Cargando…

Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities

INTRODUCTION: Mobile health (mHealth) has the potential to change how patients make healthcare decisions. We sought to determine the readiness to use mHealth technology in underserved communities. METHODS: We conducted a cross-sectional survey of patients presenting with low-acuity complaints to an...

Descripción completa

Detalles Bibliográficos
Autores principales: van Veen, Tara, Binz, Sophia, Muminovic, Meri, Chaudhry, Kaleem, Rose, Katie, Calo, Sean, Rammal, Jo-Ann, France, John, Miller, Joseph B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Department of Emergency Medicine, University of California, Irvine School of Medicine 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754190/
https://www.ncbi.nlm.nih.gov/pubmed/31539337
http://dx.doi.org/10.5811/westjem.2019.6.41911
_version_ 1783453037766901760
author van Veen, Tara
Binz, Sophia
Muminovic, Meri
Chaudhry, Kaleem
Rose, Katie
Calo, Sean
Rammal, Jo-Ann
France, John
Miller, Joseph B.
author_facet van Veen, Tara
Binz, Sophia
Muminovic, Meri
Chaudhry, Kaleem
Rose, Katie
Calo, Sean
Rammal, Jo-Ann
France, John
Miller, Joseph B.
author_sort van Veen, Tara
collection PubMed
description INTRODUCTION: Mobile health (mHealth) has the potential to change how patients make healthcare decisions. We sought to determine the readiness to use mHealth technology in underserved communities. METHODS: We conducted a cross-sectional survey of patients presenting with low-acuity complaints to an urban emergency department (ED) with an underserved population. Patients over the age of two who presented with low-acuity complaints were included. We conducted structured interview with each patient or parent (for minors) about willingness to use mHealth tools for guidance. Analysis included descriptive statistics and univariate analysis based on age and gender. RESULTS: Of 560 patients included in the survey, 80% were adults, 64% female, and 90% Black. The mean age was 28 ± 9 years for adults and 9 ± 5 years for children. One-third of patients reported no primary care physician, and 55% reported no access to a nurse or clinician for medical advice. Adults were less likely to have access to phone consultation than parents of children (odds ratio [OR] 0.49, 95% confidence interval [CI], 0.32 – 0.74), as were males compared to females (OR 0.52, 95% CI, 0.37–0.74). Most patients (96%) reported cellular internet access. Two-thirds of patients reported using online references. When asked how they would behave if an mHealth tool advised them that their current health problem was low risk, 69% of patients responded that they would seek care in an outpatient clinic instead of the ED (30%), stay home and not seek urgent medical care (28%), or use telehealth (11%). CONCLUSION: In this urban community we found a large capacity and willingness to use mHealth technology in medical triage.
format Online
Article
Text
id pubmed-6754190
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Department of Emergency Medicine, University of California, Irvine School of Medicine
record_format MEDLINE/PubMed
spelling pubmed-67541902019-09-25 Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities van Veen, Tara Binz, Sophia Muminovic, Meri Chaudhry, Kaleem Rose, Katie Calo, Sean Rammal, Jo-Ann France, John Miller, Joseph B. West J Emerg Med Population Health INTRODUCTION: Mobile health (mHealth) has the potential to change how patients make healthcare decisions. We sought to determine the readiness to use mHealth technology in underserved communities. METHODS: We conducted a cross-sectional survey of patients presenting with low-acuity complaints to an urban emergency department (ED) with an underserved population. Patients over the age of two who presented with low-acuity complaints were included. We conducted structured interview with each patient or parent (for minors) about willingness to use mHealth tools for guidance. Analysis included descriptive statistics and univariate analysis based on age and gender. RESULTS: Of 560 patients included in the survey, 80% were adults, 64% female, and 90% Black. The mean age was 28 ± 9 years for adults and 9 ± 5 years for children. One-third of patients reported no primary care physician, and 55% reported no access to a nurse or clinician for medical advice. Adults were less likely to have access to phone consultation than parents of children (odds ratio [OR] 0.49, 95% confidence interval [CI], 0.32 – 0.74), as were males compared to females (OR 0.52, 95% CI, 0.37–0.74). Most patients (96%) reported cellular internet access. Two-thirds of patients reported using online references. When asked how they would behave if an mHealth tool advised them that their current health problem was low risk, 69% of patients responded that they would seek care in an outpatient clinic instead of the ED (30%), stay home and not seek urgent medical care (28%), or use telehealth (11%). CONCLUSION: In this urban community we found a large capacity and willingness to use mHealth technology in medical triage. Department of Emergency Medicine, University of California, Irvine School of Medicine 2019-09 2019-08-06 /pmc/articles/PMC6754190/ /pubmed/31539337 http://dx.doi.org/10.5811/westjem.2019.6.41911 Text en Copyright: © 2019 Veen et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) License. See: http://creativecommons.org/licenses/by/4.0/
spellingShingle Population Health
van Veen, Tara
Binz, Sophia
Muminovic, Meri
Chaudhry, Kaleem
Rose, Katie
Calo, Sean
Rammal, Jo-Ann
France, John
Miller, Joseph B.
Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title_full Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title_fullStr Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title_full_unstemmed Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title_short Potential of Mobile Health Technology to Reduce Health Disparities in Underserved Communities
title_sort potential of mobile health technology to reduce health disparities in underserved communities
topic Population Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754190/
https://www.ncbi.nlm.nih.gov/pubmed/31539337
http://dx.doi.org/10.5811/westjem.2019.6.41911
work_keys_str_mv AT vanveentara potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT binzsophia potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT muminovicmeri potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT chaudhrykaleem potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT rosekatie potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT calosean potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT rammaljoann potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT francejohn potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities
AT millerjosephb potentialofmobilehealthtechnologytoreducehealthdisparitiesinunderservedcommunities