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A data-driven approach to patient blood management

Authors

  • Claudia S. Cohn,

    Corresponding author
    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
    • Address reprint requests to: Claudia S. Cohn, MD, PhD, Laboratory Medicine & Pathology, University of Minnesota, MMC 609, 420 Delaware Street SE, Minneapolis, MN 55455; e-mail: cscohn@umn.edu.

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  • Julie Welbig,

    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
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  • Robert Bowman,

    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
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  • Susan Kammann,

    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
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  • Katherine Frey,

    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
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  • Nicole Zantek

    1. Department of Laboratory Medicine and Pathology, University of Minnesota Medical School
    2. Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota
    3. Fairview Health Services, Southdale Hospital, Edina, Minnesota
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Abstract

Background

Patient blood management (PBM) has become a topic of intense interest; however, implementing a robust PBM system in a large academic hospital can be a challenge. In a joint effort between transfusion medicine and information technology, we have developed three overlapping databases that allow for a comprehensive, semiautomated approach to monitoring up-to-date red blood cell (RBC) usage in our hospital. Data derived from this work have allowed us to target our PBM efforts.

Study Design and Methods

Information on transfusions is collected using three databases: daily report, discharge database, and denominator database. The daily report collects data on all transfusions in the past 24 hours. The discharge database integrates transfusion data and diagnostic billing codes. The denominator database allows for rate calculations by tracking all patients with a hemoglobin test ordered. A set of algorithms is applied to automatically audit RBC transfusions. The transfusions that do not fit the algorithms' rules are manually reviewed. Data from audits are compiled into reports and distributed to medical directors. Data are also used to target education efforts.

Results

Since our PBM program began, the percentage of appropriate RBC orders increased from an initial 70%-80% to 90%-95%, and the overall RBC transfusions/1000 patient-days has decreased by 67% in targeted areas of the hospital. Our PBM program has shaved approximately 3% from our hospital's blood budget.

Conclusion

Our semiautomated auditing system allows us to quickly and comprehensively analyze and track blood usage throughout our hospital. Using this technology, we have seen improvements in our hospital's PBM.

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