Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic. (June 2019)

• Record Type: Journal Article
• Authors: Chua, Siang Li; Chow, Wai Leng
• Is Part Of: Proceedings of Singapore healthcare. Volume 28:Number 2(2019)
• Page Start: 96
• Page End: 104
• Publication Date: 2019-06
• Subjects: Non-attendance -- scheduling -- logistic regression -- healthcare operations; Medical care -- Singapore -- Periodicals; Medical care; Singapore; Periodicals; 362.1095957
• DOI: 10.1177/2010105818793155
• Languages: English
• ISSNs: 2010-1058

Abstract

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.