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Enregistrement W1992795506 · doi:10.1001/jama.2009.1821

Eliminating “Waste” in Health Care

2009· article· en· W1992795506 sur OpenAlexaboutno aff
Victor R. Fuchs

Notice bibliographique

RevueJAMA · 2009
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealthcare cost, quality, practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineHealth careWaste managementIntensive care medicineMedical emergencyEconomic growth

Résumé

récupéré en direct d'OpenAlex

PRESIDENT OBAMA IS THE MOST RECENT IN A LONG LINE of US presidents to seek reductions in health care spending through elimination of “waste.” However, the stakes this time are unusually high—the president has reported that eliminating waste is needed to fund two-thirds of the approximately $900 billion needed (over 10 years) for expanded health care coverage. To achieve this goal requires defining waste, identifying contexts in which it occurs, determining why it occurs, and implementing policies that prevent reoccurrence. Defining waste in medical care is not simple. Consider, for example, a patient who has experienced frequent, intermittent headaches for several weeks. Her physician thinks it is unlikely that the headaches are caused by a brain tumor or lesion (less than 1 chance in 10). A magnetic resonance imaging scan would provide more definite information. If the physician orders the scan, is that waste? What if the chances were 1 in 100 or 1 in 1000? What if the patient is so anxious about the headaches that she has difficulty with daily functions? Should that affect the definition of waste? As another example, consider 10 members of a college football team who are found to have a disease that has 2 possible interventions. Bed rest, fluids, and over-the-counter medications for relief of symptoms would result in recovery of all 10 patients in about 2 weeks; administration of a new, expensive drug would likely cure 7 patients within 2 or 3 days, send 1 patient to the hospital, and have no effect on the others. Is it wasteful to give the drug—or not to give it? These examples lead to considering 2 possible definitions of waste in medical care. Medical waste is defined as any intervention that has no possible benefit for the patient or in which the potential risk to the patient is greater than potential benefit. Economic waste is defined as any intervention for which the value of expected benefit is less than expected costs. The proportion of care deemed wasteful using the medical definition is much smaller than that deemed wasteful using the economic definition. Medical waste could occur only if the physician is misinformed, if the patient is misinformed and the physician succumbs to patient demands, or if the physician behaves unethically. Economic waste is much more common because of third-party payment. A conscientious clinician treating an insured patient would tend to recommend any intervention with a potential benefit greater than the potential risk. Two ubiquitous aspects of medical care make identification of waste particularly problematic. First, there is little certainty in medicine. Implicitly, if not explicitly, physicians are usually dealing with probabilities. Many interventions appear to have been wasteful in retrospect, but that is not the correct criterion; only prospective probability of success is relevant. The oft-heard promise “we will find out what works and what does not” scarcely does justice to the complexity of medical practice. Some interventions are undoubtedly useless, but those that might help some patients are much more common. Second, patients differ in unpredictable ways. The same drug given to patients with the same diagnosis often has different effects, ranging from rapid cure to serious adverse reaction. Any effort to reduce costs on a large scale requires consideration of economic waste. Where in medical practice is economic waste likely to be found? Almost everywhere. Some patients do not receive sufficient screening because of lack of insurance, inertia, or fear, but for the US population as a whole, the error is probably on the side of excess screening. On a per capita basis, patients in the United States receive almost 3 times as many magnetic resonance imaging scans as those in Canada. Are the benefits of extra scans enough to justify the extra cost? Repeated testing is another area with high potential for economic waste. There is usually little scientific foundation for the appropriate interval between tests and even less economic analysis of benefits and costs of alternative intervals. For a variety of reasons, including pressure from patients, physicians prescribe brand-name drugs when generic medications would be as effective or no drug at all would be best. An analogous situation may be the choice between a high-cost device or procedure and a less expensive alternative. For example, high-cost drug-eluting stents may be the better choice for some patients, but others would do just as well with less expensive bare-metal stents. Some patients are hospitalized for what might be wasteful reasons. For example, the patient’s insurance coverage might be better in hospital, compensation to the physician

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)0,004
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,813
Score d'incertitude au seuil0,702

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
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 Opus0,480
Tête enseignante GPT0,572
Écart entre enseignants0,092 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
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

Citations31
Publié2009
Routes d'admission1
Résumé présentoui

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