Analysis of the performance of blood donor recruitment and collection activities at a Brazilian public blood center: An evaluation based on operational data and artificial intelligence support
DOI:
https://doi.org/10.33448/rsd-v15i3.50826Keywords:
Blood Transfusion Service, Blood Donation, Data Analysis, Machine Learning.Abstract
Blood donation is essential for the functioning of health systems and for the performance of safe transfusions. However, maintaining adequate stocks remains a challenge, requiring efficient strategies for donor recruitment and mobilization. This study aimed to analyze the performance of blood donor recruitment and collection activities in a Brazilian public blood center, using statistical analysis and computational modeling applied to institutional operational data. This is a retrospective observational study with a quantitative approach, based on administrative records relating to candidate mobilization activities, clinical screening, and blood collection. The operational indicators considered were the number of campaigns carried out, candidates summoned, individual consultations, external collection activities, and clinical screenings, with the number of effective blood collections as the output variable. Initially, exploratory statistical analysis and correlation assessment between operational variables were performed, followed by the application of a linear regression model to investigate the relationship between recruitment indicators and the effectiveness of collections. The results showed a positive association between the number of clinical screenings and the number of effective collections, indicating that the volume of candidates evaluated in the screening is a determining factor for blood production. It is concluded that the integration between statistical analysis and computational modeling applied to institutional data can contribute to improving the management of blood centers and strengthening donor recruitment strategies.
References
Bertsimas, D., & Dunn, J. (2019). Machine learning under a modern optimization lens. Dynamic Ideas.
Bednall, T. C., & Bove, L. L. (2011). Donating blood: A meta-analytic review of self-reported motivators and deterrents. Transfusion Medicine Reviews, 25(4), 317–334. https://doi.org/10.1016/j.tmrv.2011.04.005
Chand, S., Amita, R., & Gupta, D. (2023). Addressing concerns and suggestions of blood donors: An assured way for donor motivation, recruitment, and retention. Asian Journal of Transfusion Science, 17(1), 3–8.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Eichler, H., Spinella, P. C., Henschler, R., Heddle, N. M., & Irony, I. (2021). Population-based analysis of the impact of demographics on the current and future blood supply in the Saarland. Transfusion Medicine and Hemotherapy, 48(3), 170–177. https://doi.org/10.1159/000512645
Ferguson, E., France, C. R., Abraham, C., Ditto, B., & Sheeran, P. (2007). Improving blood donor recruitment and retention: Integrating theoretical advances from social and behavioral science research agendas. Transfusion, 47(11), 1999–2010. https://doi.org/10.1111/j.1537-2995.2007.01423.x
Ferguson, E., Atkins, L., & Lawrence, C. (2020). A typology of blood donor motivations. Transfusion, 60(9), 2010–2020. https://doi.org/10.1111/trf.15913
France, C. R., Ditto, B., France, J. L., Himawan, L. K., & Hillyer, C. D. (2021). Assessing the impact of an automated web-based motivational interview on retention of O-negative donors. Transfusion, 61(2), 593–602. https://doi.org/10.1111/trf.16186
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer.
Kermani, F. R., et al. (2024). Development and validation of the self-regulation of blood donation scale for blood donors. Hematology, Transfusion and Cell Therapy. Advance online publication. https://doi.org/10.1016/j.htct.2024.09.2482
Li, L., et al. (2023). Mobile applications for encouraging blood donation: A systematic review and case study. Digital Health, 9, 20552076231203603. https://doi.org/10.1177/20552076231203603
Ministério da Saúde. (2024). Doação de sangue. https://www.gov.br/saude/pt-br/composicao/saes/doacao-de-sangue
Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pereira, A. S. et al. (2018). Metodologia da pesquisa científica. [Free ebook]. Santa Maria. Editora da UFSM.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Risemberg, R. I. C. et al. (2026). A importância da metodologia científica no desenvolvimento de artigos científicos. E-Acadêmica, 7(1), e0171675. https://doi.org/10.52076/eacad-v7i1.675. https://eacademica.org/eacademica/article/view/675.
Song, J. W., & Chung, K. C. (2010). Observational studies: Cohort and case-control studies. Plastic and Reconstructive Surgery, 126(6), 2234–2242. https://doi.org/10.1097/PRS.0b013e3181f44abc
Shitsuka, R. et al. (2014). Matemática fundamental para tecnologia. (2ed). Editora Érica.
Srivastava, A., Santagostino, E., Dougall, A., Kitchen, S., Sutherland, M., Pipe, S. W., et al. (2020). WFH guidelines for the management of hemophilia (3rd ed.). Haemophilia, 26(Suppl. 6), 1–158. https://doi.org/10.1111/hae.14046
Thorpe, R., et al. (2024). The health impacts of blood donation: A systematic review of donor and non-donor perceptions. Blood Transfusion, 22(1), 7–19. https://doi.org/10.2450/BloodTransfus.494
World Federation of Hemophilia. (2023). Report on the annual global survey 2022. https://www1.wfh.org/publications/files/pdf-2399.pdf
World Health Organization. (2022). Global status report on blood safety and availability 2021. https://www.who.int/publications/i/item/9789240051683
World Health Organization. (2025). Blood safety and availability. https://www.who.int/news-room/fact-sheets/detail/blood-safety-and-availability
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Weber de Santana Teles, Max Cruz da Silva, Florita Moura Aquino, Rozeli Dantas Azevedo Moura, Ana Paula Barreto Prata Silva, Douglas Abilio, Orleane Souza Rezende, Ádamo Newton Marinho Andrade, Lorena Eugênia Rosa Coelho, Carlos Henrique Santiago Martins
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
