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Transfusion ; 58(7): 1718-1725, 2018 07.
Article in English | MEDLINE | ID: mdl-29770454

ABSTRACT

BACKGROUND: The ordering process at Stanford Health Care involved twice-daily shipments predicated upon current stock levels from the blood center to the hospital transfusion service. Manual census determination is time consuming and error prone. We aimed to enhance inventory management by developing an informatics platform to streamline the ordering process and reallocate staff productivity. STUDY DESIGN AND METHODS: The general inventory accounts for more than 50 product categories based on characteristics including component, blood type, irradiation status, and cytomegalovirus serology status. Over a 5-month calibration period, inventory levels were determined algorithmically and electronically. An in-house software program was created to determine inventory levels, optimize the electronic ordering process, and reduce labor time. A 3-month pilot period was implemented using this program. RESULTS: This system showed noninferiority while saving labor time. The average weekly transfused:stocked ratios for cryoprecipitate, plasma, and red blood cells, respectively, were 1.03, 1.21, and 1.48 before the pilot period, compared with 0.88, 1.17, and 1.40 during (p = 0.28). There were 27 (before) and 31 (during) average STAT units ordered per week (p = 0.86). The number of monthly wasted products due to expiration was 226 (before) and 196 (during) units, respectively (p = 0.28). An estimated 7 hours per week of technologist time was reallocated to other tasks. CONCLUSION: An in-house electronic ordering system can enhance information fidelity, reallocate and optimize valuable staff productivity, and further standardize ordering. This system showed noninferiority to the labor-intensive manual system while freeing up over 360 hours of staff time per year.


Subject(s)
Equipment and Supplies, Hospital/statistics & numerical data , Inventories, Hospital/methods , Medical Informatics/methods , Blood Banks/statistics & numerical data
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