Rethinking Blood Testing in Pediatric Cancer Patients: A Quality Improvement Approach
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ABSTRACT: Introduction:
The overuse of blood tests burdens the healthcare system and can detrimentally impact patient care. Risks of frequent blood sampling include infection and clinician-induced anemia, which can negatively impact patients and their families. Pediatric cancer patients are particularly vulnerable as they are immunocompromised with a small blood volume. Four blood tests had become a daily practice. Therefore, we aimed to reduce the number of blood tests taken per bed day within the inpatient pediatric cancer unit by 15% within 8 months. Methods:
This quality improvement project combined several strategies to reduce test frequency and empower clinicians on the rationale for blood test ordering. Recommendations were developed collaboratively presented in a summary table. Targeted behavior-change methodology built engagement and momentum for the change. All clinicians were challenged to STOP and THINK about why a test is necessary for each patient. The primary outcome measure was the frequency of the tests taken per bed day. Frequency was compared between pre- and postimplementation plus follow-up periods across 2019–2021. Results:
26,941 blood tests were captured in 1,558 admissions. The intervention led to an overall blood test reduction of 37% over 8 months. Liver Function Tests were the standout, with a 52% decrease in test frequency. Conclusions:
A strategy incorporating education and culture change, combined with clear guidance on testing frequency, significantly reduced the ordering frequency of blood tests without increased patient harm.
SUBMITTER: Grant A
PROVIDER: S-EPMC9197359 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
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Project description:PurposeTo develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP).MethodsIn this retrospective study, a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. The test set was rated by the TDL-BAAM model as well as a GP-based deep learning model (GPDL-BAAM) and two pediatric radiologists (radiologists 1 and 2) using the GP method. All ratings were compared with chronological age using mean absolute error (MAE), and standard concordance analyses were performed.ResultsThe MAE of the TDL-BAAM model was 11.1 months, compared with 12.9 months for GPDL-BAAM (P = .0005), 14.6 months for radiologist 1 (P < .0001), and 16.0 for radiologist 2 (P < .0001). For TDL-BAAM, 95.3% of predictions were within 24 months of chronological age compared with 91.6% for GPDL-BAAM (P = .096), 86.0% for radiologist 1 (P < .0001), and 84.6% for radiologist 2 (P < .0001). Concordance was high between all methods and chronological age (intraclass coefficient > 0.93). Deep learning models demonstrated a systematic bias with a tendency to overpredict age for younger children versus radiologists who showed a consistent mean bias.ConclusionA deep learning model trained on pediatric trauma hand radiographs is on par with automated and manual GP-based methods for bone age assessment and provides a foundation for developing population-specific deep learning algorithms for bone age assessment in modern pediatric populations.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Halabi in this issue.
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