mHealth as a Component of Next-Generation Health Care

Niwa, Makoto

doi:10.1007/978-981-19-4230-3_8

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…Android and iOS smartwatches offer core health initiatives, with sensors and apps that can automatically analyze heart health, blood oxygen, sleep cycles, and fitness. This innovation is rapidly superseding traditional life alert functionality with recent developments including fall detection, automatic location-aware emergency dialing, predictive warnings of heart arrhythmia, and reported seamless syncing of medical records [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study

Weichelt,

Burke,

Kieke

et al. 2024

JMIR Hum Factors

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Background Physicians and patient-facing caregivers have increasingly used mobile health (mHealth) technologies in the past several years, accelerating during the COVID-19 pandemic. However, barriers and feedback surrounding adoption remain relatively understudied and varied across health systems, particularly in rural areas. Objective This study aims to identify provider adoption, attitudes, and barriers toward mHealth in a large, multisite, rural US health care system. We investigated (1) mHealth apps that providers use for their own benefit and (2) mHealth apps that a provider uses in conjunction with a patient. Methods We surveyed all patient-seeing providers within the Marshfield Clinic Health System with a brief, 16-item, web-based survey assessing attitudes toward mHealth, adoption of these technologies, and perceived barriers faced by providers, their peers, and the institution. Survey results were summarized via descriptive statistics, with log-binomial regression and accompanying pairwise analyses, using Kruskal-Wallis and Jonckheere-Terpstra tests for significance, respectively. Respondents were grouped by reported clinical role and specialty. Results We received a 38% (n/N=916/2410) response rate, with 60.7% (n=556) of those sufficiently complete for analyses. Roughly 54.1% (n=301) of respondents reported mHealth use, primarily around decision-making and supplemental information, with use differing based on provider role and years of experience. Self-reported barriers to using mHealth included a lack of knowledge and time to study mHealth technologies. Providers also reported concerns about patients’ internet access and the complexity of mHealth apps to adequately use mHealth technologies. Providers believed the health system’s barriers were largely privacy, confidentiality, and legal review concerns. Conclusions These findings echo similar studies in other health systems, surrounding providers’ lack of time and concerns over privacy and confidentiality of patient data. Providers emphasized concerns over the complexity of these technologies for their patients and concerns over patients’ internet access to fully use mHealth in their delivery of care.

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Section: Introductionmentioning

confidence: 99%

Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study

Weichelt,

Burke,

Kieke

et al. 2024

JMIR Hum Factors

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Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study (Preprint)

Weichelt,

Burke,

Kieke

et al. 2023

Preprint

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BACKGROUND Physicians and patient-facing caregivers have increasingly utilized mobile health (mHealth) technologies in the past several years, accelerating during the COVID-19 pandemic. However, barriers and feedback surrounding adoption remains relatively understudied and varied across health systems, particularly in rural areas. OBJECTIVE We aimed to identify provider adoption, attitudes, and barriers towards mHealth in a large, multi-site, rural U.S. healthcare system. We investigated 1) m-Health apps that providers use for their own benefit, and 2) m-Health apps that a provider uses in conjunction with a patient. METHODS We surveyed all patient-seeing providers within the Marshfield Clinic Health System with a brief, 16 item, web-based survey assessing attitudes towards mHealth, adoption of these technologies, and perceived barriers faced by providers, their peers, and the institution. Survey results were summarized via descriptive statistics, with log-binomial regression and accompanying pairwise analyses, using Kruskal-Wallis and Jonckheere-Terpstra tests for significance, respectively. Respondents were grouped by reported clinical role and specialty. RESULTS We received a 38% response rate, with 61% of those sufficiently complete for analyses. Roughly 54% of respondents reported mHealth use, primarily around decision making and supplemental information, with use differing based on provider role and years of experience. Self-reported barriers to using mHealth included a lack of knowledge and time to study mHealth technologies. Providers also reported concerns about patients’ internet access and the complexity of mHealth apps to adequately utilize mHealth technologies. Providers believed the health system’s barriers were primarily privacy, confidentiality and legal review concerns. CONCLUSIONS These findings echo similar studies in other health systems, surrounding providers’ lack of time and concerns over privacy and confidentiality of patient data. Providers emphasized concerns over the complexity of these technologies for their patients, and concerns over patients’ internet access to fully utilize mHealth in their delivery of care.

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Disease Screening in Healthcare and Pandemic Outbreak

Singh,

Jermsittiparsert,

Lal

et al. 2024

Advances in Human and Social Aspects of Technology

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Disease surveillance and a well-concerted pandemic outbreak plan have taken on greater importance within the broader realm of global health today. Artificial Intelligence (AI) is transforming this through increasingly precise and large-scale screenings, allowing to increase chances of recovery by making best use of resources. Powerful AI-based solutions, such as machine learning algorithms, can process vast amounts of data swiftly to recognize symptoms from patient records and estimate how the disease is going to spread so health authorities take corrective action in time. AI can also predict where infections will be higher this enables you to target vaccine campaigns and get vaccines distributed, ensuring an equitable distribution of said resources. This chapter focuses on the AI solutions can be used to speed up the vaccine research process by knocking out complex biological data faster than conventional methods.

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mHealth as a Component of Next-Generation Health Care

Cited by 4 publications

References 56 publications

Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study

Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study

Provider Adoption of mHealth in Rural Patient Care: Web-Based Survey Study (Preprint)

Disease Screening in Healthcare and Pandemic Outbreak

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