用机器学习来实现经济高效的医疗的解密因素
Bowen Long1, Jinfeng Zhou2, Fangya Tan1
1Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA.
Bioengineering (Basel, Switzerland)
|August 29, 2024
概括
机器学习确定了非处方药 (OTC) 药物成本效益的关键因素. 诸如FSA/HSA资格,症状范围和包装尺寸等因素显著影响消费者的感知价值.
科学领域:
- 药物经济学 药物经济学
- 医疗信息学 医疗信息学
- 消费者健康 消费者健康
背景情况:
- 非处方药 (OTC) 药物被广泛使用,但消费者并不总是清楚它们的成本效益.
- 确定影响OTC药物的感知价值的因素对于明智的购买决策至关重要.
- 现有的研究往往忽略了产品属性,定价和消费者财务账户之间的相互作用.
研究的目的:
- 使用机器学习识别影响OTC药物的成本效益的关键因素.
- 开发一种新的成本效益评级 (CER) 模型,包括用户评级和价格.
- 分析特定药物特征如何影响不同治疗类别的成本效益.
主要方法:
- 利用机器学习算法来分析来自亚马逊的大量OTC药物的数据集.
- 开发了一种基于用户评论和产品定价的专有成本效益评级 (CER) 度量.
- 研究了包括灵活支出账户 (FSA) /医疗储蓄账户 (HSA) 资格,症状治疗范围,安全警告,特殊效应,活性成分和包装尺寸在内的因素的影响.
主要成果:
- FSA/HSA资格,更广泛的症状治疗范围和更小的包装尺寸与更高的成本效益正相关.
- 带有安全警告的感冒药物,包括乙烯和乙烯等成分,由于价格较低,证明了成本效益.
- 具有儿童友好的特征的过敏药物和含有, famotidine 或的消化药物显示出更高的成本效益.
结论:
- 机器学习可以有效地识别OTC药物成本效益的关键驱动因素.
- 消费者对价值的看法受到金融可访问性 (FSA/HSA),治疗范围和产品属性的结合的影响.
- 这些发现为消费者,制造商和零售商提供了可操作的见解,以优化OTC药物选择和市场战略.
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