背景:患者导航(PN)计划已证明通过解决医疗保健障碍,在一系列临床环境中改善边缘化人群的健康结果方面具有功效。包括健康的社会决定因素(SDoHs)。然而,由于许多因素,导航员通过直接询问患者来识别SDoHs可能是具有挑战性的,包括不愿透露信息的患者,沟通障碍,以及患者导航员的可变资源和经验水平。导航员可以从增强其收集SDoH数据能力的策略中受益。机器学习可以作为这些策略之一来识别与SDoH相关的障碍。这可以进一步改善健康结果,特别是在服务不足的人群中。
目的:在本形成性研究中,我们在2项芝加哥地区PN研究中探索了基于机器学习的新方法来预测SDoHs。在第一种方法中,我们将机器学习应用于包括患者和导航员之间的评论和交互细节的数据,而第二种方法增加了患者的人口统计信息。本文介绍了这些实验的结果,并为数据收集和机器学习技术更广泛地应用于预测SDoHs的问题提供了建议。
方法:我们进行了2次实验,以探索使用从PN研究中收集的数据使用机器学习来预测患者\'SDoHs的可行性。机器学习算法是根据从2个芝加哥地区PN研究中收集的数据进行训练的。在第一个实验中,我们比较了几种机器学习算法(逻辑回归,随机森林,支持向量机,人工神经网络,和高斯朴素贝叶斯)来预测SDoHs从患者的人口统计和导航员的相遇数据随着时间的推移。在第二个实验中,我们使用了具有增强信息的多类别分类,比如去医院的运输时间,预测每位患者的多个SDoHs。
结果:在第一个实验中,随机森林分类器在测试的分类器中获得了最高的准确度。预测SDoHs的总体准确率为71.3%。在第二个实验中,多类别分类仅基于人口统计学和增强数据有效地预测了一些患者的SDoHs。这些预测的最佳准确率为73%。然而,这两个实验在单个SDoH预测和相关性中都产生了很高的变异性,这些相关性在SDoH之间变得很明显。
结论:据我们所知,这项研究是应用PN遭遇数据和多类学习算法来预测SDoHs的第一个方法。讨论的实验产生了宝贵的教训,包括对模型局限性和偏见的认识,规划数据源和测量的标准化,以及识别和预测SDoHs的交叉性和聚类的需要。虽然我们的重点是预测患者的SDoHs,机器学习在PN领域可以有广泛的应用,从定制干预交付(例如,支持PN决策)以通知测量的资源分配,和PN监督。
BACKGROUND: Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes for marginalized populations across a range of clinical contexts by addressing barriers to health care, including social determinants of health (SDoHs). However, it can be challenging for navigators to identify SDoHs by asking patients directly because of many factors, including patients\' reluctance to disclose information, communication barriers, and the variable resources and experience levels of patient navigators. Navigators could benefit from strategies that augment their ability to gather SDoH data. Machine learning can be leveraged as one of these strategies to identify SDoH-related barriers. This could further improve health outcomes, particularly in underserved populations.
OBJECTIVE: In this formative study, we explored novel machine learning-based approaches to predict SDoHs in 2 Chicago area PN studies. In the first approach, we applied machine learning to data that include comments and interaction details between patients and navigators, whereas the second approach augmented patients\' demographic information. This paper presents the results of these experiments and provides recommendations for data collection and the application of machine learning techniques more generally to the problem of predicting SDoHs.
METHODS: We conducted 2 experiments to explore the feasibility of using machine learning to predict patients\' SDoHs using data collected from PN research. The machine learning algorithms were trained on data collected from 2 Chicago area PN studies. In the first experiment, we compared several machine learning algorithms (logistic regression, random forest, support vector machine, artificial neural network, and Gaussian naive Bayes) to predict SDoHs from both patient demographics and navigator\'s encounter data over time. In the second experiment, we used multiclass classification with augmented information, such as transportation time to a hospital, to predict multiple SDoHs for each patient.
RESULTS: In the first experiment, the random forest classifier achieved the highest accuracy among the classifiers tested. The overall accuracy to predict SDoHs was 71.3%. In the second experiment, multiclass classification effectively predicted a few patients\' SDoHs based purely on demographic and augmented data. The best accuracy of these predictions overall was 73%. However, both experiments yielded high variability in individual SDoH predictions and correlations that become salient among SDoHs.
CONCLUSIONS: To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients\' SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.