ANALYTICAL METHODS TO QUANTIFY RISK OF HARM FOR ALERT-OVERRIDDEN HIGH-RISK INTRAVENOUS MEDICATION INFUSIONS
Creators: name: Su, Wan-Ting
Publisher: name: Purdue University Graduate School
Year: 2020
Subjects: Health care administration

description: The medication errors associated with intravenous (IV) administration may cause severe patient harm. To address this issue, smart infusion pumps now include a built-in dose error reduction system (DERS) to help ensure the safety of IV administration in clinical settings. However, a drug limit alert triggered by DERS may be overridden by the practitioners which can potentially cause patient harm, especially for high-risk medications. Most analytical measures used to estimate the associated risk of harm are frequency-based and only consider the overall drug performance rather than the severity impact from individual alerts. Unlike these other measures, the IV medication harm index attempts to quantify risk of harm for individual alerts. However, it is not known how well these measures describe the risk associated with alert-overridden scenarios. The goal of this research was (1) to quantitatively measure the risk for simulated individual alert-overridden infusions, (2) to compare these assessments against the risk scores obtained among four different analytical methods, and (3) to propose better risk quantification methods with a higher correlation to risk benchmarks than traditional measures, such as the IV Harm index. In this study, 25 domain experts (20 pharmacists and 5 nurses) were recruited to assess the risk (adjusted for risk benchmarks) for representative scenarios created based on hospital alert data. Four analytical methods were applied to quantify risk for the scenarios: the linear mixed models (Method A), the IV harm index (Method B), Huang and Moh’s matrix-based ranking method matrix-based method (Method C), and the analytical hierarchy process method, adjusted by linear mixed models (Method D). Method A used seven alert factors (identified as key risk factors) to build models for risk prediction, and Methods B and C used two out of seven factors to obtain risk scores. Method D used pairwise comparison surveys to calculate the risk priorities. The quantified scores from the four methods were evaluated in comparison to the risk benchmarks. Risk assessment results from the domain experts indicated that overdosing scenarios with continuous and bolus dose field limit types had significantly higher risks than those of bolus dose rate type. About the soft limit type, the expected risk in the group with a large soft maximum limit was significantly higher than the group with a small soft maximum limit. This significant difference could be found in the adult intensive care unit (AICU), but not in adult medical/surgical care unit (AMSU). The comparisons between four analytical methods and risk benchmarks showed that the risk scores from Method A (<i>ρ</i> = 0.94) and Method D (<i>ρ </i>= 0.87) were highly correlated to the risk benchmarks. The risk scores derived from Method B and Method C did not have a positive correlation with the benchmarks. This study demonstrated that the traditional IV harm index should include more risk factors, along with their interaction effects, for increased correlation with risk benchmarks. Furthermore, the linear mixed models and the adjusted AHP method allow for better risk quantification methods where the quantified scores most correlated with the benchmarks. These methods can provide risk-based analytical support to evaluate alert overrides of four high-risk medications, propofol, morphine, insulin, and heparin in the settings of adult intensive care unit (AICU) and adult medical/surgical care unit (AMSU). We believe that healthcare systems can use these analytical methods to efficiently identify the riskiest medication-care unit combinations (e.g. propofol in AICU), and reduce medication error/harm associated with infusions to enhance patient safety.