未参保患者在急诊就医期间的阿片类药物和抗菌药物处方模式。

Michael A Grasso, Anantaa Kotal, Anupam Joshi
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引用次数: 0

摘要

本研究的目的是描述未参保急诊患者阿片类药物和抗菌药物处方的特点,并建立预测性机器学习模型。未参保患者接受阿片类药物治疗的可能性较低,接受非阿片类药物替代治疗的可能性较高,接受抗菌药物处方的可能性较低。影响最大的因素是住房状况、合并症和累犯。
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Opioid and Antimicrobial Prescription Patterns During Emergency Medicine Encounters Among Uninsured Patients.

The purpose of this study was to characterize opioid and antimicrobial prescribing among uninsured patients seeking emergency medical care and to build predictive machine learning models. Uninsured patients were less likely to receive an opioid medication, more likely to receive non-opioid alternatives, and less likely to receive an antimicrobial prescription. The most impactful contributing factors were housing status, comorbidities, and recidivism.

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