Appointment attendance behaviors in multiple sclerosis: Understanding the factors that differ between no shows, short notice cancellations, and attended appointments

BACKGROUND

There has yet to be an examination of how appointment attendance behaviors in multiple sclerosis (MS) are related to scheduling metrics and certain demographic, clinical, and behavioral factors such as cognitive functioning and personality traits. This study aimed to examine the factors that differ between no shows (NS), short notice cancellations (SNC), and attended appointments.

METHODS

Participants (n = 110) were persons with MS who were enrolled in a larger cross-sectional study, during which they completed a battery of neuropsychological measures. Data about their appointments in three MS-related clinics the year prior to their study evaluation were extracted from the medical record. Bivariate analyses were done, with post-hoc tests conducted with Bonferroni corrections if there was an overall group difference.

RESULTS

A higher number of SNC were noted during the winter, with 22.4% being due to the weather. SNC were also more common on Thursdays, but less frequent during the early morning time slots (7am to 9am). In contrast, NS were associated with lower annual income, weaker healthcare provider relationships, lower self-efficacy, higher levels of neuroticism, depressive symptom severity, and health distress, and greater cognitive difficulties, particularly with prospective memory.

CONCLUSIONS

While SNC are related to clinic structure and situational factors like the weather, NS may be more influenced by behavioral issues, such as difficulty remembering an appointment and high levels of distress. These findings highlight potential targets for reducing the number of missed appointments in the clinic, providing opportunities for improved healthcare efficiency and most importantly health.

Role of Visit Modality in the HIV-Related No-Shows During the COVID-19 Pandemic: A Multisite Retrospective Cohort Study

The emergence of the COVID-19 pandemic necessitated rapid expansion of telehealth as part of healthcare delivery. This study compared HIV-related no-shows by visit type (in-person; video; telephone) during the COVID-19 pandemic (April 2020-September 2021) from the Data for Care Alabama project. Using all primary care provider visits, each visit's outcome was categorized as no-show or arrived. A logistic regression model using generalized estimating equations accounting for repeat measures in individuals and within sites calculated odds ratios (OR) and their accompanying 95% confidence interval (CI) for no-shows by visit modality. The multivariable models adjusted for sociodemographic factors. In-person versus telephone visits [OR (95% CI) 1.64 (1.48-1.82)] and in-person versus video visits [OR (95% CI) 1.53 (1.25-1.85)] had higher odds of being a no-show. In-person versus telephone and video no-shows were significantly higher. This may suggest success of telehealth visits as a method for HIV care delivery even beyond COVID-19.

Who is not coming to clinic? A predictive model of excessive missed appointments in persons with multiple sclerosis

BACKGROUND

Missed appointments can have negative effects on several facets of healthcare, including disruption of services, worse patient health outcomes, and increased costs. The influence of demographic and clinical factors on missed appointments has been studied in a number of chronic conditions, but not yet in multiple sclerosis (MS). Engagement in healthcare services is a particular concern with this population, given the complexity of the condition. Furthermore, excessive missed appointments has emerged as a risk factor for suboptimal adherence to disease modifying therapies (DMTs), prompting further exploration into this issue and whether a tool could be developed to triage possible interventions for persons with MS on DMTs who are missing their appointments. As such, this study aimed to investigate the rate and factors associated with missed appointments among a large national sample of persons with MS and develop a predictive model of excessive missed appointments.

METHODS

Administrative data from 01/01/2013 to 12/31/2015 were extracted from the VA MS Center of Excellence Data Repository. Variables not related to excessive missed appointments, defined as missing more than 20% of scheduled appointments, in bivariate analyses (p > 0.20) were excluded. Remaining baseline co-occurring conditions, demographic, and healthcare utilization variables were entered into a logistic regression model, using a backward elimination criteria of p < 0.05. Calibration and discrimination of the model were assessed. An initial predictive score was generated based on the value of the variable and its β-value from the final model.

RESULTS

The number of missed appointments ranged from 0 to 84 over a two-year period. Over 59% missed at least one appointment, though only 4.28% had excessive missed appointments. Seven variables were retained in the model: adherence to DMTs, age, distance, histories of post-traumatic stress disorder, congestive heart failure, and chronic obstructive pulmonary disease, and emergency visits. Predictive scores ranged from -6.42 to 0.96 (M = -2.61, SD = 1.15). The final model had good discrimination, calibration, and fit.

CONCLUSIONS

By using this model and accompanying score, clinicians could have a good chance of predicting individuals who will miss more than 20% of their appointments and triaging interventions.