What’s in a Name? Comparative Analysis of Laboratory Test Naming Guidelines as Applied to Common Confusing Test Names
Notice bibliographique
Résumé
Abstract Laboratory test names frequently do not enable easy understandability or promote correct test utilization, which leads to difficulty for providers in finding the correct test and results in unnecessary cost and medical errors. As a further complication, laboratory test names are largely unstandardized and are not named based on a consistent set of conventions. To address these issues, the TRUU-Lab (Test Renaming for Understanding & Utilization) initiative aims to generate a consensus laboratory test naming guideline for better human understandability of laboratory test names. These studies address the first and second aims of the TRUU-Lab initiative: 1) to identify root causes and challenges in understanding and using laboratory test names, and 2) to share resources related to potential solutions. We initially conducted survey studies to capture the most commonly problematic laboratory test names, then performed analysis of these names to identify aspects of these names that led to confusion among providers. 274 survey responses yielded ~100 unique laboratory tests that respondents felt were confusing, and highlighted substantial diversity both in the names of these tests between institutions and in respondent opinion on the best alternative names, with the top 10 most commonly-cited tests having at least 3 unique names, and the top 2 tests (Vitamin D and anti-factor Xa) having at least 10 unique names. Post-survey analysis identified eight common characteristics associated with poor understandability of a test name, including ambiguity, abbreviations, homophones, multiple indications for a single test, non-descriptive proprietary names, synonyms, truncation due to software limitations, and €œpanels where test components are obfuscated. A subset of the survey-identified confusing test names were used to evaluate existing laboratory test naming guidelines for their ability to produce understandable test names. Five guidelines, including LOINC, ONC TigerTeam, Pan-Canadian iEHR Viewer Name, Standards for Pathology Informatics (Australia), and ARUP Laboratories internal style guides, were evaluated, and produced highly variable names given the same test name prompt. Further, existing guidelines also varied in their ability to avoid pitfalls previously identified as associated with poor understandability. Together, these studies highlight the aspects of existing laboratory test names that lead to confusion among ordering providers, and identify the inability of existing laboratory test naming practices to adequately address these issues. Efforts are ongoing within TRUU-Lab to use these results to inform novel laboratory test naming guidelines that promote universal human understandability. Work is also ongoing to apply these novel guidelines to generate new candidate test names, and conduct survey analysis to evaluate the effects of new test naming guidelines on understandability and correct test utilization.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».