Time dependent patient no-show predictive modelling development. Issue 4 (9th May 2016)

• Record Type: Journal Article
• Authors: Huang, Yu-Li; Hanauer, David A.
• Is Part Of: International journal of health care quality assurance. Volume 29:Issue 4(2016)
• Page Start: 475
• Page End: 488
• Publication Date: 2016-05-09
• Subjects: Performance measurement -- Modelling -- Continuous quality improvement -- Efficiency -- Evidence-based practice -- Improvement models; Medical care -- Quality control -- Periodicals; 362.1068
• DOI: 10.1108/IJHCQA-06-2015-0077
• Languages: English
• ISSNs: 0952-6862

Abstract

Abstract : Purpose: – The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach: – A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7, 291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings: – The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications: – The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value: – This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.