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Enregistrement W2736005035 · doi:10.1097/lbr.0000000000000415

Superspecialization and Health Care Cost

2017· editorial· en· W2736005035 sur OpenAlexaboutno aff
Manuel L. Ribeiro Neto, Atul C. Mehta

Notice bibliographique

RevueJournal of Bronchology & Interventional Pulmonology · 2017
Typeeditorial
Langueen
DomaineMedicine
ThématiqueClinical practice guidelines implementation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineHealth careFamily medicinePublic relationsEconomic growthPolitical scienceEconomics

Résumé

récupéré en direct d'OpenAlex

Who strive—you don’t know how the others strive To paint a little thing like that you smeared Carelessly passing with your robes afloat,- Yet do much less, so much less, Someone says, (I know his name, no matter)—so much less! Well, less is more, Lucrezia … (poem “Andrea del Sarto”, by Robert Browning, 1855). Health care cost in the United States is the highest in the world. In addition, it has been increasing linearly in the past 2 decades. A recent study estimated that health care expenditure on treatment of chronic respiratory diseases alone is increasing by 3.7% per year, reaching approximately 130 billion dollars in 2013.1 It is imperative that we attempt to identify the causes of this high-cost care, to be able to bend the curve of health care expenditure.2 Whether medical specialization, subspecialization, and superspecialization such as Interventional Pulmonology is one of the causes of this high-cost care is an interesting and unsettled debate. Is specialization a part of the problem or a part of the solution? There are good arguments on both sides. Training in specialization is more expensive and specialists may use expensive technology more commonly than do generalists. In addition, the ratio of specialists to primary care physicians is higher in the United States compared with that in other countries. These factors may contribute to the high cost of health care in the country.2,3 Challenging this belief, however, studies have shown that specialized care has reduced cost in many medical areas.4–6 We believe that specialization could and should be a part of the solution. Health care is experiencing a paradigm shift from volume-based care to value-based care. Value-based care entails the achievement of best outcomes at lowest cost. One essential component of the shift toward value-based care is the organization of patient care around specific medical conditions. In this model of care, physicians are experts in their field, familiar with the best available data to diagnose, treat, and prognosticate patients with those specific conditions. This scenario allows specialized physicians to develop and apply cost-reducing strategies while achieving the best outcomes for a specific patient population.7 Some examples from the field of bronchoscopy illustrate how specialized care can increase the value of the product—that is, ensuring the best outcomes while reducing cost for the patients. In patients with stage I pulmonary sarcoidosis, it is common to perform endobronchial ultrasound (EBUS) with transbronchial needle aspiration (TBNA) to confirm the presence of noncaseating granulomas. This has been well demonstrated by many studies in the past decade; in these studies a significant number of patients with stage I pulmonary sarcoidosis were included and they underwent EBUS-TBNA.8–10 However, a landmark study from Winterbauer et al11 from 1973 showed that patients with bilateral symmetric hilar adenopathy with uveitis, erythema nodosum, or no symptoms can be safely diagnosed with sarcoidosis without histologic proof. Thus, clinical acumen still remains the most reliable technique for making a diagnosis of stage I sarcoidosis. Physicians specialized in sarcoidosis can potentially gain enough experience to reach the same outcome—that is, diagnosis of sarcoidosis at lowest cost. We believe that with training and enough enthusiasm anyone can perform a procedure; yet, the “best interventionalist is the one who knows when not to perform an intervention.” Subspecialization can play a major role in this respect to reduce the health care cost while preserving patient welfare. The use of bronchoscopy to diagnose ventilator-associated pneumonia is probably a less-disputed scenario, but it is still a good example of specialization contributing to value-based care. A randomized controlled trial from the Canadian Critical Care Trials Group elegantly showed that we can achieve the same outcomes (eg, 28-day mortality) doing less (endotracheal aspiration instead of bronchoscopy with bronchoalveolar lavage).12 Critical care specialists familiar with this topic may be more prone to follow this less-invasive approach. Finally, we cite one more example from the lung cancer literature. In patients with suspected non–small-cell lung cancer, a staging strategy combining endosonography (EBUS and endoscopic ultrasound) and surgical stating, when compared with surgical staging alone, improved outcomes (ie, higher diagnostic accuracy) with fewer thoracotomies.13 Bronchoscopists with greater experience in EBUS should be able to perform better staging compared with less-experienced bronchoscopists, and consequently contribute to this value-based care.14 In other words, “Medical Mediastinoscopy” should preferably be performed at the centers of excellence to achieve the most cost-effective outcomes. There is no dearth of such examples in the literature in the diverse fields of superspecialization. The debate around specialization and health care cost will probably continue for a long time. In the meantime, specialized physicians should simply do their part. The responsibility of reducing heath care costs rests on the shoulders of superspecialists. This could be achieved by striking a balance between applying their clinical acumen and relying on technology. Authors strongly believe that, case by case, interventional pulmonologists should try to achieve the best outcomes for their patients with the least amount of interventions at the lowest possible cost—because it has been known for a long time that, sometimes, less is more. Manuel L. Ribeiro Neto, MD Atul C. Mehta, MD■ ■ ■ ■

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,161
Tête enseignante GPT0,529
Écart entre enseignants0,368 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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