Optimizing Inpatient Blood Utilization Using Real-Time Clinical Decision Support.


Journal

Applied clinical informatics
ISSN: 1869-0327
Titre abrégé: Appl Clin Inform
Pays: Germany
ID NLM: 101537732

Informations de publication

Date de publication:
01 2021
Historique:
entrez: 28 1 2021
pubmed: 29 1 2021
medline: 17 11 2021
Statut: ppublish

Résumé

Red blood cell (RBC) transfusion is a common medical procedure. While it offers clinical benefits for many, hemodynamically stable patients are often subjected to unwarranted transfusions, with the potential to lead to adverse consequences. We created a real-time clinical decision support (CDS) tool in the electronic health record system to address this problem and optimize transfusion practice as part of an institutional multidisciplinary, team-based patient blood management program. The real-time CDS tool incorporated the transfusion guidelines published by the AABB. The tool was deployed as a dynamic order set within the computerized provider order entry interface. Prior to implementation, extensive education and outreach to increase provider engagement were provided. The CDS tool was launched in September 2015. The percentage of guideline-indicated RBC transfusions increased from a baseline of 43.6 to 54.2% while the percentage of multiunit (≥ 2 units) RBC transfusions decreased from 31.3 to 22.7% between September 2014 and July 2019. The estimated minimum cost saving over the entire study period was $36,519.36. Our intervention increased guideline-indicated transfusions by 10.6% and reduced multiunit transfusions by 8.6%. The adoption of a dynamic order set for the CDS tool, as opposed to an interruptive alert that displays static alert messages, allowed for more customized and tighter control of RBC orders, leading to a sustained improvement in our transfusion practice.

Sections du résumé

BACKGROUND
Red blood cell (RBC) transfusion is a common medical procedure. While it offers clinical benefits for many, hemodynamically stable patients are often subjected to unwarranted transfusions, with the potential to lead to adverse consequences. We created a real-time clinical decision support (CDS) tool in the electronic health record system to address this problem and optimize transfusion practice as part of an institutional multidisciplinary, team-based patient blood management program.
METHODS
The real-time CDS tool incorporated the transfusion guidelines published by the AABB. The tool was deployed as a dynamic order set within the computerized provider order entry interface. Prior to implementation, extensive education and outreach to increase provider engagement were provided. The CDS tool was launched in September 2015.
RESULTS
The percentage of guideline-indicated RBC transfusions increased from a baseline of 43.6 to 54.2% while the percentage of multiunit (≥ 2 units) RBC transfusions decreased from 31.3 to 22.7% between September 2014 and July 2019. The estimated minimum cost saving over the entire study period was $36,519.36.
CONCLUSION
Our intervention increased guideline-indicated transfusions by 10.6% and reduced multiunit transfusions by 8.6%. The adoption of a dynamic order set for the CDS tool, as opposed to an interruptive alert that displays static alert messages, allowed for more customized and tighter control of RBC orders, leading to a sustained improvement in our transfusion practice.

Identifiants

pubmed: 33506477
doi: 10.1055/s-0040-1721779
pmc: PMC7840436
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

49-56

Informations de copyright

Thieme. All rights reserved.

Déclaration de conflit d'intérêts

None declared.

Références

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Auteurs

Shohei Ikoma (S)

Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States.

Meg Furukawa (M)

Health Information Technology, University of California, Los Angeles, California, United States.

Ashley Busuttil (A)

Division of General Internal Medicine, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States.

Dawn Ward (D)

Department of Pathology and Laboratory Medicine, Wing-Kwai and Alice Lee-Tsing Chung Transfusion Service, David Geffen School of Medicine, University of California, Los Angeles, California, United States.

Kevin Baldwin (K)

Health Information Technology, University of California, Los Angeles, California, United States.

Jeffrey Mayne (J)

Division of Hospital Medicine, Department of Medicine, Nuvance Health, Rhinebeck, New York, United States.

Robin Clarke (R)

Ursa Health, Nashville, Tennessee, United States.

Alyssa Ziman (A)

Department of Pathology and Laboratory Medicine, Wing-Kwai and Alice Lee-Tsing Chung Transfusion Service, David Geffen School of Medicine, University of California, Los Angeles, California, United States.

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Classifications MeSH