Rural Health
Resources by Topic: AI
Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023-2024
Describes trends in the foundational use, evaluation, and governance of predictive artificial intelligence (AI) in hospitals using data from the 2023 and 2024 American Hospital Association (AHA) Information Technology (IT) supplement. Presents data on the differences in acute care hospitals' use of predictive AI by hospital size, CAH status, rural/urban location, independent/system affiliation, and electronic health record (EHR) vendor.
Date: 09/2025
Sponsoring organization: Office of the National Coordinator for Health Information Technology
view details
Describes trends in the foundational use, evaluation, and governance of predictive artificial intelligence (AI) in hospitals using data from the 2023 and 2024 American Hospital Association (AHA) Information Technology (IT) supplement. Presents data on the differences in acute care hospitals' use of predictive AI by hospital size, CAH status, rural/urban location, independent/system affiliation, and electronic health record (EHR) vendor.
Date: 09/2025
Sponsoring organization: Office of the National Coordinator for Health Information Technology
view details
Machine Learning to Evaluate the Effects of Non-Clinical Social Determinant Features in Predicting Colorectal Cancer Mortality in a Medically Underserved Appalachian Population
Examines colorectal cancer (CRC) survival rates in rural Appalachia and the related impact of demographic, clinical, and social determinant of health (SDOH) factors. Utilizes machine learning (ML) models to analyze data from Appalachian cancer centers and examines what factors contribute most to CRC survival in Appalachia. Discusses the impact of including SDOH in ML prediction models and implications for further CRC research.
Author(s): Aisha Montgomery, Ravi Vadapalli, Frank A. Dinenno, et al.
Citation: Scientific Reports, 15, 25781
Date: 07/2025
view details
Examines colorectal cancer (CRC) survival rates in rural Appalachia and the related impact of demographic, clinical, and social determinant of health (SDOH) factors. Utilizes machine learning (ML) models to analyze data from Appalachian cancer centers and examines what factors contribute most to CRC survival in Appalachia. Discusses the impact of including SDOH in ML prediction models and implications for further CRC research.
Author(s): Aisha Montgomery, Ravi Vadapalli, Frank A. Dinenno, et al.
Citation: Scientific Reports, 15, 25781
Date: 07/2025
view details
COVID-19 Risk Stratification among Older Adults: A Machine Learning Approach to Identify Personal and Health-Related Risk Factors
Examines risk perceptions and health behaviors related to COVID-19 to create a predictive model to classify individuals into risk categories. Analyzes individual features such as demographic variables, health conditions, adherence to public health guidelines, health literacy, and urbanicity.
Author(s): Arezoo Abasi, Seyed Abbas Motevalian, Haleh Ayatollahi
Citation: BMC Public Health 25, 2577
Date: 07/2025
view details
Examines risk perceptions and health behaviors related to COVID-19 to create a predictive model to classify individuals into risk categories. Analyzes individual features such as demographic variables, health conditions, adherence to public health guidelines, health literacy, and urbanicity.
Author(s): Arezoo Abasi, Seyed Abbas Motevalian, Haleh Ayatollahi
Citation: BMC Public Health 25, 2577
Date: 07/2025
view details
Innovative Rural Hospitals Think Beyond Tradition to Improve Access to Care
Provides examples of how rural hospitals are utilizing technology to improve access to care for their patients. Discusses artificial intelligence (AI), drone delivery of medicines, nurse-run telehealth hubs, and digital front doors.
Date: 04/2025
Sponsoring organization: American Hospital Association
view details
Provides examples of how rural hospitals are utilizing technology to improve access to care for their patients. Discusses artificial intelligence (AI), drone delivery of medicines, nurse-run telehealth hubs, and digital front doors.
Date: 04/2025
Sponsoring organization: American Hospital Association
view details
Machine Learning to Predict Dementia for American Indian and Alaska Native Peoples: A Retrospective Cohort Study
Describes a dementia risk prediction model for American Indian and Alaska Native (AI/AN) populations who use Tribal health services. Utilizes data from the Indian Health Service (IHS) National Data Warehouse to analyze dementia risk for 17,398 AI/AN adults, discussing health and demographic predictors of risk.
Author(s): Kayleen Ports, Jiahui Dai, Kyle Conniff, et al.
Citation: The Lancet Regional Health - Americas, 43, 101013
Date: 03/2025
view details
Describes a dementia risk prediction model for American Indian and Alaska Native (AI/AN) populations who use Tribal health services. Utilizes data from the Indian Health Service (IHS) National Data Warehouse to analyze dementia risk for 17,398 AI/AN adults, discussing health and demographic predictors of risk.
Author(s): Kayleen Ports, Jiahui Dai, Kyle Conniff, et al.
Citation: The Lancet Regional Health - Americas, 43, 101013
Date: 03/2025
view details
The State of Rural Healthcare: Research and Outlook for 2025
Presents results of a survey of 75 rural healthcare regarding their financial health and strategic priorities. Identifies the top challenges identified by the surveyed organizations. Discusses how providers are addressing strategic priorities in 2025, including cybersecurity and the adoption of digital and artificial intelligence (AI) tools. Requires name, email address, and organization information to download.
Date: 02/2025
Sponsoring organization: Wipfli, LLP
view details
Presents results of a survey of 75 rural healthcare regarding their financial health and strategic priorities. Identifies the top challenges identified by the surveyed organizations. Discusses how providers are addressing strategic priorities in 2025, including cybersecurity and the adoption of digital and artificial intelligence (AI) tools. Requires name, email address, and organization information to download.
Date: 02/2025
Sponsoring organization: Wipfli, LLP
view details
Large Language Models for Agricultural Injury Surveillance
Describes the use of large language models (LLMs) in automating a previously manual agricultural injury surveillance workflow. Discusses reliability, accuracy, costs, and remaining challenges.
Author(s): Jacob Muller, Daniel Petti, Changying Li, et al.
Citation: Safety, 11(1), 15
Date: 02/2025
view details
Describes the use of large language models (LLMs) in automating a previously manual agricultural injury surveillance workflow. Discusses reliability, accuracy, costs, and remaining challenges.
Author(s): Jacob Muller, Daniel Petti, Changying Li, et al.
Citation: Safety, 11(1), 15
Date: 02/2025
view details
Artificial Intelligence (AI) in Rural Health
Provides an overview of artificial intelligence (AI), applications of AI, and how AI can be used in healthcare. Discusses how AI can impact rural healthcare and considerations for AI use in healthcare.
Date: 12/2024
Sponsoring organization: National Consortium of Telehealth Resource Centers
view details
Provides an overview of artificial intelligence (AI), applications of AI, and how AI can be used in healthcare. Discusses how AI can impact rural healthcare and considerations for AI use in healthcare.
Date: 12/2024
Sponsoring organization: National Consortium of Telehealth Resource Centers
view details
2024 Nebraska Rural Poll Research Report: Artificial Intelligence
Analyzes 2023 Nebraska Rural Poll results of a survey of 1,010 rural Nebraskans regarding their use of and opinions on artificial intelligence (AI). Includes data on their perceptions of AI use in healthcare, personal well-being, workforce, and more.
Author(s): Heather Akin, Cheryl Burkhart-Kriesel, Mary Emery, et al.
Date: 12/2024
Sponsoring organization: University of Nebraska-Lincoln
view details
Analyzes 2023 Nebraska Rural Poll results of a survey of 1,010 rural Nebraskans regarding their use of and opinions on artificial intelligence (AI). Includes data on their perceptions of AI use in healthcare, personal well-being, workforce, and more.
Author(s): Heather Akin, Cheryl Burkhart-Kriesel, Mary Emery, et al.
Date: 12/2024
Sponsoring organization: University of Nebraska-Lincoln
view details
Clinician Voices on Ethics of LLM Integration in Healthcare: A Thematic Analysis of Ethical Concerns and Implications
Explores large language model (LLM) integration into healthcare. Analyzes 3,049 online clinician posts to examine themes such as communication, quality improvement, data use, ethical considerations, user experiences, LLM challenges specific to rural healthcare, and more.
Author(s): Tala Mirzaei, Leila Amini, Pouyan Esmaeilzadeh
Citation: BMC Medical Informatics and Decision Making, 24, 250
Date: 09/2024
view details
Explores large language model (LLM) integration into healthcare. Analyzes 3,049 online clinician posts to examine themes such as communication, quality improvement, data use, ethical considerations, user experiences, LLM challenges specific to rural healthcare, and more.
Author(s): Tala Mirzaei, Leila Amini, Pouyan Esmaeilzadeh
Citation: BMC Medical Informatics and Decision Making, 24, 250
Date: 09/2024
view details