Attitudinal predictors of no-show to substance use disorder treatment intake in Veterans

Abstract: Background: Substance use disorder (SUD) exacts a devastating psychosocial and medicolegal toll in Veterans. Despite the availability of quality SUD care provided by the Veterans Health Administration, many Veterans with SUD who initially seek treatment do not attend their intake appointment that helps determine their assigned level of care. Demographic and administrative factors have been used to predict outpatient no-shows, but whether patient-reported attitudes can be feasibly probed prior to the intake appointment to potentially predict intake no-show is largely unexplored. Objectives: We wished to determine whether patient attitudes about SUD and SUD treatment can be feasibly probed in veterans and whether such attitudes could potentially predict intake no-show. Methods: In a preliminary feasibility study, we administered by mail, smartphone app, or by in-person or telephone verbal interview a list of potential standard-of-care intake interview questions regarding attitudes about self and SUD treatment in n = 79 veterans scheduled for SUD treatment intake, of whom 29% did not attend the intake. To examine the power of attitudinal factors to predict intake no-show, we utilized decision tree-based machine learning (ML) analysis of veteran responses about addiction beliefs and other patient factors. Results: ML analyses indicated that older age, longer time before intake date as well as self-reported low levels of medical adherence were independently predictive of intake no-show. Regarding attitudinal factors, no-show was also predicted by high endorsements of craving, having "an addiction," trust in previous SUD treatment providers, and existing engagement in the recovery community. Conclusion: These preliminary data suggest the feasibility and potential predictive utility of querying Veterans slated for treatment intake about their attitudes regarding SUD and SUD treatment. Future replication studies with a larger sample size could yield briefer, lower-burden assessments of attitudinal no-show risk factors that could be targeted by pre-intake motivational interventions.