Vidar Bosnes1, Magne Aldrin, Hans Erik Heier. 1. Department of Immunology and Transfusion Medicine, Ullevål University Hospital, Oslo, Norway. vidar.bosnes@uus.no
Abstract
BACKGROUND: Keeping waiting time at blood donation short is important for making donation a good experience for the donors and hence to motivate for repeat donations. At the Blood Bank of Oslo, fixed appointments are used, and few donors arrive without appointments. On average, 59 percent of scheduled donors arrive, but day-to-day variations are large. Methods for predicting the number of donors that will arrive on a given day would be valuable in reducing waiting times. STUDY DESIGN AND METHODS: Information about candidate explanatory variables was collected for all appointments made in a 971-day period (179,121 appointments). A logistic regression model for the prediction of blood donor arrival was fitted. RESULTS: Among 18 explanatory variables, the most important were the time from appointment making to appointment date; the contact medium used; the donor age and total number of donations; and the number of no-shows, arrivals, and deferrals during the preceding 2 years. Compared to taking only the average arrival rate into account, prediction intervals were reduced by 43 percent. CONCLUSION: Statistical modeling can provide useful estimates of blood donor arrival, allowing for better planning of donation sessions.
BACKGROUND: Keeping waiting time at blood donation short is important for making donation a good experience for the donors and hence to motivate for repeat donations. At the Blood Bank of Oslo, fixed appointments are used, and few donors arrive without appointments. On average, 59 percent of scheduled donors arrive, but day-to-day variations are large. Methods for predicting the number of donors that will arrive on a given day would be valuable in reducing waiting times. STUDY DESIGN AND METHODS: Information about candidate explanatory variables was collected for all appointments made in a 971-day period (179,121 appointments). A logistic regression model for the prediction of blood donor arrival was fitted. RESULTS: Among 18 explanatory variables, the most important were the time from appointment making to appointment date; the contact medium used; the donor age and total number of donations; and the number of no-shows, arrivals, and deferrals during the preceding 2 years. Compared to taking only the average arrival rate into account, prediction intervals were reduced by 43 percent. CONCLUSION: Statistical modeling can provide useful estimates of blood donor arrival, allowing for better planning of donation sessions.
Authors: Anne Wevers; Daniël H J Wigboldus; Wim L A M de Kort; Rick van Baaren; Ingrid J T Veldhuizen Journal: Blood Transfus Date: 2013-03-01 Impact factor: 3.443