Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic

• Published in: Proceedings of Singapore healthcare Vol. 28; no. 2; pp. 96 - 104
• Main Authors: Chua, Siang Li, Chow, Wai Leng
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2019; Sage Publications Ltd; SAGE Publishing
• Subjects: Outpatient care facilities; Patient compliance; Risk factors; Scheduling; Singapore; scheduling; Non-attendance; healthcare operations; logistic regression
• ISSN: 2010-1058; 2059-2329
• DOI: 10.1177/2010105818793155

Aim: 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. Method: 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. Result: 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. Conclusion: 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.