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Introduction: Many patients used telehealth services during the COVID-19 pandemic. In this study, we evaluate how different factors have affected telehealth utilization in recent years. Decision makers at the federal and state levels can use the results of this study to inform their healthcare-related policy decisions. Methods: We implemented data analytics techniques to determine the factors that explain the use of telehealth by developing a case study using data from Arkansas. Specifically, we built a random forest regression model which helps us identify the important factors in telehealth utilization. We evaluated how each factor impacts the number of telehealth patients in Arkansas counties. Results: Of the 11 factors evaluated, five are demographic, and six are socioeconomic factors. Socioeconomic factors are relatively easier to influence in the short term. Based on our results, broadband subscription is the most important socioeconomic factor and population density is the most important demographic factor. These two factors were followed by education level, computer use, and disability in terms of their importance as it relates to telehealth use. Discussion: Based on studies in the literature, telehealth has the potential to improve healthcare services by improving doctor utilization, reducing direct and indirect waiting times, and reducing costs. Thus, federal and state decision makers can influence the utilization of telehealth in specific locations by focusing on important factors. For example, investments can be made to increase broadband subscriptions, education levels, and computer use in targeted locations. [ABSTRACT FROM AUTHOR]

The article discusses the attrition of remote patient monitoring (RPM) use for hypertension management in Medicare fee-for-service beneficiaries from 2018 to 2021. The study found that around half of RPM initiators did not have an RPM claim 12 months after initiation, with a gradual decline in use over time. Female beneficiaries were less likely to have persistent RPM use, while Asian beneficiaries and rural residents had higher probabilities of persistent use. The study highlights the need for further research on sustained RPM use in older adults with chronic conditions to prevent complications. [Extracted from the article]

Aim Design, setting and participants Measurements Findings Conclusion This study: (1) estimated the effect of early discontinuation of medication for opioid use disorder (MOUD) on overdose probability and (2) measured the relationship between patient characteristics and early discontinuation probability for each MOUD type.This was a retrospective cohort using electronic health record data from the US Veterans Healthcare Administration. Participants were veterans initiating MOUD with buprenorphine (BUP), methadone (MET) or extended‐release naltrexone (XR‐NTX) from fiscal years 2012–19. A total of 39 284 veterans met eligibility with 22 721 (57.8%) initiating BUP, 12 652 (32.2%) initiating MET and 3911 (10.0%) initiating XR‐NTX.Measurements (1) determined whether the veteran experienced an overdose in the 365 days after MOUD initiation (primary) and (2) early discontinuation of MOUD, defined as discontinuation before 180 days (secondary). We assumed that unobserved patient characteristics would jointly influence the probability of discontinuation and overdose. and estimated the joint distribution with a bivariate probit model.We found that 9.0% of BUP initiators who experienced an overdose above the predicted 3.9% had no veteran‐discontinued BUP early; findings for XR‐NTX were similar, with 12.2% of initiators overdosing above the predicted 4.5%, but this was statistically inconclusive. We found no relationship between early discontinuation and overdose for MET initiators, probably due to the high risk of both events. The patient characteristics included in our post‐estimation exploratory analysis of early discontinuation varied by MOUD type, with between 14 (XR‐NTX) and 25 (BUP) tested. The only characteristics with at least one level showing a statistically significant change in probability of early discontinuation for all three MOUD types were geography and prior‐year exposure to psychotherapy, although direction and magnitude varied.Early discontinuation of buprenorphine, and probably extended‐release naltrexone, appears to be associated with a greater probability of experiencing a fatal or non‐fatal overdose among US veterans receiving medication for opioid use disorder (MOUD); methadone does not show the same association. There is no consistent set of characteristics among early discontinuers by MOUD type. [ABSTRACT FROM AUTHOR]

Introduction: During the COVID-19 pandemic, health care shifted to virtual interactions with health professionals. The aim of this study was to examine the determinants of telehealth use in a nationally representative sample of the United States adult population. Methods: The study used data from the 2020 National Health Interview Survey of 17,582 respondents aged ≥18. Andersen's model of health services utilization was employed to examine predisposing, enabling, and needs factors associated with past-year telehealth use. Multivariable logistic regression was conducted to examine statistical associations. Results: 32.5% of adults (n = 6402; mean age 51.6, SE = 0.4) reported telehealth use. Predisposing factors: Women and married/partnered adults and those with higher levels of education had greater odds of using telehealth. Adults living in Midwest and South and adults living in medium-small and non-metropolitan areas had decreased odds of using telehealth. Enabling factors: Income and having a usual source of care were positively associated with telehealth use. A negative association was found for those with no insurance and telehealth use, whereas a positive association was found for military insurance. Needs factors: Odds of using telehealth were increased for adults who had well-visits and ER visits in the past 12 months. Mental health services quadrupled the odds of telehealth use. Odds of using telehealth increased with each additional chronic disease, including COVID-19. Conclusion: There are disparities in telehealth use according to sex, education, rurality, access to care, and health needs. Tackling these disparities is pivotal to ensure barriers to telehealth use are not exacerbated post-pandemic. [ABSTRACT FROM AUTHOR]

Introduction:Telehealth is becoming integral to health care delivery, which may create a higher need for better internet. This is the first study to examine whether users are willing to spend more on their internet if they are (1) more experienced in using telehealth, (2) more optimistic about telehealth benefits, and (3) less concerned about the difficulty of using telehealth. Methods:We surveyed 5,200 Americans about their willingness to pay (WTP) for internet speed and quality on the one hand, and their (1) prior use of telehealth, (2) opinion about the benefits of telehealth, and (3) opinion about the difficulties of using telehealth. We stratified the sample using the three aspects above and conducted a conjoint analysis to estimate the WTP for internet speed and quality within each stratum. Results:Compared with respondents who had never used telehealth, respondents who had used five to six different types of telehealth services were willing to spend 50.4% more on their internet plans. Users who most firmly believed in the benefits of telehealth were willing to spend 59.2% more than the most pessimistic users. Users who had the lowest concerns about the difficulty of using telehealth were willing to spend 114% more than users with the most perceived difficulty. Discussion:We found strong ties between the WTP for internet and prior telehealth use and perceptions of telehealth benefits and barriers. Thus, internet expenditures may be influenced by users' anticipation of using telehealth. Future studies may investigate the causality of the relationship. [ABSTRACT FROM AUTHOR]

AbstractBackgroundObjectiveMethodsResultsConclusionsBuprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.To develop and validate machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans initiating B-MOUD.Veterans initiating B-MOUD from fiscal years 2006-2020 were identified. Veterans’ B-MOUD episodes were randomly divided into training (80%;n = 45,238) and testing samples (20%;n = 11,309). Candidate algorithms [multiple logistic regression, least absolute shrinkage and selection operator regression, random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN)] were used to build and validate classification models to predict six binary outcomes: 1) B-MOUD retention, 2) any overdose, 3) opioid-related overdose, 4) overdose death, 5) opioid overdose death, and 6) all-cause mortality. Model performance was assessed using standard classification statistics [e.g., area under the receiver operating characteristic curve (AUC-ROC)].Episodes in the training sample were 93.0% male, 78.0% White, 72.3% unemployed, and 48.3% had a concurrent drug use disorder. The GBM model slightly outperformed others in predicting B-MOUD retention (AUC-ROC = 0.72). RF models outperformed others in predicting any overdose (AUC-ROC = 0.77) and opioid overdose (AUC-ROC = 0.77). RF and GBM outperformed other models for overdose death (AUC-ROC = 0.74 for both), and RF and DNN outperformed other models for opioid overdose death (RF AUC-ROC = 0.79; DNN AUC-ROC = 0.78). RF and GBM also outperformed other models for all-cause mortality (AUC-ROC = 0.76 for both). No single predictor accounted for >3% of the model’s variance.Machine-learning algorithms can accurately predict OUD-related outcomes with moderate predictive performance; however, prediction of these outcomes is driven by many characteristics. [ABSTRACT FROM AUTHOR]

Introduction:The COVID-19 pandemic brought about renewed interest and investment in telehealth, while also highlighting persistent health disparities in the Southern states. Little is known about the characteristics of those utilizing telehealth services in Arkansas, a rural Southern state. We sought to compare the characteristics of telehealth utilizers and nonutilizers among Medicare beneficiaries in Arkansas before the COVID-19 public health emergency to provide a baseline for future research investigating disparities in telehealth utilization. Methods:We used Arkansas Medicare beneficiary data (2018–2019) to model telehealth use. We included interactions to assess how the association between the number of chronic conditions and telehealth was moderated by race/ethnicity and rurality, adjusted for covariates. Results:Overall telehealth utilization in 2019 was low (n = 4,463; 1.1%). The adjusted odds of utilizing telehealth was higher for non-Hispanic Black/African Americans (vs. white, adjusted odds ratio [aOR] = 1.34, 95% confidence interval [CI] = 1.17–1.52), rural beneficiaries (aOR = 1.99, 95% CI = 1.79–2.21), and those with more chronic conditions (aOR = 1.23, 95% CI = 1.21–1.25). Race/ethnicity and rurality were significant moderators, such that the association between the number of chronic conditions and telehealth was strongest among white and among rural beneficiaries. Discussion:Among the 2019 Arkansas Medicare beneficiaries, having more chronic conditions was most strongly associated with telehealth use among white and rural individuals, while the effect was not as pronounced for Black/African American and urban individuals. Our findings suggest that advances in telehealth are not benefiting all Americans equally, with aging minoritized communities continuing to engage with more strained and underresourced health systems. Future research should investigate how upstream factors such as structural racism perpetuate poor health outcomes. [ABSTRACT FROM AUTHOR]