Notice bibliographique
Résumé
[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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
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.
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 tête enseignante, 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 ».