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Record W2888198711 · doi:10.1136/bmjoq-2018-000319

Knowledge translation and process improvement interventions increased pain assessment documentation in a large quaternary paediatric post-anaesthesia care unit

2018· article· en· W2888198711 on OpenAlexafffund
Daniel Stocki, Conor Mc Donnell, Gail Wong, Gloria Kotzer, Kelly Shackell, Fiona Campbell

Bibliographic record

VenueBMJ Open Quality · 2018
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsPacuDocumentationMedicineAuditPain assessmentPsychological interventionIntervention (counseling)Post-anesthesia care unitQuality managementPhysical therapyNursingMedical emergencyPain managementAnesthesiaService (business)

Abstract

fetched live from OpenAlex

BACKGROUND: Due to inadequate pain assessment documentation in our paediatric post-anaesthetic care unit (PACU), we were unable to monitor pain intensity, and target factors contributing to moderate and severe postoperative pain in children. The purpose of this study was to improve pain assessment documentation in PACU through a process improvement intervention and knowledge translation (KT) strategy. The study was set in a PACU within a large university affiliated paediatric hospital. Participants included PACU and Acute Pain Service nursing staff, administrative staff and anaesthesiologists. METHODS: The Plan-Do-Study-Act method of quality improvement was used. Benchmark data were obtained by chart review of 99 patient medical records prior to interventions. Data included pain assessment documentation (pain intensity score, use of validated pain intensity measure) during PACU stay. Repeat chart audit took place at 4, 5 and 6 months after the intervention. INTERVENTION: Key informant interviews were conducted to identify barriers to pain assessment documentation. A process improvement was implemented whereby the PACU flowsheets were modified to facilitate pain assessment documentation. KT strategy was implemented to increase awareness of pain assessment documentation and to provide the knowledge, skill and judgement to support this practice. The KT strategy was directed at PACU nursing staff and comprised education outreach (educational meetings for PACU nurses, discussions at daily huddles), reminders (screensavers, bedside posters, email reminders) and feedback of audit results. RESULTS: The proportion of charts that included at least one documented pain assessment was 69%. After intervention, pain assessment documentation increased to >90% at 4 and 5 months, respectively, and to 100% after 6 months. CONCLUSION: After implementing process improvement and KT interventions, pain assessment documentation improved. Additional work is needed in several key areas, specifically monitoring moderate to severe pain, in order to target factors contributing to significant postoperative pain in children.

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)0.007
metaresearch head score (Gemma)0.000
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 score0.196
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.117
GPT teacher head0.493
Teacher spread0.377 · 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".

Quick stats

Citations9
Published2018
Admission routes2
Has abstractyes

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