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Record W4417296753 · doi:10.1093/pch/pxaf116.069

69 Fewer pokes with faster PICCs: A quality improvement initiative to improve timely access to PICCs in paediatric care

2025· article· en· W4417296753 on OpenAlexaff
Jennifer Smitten, Elizabeth A. Lamb

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsQuality managementVascular accessPeripherally inserted central catheterCatheterPatient safetyIntervention (counseling)Quality (philosophy)Quality assurance

Abstract

fetched live from OpenAlex

Abstract Background Children requiring long courses of intravenous (IV) therapy often require a peripherally inserted central catheter (PICC). PICCs ensure stable access for IV antibiotics, parenteral nutrition, and other therapies. PICCs also help avoid pain from frequent needle pokes for bloodwork and new IV catheter insertions. Until recently, all PICC insertions at our tertiary hospital were performed by Interventional Radiology (IR) physicians. However, demand for PICCs exceeded IR’s capacity to insert them, leading to delays. In 2022, mean wait time for a PICC insertion was 3.9 days, with only 43% of patients receiving a PICC within 3 days of the request, and 12% of patients who needed a PICC were not able to receive one at all due to delays. Objectives The aim of this quality improvement (QI) initiative was to have >90% of paediatric inpatients who require a PICC receive one within 3 days of the request. Design/Methods The Model for Improvement framework was used. An interdisciplinary QI team started by collecting baseline data. They analyzed the root causes of delayed PICC insertions and identified that scarce IR resources were a primary driver of delays. They conducted a literature review and international environmental scan, created a process map, and engaged with groups across the hospital to identify change ideas. Change ideas were prioritized using an effort-impact matrix. The chosen intervention was to implement a nurse-inserted PICC service to complement existing IR services. Nurses from the Vascular Access Team were specially trained to insert PICCs in children, and the PICC Nurse pilot was launched in June 2023 using Plan-Do-Study-Act (PDSA) methodology to test and adapt changes. Data to track process, balancing, and outcome measures were collected in a REDCap database. Patient, caregiver, and healthcare provider satisfaction data were collected by electronic surveys accessed via a quick response (QR) code. Results In the first 16 months after the launch of the PICC Nurse service, 90 PICCs were inserted by the team. The low numbers in the initial phase (an average of 3.5 PICCs per month) were largely due to conservative eligibility criteria and lack of a formal process for sedation support, resulting in a small number of children being eligible. Ongoing PDSA cycles led to a partnership with Anesthesia for sedation support, and re-evaluation of eligibility criteria with stakeholders. These changes resulted in an increase from an average of 3.5 PICCs per month by the nurse team in the first 6 months, to an average of 7.5 PICCs per month in the most recent 6 months (Figure 1). The mean wait time for PICC insertions by the PICC Nurses was 1.6 days, down from the previous baseline of 3.9 days. 87% of patients who had their PICC inserted by a nurse received their PICC within 3 days. The majority of delays longer than 3 days occurred due to coordination with other procedures under sedation. PICC cancellations also significantly decreased. No adverse events during the PICC insertion procedure were reported. 46 people responded to the satisfaction survey; 16 patients/caregivers and 30 healthcare providers. 98% of respondents reported they were “satisfied” or “extremely satisfied” with the PICC insertion procedure by the PICC Nurse team. Conclusion The nurse-inserted PICC service in paediatrics was feasible, well-accepted, and safe. It enabled patients to more readily access optimum therapies and avoid the pain of repeated pokes. Patients with PICCs may be repatriated to community hospitals to complete treatment, or may be discharged home where outpatient programs exist. By empowering nurses to practice to their full scope, capacity within IR is increased, bottlenecks are reduced, and scarce tertiary care resources can be more appropriately allocated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0030.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.383
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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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Citations0
Published2025
Admission routes1
Has abstractyes

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