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Record W2067975277 · doi:10.1017/s1047951107001230

Improving safety for children with cardiac disease

2007· review· en· W2067975277 on OpenAlexaff
Ravi R. Thiagarajan, Geoffrey L. Bird, Karen Harrington, John R. Charpie, Richard C. Ohye, James M. Steven, Michael L. Epstein, Peter C. Laussen

Bibliographic record

VenueCardiology in the Young · 2007
Typereview
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicinePatient safetyHarmHuman errorQuality (philosophy)TerminologySet (abstract data type)Health careAction (physics)Medical emergencyRisk analysis (engineering)Computer science

Abstract

fetched live from OpenAlex

The complexity of the modern systems providing health care presents a unique challenge in delivering care of the required quality in a safe environment. Issues of safety have been thrust into the limelight because of adverse events highly publicized in the general media. In the United States of America, improving the safety and quality in health care has been set forth as a priority for improvements in the 21st century in the report from the Institute of Medicine. Many measures have now been initiated for improving the safety of patients at hospital, regional, and national level, and through initiatives sponsored by governments and private organizations. In this review, we summarize known concepts and current issues on the safety of patients, and their applicability to children with congenital cardiac disease. Prior to examining the issues of medical error and safety, it is important to define the terminology. An error is defined as the failure of a planned action to be completed as intended, also known as an execution error, or the use of a wrong plan to achieve an aim, this representing a planning error. An active error is an error that occurs at the level of the frontline operator, and the effects of which are felt immediately. A latent error is an error in the design, organization, training and maintenance, that leads to operator errors, and the effects of which are typically dormant in the system for lengthy periods of time. Latent errors may cause harm given the right circumstances and environment. An adverse event is defined as an injury resulting from medical intervention. A preventable adverse event is an adverse event that occurs due to medical error. Negligent adverse events are a subset of preventable adverse events where the care provided did not meet the standard of care expected of that practitioner. The study of improving the delivery of safe care for our patients is a rapidly growing field. Important components for development of programmes to improve the safety of patients include the leadership for the programme, the implementation of process design based on human limitations, the promotion of teamwork and function, the anticipation of unexpected events, and the creation of a learning environment. Much is yet to be learned about the risk and incidence of adverse events during hospitalization of children with congenital cardiac disease. Errors due to human factors, such as poor communication, poor coordination, and suboptimal team work, have shown to be important causes of adverse outcomes in children undergoing cardiac surgery, and should be a focus for improvement. Future research on evaluating causes and prevention of medical errors and adverse events in this population at high risk, and consuming high resources, is essential. Issues of inadequate safeguards for patients have been prominent in the media, and have been highlighted in reports from the Institute of Medicine. Our review discusses research on the causes of medical error, and proposes concepts to design successful programmes to improve safety for the patients on a local level.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.101
GPT teacher head0.447
Teacher spread0.345 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2007
Admission routes1
Has abstractyes

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