The Impact of Expanded Tele-Mental Health on Quality-Of-Care Indicators: A Three-Pronged Regression Analysis at Los Angeles County’s Department of Mental Health
Background: The use of Tele-Mental Health (TMH) skyrocketed after the COVID-19 pandemic led to the announcement of a public health emergency in March 2020. This rise coincided with soaring rates of mental health issues and increasing demand for accessible and sustainable treatment, all while meeting physical distancing requirements. TMH use is theorized to improve timely access to care and provide opportunities to improve quality of care indicators in individuals and at the health systems level. Research Question: How has the widespread adoption of Tele-Mental Health changed quality of care (QoC) indicators among patients of LA County Department of Mental Health’s (LAC DMH) Directly Operated (DO) clinics? Methods: The study design for this analysis is a multivariate quasi-experimental study with a pseudo-control. A three-pronged approach to the analysis was used to tackle the research question and two QoC indicators are defined as the binary “Timely” variable and the continuous “Appointment Adherence” variable. All the models adjusted for covariates (demographic variables and the ratio of patients to providers) and mediators (the Request Type, which determines the timely standards of care). A “Pandemic Time” variable referred to if the data point took place before March 19, 2020, which referred to the date that the Safer-at-Home Order (SHO) was announced, or after. The first prong, approach A, used a logistic regression for the Timely variable and an OLS regression for Appointment Adherence; it compared users of TMH to those receiving in-person care and included the pandemic time variable. Approach B did the same but accounted for crowding effects over time by adding an offset variable for the ratio of appointment requests to providers. An ANOVA for the first two approaches determined the effect size of the variables and those that had an effect size over 0.01 were used to build a parsimonious model for Approach C. Approach C used Interrupted Time Series models to compare the actual changes in QoC indicators from March 2017 to February 2021 with the expansion of TMH taking place post-SHO (March 2020-February 2021) to a pseudo-control for the whole health system. Approach C transformed the “Timely” and “TMH” variables to be continuous by transforming them to the percent of the total patients that received timely care and the percent of services delivered via TMH. Results: Approach A found that TMH use was significantly associated (p=0.00) with a 15% reduced probability of receiving a timely appointment compared to those that received in-person care, though the probability of receiving a timely appointment increased 10% post-SHO compared to pre-SHO (p=0.00). Approach A also found that TMH use was significantly associated with a 2.5% increase in Appointment Adherence (p=0.00) compared to those receiving in-person care, but that post-SHO there was a 4% decrease in Appointment Adherence as compared to pre-SHO (p=0.00). Approach B found that TMH use was significantly associated (p=0.00) with a 6% decrease in the probability to receive a timely appointment when accounting for the crowding effect; TMH use was not significantly associated with Appointment Adherence. Approach C used Interrupted Time Series regression to find that there was no significant association between TMH use and receiving a timely appointment and that the fluctuations in timely care both exceeded and fell short of the pseudo-control. TMH adoption did however have a significant relationship at a 10% level (p=0.09) with appointment adherence, in which every additional percent of TMH adoption by DMH was associated with a 7% increase in appointment adherence compared to the pseudo-control. Conclusion: TMH use, timely access to care, and Appointment Adherence all increased post-SHO. DMH’s adoption to TMH is associated with an increased likelihood of Appointment Adherence compared to if DMH kept TMH use at pre-SHO levels. Request Types with shorter timely standards are more likely to receive a timely appointment and to adhere to appointment plans when the health system had adopted TMH. However, there was no significant association exists between the adoption of TMH and Timely care within the health system. Among individuals that used TMH, there was a decreased likelihood to receive Timely care as compared to those receiving in-person care, though the likelihood of receiving timely appointments increase post-SHO. Individuals that used TMH were more likely than those that received in-person care to adhere to their appointment schedules. Future research should examine the impact of TMH use on QoC indicators over a longer time-period. Additionally, TMH should be evaluated as a promising intervention to reduce disparities in care, especially when adjusting for language and racial concordance, and to improve cost-effectiveness through redistribution of resources.
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