Variations in Resource Intensity and Cost Among High Users of the Emergency Department
Notice bibliographique
Résumé
OBJECTIVES: High users of emergency department (ED) services are often identified by number of visits per year, with little exploration of the distribution/pattern of visits over time. The purpose of this study was to examine patient- and encounter-level factors and costs related to periods of short-term resource intensity among high users of the ED within a tertiary care teaching facility. METHODS: We identified all adults with at least three visits to the Ottawa Hospital ED within a 1-year period from April 1, 2012, to March 31, 2013. Within this high-user cohort, we then measured intensity of use by calculating average daily visit rates to identify individuals with a cluster of ED visits. Those with at least three ED visits/7 days at any point during follow-up were considered patients with clustered ED use (i.e., a period of short-term resource intensity). Detailed clinical and administrative data were used to compare patient- and encounter-level characteristics and cost profiles between the clustered and nonclustered groups. Analyses were repeated using varying cut points to define high users (at least five and at least eight visits per year). RESULTS: Of the 16,153 patients identified as high ED users during the study period, 13.5% had their visits clustered within a short period of time. These clustered users were more likely to be homeless, to require psychiatric services, and to leave without being seen by a physician and less likely to be admitted to the hospital. Approximately one in three (31.2%) high ED users with clustered visits returned for the same medical problem (namely pain-related disorders, shortness of breath, and cellulitis) within a 1-week period. Similar trends were observed when the high-user cohort was restricted to those with at least five and at least eight ED visits/year. Finally, patients with short-term intensity periods had lower direct and indirect costs per encounter than those without. CONCLUSIONS: Using a novel methodology that accounts for both number and intensity of ED encounters over time, we were able to identify specific subpopulations of high ED users. Further work is required to determine if this methodology has utility for targeting care pathways within this heterogeneous and high-risk patient group.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».