Notice bibliographique
Résumé
Imagine the impact of a paper entitled “Operative mortality increased by 3%.” Or perhaps a newspaper headline, “Woman in labor waits in hospital hallway for 6 hours.” In this month's Academic Emergency Medicine (AEM), we have an article that once again demonstrates serious consequences of boarding inpatients in our emergency departments (EDs) while they wait for hospital beds.1 Unfortunately for patients throughout the United States and some parts of Canada, this article will not have the impact it should. It will most likely become just another crowding paper. As a decision editor for AEM, my job is to select the best science on the best topics for our readers. As many of you who are authors realize, we take only a few of the many papers that are submitted. So my first reaction when receiving this paper was to reject it, not because the science was bad, but because it was yet another crowding paper. It, like all the others, shows that crowding is bad, very bad for our patients. So why publish another crowding paper? A year or so ago a group of us met with some federal policy-makers in Washington, DC, to discuss crowding and its consequences. The policy wonks were perplexed, as they thought the crowding issue had been solved. They had not seen it in the recent literature for more than 6 months, so it must be fixed. Lesson learned: we have to keep our issues in the forefront. Many emergency physicians who work in large crowded EDs have resigned themselves to boarding. There will always be boarding and crowding; they have given up hope that a solution will be found. It is easy to understand this, but such feelings lead to the normalization of deviance.2 In the space shuttle program there was an unsolvable problem with the heat shields. Over time workers accepted the heat shield failure as a natural part of business and no longer looked for a solution. The failure of the heat shield caused the shuttle disaster in the 1980s and cost the lives of many talented scientists. We cannot allow ourselves to consider boarding and crowding as normal. It will cost the lives of our patients. In 2002, an elderly woman spent 72 hours in the hallway of an ED in England. Her story became front page news and the public outcry led to the 4-hour target where 90%+ patients must leave the ED within 4 hours. While this rule led to some gaming of the data, most hospitals met the target to avoid financial penalties.3 The 4-hour target has been slackened somewhat, but there still is a priority to move patients swiftly through the ED and onto inpatient floors. Recently Australia has enacted its own brand of time targets.4 Even Canada has begun to implement time targets, with each province taking a slightly different approach. Of the four major countries that first developed emergency medicine as a specialty, only the United States has not dealt with boarding and crowding in its EDs. Hospitals now publicly report the ED length of stay for admitted patients. In the most recent reporting cycle, nearly 25% of hospitals did not report this measurement, according to HospitalCompare.gov. Of the hospitals that did report, nearly 60% reported median times of greater than 4 hours (recall the target in England is 90%+ patients leave within 4 hours) and 14% reported median times greater than 6 hours! Included in the group of hospitals that perform the worst are many hospitals listed in ratings of our “best hospitals.”5 So what will it take to end the practice of boarding inpatients in the ED? Clearly it will not be an elderly woman in the hall for 3 days. What will it take to move our patients into care areas that are appropriate for their disease? When we will stop having specialty care floors (oncology, cardiology, etc.) where patients are congregated to get the best care, but overflow patients are kept in a crowded generic ED? How much proof do we need to prove this practice is the most important safety issue facing our patients? The answers are not clear. What is clear is that we must continue to publish “another crowding paper.” We cannot become immune to boarders in our EDs, to see them as inevitable. Our research should be targeted to get the attention of the public and of special interest groups such as AARP. Our research should be targeted and distributed to policy-makers in Washington, DC, and our state health departments. We need to work in concert with these policy groups to devise ways to remove boarders from our EDs. The solutions are complex, tied to hospital reimbursement and operations. But unless hospitals see this as a problem, they will not go looking for a solution. In many cases they have already normalized this deviance. My hope is that someday future emergency physicians will look at the large body of work devoted to crowding and consider it as foreign as phrenology or urine therapy. Tales of inpatients waiting for hours or days will be akin to doctors using their bare hands to perform thoracotomies. Or, if we do not continue to pursue this research, they may wonder why we gave up.
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 | 0,163 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,023 | 0,017 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,014 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,094 | 0,061 |
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 ».