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Objectives: Complete blood count and differential (CBC diff) is a common laboratory test that may be overused or misordered, particularly in an inpatient setting. We assessed the ability of a clinical decision support (CDS) alert to decrease unnecessary orders for CBC diff and analyzed its impact in the laboratory.
Methods: We designed 3 CDS alerts to provide guidance to providers ordering CBC diff on inpatients at frequencies of daily, greater than once daily, or as needed.
Results: The 3 alerts were highly effective in reducing orders for CBC diff at the frequencies targeted by the alert. Overall, test volume for CBC diff decreased by 32% (mean of 5257 tests per month) after implementation of the alerts, with a corresponding decrease of 22% in manual differentials performed (mean of 898 per month). Turnaround time for manual differentials decreased by a mean of 41.5 minutes, with a mean decrease of up to 90 minutes during peak morning hours.
Conclusions: The 3 CDS alerts successfully decreased inpatient orders for CBC diff and improved the quality of patient care by decreasing turnaround time for manual differentials.
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http://dx.doi.org/10.1093/ajcp/aqae024 | DOI Listing |
Clin Microbiol Infect
August 2025
University of Cologne, Faculty of Medicine and University Hospital of Cologne, Department I of Internal Medicine, Cologne, Germany; German Centre for Infection Research (DZIF), Braunschweig, Germany; Goethe University Frankfurt, Faculty of Medicine and University Hospital of Frankfurt, Institute for
Objectives: Infection prevention and control (IPC) and antimicrobial stewardship (AMS) measures are critical to reduce transmission and infection by Clostridioides difficile (CDI) and other enteric pathogens. This study evaluated the impact of enhanced IPC and AMS on CDI and bloodstream infections (BSI) by vancomycin-resistant enterococci (VRE), and third-generation cephalosporin-resistant Enterobacterales (3GCREB).
Methods: The study was conducted in five German university hospitals from January 2016 to July 2019.
JMIR Cancer
May 2025
Department of Gynecology and Obstetrics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 21/23, Erlangen, 91054, Germany, 49 9131 8533553.
Background: The introduction of oral anticancer therapies has, at least partially, shifted treatment from clinician-supervised hospital care to patient-managed home regimens. However, patients with breast cancer receiving oral cyclin-dependent kinase 4/6 inhibitor therapy still require regular hospital visits to monitor side effects. Telemonitoring has the potential to reduce hospital visits while maintaining quality care.
View Article and Find Full Text PDFClin Chem Lab Med
July 2025
Emergency Department, APHP-Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.
Objectives: Traditional biomarkers used for sepsis diagnosis have limited sensitivity and specificity and, so far, are not recommended for sepsis diagnosis. We aimed to evaluate diagnostic accuracy of XN-9000 hematology analyzer derived cell population data (CPD) for sepsis.
Methods: We conducted a cross-sectional cohort study on patients admitted to an emergency department (ED) with a suspicion of infection, having a complete blood count with differential (CBC-Diff).
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFClin Chem
March 2025
Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
View Article and Find Full Text PDF