Alice Shijia Yan
Dados Biográficos
| ID | 5544337 |
|---|---|
| NOME | Alice Shijia Yan |
| PRENOMES | Alice Shijia |
| SOBRENOME | Yan |
| ASSINATURA | YAN A S |
| AFILIAÇÕES | Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD |
| ORCID | 0009-0002-5299-2649 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2025 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Revisiting the rural and urban divide in hospital health information technology adoption
PURPOSE: We assessed the adoption of telehealth, patient engagement (PE), and health information exchange (HIE) functionalities among hospitals in 2023, comparing adoption rates between rural and urban hospitals. METHODS: We used the linked 2023 American Hospital Association Annual Survey and Information Technology Survey data. We examined average adoption rates of eight telehealth, eight PE, and three HIE functionalities across metropolitan, mic…
Adoption of Health Information Technologies by Area Socioeconomic Deprivation Among US Hospitals
In this study, hospitals in more socioeconomically disadvantaged HSAs remained likely to adopt telehealth and HIE functionalities. Nevertheless, HIT adoption has grown steadily over time. Accountable care organization participation may support HIT infrastructure and help reduce geographic disparities in adoption and access to care
Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation
OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. BACKGROUND: AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI's impact on health equity. METHODS: We used linked datasets from the 2022 Amer…
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Revisiting the rural and urban divide in hospital health information technology adoption
PURPOSE: We assessed the adoption of telehealth, patient engagement (PE), and health information exchange (HIE) functionalities among hospitals in 2023, comparing adoption rates between rural and urban hospitals. METHODS: We used the linked 2023 American Hospital Association Annual Survey and Information Technology Survey data. We examined average adoption rates of eight telehealth, eight PE, and three HIE functionalities across metropolitan, mic…
Adoption of Health Information Technologies by Area Socioeconomic Deprivation Among US Hospitals
In this study, hospitals in more socioeconomically disadvantaged HSAs remained likely to adopt telehealth and HIE functionalities. Nevertheless, HIT adoption has grown steadily over time. Accountable care organization participation may support HIT infrastructure and help reduce geographic disparities in adoption and access to care
Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation
OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. BACKGROUND: AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI's impact on health equity. METHODS: We used linked datasets from the 2022 Amer…
Health care (3 obras) · Health information technology (2 obras) · Medicine (2 obras) · Mobile Health and mHealth Applications (2 obras) · Telemedicine and Telehealth Implementation (2 obras) · Artificial Intelligence (1 obras) · Artificial Intelligence (1 obras) · Artificial Intelligence in Healthcare and Education (1 obras) · Computer Science (1 obras) · COVID-19 Digital Contact Tracing (1 obras)