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Enregistrement W4411134776 · doi:10.1093/jalm/jfaf065

Healthcare Excellence is a Global Phenomenon

2025· article· en· W4411134776 sur OpenAlexaff
Melissa Ryan, Colleen Strain, Tricia Ravalico

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueQuality and Safety in Healthcare
Établissements canadiensAbbott (Canada)
Organismes subventionnairesnon disponible
Mots-clésPhenomenonExcellenceHealth careEngineering ethicsPolitical scienceEpistemologyEngineeringPhilosophyLaw

Résumé

récupéré en direct d'OpenAlex

Change can be uncomfortable, and yet, it is essential for growth. Healthcare excellence requires growth and the pursuit of more effective, safe, people-centered, equitable, timely, integrated, and efficient care. Healthcare teams with recognition through the UNIVANTS of Healthcare Excellence award program have consistently and intrinsically risen above these challenges, choosing “change” over “comfort,” for the betterment of healthcare overall. Winning teams associated with the 2025 UNIVANTS of Healthcare Excellence award program were recently announced (Fig. 1). The 9 newest teams span 7 countries (China, France, Iceland, Japan, Spain, Taiwan, and the United States), including 2 unique country additions to the previously recognized conglomeration of winner alumni. New country representation accounts for 29% of recognized sites in 2025. This trend is not unlike the past 3 years in which 30% to 33% of recognized sites involve health systems in countries new to UNIVANTS. Thus, UNIVANTS alumni now include over 422 innovators across 85 teams in 31 or more countries over 6 years. 2025 UNIVANTS of Healthcare Excellence award winners. Not unlike the name of the award implies, every winner alumnus associated with the UNIVANTS of Healthcare Excellence program has (a) unified across disciplines (i.e., embodied teamwork in demonstrative ways) and has (b) applied avant-garde thinking to traditional challenges, maximizing insights from laboratory medicine to achieve measurably better outcomes for patients, payors, clinicians, and health systems (1). The 2025 award-winning teams tackled common themes, including timely identification of previously unknown infectious disease (hepatitis C and syphilis), timely triage and enhanced efficiencies within emergency departments, and timely personalized approaches for enhanced clinical care pathways in patients at risk of and/or suffering from diverse disease (chronic kidney disease, hepatocellular carcinoma, and H. pylori). Another focus of one 2025 best practice was reduced anticoagulation variability in patients taking warfarin (2). As healthcare excellence is a global phenomenon, it has been rewarding to appreciate that the recognized best practices associated with the UNIVANTS of Healthcare Excellence program in 2025, and since its inception in 2018, have been geographically diverse. With that said, 3 countries (China, England, and the United States) have had sustained recognition through the UNIVANTS of Healthcare Excellence award program for at least 5 of the 6 years. Interestingly, these countries (and all countries associated with recognition through the UNIVANTS of Healthcare Excellence program) have only marginal overlap (35.5% to 41.9%) with the top 30 countries that are most commonly cited for healthcare excellence (3–5). This underscores the importance of continual improvement, and a leading premise associated with recognition from the UNIVANTS of Healthcare Excellence program, which is that submitted best practices must involve measurable improvements in the form of key performance indicators when compared to existing processes, and not necessarily a gold standard. All best practices with recognition through the UNIVANTS of Healthcare Excellence award program, however, must be implemented into clinical practice with a measurable benefit to patients, payors, clinicians, and health systems. The scoring rubric is multifactorial with at least 7 judge assessments per application spanning the following 7 organizations: International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), Association for Diagnostics & Laboratory Medicine (ADLM), European Health Management Association (EHMA), Modern Healthcare, Health Information and Management Systems Society (HIMSS), National Association of Healthcare Quality (NAHQ), and the Institute of Health Economics (IHE). While Abbott funds and founded this prestigious and highly coveted healthcare honor, it has no role in the scoring process (1). For more details about the 2025 winning teams and/or to learn more about the UNIVANTS of Healthcare Excellence awards, partners or more, visit www.UnivantsHCE.com. Author Contributions: The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Melissa Ryan (Data curation-Equal, Formal analysis-Equal), Colleen Strain (Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal), and Tricia Ravalico (Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal) Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Research Funding: None declared. Disclosures: C. Strain receives a salary from Abbott Diagnostics and holds relevant stocks, and serves as the Corporate Representative to the IFCC Communications and Publications Division. M. Ryan receives a salary from Abbott Diagnostics and holds relevant stocks. T. Ravalico receives a salary from Abbott Diagnostics and holds relevant stocks, serves on the ADLM Corporate Advisory Board, and is the Corporate Representative to the IFCC Executive Board and a member of the IFCC-TF Corporate Members.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,626
Score d'incertitude au seuil0,672

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,058
Tête enseignante GPT0,449
Écart entre enseignants0,391 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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