Cargando…

Predicting Social Determinants of Health in Patient Navigation: Case Study

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Iacobelli, Francisco, Yang, Anna, Tom, Laura, Leung, Ivy S, Crissman, John, Salgado, Rufino, Simon, Melissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131925/
https://www.ncbi.nlm.nih.gov/pubmed/36976634
http://dx.doi.org/10.2196/42683
_version_ 1785031286463660032
author Iacobelli, Francisco
Yang, Anna
Tom, Laura
Leung, Ivy S
Crissman, John
Salgado, Rufino
Simon, Melissa
author_facet Iacobelli, Francisco
Yang, Anna
Tom, Laura
Leung, Ivy S
Crissman, John
Salgado, Rufino
Simon, Melissa
author_sort Iacobelli, Francisco
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10131925
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-101319252023-04-27 Predicting Social Determinants of Health in Patient Navigation: Case Study Iacobelli, Francisco Yang, Anna Tom, Laura Leung, Ivy S Crissman, John Salgado, Rufino Simon, Melissa JMIR Form Res Original Paper 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. JMIR Publications 2023-03-28 /pmc/articles/PMC10131925/ /pubmed/36976634 http://dx.doi.org/10.2196/42683 Text en ©Francisco Iacobelli, Anna Yang, Laura Tom, Ivy S Leung, John Crissman, Rufino Salgado, Melissa Simon. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.03.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Iacobelli, Francisco
Yang, Anna
Tom, Laura
Leung, Ivy S
Crissman, John
Salgado, Rufino
Simon, Melissa
Predicting Social Determinants of Health in Patient Navigation: Case Study
title Predicting Social Determinants of Health in Patient Navigation: Case Study
title_full Predicting Social Determinants of Health in Patient Navigation: Case Study
title_fullStr Predicting Social Determinants of Health in Patient Navigation: Case Study
title_full_unstemmed Predicting Social Determinants of Health in Patient Navigation: Case Study
title_short Predicting Social Determinants of Health in Patient Navigation: Case Study
title_sort predicting social determinants of health in patient navigation: case study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131925/
https://www.ncbi.nlm.nih.gov/pubmed/36976634
http://dx.doi.org/10.2196/42683
work_keys_str_mv AT iacobellifrancisco predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT yanganna predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT tomlaura predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT leungivys predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT crissmanjohn predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT salgadorufino predictingsocialdeterminantsofhealthinpatientnavigationcasestudy
AT simonmelissa predictingsocialdeterminantsofhealthinpatientnavigationcasestudy