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Enregistrement W1966598618 · doi:10.2345/i0899-8205-40-6-418.1

Who Drank My Wine?

2006· letter· en· W1966598618 sur OpenAlexaboutno aff
Binseng Wang

Notice bibliographique

RevueBiomedical Instrumentation & Technology · 2006
Typeletter
Langueen
DomaineHealth Professions
ThématiqueQuality and Safety in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBenchmarkingSurpriseBenchmark (surveying)Action (physics)Work (physics)MarketingPsychologyBusinessMedical educationPublic relationsMedicineEngineeringPolitical scienceSocial psychology

Résumé

récupéré en direct d'OpenAlex

I was, at the same time, happy and sad reading the two Final Word editorials in the July/August issue of BI&T. I was happy because it has been almost a decade since the subject of benchmarking has been discussed among clinical engineering (CE) professionals. I was sad, however, to see that suspicions and skepticisms, as well as misunderstandings, are still so prevalent among our colleagues.In spite of agreeing with the positive aspects that Ken Maddock pointed out, I am afraid that I have to disagree with his choice of title (“Glass is Half Full”). While we have been arguing with each other over benchmarking minutia (what is the right definition of inventory, how to count work orders, which expenses should be included or excluded, etc.), hospital administrators and chief financial officers have long since gone ahead to benchmark CE using whatever measures they—not us—believe are correct. Today, almost every hospital participates in one of the benchmarking projects offered by a number of “performance solutions” consulting companies and each one of these companies has created some type of benchmark for CE services. In other words, our “wine” has already been consumed by others while we are busy fighting each other!For example, I recently reported at the 29th Canadian Medical and Biological Engineering Conference held in Vancouver, BC, a preliminary analysis of CE “measures” collected by Solucient LLC, one of the leading performance improvement companies. To my surprise, over 170 hospitals (among the over 850 subscribers to Solucient's Action O-I® database) have been reporting CE- and technology-related data for numerous years. More surprising even is the fact that in spite of all the well-known and discussed concerns and disagreements among CE professionals on how to measure each indicator, a fairly clear picture of what is happening with medical technology adoption and management has emerged.For instance, most hospitals have about 13 pieces of capital equipment per staffed (not licensed) bed and invest about $3,000 for each patient discharge, regardless of hospital size or teaching nature. Typically, a hospital spends per year about 4% of its total capital equipment investment to maintain it. The annual total CE budget (including labor, parts, service contracts, etc.) is generally less than 1% of the hospital's annual total operating budget (thus our “invisibility” to the C-suite). Furthermore, the amount of CE FTEs hovers around 2.6 per 100 staffed beds or each CE FTE covers about 520 pieces of capital equipment. Obviously, these are “averages” and not benchmark goals for others to aspire to, but they seem to provide useful insights and “rules of thumb” for healthcare administrators and CFOs.Perhaps more alien yet to the CE community is the direction healthcare benchmarking has taken. Instead of using “capacity” metrics (e.g., beds or pieces of equipment), hospitals are adopting “operational” and “output” metrics such as patient days and discharges as the denominators. This trend reflects the realization that hospitals are production sites that should be measured by their operations and output rather than capacity, as well as the fact that most reimbursements nowadays are for cases (diagnostic related group—DRG) or amount of patient covered (“capitation”) rather than equipment used or procedures performed.While I am not sure suspicion and skepticism are the fundamental reasons for our lack of consensus (e.g., while scientists are required to have similar professional traits, they seem to agree on most fundamental laws of nature), I think Boyd Hutchins is right in pointing out that CE professionals are likely to remain for sometime in the dungeons fighting themselves and invisible dragons before realizing that the sun is shining above and there are numerous other challenges and opportunities waiting for us above ground (e.g., licensing the profession, assuming broader roles, or simply going fishing).

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut 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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,129

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,024
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,003
Communication savante0,0110,010
Science ouverte0,0010,003
Intégrité de la recherche0,0050,015
Charge utile insuffisante (le modèle a refusé de juger)0,0390,026

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 Opus0,066
Tête enseignante GPT0,427
Écart entre enseignants0,360 · 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 source (Gemma direct ou Codex distillé), 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
GenreCommentaire

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

En bref

Citations3
Publié2006
Routes d'admission1
Résumé présentoui

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