Evidence map›Paper›PMID 42333648›Full record

ArticleTransfusion2026

Monitoring and feedback using data in transfusion medicine: A study of practices from the BEST Collaborative.

Ryan A Metcalf, Allison Mo, Sandra K White, Gustavo C Duarte, Nancy M Dunbar, Katie Hands, Anil K Panigrahi, Jessica L Poisson, Nabiha H Saifee, Hua Shan and 3 more

Abstract read
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In one paragraph

Article in Transfusion, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ryan A MetcalfDepartment of Pathology, University of Utah, Salt Lake City, Utah, USA.ORCID https://orcid.org/0000-0001-6074-7147
Allison MoTransfusion Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0002-1923-3133
Sandra K WhiteDepartment of Pathology, University of Utah, Salt Lake City, Utah, USA.
Gustavo C DuarteNew Zealand Blood Service, Auckland, New Zealand.ORCID https://orcid.org/0000-0002-4552-7303
Nancy M DunbarDartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA.ORCID https://orcid.org/0000-0001-8601-5438
Katie HandsScottish National Blood Transfusion Service, Ninewells Hospital, Dundee, UK.
Anil K PanigrahiDepartment of Anesthesiology, Stanford University School of Medicine, Stanford, California, USA.
Jessica L PoissonDuke University Hospital, Durhan, North Carolina, USA.ORCID https://orcid.org/0000-0002-6047-6436
Nabiha H SaifeeDepartment of Laboratory Medicine and Pathology, Seattle Children's Hospital, Seattle, Washington, USA.ORCID https://orcid.org/0000-0003-1646-9201
Hua ShanDepartment of Pathology, Stanford University School of Medicine, Stanford, California, USA.
Erica M WoodTransfusion Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-7527-2340
Michael F MurphyNHS Blood and Transplant, Oxford University Hospitals NHS Trust, Oxford, UK.ORCID https://orcid.org/0000-0002-2375-7503
Simon J StanworthNHS Blood and Transplant, Oxford University Hospitals NHS Trust, Oxford, UK.ORCID https://orcid.org/0000-0002-7414-4950

Funding

National Institute for Health and Care Research NIHR203334
6 · The paper itself

Abstract

backgroundIt is imperative that health systems monitor transfusion practice quality and implement improvements when evidence-based practice gaps are identified. Despite the importance of data-driven quality activities, little is known about approaches employed by hospitals. This study aimed to describe strategies used by hospitals to acquire, analyze, and apply transfusion-related quality data. STUDY DESIGN AND

methodsA pre-piloted survey was developed by the core team, and Biomedical Excellence for Safer Transfusion (BEST) Collaborative attendees of meetings were invited to participate. Topics spanned five domains: hospital characteristics, data acquisition, quality metric prioritization, data analysis/visualization, and feedback processes. Responses were summarized to identify patterns and variation across institutions.

resultsNine hospitals of varying size, specialty mix, and transfusion volumes from three continents participated. Most transfusion services acquired transfusion data from both the electronic health record and the laboratory information system (6/9, 67%). Although all sites tracked the number of transfusions, there was wide variation in other metrics prioritized. Transfusion appropriateness was prioritized by 7 of 9 centers, but few reported monitoring antifibrinolytic agent use (2/9, 22%), anemia management (4/9, 44%), and consent (2/9, 22%). All hospitals performed some form of data analysis, but chart/table types and functionalities differed. Feedback was typically provided by transfusion medicine specialists or hematology, but frequency and means differed between sites. DISCUSSION: This descriptive study highlights significant variation in how selected hospitals use data to monitor transfusion practices and provide feedback. There are many opportunities to enhance optimal uses of data to deliver effective quality improvement.

Indexed as

Blood TransfusionTransfusion MedicineElectronic Health RecordsFeedbackHospitalsHumans

Identifiers

PMID42333648

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.