Impact of predictive scoring model and e‐mail messages on African American blood donors. Issue 6 (5th May 2017)

• Record Type: Journal Article
• Authors: Bachegowda, Lohith S.; Timm, Brad; Dasgupta, Pinaki; Hillyer, Christopher D.; Kessler, Debra; Rebosa, Mark; France, Christopher R.; Shaz, Beth H.
• Is Part Of: Transfusion. Volume 57:Issue 6(2017)
• Page Start: 1515
• Page End: 1521
• Publication Date: 2017-05-05
• Subjects: Hematology -- Periodicals; Blood -- Transfusion -- Periodicals; Blood Group Antigens -- Periodicals; Blood Preservation -- Periodicals; Blood Transfusion -- Periodicals; 615
• DOI: 10.1111/trf.14113
• Languages: English
• ISSNs: 0041-1132

Abstract

Abstract : BACKGROUND: Expanding the African American (AA) donor pool is critical to sustain transfusion support for sickle cell disease patients. STUDY DESIGN AND METHODS: The aims were to: 1) apply cognitive computing on donation related metrics to develop a predictive model that effectively identifies repeat AA donors, 2) determine whether a single e‐mail communication could improve AA donor retention and compare retention results on higher versus lower predictive score donors, and 3) evaluate the effect of e‐mail marketing on AA donor retention with culturally versus nonculturally tailored message. RESULTS: Between 2011 and 2012, 30, 786 AA donors donated blood at least once on whom predictive repeat donor scores (PRDSs) was generated from donor‐related metrics (frequency of donations, duration between donations, age, blood type, and sex). In 2013, 28% (8657/30, 786) of 2011 to 2012 donors returned to donate on whom PRDS was validated. Returning blood donors had a higher mean PRDS compared to nonreturning donors (0.649 vs. 0.268; p < 0.001). In the e‐mail pilot, high PRDS (≥0.6) compared to low PRDS (<0.6) was associated with 89% higher donor presentation rate (p < 0.001), 20% higher e‐mail opening rate (p < 0.001), and, specifically among those who opened the e‐mail, 159% higher presentation rate (p < 0.001). Finally, blood donation rate did not differ (p = 0.79) as a function of generic (n = 9312, 1.4%) versus culturally tailored (n = 9326, 1.3%) message. CONCLUSION: Computational algorithms utilizing readily available donor metrics can identify highly committed AA donors and in conjunction with targeted e‐mail communication has the potential to increase the efficiency of donor marketing.