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Record W3202575034 · doi:10.1136/bmjoq-2021-001570

Exploration of a quality improvement process to standardised preoperative tests for a surgical procedure to reduce waste

2021· article· en· W3202575034 on OpenAlexaff
Rabia Shahid, Malone Chaya, Ian Lutz, B. G. Taylor, Lily Dongxia Xiao, Gary Groot

Bibliographic record

VenueBMJ Open Quality · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineQuality managementPDCASurgeryMedical physicsOperations managementEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Preoperative tests are done to determine a patient's fitness for anaesthesia and surgery. LOCAL PROBLEM: Although routine tests before surgery in the absence of specific clinical indications are not recommended, we observed high volumes of routine preoperative tests were performed in our institution. We describe a process to implement a standardised preoperative investigational approach to reduce unnecessary testing before surgeries. METHODS: A series of six Plan-Do-Study-Act (PDSA) cycles was conducted for root cause analysis and process mapping, development of standardised tool (GRID), collection of baseline data, education and feedback, pilot testing and implementation and uptake of GRID.Root cause analysis revealed a lack of awareness of guidelines and a lack of a standardised tool to guide preoperative testing. We undertook a pilot quality improvement project to reduce unnecessary testing before knee and hip arthroplasty by developing and implementing a standardised tool (GRID) and engaging all stakeholders. INTERVENTIONS: A clinical development team (CDT) was formed, including all the stakeholders. Our CDT focused on a continuous rapid cycle improvement strategy. RESULTS: After implementation of the tool in a subgroup of patients undergoing elective hip or knee arthroplasty, unnecessary coagulation tests (activated partial thromboplastin time and the international normalised ratio), electrolyte/renal panel tests and electrocardiograms were reduced by 81% (91%-17%), 81% (41%-7%) and 68% (35%-11%), respectively. No surgery was delayed or cancelled due to tests not performed before surgery. CONCLUSIONS: A standardised preoperative investigational approach based on patients' medical conditions rather than routine testing can reduce unnecessary tests before surgery. Further, implementing guidelines is more complex than developing guidelines. Hence, continuous PDSA cycles are essential to evaluate the processes in a quality improvement project. It can take time to build teams and have shared goals; however, once this is achieved, the success of a quality improvement project is certain.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.156
GPT teacher head0.505
Teacher spread0.349 · 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 designBench or experimental
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

Citations8
Published2021
Admission routes1
Has abstractyes

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