Wen-Chien Tsai1, Wui-Chiang Lee2,3, Shu-Chiung Chiang3,4, Yu-Chun Chen3,5, Tzeng-Ji Chen3,5. 1. Superintendent Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC. 2. Department of Medical Affairs and Planning, Taipei Veterans General Hospital, Taipei, Taiwan, ROC. 3. School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. 4. Department of Financial Engineering and Actuarial Mathematics, Soochow University, Taipei, Taiwan, ROC. 5. Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Abstract
BACKGROUND: Missed appointments mean appointments neither attended nor canceled by patients. Missed appointments belong to one of the important subjects of hospital management because they would incur the inactivity of medical professionals and devices, occupy the health resources for other patients, and thus impair the quality of healthcare services. The aim of this study was to explore the factors of missed appointments at the outpatient department of an academic medical center in Taiwan. METHODS: This was a cross-sectional study based on registration records of an academic medical center in Northern Taiwan in 2015. Fifteen variables of patients, appointments, and weathers were taken into analysis. Logistic regression was used to calculate the adjusted odds ratio of each variable. For nonfirst visits, we further built a logistic regression model with the five most influential variables and the personal attendance pattern of the previous three appointments. RESULTS: Of 2 132 577 eligible appointments in 2015, the overall no-show rate was 16.9%. The influential factors included the following: (1) patient characteristics: younger than 40 years, ≤6 visits, and a no-show rate between 50% and 75% in the previous year; (2) appointment characteristics: Saturdays, evenings, on the last third of the waiting list, only one appointment on the same day, online appointments, appointment-to-visit intervals (wait time) in 7 to 14 days, appointments to obstetrics/gynecology or pediatrics, first-time appointments, and the interval between the appointed visit and the previous visit in <7 days; and (3) weather characteristics: warm weathers and heavy rains. For nonfirst appointments, the influences in decreasing order were heavy rain, shorter interval between the appointed visit and the previous visit to the same department, higher no-show rate in the previous year, total absence in the personal attendance pattern of the previous three appointments, longer wait time, and children. CONCLUSION: The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.
BACKGROUND: Missed appointments mean appointments neither attended nor canceled by patients. Missed appointments belong to one of the important subjects of hospital management because they would incur the inactivity of medical professionals and devices, occupy the health resources for other patients, and thus impair the quality of healthcare services. The aim of this study was to explore the factors of missed appointments at the outpatient department of an academic medical center in Taiwan. METHODS: This was a cross-sectional study based on registration records of an academic medical center in Northern Taiwan in 2015. Fifteen variables of patients, appointments, and weathers were taken into analysis. Logistic regression was used to calculate the adjusted odds ratio of each variable. For nonfirst visits, we further built a logistic regression model with the five most influential variables and the personal attendance pattern of the previous three appointments. RESULTS: Of 2 132 577 eligible appointments in 2015, the overall no-show rate was 16.9%. The influential factors included the following: (1) patient characteristics: younger than 40 years, ≤6 visits, and a no-show rate between 50% and 75% in the previous year; (2) appointment characteristics: Saturdays, evenings, on the last third of the waiting list, only one appointment on the same day, online appointments, appointment-to-visit intervals (wait time) in 7 to 14 days, appointments to obstetrics/gynecology or pediatrics, first-time appointments, and the interval between the appointed visit and the previous visit in <7 days; and (3) weather characteristics: warm weathers and heavy rains. For nonfirst appointments, the influences in decreasing order were heavy rain, shorter interval between the appointed visit and the previous visit to the same department, higher no-show rate in the previous year, total absence in the personal attendance pattern of the previous three appointments, longer wait time, and children. CONCLUSION: The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.