Details

Autor(en) / Beteiligte

• Kaufman, Richard M.
• Dinh, Anh
• Cohn, Claudia S.
• Fung, Mark K.
• Gorlin, Jed
• Melanson, Stacy
• Murphy, Michael F.
• Ziman, Alyssa
• Elahie, Allahna L.
• Chasse, Danielle
• Degree, Lynsi
• Dunbar, Nancy M.
• Dzik, Walter H.
• Flanagan, Peter
• Gabert, Kimberly
• Ipe, Tina S.
• Jackson, Bryon
• Lane, Debra
• Raspollini, Elisabetta
• Ray, Charles
• Sharon, Yudit
• Ellis, Martin
• Selleng, Kathleen
• Staves, Julie
• Yu, Philip
• Zeller, Michelle
• Yazer, Mark

Titel

Electronic patient identification for sample labeling reduces wrong blood in tube errors

Ist Teil von

• Transfusion (Philadelphia, Pa.), 2019-03, Vol.59 (3), p.972-980

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• BACKGROUND Wrong blood in tube (WBIT) errors are a preventable cause of ABO‐mismatched RBC transfusions. Electronic patient identification systems (e.g., scanning a patient's wristband barcode before pretransfusion sample collection) are thought to reduce WBIT errors, but the effectiveness of these systems is unclear. STUDY DESIGN AND METHODS Part 1: Using retrospective data, we compared pretransfusion sample WBIT rates at hospitals using manual patient identification (n = 16 sites; >1.6 million samples) with WBIT rates at hospitals using electronic patient identification for some or all sample collections (n = 4 sites; >0.5 million samples). Also, we compared WBIT rates after implementation of electronic patient identification with preimplementation WBIT rates. Causes and frequencies of WBIT errors were evaluated at each site. Part 2: Transfusion service laboratories (n = 18) prospectively typed mislabeled (rejected) samples (n = 2844) to determine WBIT rates among samples with minor labeling errors. RESULTS Part 1: The overall unadjusted WBIT rate at sites using manual patient identification was 1:10,110 versus 1:35,806 for sites using electronic identification (p < 0.0001). Correcting for repeat samples and silent WBIT errors yielded overall adjusted WBIT rates of 1:3046 for sites using manual identification and 1:14,606 for sites using electronic identification (p < 0.0001), with wide variation among individual sites. Part 2: The unadjusted WBIT rate among mislabeled (rejected) samples was 1:71 (adjusted WBIT rate, 1:28). CONCLUSION In this study, using electronic patient identification at the time of pretransfusion sample collection was associated with approximately fivefold fewer WBIT errors compared with using manual patient identification. WBIT rates were high among mislabeled (rejected) samples, confirming that rejecting samples with even minor labeling errors helps mitigate the risk of ABO‐incompatible transfusions. See article on page 899–902, in this issue