which represented cost savings of $1,621,730. There were no statistically significant changes in antimicrobial resistance, and no adverse patient outcomes were noted.
The addition of a data-mining tool to an antimicrobial stewardship program can further decrease inappropriate use of antimicrobials, provide a greater reduction in overall antimicrobial use, and provide increased cost savings without negatively affecting patient outcomes.
抗生素管理已被视为减少或预防抗生素耐药性的重要途径。2001年,一家社区医院实施了抗菌药物管理计划。该计划成功降低了抗菌药物的使用量和支出。2011年,随着数据挖掘工具的实施,该计划得以扩展,其重点从控制抗菌药物的使用转向指导合理使用抗菌药物。
检验在现有抗菌药物管理计划中添加数据挖掘工具将进一步提高抗菌药物合理使用的假设。
采用历史对照的干预性研究。
在数据挖掘工具中设置规则和警报,以帮助识别不适当的抗生素使用情况。分散的药剂师对静脉转口服、围手术期抗生素使用时长以及限制使用的抗菌药物的警报采取行动。传染病(ID)药剂师和ID医师/医院流行病学家关注所有其他已识别的警报类型,如抗生素降阶梯、细菌与药物不匹配以及双重覆盖。利用电子病历记录和与医生的电话沟通提出建议。
2012年共进行了2003次抗菌药物干预,接受率为90%。与2010年相比,2012年针对性的广谱抗菌药物使用量下降了15%,节省成本1,621,730美元。抗菌药物耐药性无统计学显著变化,也未观察到不良患者结局。
在抗菌药物管理计划中添加数据挖掘工具可进一步减少抗菌药物的不当使用,更大程度地降低总体抗菌药物使用量,并增加成本节约,且不会对患者结局产生负面影响。