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A Telebehavioral Health Article Analysis

Table of Contents

Introduction
Telebehavioral Health Use
Conclusion
References

Introduction

Telebehavioral health is explained as telehealth services provided by behavioral health experts including psychologists, social workers, and psychiatrists. There are instances where telehealth generates behavioral health, including psychotherapy, problem-solving therapy, behavioral activation, and cognitive behavioral therapy (Talbot et al., 2020). This studys main goal was to determine how facility fee payment interacts with telehealth-specific conversant agreement policies linked with telebehavioral health services users in Medicaid, before and after guiding for covariates. In addition, examination of how beneficiary features were allied to telebehavioral health use in the people of interest.

Telebehavioral Health Use

Key variables manifest in the selected article: telebehavioral health, telehealth policies in state Medicaid programs, and covariates. The delivery of substance abuse or mental health treatment via interactive video or live is termed telebehavioral health or the outcome variable. The second is telehealth policies in the state Medicaid program, which has two explanatory variables (Talbot et al., 2019). That is, facility fee payment, telehealth-specific informed consent, and Medicaid telehealth policies. The payment made by the insurer to an originating site refers to as a telehealth facility fee that is a facility hosting patients receiving telehealth services. The fee aid in compensating the originating site for the use of its telehealth equipment and space. On the other hand, the American Telemedicine Association has obliquely shared negative of these policies (Butler and Reck, 2018). During an evaluation of how their policies encourage telehealth adoption, the ATA provides states with lower grades if their accord conditions for telehealth are tougher than for in-person services.

Covariates and other variables such as individual, state-level, and county variables. At this level, beneficiary characteristics are measured in age, gender, serious mental illness, and race (Talbot et al., 2020). To be beneficiaries one have to be identified with serious mental illnesses such as bipolar disorder, schizophrenia, episodic mood disorder with symptoms of mania, psychotic disorder, and other major depressive disorders. Furthermore, beneficiaries were grouped as those living in rural counties adjacent or nonadjacent to metropolitan areas. Therefore, RUCCs 5, 7, and 9 identified nonadjacent counties, while RUCCs 4, 6, and 8 designate adjacent counties. Lastly, private-payer telehealth parity requirements are conceptualized as a covariate (Zhou et al., 2020). Purity rules mandate the coverage of telehealth by private insurers. It is assumed to strengthen telehealth facilities by broadening the payer mix and forming extra revenue streams for telehealth.

Rural Medicaid beneficiaries were more likely than their urban counterparts to use telehealth services. However, absolute rates of telehealth use were low in 2011: only 0.26 percent of rural nondual Medicaid beneficiaries used telemedicine (Butler and Reck, 2018). For rural and urban Medicaid patients, psychotropic medication management was the most common use of telehealth. Nevertheless, the proportion of users who used telehealth to receive nonbehavioral health treatments raised radically as the population in the rural area increased (Zhou et al., 2020). Mood disorders were the most common cause for telehealth users to seek assistance, regardless of where they lived. As the rural individual expanded, more telehealth users obtained services for attention deficit hyperactivity disorder (ADHD) and other behavioral health issues often diagnosed in childhood.

Internal validity can be described as an assertive that other factors cannot expound a cause and impact association established in a study. Thus, it makes the conclusions of a relationship sincere and trustworthy. Internal validity threats must be identified and countered in a research design for a robust study. Different threats can apply to single-group and multi-group studies (Zhou et al., 2020). Those threats are as follows the results do not encourage definitive assumptions about a causal relationship between telebehavioral health usage and explanatory variables since the study design was cross-sectional. Individuals with double entitlement and Medicaid managed care enrollees were excluded from the sample, the internal validity of the study may have been jeopardized because the findings were not generalizable to rural OPB (Zhou et al., 2020). As a result, the sample may not be an accurate representation. This is yet another threat to internal consistency.

Multivariate analysis is a statistical method that measures the relationships between two or more response variables. Multivariate techniques attempt to model reality by involving more than one factor in each situation, product, or decision. For example, when purchasing a car, you may consider price, safety features, color, and functionality (Butler and Reck, 2018). Modern society has amassed massive amounts of data in virtually every field. Still, using that data to understand what is going on and make informed decisions remains a challenge.

Multivariate techniques enable researchers to examine and quantify relationships between variables in a broader context. They can control the association between variables using cross-tabulation, partial correlation, and multiple regressions. They can introduce other variables to determine the links between the independent and dependent variables or specify the conditions under which the association occurs (Talbot et al., 2020). The ability to obtain a more realistic picture than looking at a single variable is one of the benefits of multivariate analysis. Furthermore, compared to univariate techniques, multivariate techniques provide a powerful significance test.

Multivariate techniques are complex and involve high-level mathematics, necessitating a statistical program to analyze the data. These statistical programs can be costly for an individual to obtain. One of the most significant limitations of multivariate analysis is that statistical modeling outputs are not always easy for students to interpret (Butler and Reck, 2018). Multivariate techniques require a large sample of data to yield meaningful results; otherwise, the results are meaningless due to high standard errors. Standard errors determine how confident you can be in the results, and a large sample can provide more confidence than a small one (Talbot et al., 2020). Running statistical programs is relatively simple, but statistical training is required to make sense of the data.

Experimental design refers to how participants in an experiment are assigned to different groups. The three key aspects of experimental design are independent groups, repeated measures, and matched pairs designs (Talbot et al., 2020). Moreover, matched pairs design utilizes a comparison group model where a pair of participants are matched based on key variables. Each pair is assigned one member to the experimental group and the other to the control group (Talbot et al., 2019). Reliability and validity are determined; include the results alongside your main findings if you calculate reliability and validity. Methodology how did you design your study to ensure the reliability and validity of the measures you used.

Conclusion

In conclusion, this study suggests that Medicaid programs consider how all of their telehealth policies relate and whether they moderate one anothers relationships with TBH usage among rural Medicaid OPBHS users. Definitely, the findings question the assumption that informed consent rules impede membership in telehealth services. Building and consolidating telehealth setups could be a vital component of efforts to escalate the obtainability of TBH services for rural beneficiaries. HRSA and the Substance Abuse and Mental Health Services Administration, for instance, have made significant stashes in telehealth structures, some of which are specially intended to subsidy rural residents.

References

Butler, J., & Reck, J. (2018). Overcoming payment challenges to realize the promise of telehealth. Web.

Talbot, J. A., Burgess, A. R., Thayer, D., Parenteau, L., Paluso, N., & Coburn, A. F. (2019). Patterns of telehealth use among rural Medicaid beneficiaries. The Journal of Rural Health, 35, 298307.

Talbot, J. A., Jonk, Y. C., Burgess, A. R., Thayer, D., Ziller, E., Paluso, N., & Coburn, A. F. (2020). Telebehavioral health (TBH) use among rural Medicaid beneficiaries: Relationships with telehealth policies. Journal of Rural Mental Health, 44(4), 217231.

Zhou, X., Snoswell, C. L., Harding, L. E., Bambling, M., Edirippulige, S., Bai, X., & Smith, A. C. (2020). The role of telehealth in reducing the mental health burden from COVID-19. Telemedicine Journal and e-Health, 26, 377379.

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