Using Applied Machine Learning to Predict Healthcare Utilization Based on Socioeconomic Determinants of Care
Using Applied Machine Learning to Predict Healthcare Utilization Based on Socioeconomic Determinants of Care
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DOI：
10.37765/ajmc.2020.42142
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发表时间：
2020-01-01
影响因子：
3.2
通讯作者：
Showalter, John
中科院分区：
文献类型：
--
作者：
Chen, Soy;Bergman, Danielle;Showalter, John
目的：为了确定是否有可能进行风险分层，避免利用没有临床数据和有限的患者水平data.Study设计：本研究的目的是证明社会经济因素的健康（SDH）方面的影响，可避免的患者水平的医疗保健利用。该研究调查了机器学习模型仅使用公开和购买的SDH数据预测风险的能力。从一个去识别的数据库中，代表3个卫生系统在美国，共138,115例进行了分析：一个坚持的方法，以确保该模型的性能可以测试一个完全独立的一组科目。一个专有的决策树方法被用来进行预测。只有社会经济特征-年龄组，性别和种族-被用于预测患者的入院风险。结果：本研究中分析的基于决策树的机器学习方法能够预测住院和急诊科的利用率，仅使用购买和公开可用的SDH数据，具有高度的歧视性。这项研究表明，有可能在不与患者互动或收集患者年龄、性别、种族和地址以外的信息的情况下对患者的使用风险进行风险分层。这一应用的影响是广泛的，并有可能积极影响卫生系统，通过促进有针对性的患者外展与具体的，个性化的干预措施，以解决有害的SDH不仅在个人层面，而且在社区层面。
OBJECTIVES: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data.STUDY DESIGN: The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States.METHODS: A hold-out methodology was used to ensure that the model's performance could be tested on a completely independent set of subjects. A proprietary decision tree methodology was used to make the predictions. Only the socioeconomic features-age group, gender, and race-were used in the prediction of a patient's risk of admission.RESULTS: The decision tree-based machine learning approach analyzed in this study was able to predict inpatient and emergency department utilization with a high degree of discrimination using only purchasable and publicly available data on SDH.CONCLUSIONS: This study indicates that it is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the patient's age, gender, race, and address. The implications of this application are wide and have the potential to positively affect health systems by facilitating targeted patient outreach with specific, individualized interventions to tackle detrimental SDH at not only the individual level but also the neighborhood level.