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Record W3033645844 · doi:10.29173/cjen54

Implementing a multi-site cardiac arrest quality improvement initiative

2020· article· en· W3033645844 on OpenAlexvenueaboutno aff
Christopher Picard, Domhnall O’Dochartaigh, Richard H. Drew, Warren Ma, Matthew J. Douma, Candice Keddie

Bibliographic record

VenueCanadian Journal of Emergency Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Quality managementBusinessMedicineEngineeringOperations managementPhysics

Abstract

fetched live from OpenAlex

Background: Medical cardiac arrest care in Edmonton Zone Emergency Departments does not undergo structured quality monitoring or continuous improvement. Prior to this work, quality indicators had not been selected, nor had tracking or reporting activities been undertaken. This work brings the Edmonton Zone EDs to the forefront of the continuous quality improvement recommendations made by Heart and Stroke Canada and the American Heart Association that are believed to improve both patient outcomes and overall system performance. For this project quality indicator development and implementation takes three perspectives: patients and families, frontline staff and the health care system. This work is informed by the Institute for Healthcare Improvement, the National Institute of Science and the International Liaison Committee on Resuscitation’s work on Systems of Care and Continuous Quality Improvement for Emergency Cardiovascular Care. This work is motivated by the desire to improve patient/family experience and outcome, provider experience while improving system performance. Implementation: An iterative process identified the lowest resource/highest impact areas for improvement. This process was informed through a Delphi survey conducted by the Alberta Cardiac Arrest Stakeholders group and stakeholder engagement. Four areas for improvement were identified: support of patients and families, support of staff, improvement in care metrics, and system level interventions. Support of patients and families was accomplished through the development of an advisory network, by linking families with existing supports, and through the implementation of a bereavement package. Supporting staff was accomplished through the development of a formal and informal debriefing processes. Improving clinical care was accomplished through the integration of chest compression feedback devices into clinical care. Improvements at the system level will be accomplished through the creation of a cardiac arrest registry. Evaluation Methods: Mixed methods approaches are used to evaluate this project. Post cardiac arrest quality track forms are being filled out. Chest compression feedback device data was obtained through simulated patient-care scenarios, staff experiences were obtained through a structured survey. Clinically chest compression data was collected from the feedback devices by Clinical educators, through tracking forms, and pre-and-post surveys of frontline staff measuring burnout and occupational stress are underway. Data is being collected in a local registry to generate accurate incidence and survival rates. Eventual post-implementation interviews with providers, survivors and families will be conducted. Results: A patient/family advisor network has been established. Survivor and families can be connected with the Bystander Support Network and the Heart and Stroke Foundation portal through the bereavement packages being offered at one of the QI sites. Two sites have developed staff debriefing processes: an interdisciplinary Critical Incident Stress Management (CISM) team at one site, and referral to an existing CISM team at two other sites. Chest compression feedback is being used at two sites, staff feedback has been positive. One site is tracking resuscitation metrics which are being used to guide and evaluate the interventions: continued improvement in chest compression quality has been noted. Data analytics are being used at all sites to identify additional opportunities to improve resuscitation care and efforts are underway to expand data collection to other sites and to unify pre-hospital and in-hospital cardiac arrest data. Advice and Lessons Learned: Pre-intervention data would have allowed for more meaningful comparisons in patient care. Efforts should be put into identifying what these measures could be. High levels of staff engagement at one site appear to have influenced the uptake of chest compression feedback. Effort should identify key stakeholders and gain buy in to increase uptake There are significant barriers to unifying pre-hospital and in-hospital cardiac arrest data. It is our belief that a continuous record offers some greatest opportunity to collect data on resuscitation care. Efforts should focus on building a linkage between these data sources and creating a shared data set.

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)0.056
metaresearch head score (Gemma)0.028
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 score0.056
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.002
Scholarly communication0.0060.003
Open science0.0040.017
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.362
Teacher spread0.289 · 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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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