Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules.

Biomedical informatics insights Pub Date : 2019-05-28 eCollection Date: 2019-01-01 DOI:10.1177/1178222619829079
Michael Z Grabel, Benjamin L Vaughan, Judith W Dexheimer, Eric S Kirkendall
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引用次数: 0

Abstract

Objective: 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.

Materials and methods: 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.

Results: 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.

Discussion: 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.

Conclusions: 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.

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计算机辅助修改用药剂量规则的数学模型。
目的:儿科用药是一项复杂的工作,很容易出错,从而对患者造成伤害。为了改进计算机辅助给药,我们开发了一种数学模型和算法来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配药规则的可行性:结合历史订购数据,开发了一种数学模型和算法,用于自动确定最佳配药规则参数。该算法通过平衡一种药物产生的警报数量和最小剂量间隔时间来优化配药规则。总共测试了 5 种候选药物。对新模型与当前配药规则生成的警报数量进行了分析比较:结果:对于这 5 种药物,算法生成了多种临床相关规则的可能性,返回的规则与当前的配药规则一样好,或者与历史处方行为相匹配。在减少总警报负担方面,这些规则与现有系统规则相当或更好:讨论:该数学模型和算法是调整药物剂量规则的一种精确且可扩展的解决方案。讨论:数学模型和算法是一种准确且可扩展的调整用药规则的解决方案,与目前的人工方法相比,它们可以更快地改变次优规则,并可用于帮助识别和纠正质量较差的规则:结论:利用历史处方数据建立数学模型可生成适合临床的电子配药规则参数。这种方法是一种可自动扩展的解决方案,有助于减轻警报疲劳和减少用药错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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