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Objective: In this study, after the examination, most patients apply to phlebotomy units to perform the necessary examinations. Sufficient plebotomists should be taken to the phlebotomy unit to serve a large number of patients. The aim of this study is to determine the reguired number of phlebotomists in blood center using the artificial intelligence.
Material and Methods: This study was conducted in the Health Sciences University Tepecik Training and Research Hospital Blood center between the September-November 2019 . The required number of phlebotomists in the unit was determined with an artificial intelligence-based method. With this system, the number of patients coming to the phlebotomy unit is estimated in real time and considering the past performance of the working phlebotomists, how many phlebotomists are needed in real time is calculated.
Results: The number of phlebotomists who both serve patients as quickly as possible and use the personnel resources of hospital efficiently needs to be optimized. In order to solve this problem, an AI-based system has been developed. With this system, the number of patients coming to phlebotomy unit is estimated in real time and considering the past performances of the working phlebotomists, it calculates how many phlebotomists are needed in real time
Conclusion: The suggestions made by this AI-based system have made a great contribution to the management of the phlebotomy unit. Managers used hospital staff resources in the most efficient way and at the same time, they were able to ensure that patients receive phlebotomy service by following the system's recommendations.
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2. Hutzschenreuter A.K., Bosman P.A.N., La Poutré H. (2009) Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management. In: Ehrgott M., Fonseca C.M., Gandibleux X., Hao JK., Sevaux M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg
3. Gupta A, Dwivedi T, Sadhana, Chaudhary R. Analysis of Patient's Satisfaction with Phlebotomy Services in NABH Accredited Neuropsychiatric Hospital: An Effective Tool for Improvement. J Clin Diagn Res. 2017;11(9):EC05–EC08. doi:10.7860/JCDR/2017/26190.10562
4. Timothy Bartram & Peter J. Dowling (2013) An international perspective on human resource management and performance in the health care sector: toward a research agenda, The International Journal of Human Resource Management, 24:16, 3031-3037, DOI: 10.1080/09585192.2013.775024
5. Aileen P. Morrison, Milenko J. Tanasijevic, Joi N. Torrence-Hill, Ellen M. Goonan, Michael L. Gustafson, Stacy E.F. Melanson, (2011) A Strategy for Optimizing Staffing to Improve the Timeliness of Inpatient Phlebotomy Collections. Archives of Pathology & Laboratory Medicine: December 2011, Vol. 135, No. 12, pp. 1576-1580. doi:https://doi.org/10.5858/arpa.2011-0061-OA
6. Leaven L, Qu X. A two-stage stochastic programming model for phlebotomist scheduling in hospital laboratories. Health Syst (Basingstoke). 2017;7(2):100–110. Published 2017 Dec 6. doi:10.1057/s41306-017-0033-8
7. Aleksandar S. Mijailovic, Milenko J. Tanasijevic, Ellen M. Goonan, Rachel D. Le, Jonathan M. Baum, and Stacy E.F. Melanson (2014) Optimizing Outpatient Phlebotomy Staffing: Tools to Assess Staffing Needs and Monitor Effectiveness. Archives of Pathology & Laboratory Medicine: July 2014, Vol. 138, No. 7, pp. 929-935.
8. Orbatu D., Yıldırım O, “Use of Artificial Intelligence in Phlebotomy Unit” Turkish Journal of Biochemistry (2018), 43(Supplement), pp. 22-29. Retrieved 15 Oct. 2019, from doi:10.1515/tjb-2018-43s144
9. Jones K, Lemaire C, Naugler C. Phlebotomy Cycle Time Related to Phlebotomist Experience and/or Hospital Location. Lab Med. 2016;47(1):83–86. doi:10.1093/labmed/lmv006