Rural Health
Resources by Topic: AI
American Clusters: Using Machine Learning to Understand Health and Health Care Disparities in the United States
Demonstrates the strengths and weaknesses of using machine learning (ML) to identify social determinants of health, healthcare access, healthcare infrastructure, and geography factors contributing to health inequalities. Discusses use of ML in policy, with different levels of geography, and in future research.
Author(s): Diane M. Bowser, Kaili Maurico, Brielle A. Ruscitti, William H. Crown
Citation: Health Affairs Scholar, 2(3)
Date: 03/2024
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Demonstrates the strengths and weaknesses of using machine learning (ML) to identify social determinants of health, healthcare access, healthcare infrastructure, and geography factors contributing to health inequalities. Discusses use of ML in policy, with different levels of geography, and in future research.
Author(s): Diane M. Bowser, Kaili Maurico, Brielle A. Ruscitti, William H. Crown
Citation: Health Affairs Scholar, 2(3)
Date: 03/2024
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How Rural Hospitals Can Use AI to Address AR Challenges and Workforce Shortages with Kelly Arduino, MA, MBA
Podcast episode discussing some of the challenges rural healthcare organizations face, including workforce shortages, artificial intelligence (AI) and technology, finances, and cybersecurity. Describes the impact recruiting local leaders can have on rural health organizations.
Date: 02/2024
Sponsoring organization: Impact! Communications, Inc.
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Podcast episode discussing some of the challenges rural healthcare organizations face, including workforce shortages, artificial intelligence (AI) and technology, finances, and cybersecurity. Describes the impact recruiting local leaders can have on rural health organizations.
Date: 02/2024
Sponsoring organization: Impact! Communications, Inc.
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Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up
Evaluates the effectiveness of a research study recruitment strategy using multiple contact methods for the rural, older adult population in Appalachian Pennsylvania. Includes rural and urban comparisons within Appalachian Pennsylvania and discusses common barriers to research participation for this group.
Author(s): Tabitha Milliken, Donielle Beiler, Samantha Hoffman, Ashlee Olenginski, Vanessa Troiani
Citation: JMIR Formative Research, 8(2024), 38189
Date: 2024
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Evaluates the effectiveness of a research study recruitment strategy using multiple contact methods for the rural, older adult population in Appalachian Pennsylvania. Includes rural and urban comparisons within Appalachian Pennsylvania and discusses common barriers to research participation for this group.
Author(s): Tabitha Milliken, Donielle Beiler, Samantha Hoffman, Ashlee Olenginski, Vanessa Troiani
Citation: JMIR Formative Research, 8(2024), 38189
Date: 2024
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Health Information Workforce: Survey Results on Workforce Challenges and the Role of Emerging Technologies
Presents results of an August 2023 survey of health information professionals on workforce challenges and the impact of emerging technologies, including artificial intelligence and machine learning, on the health information workforce. Describes challenges for recruiting and retaining health information professionals, factors that influence turnover, and the impacts of understaffing on healthcare quality and reimbursement. Discusses the future outlook for the health information profession and offers policy recommendations to enhance workforce development. Includes rural references throughout.
Date: 10/2023
Sponsoring organizations: American Health Information Management Association, NORC at the University of Chicago
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Presents results of an August 2023 survey of health information professionals on workforce challenges and the impact of emerging technologies, including artificial intelligence and machine learning, on the health information workforce. Describes challenges for recruiting and retaining health information professionals, factors that influence turnover, and the impacts of understaffing on healthcare quality and reimbursement. Discusses the future outlook for the health information profession and offers policy recommendations to enhance workforce development. Includes rural references throughout.
Date: 10/2023
Sponsoring organizations: American Health Information Management Association, NORC at the University of Chicago
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Predicting Firm Creation in Rural Texas: A Multi-Model Machine Learning Approach to a Complex Policy Problem
Evaluates Texas counties between 2008 and 2018 to identify factors contributing to entrepreneurship for rural areas. Discusses how healthcare, elder care, and child care predict growth in entrepreneurship. Compares predictive factors for rural areas with urban predictive factors. Makes recommendations for research, policy, and scholarship for rural entrepreneurship.
Author(s): Mark C. Hand, Vivek Shastry, Varun Rai
Citation: PLOS ONE, 18(6), e0287217
Date: 06/2023
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Evaluates Texas counties between 2008 and 2018 to identify factors contributing to entrepreneurship for rural areas. Discusses how healthcare, elder care, and child care predict growth in entrepreneurship. Compares predictive factors for rural areas with urban predictive factors. Makes recommendations for research, policy, and scholarship for rural entrepreneurship.
Author(s): Mark C. Hand, Vivek Shastry, Varun Rai
Citation: PLOS ONE, 18(6), e0287217
Date: 06/2023
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Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study
Analyzed wastewater samples from 12 locations in the United States to determine feasibility in using wastewater surveillance for monitoring substance use. Discusses using machine learning as a public health tool for analyzing data to identify communities with similar resource needs. Includes rural-specific concerns.
Author(s): Marie A. Severson, Sathaporn Onanong, Alexandra Dolezal, et al.
Citation: JMIR Formative Research, 7(2023), 45353
Date: 2023
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Analyzed wastewater samples from 12 locations in the United States to determine feasibility in using wastewater surveillance for monitoring substance use. Discusses using machine learning as a public health tool for analyzing data to identify communities with similar resource needs. Includes rural-specific concerns.
Author(s): Marie A. Severson, Sathaporn Onanong, Alexandra Dolezal, et al.
Citation: JMIR Formative Research, 7(2023), 45353
Date: 2023
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Using Machine Learning to Identify Factors Associated with Practice Location of the Healthcare Workforce
Results of a study applying machine learning techniques to identify factors predicting the decision of physicians, nurses, dentists, and pharmacists to practice in a rural area. Uses data sets collected by the Utah Medical Education Council in 2014, 2016, and 2017. Features statistics with breakdowns by rural and non-rural location.
Author(s): Jerry Bounsanga, Martin S. Lipsky, Eric S. Hon, et al.
Citation: Rural and Remote Health, 22(1), 7050
Date: 02/2022
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Results of a study applying machine learning techniques to identify factors predicting the decision of physicians, nurses, dentists, and pharmacists to practice in a rural area. Uses data sets collected by the Utah Medical Education Council in 2014, 2016, and 2017. Features statistics with breakdowns by rural and non-rural location.
Author(s): Jerry Bounsanga, Martin S. Lipsky, Eric S. Hon, et al.
Citation: Rural and Remote Health, 22(1), 7050
Date: 02/2022
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AI in Rural Healthcare Toolkit
Resources and best practices to help rural healthcare organizations explore how AI (artificial intelligence) can help improve access to care. Identifies program models and examples, and discusses implementation, evaluation, sustainability, and dissemination of program results.
Sponsoring organization: Rural Health Information Hub
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Resources and best practices to help rural healthcare organizations explore how AI (artificial intelligence) can help improve access to care. Identifies program models and examples, and discusses implementation, evaluation, sustainability, and dissemination of program results.
Sponsoring organization: Rural Health Information Hub
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Heartland Telehealth ECHO Series
A Project ECHO (Extension for Community Healthcare Outcomes) series focused on providing access to specialty care expertise by connecting healthcare providers to specialist teams via videoconferencing. Covers a wide array of topics such as clinician burnout, artificial intelligence (AI), effective billing strategies, and more.
Sponsoring organization: Heartland Telehealth Resource Center
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A Project ECHO (Extension for Community Healthcare Outcomes) series focused on providing access to specialty care expertise by connecting healthcare providers to specialist teams via videoconferencing. Covers a wide array of topics such as clinician burnout, artificial intelligence (AI), effective billing strategies, and more.
Sponsoring organization: Heartland Telehealth Resource Center
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