How do we monitor hospital transfusion practice using an end‐to‐end electronic transfusion management system?

Murphy, Michael; Fraser, Edward; Miles, David; Noel, S; Staves, Julie; Cripps, Barbara; Kay, Jonathan

doi:10.1111/j.1537-2995.2011.03509.x

Cited by 49 publications

(40 citation statements)

References 19 publications

(40 reference statements)

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“…Such tools can alert clinicians to inappropriate orders and facilitate audit. In the case of blood ordering, clinical decision tools have been shown to reduce inappropriate blood transfusion, however data are limited [32,33].…”

Section: Action To Reduce Transfusionmentioning

confidence: 99%

The scientific basis for patient blood management

Murphy

Goodnough

2015

Transfusion Clinique et Biologique

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Section: Action To Reduce Transfusionmentioning

confidence: 99%

The scientific basis for patient blood management

Murphy

Goodnough

2015

Transfusion Clinique et Biologique

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“…Errors leading to ABO mistransfusion can occur at every step in the blood collection and transfusion process . Wrong blood in tube (WBIT) errors, in which the blood inside the tube was drawn from someone other than the patient identified on the label, represent an important and preventable preanalytic source of error.…”

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confidence: 99%

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

et al. 2018

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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.

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“…All three Level 2 and 3 studies [5, 6, [17][18][19][20][21][22] reported on these outcome measures to some extent. However, only Wong et al [21] provide detailed figures with a decrease in returns of 8Á2% (to 13Á6%) and maintenance of low discard rates at 1Á27%.…”

Section: Synthesis -Quantitativementioning

confidence: 99%