Grabel Michael Z, Vaughan Benjamin L, Dexheimer Judith W, Kirkendall Eric S
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA.
Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH, USA.
Biomed Inform Insights. 2019 May 28;11:1178222619829079. doi: 10.1177/1178222619829079. eCollection 2019.
Medication dosing in pediatrics is complex and prone to errors that may lead to patient harm. To improve computer-assisted dosing, a mathematical model and algorithm were developed to optimize clinical decision support dosing rules and reduce spurious alerts. The objective was to evaluate the feasibility of using this algorithm to adjust dosing rules.
Incorporating historical ordering data, a mathematical model and algorithm were developed to automatically determine optimal dosing rule parameters. The algorithm optimizes the dosing rules by balancing the number of alerts generated for a medication with a minimal length dose interval. In all, 5 candidate medications were tested. An analysis was performed to compare the number of alerts generated by the new model with the current dosing rules.
For the 5 medications, the algorithm generated multiple clinically relevant rule possibilities and the rules returned performed as well as current dosing rule or matched historical prescriber behavior. The rules were comparable to or better than the existing system rules in reducing the total alert burden.
The mathematical model and algorithm are an accurate and scalable solution to adjusting medication dosing rules. They can be implemented to change suboptimal rules more quickly than current manual methods and can be used to help identify and correct poor quality rules.
Mathematical modeling using historic prescribing data can generate clinically appropriate electronic dosing rule parameters. This approach represents an automatable and scalable solution that could help reduce alert fatigue and decrease medication dosing errors.
儿科用药剂量计算复杂且容易出错，可能会对患者造成伤害。为改进计算机辅助给药，开发了一种数学模型和算法，以优化临床决策支持给药规则并减少虚假警报。目的是评估使用该算法调整给药规则的可行性。
结合历史医嘱数据，开发了一种数学模型和算法，以自动确定最佳给药规则参数。该算法通过平衡针对一种药物产生的警报数量与最小剂量间隔来优化给药规则。总共测试了5种候选药物。进行了一项分析，以比较新模型与当前给药规则产生的警报数量。
对于这5种药物，该算法生成了多种临床相关的规则可能性，返回的规则与当前给药规则表现相当或与历史开方者行为相符。在减轻总警报负担方面，这些规则与现有系统规则相当或更好。
该数学模型和算法是调整用药剂量规则的准确且可扩展的解决方案。与当前的手动方法相比，它们可以更快地实施以更改次优规则，并且可用于帮助识别和纠正质量不佳的规则。
使用历史处方数据进行数学建模可以生成临床上合适的电子给药规则参数。这种方法代表了一种可自动化且可扩展的解决方案，有助于减少警报疲劳并减少用药剂量错误。
