Cycle 1
death
REGRESSION, EVENT, SECONDARY
Two outcomes were assessed in evaluating the data collection tool: 1) a pre-specified outcome of treatment continuation, defined as the placement and receipt of a prescription refill, and 2) a post-hot outcome of medication adjustment, defined as a change in medication type, quantity, and/or dosage at any point during their treatment plan. Treatment continuation was assessed within a specific time frame depending on the quantity of medication in their initial prescription. Patients were able to select either a monthly or quarterly (3-month) supply of medication. For monthly patients, treatment continuation was assessed within a time frame of 91 days. For quarterly patients, treatment continuation was assessed within a time frame of 123 days. These time frames were chosen in order to reflect individual and situational variation that may impact how often patients take their ED medication. All data was extracted from the telehealth platform’s electronic health record (EHR) database, where information collected from patients’ intake forms, telehealth visits, and other care-related interactions is stored.Kaplan–Meier curves were used to examine unadjusted time-to-event treatment continuation. Due to the nature of the treatment continuation measure (placement and receipt of a prescription refill order), an “alive” event was used in place of the traditional death event. Patients were considered censored if there was no prescription refill within the specified timeframe. Log rank tests were used to determine statistical significance. Measuring the impact of the RxCI on treatment continuation served a dual purpose; it provided a secondary measure of patient uptake and tolerance of an additional point of contact. We used a chi square test of association to assess our post-hoc outcome of whether the proportion of patients who adjusted their treatment differed between groups. Lastly, we conducted an ancillary analysis using multivariable logistic regression to examine the effects of randomization into the intervention group. In this model, we employed medication adjustment as a control variable, along with age, to predict treatment continuation. An additional multivariable model including an intervention group-by-medication adjustment interaction term was also examined. To gauge the effectiveness of the intervention's implementation, we assessed the response rate to the questionnaire.