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Record W2983489157 · doi:10.1182/blood-2019-130510

Reducing Repetitive and Reflexive Diagnostic Phlebotomy in an Intensive Care Unit: A Quality Improvement Project

2019· article· en· W2983489157 on OpenAlexaffabout
Olga Levi, Maverick Chan, Thomas Bodley, Smith Orla, Michelle Sholzberg, Shannon Swift, Hina Chaudhry, Jan O. Friedrich, Lisa K. Hicks

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsPhlebotomyMedicineIntensive care unitEmergency medicineIntensive careIntensive care medicineAnemiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background Laboratory testing is a core component of contemporary medical care. However, repetitive diagnostic phlebotomy has been associated with anemia and higher transfusion burden, both of which are associated with increased mortality. The potential harm of repetitive blood testing is particularly pronounced in the intensive care unit (ICU). Critically ill patients receive a high volume of blood tests due to their need for intensive intervention and monitoring. In addition, ICU patients often suffer from anemia of inflammation which impairs effective erythropoiesis and may exacerbate net red cell loss resulting from repetitive blood testing. Aim Modeling data suggests that a 15% decrease in the amount of blood drawn may result in clinically meaningful decrease in ICU anemia.We aimed to reduce the average volume of blood collected per patient-day by 15% by June 30, 2019 by developing and implementing a patient-centered, diagnostic phlebotomy strategy in the Medical-Surgical Intensive Care Unit (MSICU) at an academic tertiary care center in Toronto, Canada. Methods A series of change strategies were implemented in a single-site, 25-bed MSICU between February and July 2019. The strategy included: stake-holder engagement, education sessions, process changes to encourage patient-centered lab ordering, electronic order set modifications to deter open-ended and unnecessary lab ordering, order changes to facilitate add-on testing, and audit and feedback regarding the average volume of blood collected per patient-day in the MSICU. Baseline data from July 2018 to January 2019 was collected retrospectively using administrative data. The main outcome measure was average volume of blood collected per patient-day in the MSICU. Balance measures included average discrete blood draws, ICU length of stay and mortality. The statistical stability of the main outcome measure over time was explored with Shewhart chart (I-chart) analysis. The student's T-Test was also used to compare the mean blood collected per patient-day in the ICU at baseline versus post-intervention. Results Baseline data from July 2018 to January 2019 revealed a mean of 46.8 mL of blood was collected per patient-day (including wastage). The volume of blood collected per patient-day was stable during the baseline observation period. Direct observations and process mapping of the blood ordering processes suggested that repetitive and reflexive blood testing was common in the MSICU. Change strategies were successively introduced between February and June 2019. During the study period average blood volume collected decreased from 46.8ml per patient-day to 36.0 mL per patient day. Special cause variation was observed 6 weeks into the intervention (p < 0.001). The average number of discrete blood draws also decreased from 4.1 to 3.7 per patient day. Conclusion Sequential, patient-centered interventions discouraging reflexive and unnecessarily repetitive blood testing in an ICU were associated with a significant decrease in average blood volumes collected per patient-day. Future analyses will explore whether the observed decrease in daily phlebotomy volumes was associated with any changes in patient outcomes such as transfusion burden, ICU length of stay and mortality. Disclosures Sholzberg: Novartis: Honoraria; Amgen: Honoraria, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.433
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
Admission routes2
Has abstractyes

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