Income Verification On The Exchanges: The Broader Policy Picture
Notice bibliographique
Résumé
The Affordable Care Act scandal de jour (or at least one of them) is the difficulty the exchanges have faced in verifying the eligibility of many premium tax credit applicants. Two Department of Health and Human Services Office of Inspector General Reports in early July documented the existence of these problems. One reported that as of the first quarter of 2014, the federal exchange alone had been unable to resolve 2.6 or 2.9 million data inconsistencies. Another reported that internal controls at the federal and two state exchanges were not fully effective in ensuring that individuals enrolled in exchanges were in fact eligible. House Republicans claim that in fact there are 4 million data inconsistencies affecting half of all enrollments. In House Energy and Commerce hearings on June 10, 2014, Republican Representative Charles Bustany Jr. claimed that $44 billion in improper payments would be made over the next 10 years. Douglas Holtz-Eakin, a former Bush Administration official, who testified at the hearings claims that improper payments may equal $152 billion. The House Energy and Commerce Health Subcommittee is holding further hearings on data inconsistencies on July 16. The seriousness of verification issues should not be overestimated. The administration has been put in place procedures to verify carefully premium tax credit applications. Many of the discrepancies CMS is attempting to resolve do not relate to income eligibility, and those that do may result ultimately in a finding of eligibility for increased, rather than decreased, premium tax credits. A discrepancy that could result in the need for additional documentation may be as trivial as a hyphen left out of a name or a digit transposed on a Social Security number. Unfortunately, programs proposed by Republicans and other ACA opponents that in fact make a serious attempt to cover the uninsured will require income reporting and face similar difficulties. Current reform proposals that avoid coverage eligibility determinations will not in fact cover the uninsured. While the administration could have perhaps done a better job in making eligibility determinations, any means-tested program faces a similar challenge. It is possible to design a system that does not rely on means testing and could cover low-income and high-cost uninsured Americans, as I describe below. But it would be a very different system than the ACA or alternatives currently being proposed.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,011 |
| Communication savante | 0,027 | 0,037 |
| Science ouverte | 0,005 | 0,010 |
| Intégrité de la recherche | 0,045 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».