Telemedicine and e-Health最新文献

Background: Mississippi faces significant health disparities and barriers to health care access, particularly in its most rural areas. Telehealth offers a promising solution to address these challenges, but its adoption remains uneven. The purpose of this study was to investigate the potential factors associated with self-reported telehealth utilization among adult Mississippi residents, focusing on individual-, household-, and area-level characteristics. Methods: Data were collected from a state-representative survey of adult Mississippi residents (N = 821) using both online- and phone-based platforms, supplemented with secondary internet quality and local health care access data. A two-stage hurdle regression model was used to examine factors associated with telehealth use and conditional on any use, utilization frequency. A regression estimating associations with the use of in-person medical care was also estimated for comparison purposes. Results: Telehealth use was significantly associated with specific health conditions and health insurance status. However, local internet quality did not significantly impact the likelihood of telehealth use aside from a marginally significant association with local upload speed. Findings suggest that other demographic- and health-related factors may play a more prominent role. We also find differential telehealth utilization rates by region, suggesting that area-level characteristics like health care infrastructure may affect telehealth use likelihood. Conclusions: Telehealth adoption in Mississippi is associated with individual factors like health and insurance status rather than broadband access alone. Efforts to expand telehealth use should also address noninfrastructure barriers, such as digital literacy and awareness, particularly in rural and underserved populations.

Background: Telehealth has grown as a common treatment modality for substance use disorders following expanded telehealth flexibilities during the COVID-19 pandemic. Telehealth can increase access to treatment in rural areas, where there are limited local addiction providers. Methods: We conducted a cross-sectional survey of adults in telehealth treatment for opioid use disorder and compared satisfaction with care and provider-patient relationship quality between participants in rural and nonrural areas. Results: Respondents scored a mean of 4.51 ± 0.694 on the Telemedicine Satisfaction Questionnaire (range: 1-5) and 27.12 ± 5.633 on the Provider-Patient Depth of Relationship Questionnaire (range: 0-32), indicating high overall satisfaction and a deep provider-patient relationship. There were no significant differences based on rural residence. Conclusions: Based on high patient satisfaction, our findings support the future expansion of telemedicine treatment platforms across rural and nonrural areas to address the substantial unmet need for substance use treatment across the United States.

Background: Missed appointments adversely affect clinical outcomes, clinic efficiency, and quality of care and could worsen the impact of pediatric workforce shortages on health care access. Telemedicine has the potential to reduce missed appointments. However, interventions that do not account for neighborhood factors could widen disparities. We analyzed the relationship between missed appointments and type of pediatric appointment and the role of telemedicine and neighborhood factors. Methods: This retrospective cohort study used three data sources: (1) electronic health records, (2) American Community Survey, and (3) Housing and Urban Development crosswalk data. The analyses were restricted to pediatric patients (<18 years) with completed or missed outpatient visits (March 2020-December 2022). The outcome was missed appointments. The primary predictors were pediatric visit type, appointment modality, census tract (CT) residential segregation, and CT poverty level. Generalized estimating equations were used. Results: The final sample size was 90,712 appointments for 32,305 unique patients. The overall no-show rate was 20.75%. The no-show rate for general pediatrics was 20.36% and 27.82% for specialty appointments. In multivariable analyses, there was an increased likelihood of missed appointments for pediatric subspecialty appointments compared to general pediatrics (Odds Ratio (OR): 1.62; 95% Confidence Interval (CI): 1.51, 1.74). Telemedicine appointments were associated with a decreased likelihood of missed appointments compared to in-person appointments (OR: 0.41; 95% CI:0.39, 0.44). There was a positive interaction between appointment type and pediatrics visit type with a larger effect for subspecialty visits. Conclusions: Tailored interventions that integrate telemedicine uptake and contextual factors have the potential to reduce missed appointments.

Objectives: To compare the health outcomes, specifically hospitalization and mortality rates, of primary care physicians' referrals to the cardiology department for ambulatory assistance in heart failure (HF) over three clearly defined periods: before, during the electronic consultation program implementation (e-consult), and during the COVID-19 pandemic. Methods: Between 2010 and 2021, 6,859 HF patients were referred at least once. Of these, 4,851 received e-consultations, and 2,008 underwent single-act consultations. A time series regression model was used to analyze the impact of e-consult implementation (started in 2013) on all-cause, cardiovascular (CV), and HF-related hospital admissions and mortality rates. Results: e-Consults reduced the waiting time for cardiology care to 9 days. Hospital admissions decreased significantly after the implementation of e-consult (relative risk incidence [RRi] [95% confidence interval {CI95%}]: 0.867 [0.875-0.838] for HF, 0.838 [0.825-0.856] for cardiovascular disease, and 0.639 [0.635-0.651] for all-cause diseases), and mortality decreased (RRi [CI95%]: 0.981 [0.977-0.983] for HF, 0.977 [0.970-0.980] for CV, and 0.985 [0.984-0.985] for all causes). These improvements persisted during the COVID-19 pandemic. Conclusions: The implementation of the e-consult program for managing HF patient referrals resulted in reduced waiting times for cardiology care and decreases in hospitalizations and mortality rates. These benefits were maintained during the COVID-19 pandemic.

Objective: To characterize organizational and financial factors associated with hospital telemedicine utilization reporting. Methods: We used an explanatory sequential mixed methods design to quantitatively analyze hospital-level data from Medicare Cost Reports (2017-2021) and the American Hospital Association Annual Survey (AHAAS) (2020-2021) to assess telemedicine utilization reporting. Semistructured interviews were conducted with key informants from various health care sectors to contextualize quantitative findings. Results: Among 4,224 nonfederal acute care hospitals in our sample, most were urban (50.7%), not-for-profit (60.3%), and nonteaching hospitals (91.4%). For-profit, southern, and western hospitals were more likely to report telemedicine utilization data to the AHAAS compared to other ownership status and region categories. Qualitative interviews identified six domains that support enhanced telemedicine reporting: (1) resource and infrastructure availability, (2) organizational reporting issues, (3) survey design, (4) reconcilable vendor documentation, (5) lack of reporting requirements, and (6) lack of standardized definitions of telemedicine and telemedicine utilization. Conclusions: Addressing telemedicine reporting barriers is essential for accurate telemedicine utilization measurement and improved health care delivery. Future research should advance robust methodologies for capturing telemedicine utilization and explore the impact of reporting incentives and mandates on data completeness.

Introduction: Assessing treatment acceptability is critical to understanding patient experiences in clinical trials, especially in telehealth settings where exposure and engagement experiences are unique. However, the use of patient-reported acceptability outcomes in mental health-focused trials has been mixed, with most published studies relying on objective behavior (e.g., dropout rates) or fit-for-use measures, rather than instruments rooted in a specific theoretical model. This study introduces the Program Acceptability Tool for Telehealth (PATT), a novel, theoretically grounded instrument designed to capture patient-reported acceptability in telehealth-based trials. Methods: Here, we describe the initial development and validation of the PATT, including its performance with 123 caregivers participating in an ongoing clinical trial that includes multiple types of interventions and support programs focused on caregiver well-being. Results: The final 12-item PATT demonstrated robust psychometric properties, including high internal consistency (α = 0.82-0.90) and content validity. Convergent validity was established through significant correlations between PATT scores and behavioral engagement metrics. Conclusions: Our findings suggest that the PATT is a reliable, valid tool for capturing patient acceptability, offering a nuanced perspective on program, process, and impact-related experiences. Further validation studies are recommended to confirm the PATT's utility in broader applications.

Background: Maintaining primary care during disasters is crucial for mitigating health impacts. Telehealth can facilitate continuity but is often underutilized. The Veteran's Health Administration (VA) rapidly increased telehealth capacity in 2020, but the impact on disaster telehealth utilization remains unknown. We analyzed the impact of two hurricanes (Hurricane Michael in 2018 and Hurricane Ian in 2022) on telehealth utilization by VA primary care facilities. Design: Interrupted time series analysis of primary care visits to VA facilities in hurricane-affected states for 7 days before and 14 days following each event. Primary care visits were identified from the VA Corporate Data Warehouse. The primary outcome was the proportion of visits conducted virtually after each hurricane. Models included patient demographics, facility rurality, storm severity, and baseline facility telehealth capacity. Results: Seventy-eight VA facilities were evaluated for Hurricane Michael and 126 for Hurricane Ian. After covariate adjustment, Michael was associated with an immediate increase in the proportion of telehealth visits by 8.5 percentage points (95% confidence interval [CI]: 4.3%-12.7%, p < 0.001) and Ian by 12.3 percentage points (95% CI: 8.4%-16.1%, p < 0.001). Analyses by facility rurality demonstrated significant increases in telehealth for urban and rural facilities following Michael in 2018 (urban: 7.1%, 95% CI: 2.7%-11.5%, p < 0.001; rural: 15.1%, 95% CI: 4.5%-25.7%, p < 0.001) but only for urban facilities following Ian in 2022 (13.8%, 95% CI: 9.7%-18.0%, p < 0.001). Increases in telehealth utilization were larger for facilities in severely damaged areas. Conclusions: Telehealth was critical to VA primary care delivery during both hurricanes, with higher utilization seen in 2022 likely from the intervening scale-up of telehealth capacity. Rural facilities may be lagging in disaster telehealth utilization, exacerbating disparities in care delivery.

Introduction/Methods: Patient Monitoring at Home is a Remote Patient Monitoring (RPM) program through Michigan Medicine, which provides symptoms and vital sign monitoring via a provided cellular tablet and Bluetooth-connected devices. A team of registered nurses monitors patients 7 days per week. Results: The team examined 6-month outcomes for 1,139 encounters from November 2020 to August 2022, which showed a 59% reduction in the average number of hospital admissions 6 months after the start of enrollment (1.38 vs. 0.57, p < 0.0001) across multiple enrollment diagnoses including COVID-19, congestive heart failure, and hypertension. The duration of enrollment varied, ranging from 7 to 386 days, with a median of 38 days. A shorter duration of monitoring was associated with a more favorable outcome (hospitalization reduction). Discussion: Our findings show that RPM is effective in reducing hospital admissions for a wide variety of conditions. More research is needed to optimize patient selection, ideal method, and duration of monitoring.

Background: Implementation of telemental health care in emergency departments (EDs) in the United States (U.S.) has been increasing. Artificial intelligence (AI) can augment traditional qualitative research methods; little is known about its efficiency and accuracy. This study sought to understand ED directors' qualitative recommendations for improving telemental health care implementation and to understand how AI could facilitate analysis of qualitative survey responses. Methods: Directors at a nationally representative sample of 279 U.S. EDs that used telemental health care completed an open-ended survey question about improving telemental health care implementation between June 2022 and October 2023. Two groups of researchers completed independent qualitative coding of responses: one group used traditional qualitative methods, and one group used AI (ChatGPT 4.0) to facilitate analysis. Both groups independently developed a codebook, came to consensus on a combined codebook, and each group independently used it to code the survey responses. The two groups identified themes in ED directors' recommendations and compared codebooks and code application across traditional and AI approaches. Results: Themes included (1) recommendations for improving telemental health care directly and (2) recommendations for improving mental health care systems broadly to make telehealth more effective. ED directors' most common recommendation was enabling faster and more streamlined access to telemental health care. AI augmented human coding by identifying two valid codes not initially identified by human analysts. In codebook application, 75% of responses were coded consistently across AI and human coders. Conclusions and Relevance: For US EDs using telemental health care, there is a need to improve timeliness and efficiency of access to telemental health care.

Introduction: This study aims to assess how the adoption of telemedicine by primary care providers influenced new patient referrals to pediatric ophthalmology. Methods: Retrospective chart review of new pediatric ophthalmology referrals from primary care providers within the same 3 months (April, August, and December) each year between 2019 and 2021. Patient demographics, reason for referral, and recommended continued ophthalmical care (as a proxy for referral quality) were evaluated. Logistic regression models, chi-square tests, and Mann-Whitney tests were performed to assess the impact of telemedicine referrals. Results: Of the 1,181 referrals reviewed, 551 were included in the final analysis. Telemedicine use increased over time (p < 0.005). Comparing telemedicine and in-person referrals, there was no difference in patient age, sex, race, language, or insurance type (all p > 0.05). A significant difference was observed between the reasons for referrals by referral type (p < 0.005). The most common reason for telemedicine referrals was ophthalmic manifestations of systemic diseases, while the most common reason for in-person referrals was failed vision screening. Patients with public insurance and those referred after 2020 had higher odds of recommended continued care (adjusted odds ratio [OR]: 1.67, p = 0.01 and 1.98, p = 0.002), though referrals based on telemedicine visits were less likely to require continued ophthalmic care (adjusted OR: 0.33, p = 0.001). Conclusion: Pediatric ophthalmology referrals were influenced by the adoption of telemedicine by primary care providers. Referrals based on telemedicine visits were less likely to warrant continued ophthalmic care, suggesting that the impact of telemedicine on facilitating referrals and improving access to pediatric ophthalmology subspecialty care remains uncertain.