Discovering blood donor arrival patterns using data mining: a method to investigate service quality at blood centers

Blood centers without fixed appointments for collecting blood often experience nonconstant donor arrival rates, which vary due to time-of-day, day-of-week, etc. When a constant workforce size is employed in such blood centers, there is either idle personnel, or donor satisfaction is compromised due to long waiting times, or both conditions alternate over time. Consequently, a method to obtain adaptive workforce requirements might be valuable. This study utilized the Two-Step Cluster method and the Classification and Regression Trees method in succession to identify both daily and hourly donor arrival patterns at Hacettepe University Hospitals' Blood Center. A serial queuing network model of the donation process was then employed for each of the identified donor arrival patterns. By considering and accommodating variations in the donor arrival patterns, required workforce sizes and their decomposition among process steps were predicted to achieve predetermined target values of expected waiting times and to balance workforce utilizations in the blood donation processes. Although a blood center is considered for the proposed methodology, the approach is general and applications in various operations of healthcare organizations are possible.