Clinical Engineering Benchmarking
Notice bibliographique
Résumé
In Brief Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities. Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,090 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,013 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».