Socioeconomic and demographic factors predictive of missed appointments in outpatient radiation oncology: an evaluation of access

Abstract

PurposeWhile missed patient appointments reduce clinic efficiency and limit effective resource allocation, factors predictive of “no shows” are poorly understood in radiation oncology.Methods and materialsA prospective data registry of consecutive patients referred for initial consultation from October 2,018 to April 2022 was reviewed. Demographic characteristics recorded included age, gender, race, language preference, living situation, and insurance status. Zip code data linked to a patient’s residential address was used to determine socioeconomic status (SES) based on publicly available data on median household income. No show encounters were defined as all encounters where the patient failed to cancel their visit and did not sign-in to their scheduled appointment. Descriptive statistics were presented to identify factors predictive of missed appointments.ResultsA total of 9,241 consecutive patients were referred and logged into the database during the 4-year period, of which 5,755 were successfully scheduled and registered. A total of 523 patients (9%) failed to show for their appointments. Missed appointments were associated with low-income status, homeless living situation, and Black or Latino race (p < 0.05, for all). The proportion of White, Latino, Asian, and Black patients who missed appointments was 6%, 14%, 9%, and 12%, respectively (p < 0.001). Patient characteristics independently associated with higher odds of appointment non-adherence included low-income status ((OR) = 2.90, 95% CI (1.44–5.89) and Black or Latino race [(OR) = 3.31, 95% CI: 1.22–7.65].ConclusionsOur results highlight the influence of demographic, financial, and racial disparities on proper health care utilization among patients with cancer. Future interventions aimed at reducing appointment no shows could channel resources to the at risk-populations identified in this analysis, improving access to care, and optimize clinic efficiency.