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Enregistrement W397126405 · doi:10.1007/s11999-015-4295-9

CORR Insights®: Time-driven Activity-based Costing More Accurately Reflects Costs in Arthroplasty Surgery

2015· letter· en· W397126405 sur OpenAlexaff
Peter Cram

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2015
Typeletter
Langueen
DomaineHealth Professions
ThématiqueHealthcare Operations and Scheduling Optimization
Établissements canadiensToronto General Hospital
Organismes subventionnairesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on Aging
Mots-clésMedicineActivity-based costingAccounting methodTicketCost accountingCost estimateOperations managementAccountingComputer scienceEconomics

Résumé

récupéré en direct d'OpenAlex

Where Are We Now? Recent studies [7, 9] have conclusively demonstrated that hospital administrators and physicians in the United States often are flummoxed by a relatively simple question: “How much will my [insert name of procedure] cost?” Any reasonable person should be outraged, perplexed, and infuriated. Call your local Wal-Mart and ask them for the cost of any item. You will get answer within seconds. Go online and you can quickly find the cost of a roll of toilet paper, a new car, or a round-trip bus ticket to Peoria, IL, USA. Yet most of us are unable to provide our own patients with reasonable cost estimates for the tests, procedures, and products we prescribe every day. Akhavan et al. [1] used a rigorous accounting method (Time-driven Activity Based Costing [TDABC]) to examine the cost of TKA and THA procedures. The authors then compared the costs ascertained using TDABC with the traditional accounting method used by virtually all US hospitals. Not surprisingly, the authors found that methods matter. Specifically, the authors found that TDABC methods yielded “cost” estimates for TKA and THA that were approximately 45% lower than traditional accounting methods (USD 10,000 per case). These results have a number of important implications. First, the results provide an explanation for recent research studies that indicated hospitals were unable to provide credible estimates of prices for many of the most routine services that they provide [2, 12]. If hospitals’ internal accounting systems are fundamentally flawed and inaccurate as Akhavan and colleagues suggest, it is no wonder that hospitals are unable to provide accurate pricing data to consumers [5]. We cannot excuse our healthcare system for dysfunctional accounting systems and its inability to know costs and provide prices, but this does at least provide an explanation for the problem at hand. Second, the results have implications for hospital leadership when setting priorities and mapping strategy. Virtually all hospitals use their internal traditional accounting cost estimates to determine which services are “profitable” and which are not. Services that are profitable are often seen as priorities for investment and expansion; unprofitable services are carefully considered with a focus on how these services fit with each institutions priorities, mission, and local community needs. If estimates of service-line profitability are incorrect—as the current analysis suggests—hospitals may be making strategic blunders. Where Do We Need To Go? While research in the area of hospital accounting and cost-to-charge ratios are somewhat limited, the available data are concerning [8, 10]. Evidence suggests that cost-to-charge ratios often vary between hospitals because of differences in internal accounting practices rather than true differences in either the complexity of patients or the resources “consumed” [3, 4, 6]. The current system leads to numerous problems including: (1) Inaccurate data to guide internal hospital decisions on which services to provide and invest in; and (2) inaccurate prices when customers including individual patients and insurance providers request information. At the most foundational levels, cost-to-charge ratios are based upon faulty assumptions and odd mathematics. From the hospital perspective, the cost of providing a specific procedure—TKA, for example—would be the cost of all inputs required to perform this procedure; inputs would include consumable materials (implants, gloves, medications administered), labor (physician time, nursing time, janitor time), and indirect costs (space, heat, electricity) [11]. One option for accurately capturing costs appears to be TDABC, but there are certainly other options including relative-value-unit based accounting measures [11]. Either way, it is time to move beyond cost-to-charge ratios. As healthcare reform proceeds with increasing amounts of financial risk being transferred onto healthcare systems, it will be vital for to have accurate data. How Do We Get There? During the past 20 years, hospitals have invested heavily in information systems that allow us to measure and track many aspects of performance including mortality rates, length-of-stay, and adverse events. We now need to do the same with respect to costs. Hospital administration and physicians need to go back to school. We will need research into and implementation of new methods for how to measure costs. We will need to compare assorted costing measures as Akhavan et al. have done in this paper. We will then need to invest in systems that allow us to measure and track costs for assorted procedures and services. These data will allow delivery systems to make better decisions about which services merit expansion, which services should be avoided, and where improvements in efficiency are needed. Finally, we will need to learn how to disseminate cost information to physicians and other front-line personnel. It will be a long journey best started today.

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)0,005
score de la tête « metaresearch » (Gemma)0,095
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,300

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,095
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,005
Études des sciences et des technologies0,0000,000
Communication savante0,0040,003
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0900,017

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 Opus0,422
Tête enseignante GPT0,566
Écart entre enseignants0,144 · 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'étudeObservationnel
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

Citations3
Publié2015
Routes d'admission1
Résumé présentoui

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