Provide a brief summary of article. Then state why it relates to Patient Equity: Social Determinants to Improve Patient Care
Provide a brief summary of article. Then state why it relates to Patient Equity: Social Determinants to Improve Patient Care
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Provide a brief summary of article. Then state why it relates to Patient Equity: Social Determinants to Improve Patient Care
Transcribed Image Text:Predicting Social Determinants
of Health in Patient Navigation:
Case Study
Francisco lacobelli 1,2; Anna Yang 3,4 Ⓒ;
Laura Tom 3,4; Ivy S Leung 3,4; John Crissman ¹;
Rufino Salgado 1 D; Melissa Simon 3, 4, 5
Abstract
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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 DoHs.
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.
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