Risk Management and Healthcare Policy (Nov 2020)
Who Misses Appointments Made Online? Retrospective Analysis of the Outpatient Department of a General Hospital in Jinan, Shandong Province, China
Abstract
Wei Su,1 Cuiling Zhu,1 Xin Zhang,1 Jun Xie,2 Qingxian Gong2 1School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, People’s Republic of China; 2Shunneng Network Technology Limited Company, Jinan, Shandong, People’s Republic of ChinaCorrespondence: Wei Su; Xin Zhang Email [email protected]; [email protected]: Missed appointments in outpatient registration pose challenges for hospital administrators, especially in the context of China’s shortage of medical resources. Previous studies have identified factors that affect healthcare access via traditional appointment systems. Few studies, however, have specifically investigated Internet appointment systems. Therefore, this study explored the key factors related to missed appointments made on the Internet appointment system of a general hospital in Jinan, Shandong Province.Methods: Online appointment data were collected from the outpatient department of a general hospital in Jinan from September 2017 to February 2018. Logistic regression was used to analyze the relative importance of eight variables: gender, age, interval between scheduling and appointment, day of the week, physician’s academic rank, appointment fee, previous missed appointments, and clinical department.Results: A total of 48,777 online appointment records were collected, which included a 15% no-show rate. The key factors associated with no-shows included age, interval between scheduling and appointment, previous missed appointments, and clinical department. No significant relationships were found between no-shows and gender, day of the week, and appointment fee.Conclusion: No-show rates were influenced by many factors. Based on this study’s findings, targeted measures can be taken to decrease no-show frequency and improve medical efficiency.Keywords: online appointments, no-show appointments, influential factors, logistic regression
