To assess telehealth adoption among hospitals located in rural and urban areas, and identify barriers related to enhanced telehealth capabilities in the areas of patient engagement and health information exchange (HIE) capacity with external providers and community partners.
Methods:We used the 2018 American Hospital Association (AHA) Annual Survey and IT Supplement Survey. We applied state fixed effects multivariate analyses and Oaxaca decomposition to estimate the variation of outcomes of interest by hospital geographies.Findings: Our research showed substantial differences in telehealth adoption among hospitals located in rural, micropolitan, and metropolitan areas, where adoption rates increase with urbanicity. Rural hospitals were least likely to have telehealth systems with patient engagement capabilities such as the ability to view their health information online and electronically transmit medical information to a third party. They were also the least likely to report that clinical information was available electronically from outside providers. Our model explained 65% of the rural/urban difference in telehealth adoption, 55% of the number of telehealth services adopted, and 43%-49% of the rural/urban difference in telehealth barriers.
Conclusion:Findings demonstrated significant barriers to telehealth use among hospitals located in rural and urban areas. For rural hospitals, barriers include lack of HIE capacity among health care providers in the community, and lack of patient engagement capability.
BACKGROUND/OBJECTIVES: This study examined urban/rural differences in the frequency of preventable emergency department (ED) visits among patients with Alzheimer's disease and related dementias (ADRD), with a focus on the variation of accountable care organization (ACO) participation status for hospitals in urban and rural areas. DESIGN: We performed a cross-sectional study using the 2015 State Emergency Department Databases, the American Hospital Association Annual Survey of Hospitals, and the Area Health Resource File. Individual-, county-, and hospital-level characteristics and state fixed effects were used for model specification. SETTING: Patients with ADRD from seven states who visited the ED and had routine discharges. PARTICIPANTS: Our sample consisted of 117,196 patients with ADRD. MEASUREMENTS: The outcome was preventable ED visits classified using the New York University Emergency Department visit algorithm. We performed a multivariable logistic regression to estimate the variation of preventable ED visits by urban and rural areas. RESULTS: Rural patients with ADRD had 1.13 higher adjusted odds (P = .007) of going to the ED for a preventable visit compared with their urban counterparts. In addition, ACO-affiliated hospitals had .91 lower adjusted odds (P = .005) of preventable ED visits for ADRD patients compared with hospitals not affiliated with an ACO. Wholecounty Mental Health Care Health Professional Shortage Area (HPSA) (odds ratio = 1.14; P = .002) designation was also an indicator of higher preventable ED rates.CONCLUSION: ACO delivery systems have the potential to decrease rural preventable ED visits among ADRD patients.
Objectives: Registered nurses are uniquely positioned to fill shortages in the geropsychiatric healthcare workforce. With training, nurses can coordinate both mental and physical health care and deliver mental health interventions. Our objective was to determine how nursing educators are preparing students to care for the mental health needs of older adults and explore the challenges that they face in this effort.Design: A qualitative study using semi-structured, in-depth interviews.Setting: Participants were recruited from schools of nursing in the Northeast region of the United States.Participants: Undergraduate psychiatric nursing instructors and professors.Measurements: Semi-structured interviews focused on challenges and strategies related to preparing nursing students to meet the mental health needs of older patients. All interviews were conducted by registered nurses, audio recorded, transcribed, and thematically coded by two members of the research team using NVivo.
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