{"title":"Using a rule-based decision tool for medication dose selection to improve patient safety and the need for pharmacist intervention","authors":"Tyler Finocchio Pharm.D., MHIIM, Gregory Jaszczur Pharm.D.","doi":"10.1002/jac5.1981","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>An assortment of alerts has been employed to influence provider order entry, yet many medication orders still require dose adjustment by pharmacists upon order verification.</p>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>The primary goals of this study were to evaluate the impact of adding rule-based decision support to the computerized provider order entry system on the need for medication dose adjustment by a pharmacist and the occurrence of acute kidney injury (AKI) among patients.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This was a retrospective, pre- and post-implementation observational study on the integration of rule-based logic into the computerized provider order entry system to automatically select default doses and frequencies for weight-based or renally-cleared medications in alignment with health system guidelines. The primary end points were the proportion of medication orders that required pharmacist intervention for dose adjustment and the number of times the AKI pop-up alert was triggered.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>After inclusion and exclusion criteria were applied to all available orders, there were 47 393 and 45 767 orders included for final analysis in the pre- and post-implementation periods, respectively. The post-implementation period showed a significant reduction in pharmacist dosing interventions, with a relative risk of 0.42 (95% confidence interval [CI]: 0.40–0.43; <i>p</i> < 0.0001) and a reduction in AKI (relative risk = 0.58 [95% CI: 0.53–0.64; <i>p</i> < 0.0001]).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This study demonstrates the potential of rule-based decision support to improve initial medication dose selection, reduce the occurrence of AKI, and reduce pharmacist workload, all without increasing alert fatigue.</p>\n </section>\n </div>","PeriodicalId":73966,"journal":{"name":"Journal of the American College of Clinical Pharmacy : JACCP","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American College of Clinical Pharmacy : JACCP","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jac5.1981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction
An assortment of alerts has been employed to influence provider order entry, yet many medication orders still require dose adjustment by pharmacists upon order verification.
Objectives
The primary goals of this study were to evaluate the impact of adding rule-based decision support to the computerized provider order entry system on the need for medication dose adjustment by a pharmacist and the occurrence of acute kidney injury (AKI) among patients.
Methods
This was a retrospective, pre- and post-implementation observational study on the integration of rule-based logic into the computerized provider order entry system to automatically select default doses and frequencies for weight-based or renally-cleared medications in alignment with health system guidelines. The primary end points were the proportion of medication orders that required pharmacist intervention for dose adjustment and the number of times the AKI pop-up alert was triggered.
Results
After inclusion and exclusion criteria were applied to all available orders, there were 47 393 and 45 767 orders included for final analysis in the pre- and post-implementation periods, respectively. The post-implementation period showed a significant reduction in pharmacist dosing interventions, with a relative risk of 0.42 (95% confidence interval [CI]: 0.40–0.43; p < 0.0001) and a reduction in AKI (relative risk = 0.58 [95% CI: 0.53–0.64; p < 0.0001]).
Conclusion
This study demonstrates the potential of rule-based decision support to improve initial medication dose selection, reduce the occurrence of AKI, and reduce pharmacist workload, all without increasing alert fatigue.