Development of predictive scoring model for risk stratification of no

No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care