Academia.eduAcademia.edu

Outline

On decision making support in blood bank information systems

https://doi.org/10.1016/J.ESWA.2007.01.016

Abstract

Information and computer technology popularizes in blood banks for its potentials in working efficiency as well as service quality. But, in the face of the explosive data and information, one of the challenges in blood bank information systems is how to make good use of them for decision making support. In this paper, we desire to explore the mechanisms of decision making support in blood bank information systems. Firstly, the properties of data and decisions in a blood bank are examined carefully; then, we introduce the development of computerized decision making support with special concerns on blood donation and transfusion service; finally, a case study is presented to evidence our understanding on decision making support in blood bank information systems.

References (28)

  1. Bosnes, V., Aldrin, M., & Heier, H. E. (2005). Predicting blood donor arrival. Transfusion, 45(2), 162-170.
  2. British committee for standards in hematology (BCSH). Guidelines for blood bank computing. (2000). Transfusion Medicine, 10(4), pp. 307-314.
  3. Brittenham, G. M., Klein, H. G., Kushner, J. P., & Ajioka, R. S. (2001). Preserving the national blood supply. Hematology, 422-432.
  4. Butch, S. H. (2002). Computerization in the transfusion service. Vox Sanguinis, 83(suppl. 1), 105-110.
  5. Center for Biologics Evaluation and Research (CBER). (2005). Draft guidelines for the validation of blood establishment computer systems, <www.fda.gov/cber/guidelines.htm>.
  6. Connelly, D. P., Sielaff, B. H., & Scott, E. P. (1990). ESPRE -expert system for platelet request evaluation. American Journal of Clinical Pathology, 94(4), s19-s24.
  7. Gardner, R. M., Golubjatnikov, O. K., Laub, R. M., Jacobson, J. T., & Evans, R. S. (1990). Computer-critiqued blood ordering using the HELP system. Journal of Biomedical Informatics, 23, 514-528.
  8. Glynn, S. A., Kleinman, S. H., Schreiber, G. B., Zuck, T., McCombs, S., Bethel, J., et al. (2002). Motivations to donate blood: demographic comparisons. Transfusion, 42(2), 216-225.
  9. Gregory, P. P., & Eric, B. (1980). PBDS: a decision support system for regional blood management. Management Science, 26(5), 451-464.
  10. Gregory, P. P., & Eric, B. (1984). Blood inventory management: an overview of theory and practice. Management Science, 30(7), 777-800.
  11. Gupta, O., Priyadarshini, K., Massoud, S., & Agrawal, S. K. (2004). Enterprise resource planning: a case of a blood bank. Industrial Management and Data Systems, 104(7), 589-603.
  12. Hanson, M. (1996). Should we do another test? Decision making in blood banking. Clinical Laboratory Medicine, 16(4), 883-893.
  13. International Society of Blood Transfusion (ISBT). (2003). Guidelines for validation and maintaining the validation state of automation systems in blood banks. Vox Sanguinis, supplement 1, s1-s14.
  14. James, R. C., & Matthews, D. E. (1996). Analysis of blood donor return behavior using survival regression methods. Transfusion Medicine, 6(1), 21-30.
  15. Jelles, G. M. (1993). Costs and benefits of HIV-1 antibody testing of donated blood. Journal of Policy Analysis and Management, 12(3), 512-531.
  16. Kros, J. F., & Pang, R. Y. (2004). A decision support system for quantitative measurement of operational efficiency in a blood collec- tion facility. Computer Methods and Programs in Biomedicine, 74(1), 77-89.
  17. Li, B. N., & Dong, M. C. (2006). Banking on blood. Computing and Control Engineering(August-September), 22-25.
  18. Li, B. N., Chao, S., Dong, M. C. (in press). SIBAS: a blood bank information system and its 5-year implementation at Macau. Com- puters in Biology and Medicine, doi 10.1016/j.compbiomed.2006.03. 010. Li, B. N., Chao, S., & Dong, M. C. (2006). Barcode technology in blood bank information systems: upgrade and its impact. Journal of Medical Systems, 30(6), 449-457.
  19. Peta ¨ja ¨, J., Andersson, S., & Syrja ¨la ¨, M. (2004). A simple automatized audit system for following and managing practices of platelet and plasma transfusions in a neonatal intensive care unit. Transfusion Medicine, 14(4), 281-288.
  20. Pietersz, R. N. I. (1995). Automation/computerization in blood process- ing. Transfusion Science, 16(3), 235-241.
  21. Raj, J., & Tarun, S. (1991). Storing crossmatched blood: a perishable inventory model with prior allocation. Management Science, 37(3), 251-266.
  22. Roh, T. H., Ahn, C. K., & Han, I. (2005). The priority factor model for customer relationship management system success. Expert Systems with Applications, 28(4), 641-654.
  23. Sielaff, B. H., Connelly, D. P., & Scott, E. P. (1989). ESPRE: a knowledge- based system to support platelet transfusion decisions. IEEE Trans- actions on Biomedical Engineering, 36(5), 541-546.
  24. Sime, S. L. (2005). Strengthening the service continuum between trans- fusion providers and suppliers: enhancing the blood services network. Transfusion, 45(s4), 206S-223S.
  25. Smith, J. W., Svirbely, J. R., Evans, C. A., Strohm, P., Josephson, J. R., & Tanner, M. (1985). RED: a red-cell antibody identification expert module. Journal of Medical Systems, 9(3), 121-138.
  26. Spackman, K. A., & Beck, J. R. (1990). A knowledge-based system for transfusion advice. American Journal of Clinical Pathology, 94(4), s25-s29.
  27. Stephen, W., & John, E. S. (2000). Reducing surgical patient costs through use of an artificial neural network to predict transfusion requirements. Decision Support Systems, 30(2), 125-138.
  28. Zaller, N., Nelson, K. E., Ness, P., Wen, G., Bai, X., & Shan, H. (2005). Knowledge, attitude and practice survey regarding blood donation in a Northwestern Chinese city. Transfusion Medicine, 15(4), 277- 286.