Optimizing Inpatient Blood Utilization Using Real-Time Clinical Decision Support

Shohei Ikoma; Meg Furukawa; Ashley Busuttil; Dawn Ward; Kevin Baldwin; Jeffrey Mayne; Robin Clarke; Alyssa Ziman

doi:10.1055/s-0040-1721779

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2021; 12(01): 049-056
DOI: 10.1055/s-0040-1721779

Research Article

Optimizing Inpatient Blood Utilization Using Real-Time Clinical Decision Support

Shohei Ikoma

¹Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States

,

Meg Furukawa

²Health Information Technology, University of California, Los Angeles, California, United States

,

Ashley Busuttil

³Division of General Internal Medicine, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States

,

Dawn Ward

⁴Department 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

²Health Information Technology, University of California, Los Angeles, California, United States

,

Jeffrey Mayne

⁵Division of Hospital Medicine, Department of Medicine, Nuvance Health, Rhinebeck, New York, United States

,

Robin Clarke

⁶Ursa Health, Nashville, Tennessee, United States

,

Alyssa Ziman

⁴Department 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

› Institutsangaben

Abstract

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.

Keywords

decision support systems - clinical - blood transfusion - medical informatics - blood banks - quality improvement

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