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Record W2029294889 · doi:10.1097/pts.0000000000000139

Development of an Electronic Pediatric All-Cause Harm Measurement Tool Using a Modified Delphi Method

2014· article· en· W2029294889 on OpenAlex
David C. Stockwell, Hema Bisarya, David C. Classen, Eric S. Kirkendall, Peter Lachman, Anne Matlow, Eric Tham, Dan Hyman, Elizabeth Searles, Stephen E. Muething, Paul J. Sharek

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Patient Safety · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHarmDelphi methodVettingPatient safetyProcess (computing)DelphiMedicineComputer scienceRisk analysis (engineering)PsychologyComputer securityArtificial intelligenceHealth carePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: To have impact on reducing harm in pediatric inpatients, an efficient and reliable process for harm detection is needed. This work describes the first step toward the development of a pediatric all-cause harm measurement tool by recognized experts in the field. METHODS: An international group of leaders in pediatric patient safety and informatics were charged with developing a comprehensive pediatric inpatient all-cause harm measurement tool using a modified Delphi technique. The process was conducted in 5 distinct steps: (1) literature review of triggers (elements from a medical record that assist in identifying patient harm) for inclusion; (2) translation of triggers to likely associated harm, improving the ability for expert prioritization; (3) 2 applications of a modified Delphi selection approach with consensus criteria using severity and frequency of harm as well as detectability of the associated trigger as criteria to rate each trigger and associated harm; (4) developing specific trigger logic and relevant values when applicable; and (5) final vetting of the entire trigger list for pilot testing. RESULTS: Literature and expert panel review identified 108 triggers and associated harms suitable for consideration (steps 1 and 2). This list was pared to 64 triggers and their associated harms after the first of the 2 independent expert reviews. The second independent expert review led to further refinement of the trigger package, resulting in 46 items for inclusion (step 3). Adding in specific trigger logic expanded the list. Final review and voting resulted in a list of 51 triggers (steps 4 and 5). CONCLUSIONS: Application of a modified Delphi method on an expert-constructed list of 108 triggers, focusing on severity and frequency of harms as well as detectability of triggers in an electronic medical record, resulted in a final list of 51 pediatric triggers. Pilot testing this list of pediatric triggers to identify all-cause harm for pediatric inpatients is the next step to establish the appropriateness of each trigger for inclusion in a global pediatric safety measurement tool.

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.

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)0.006
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 integrity0.0000.001
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 head0.176
GPT teacher head0.438
Teacher spread0.262 · 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