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Enregistrement W305602965

Some Issues in US Healthcare

2008· article· en· W305602965 sur OpenAlexaboutno aff
Ajay Aggarwal

Notice bibliographique

RevueInternational management review · 2008
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth careInefficiencyHealthcare systemGovernment (linguistics)BusinessEconomicsEconomic growthMarket economy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

[Abstract] The paper highlights several problems with the current US healthcare system. The monies invested in the various healthcare areas are examined and compared with other western nations. Suggestions for controlling expenditures and bridging the care gap are made along with some implications for managers. [Keywords] Healthcare; Healthcare system; expenditure; USA Introduction Something needs to be done with the US Healthcare system. It costs too much, gives back too little, and leaves out millions without coverage. It's ironic when expenditure growth of 8.5% over a 6-month period is applauded, despite it being three times faster than the economic growth (Wechsler, 2004). According to Francis (2003), the US spent 14.1% of GDP on healthcare in 2002, and it is projected to reach 17.7% of GDP by 2012. In sharp contrast, the 28 members of the Paris-based Organization for Economic Cooperation and Development spend an average of 8%. Even Canada, spends just 9.1% of its GDP in its government-financed healthcare program. While it is true that Canadians do have to wait in line for some procedures, it can hardly justify the cost-differential between Canadian and US healthcare programs. Perhaps Herzlinger (2000) said it best when he lamented that widespread inefficiency and inconvenience characterize the current US healthcare system because it has failed to heed the lessons of knowing its customers and focusing on their needs. Problems with the Current System There have been urgent calls from big business to fix the ills of the US healthcare system. Unlike the past, where businesses routinely covered the healthcare premiums of all employees, and often their entire families, the skyrocketing premiums are causing them to shirk away from even the most basic employee coverage. The premium increased, on average, by 87% during the 2000-2006 periods, compared to an inflation adjustment of 18% for the same period. Coming as no surprise, the percentage of employees receiving employer health insurance dropped to 59% from 65% in 2001. Several experts suggest a comprehensive solution that shares the healthcare burden between the individual, government, and businesses. Wal-Mart, AT&T, and INTEL, among others, have made efforts to get their pleas noticed. Commenting on the state of affairs, Wal-Mart CEO Scott attempted to sound the alarm by commenting, Our current system hurts America's competitiveness and leaves too many people uninsured. Similar words have been sounding off across the US landscape. The Business Roundtable in Washington, the Service Employees International Union, the Heritage Foundation, and the US Chamber of Commerce, among others, are all demanding change (Trumbull, 2007, Feb 13). Despite constituting over 1/6 of the economy (evidenced by GNP), the Information Technology (IT) investments in the healthcare industry are dismal compared to the other sectors (financial). The technological reforms suggested by the Health Insurance Portability and Accountability Act (HIPAA) legislation have not been whole-heartedly embraced by the industry currently (Reisman, 2003). A survey dealing with HIPAA compliance in areas of security, transaction and code sets, and privacy requirements, conducted during the winter of 2005, reported several dismal results (US Healthcare, 2005/ For instance, only 30% of payers (up from 13% in June 2004) and only 18% of providers indicated that they were compliant with the HIPAA security regulations. While the HIPAA transaction and code set compliance numbers improved to include 73% of providers and 70% of payers indicated compliance (up from 65% and 62% respectively), they are still far from their intended goal of total compliance. In the most serious area of HIPAA privacy, only 78% of providers and 90% payers indicated that they are compliant with the privacy rule, almost two years after the deadline (April 2003). …

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)0,008
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,080

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,017
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0070,011
Communication savante0,0100,011
Science ouverte0,0020,004
Intégrité de la recherche0,0120,013
Charge utile insuffisante (le modèle a refusé de juger)0,0140,002

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 Opus0,093
Tête enseignante GPT0,341
Écart entre enseignants0,247 · 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'étudeThéorique ou conceptuel
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
Publié2008
Routes d'admission1
Résumé présentoui

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