The impact of common variables on non-attendance at a radiology centre in Singapore

With the increasing demand for medical imaging, non-attendance inhibits private and public radiology practices in Singapore from providing timely care and achieving maximal efficiency. Missed radiological appointments adversely affect clinical and economic outcomes and strain the finite healthcare resources. We examined the prevalence and predictors of patient non-attendance for radiological services at a regional public hospital in Singapore and compared them against other medical imaging centres globally.

Outpatient records of patients who were scheduled for specialised medical imaging obtained from Radiological Information System (RIS) were retrospectively reviewed. Analysed variables include patient demographics, radiology modalities, visit statuses and appointment lead times where Pearson's chi-square test and Fisher's exact test were used for categorical variables, and independent sample t-test was used for continuous variables. The association between each patient characteristic and non-attendance status was assessed using Binary Logistics Regression. Variables that showed statistical significance in univariate analysis were included in the multivariate logistic regression model to identify the independent risk factors associated with non-attendance.

Among the 59,748 outpatient appointments with medical imaging requests, 15.5% did not turn up for their appointments. Logistic regression indicated that patient's age, ethnicity, subsidy status, house ownership, living vicinity to regional hospital cluster, appointment wait times, appointment hours and appointment months were significant factors associated with the failure to attend scheduled radiological examinations.

Even though predictors of non-attendance remained consistent across medical imaging centres worldwide, Singapore reported a higher prevalence of missed appointments calling for future exploratory studies to understand the population's health-seeking behaviours and ordering patterns of clinicians.

Comparison and identification of these predictors will assist in the design of targeted interventions that may improve patient's adherence and utilisation of imaging services.