论文标题
用于延期护理预测的机器学习
Machine Learning for Deferral of Care Prediction
论文作者
论文摘要
护理延期是患者延期或无法接受医疗服务的现象,例如看医生,药物或计划的手术。护理延期可能是患者决策,服务可用性,服务限制或限制的结果。从长远来看,人口持续的护理延期可能导致人口健康和复杂健康问题的下降,从而导致更高的社会和财务成本。因此,鉴定可能有延期护理风险的患者对于改善人口健康和降低护理总成本很重要。此外,由于社会经济因素,少数群体和脆弱的人群遭到护理延期的风险更大。在本文中,我们(a)解决了预测护理延期进行良好护理访问的问题; (b)观察健康的社会决定因素是预测护理递延的相关解释因素,并且(c)计算模型在人口统计学,社会经济因素和选定合并症方面的公平程度。许多卫生系统目前使用基于规则的技术追溯识别以前推迟护理的患者。该模型的目的是确定有延期护理风险的患者,并允许卫生系统通过直接推广或社会决定性调解来防止护理延迟。
Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the result of patient decisions, service availability, service limitations, or restrictions due to cost. Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term. Consequently, identification of patients who may be at risk of deferring care is important towards improving population health and reducing care total costs. Additionally, minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors. In this paper, we (a) address the problem of predicting care deferral for well-care visits; (b) observe that social determinants of health are relevant explanatory factors towards predicting care deferral, and (c) compute how fair the models are with respect to demographics, socioeconomic factors and selected comorbidities. Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care. The objective of this model is to identify patients at risk of deferring care and allow the health system to prevent care deferrals through direct outreach or social determinant mediation.