Board certification and billing practices of international medical graduate hematologists and oncologists.
Notice bibliographique
Résumé
9007 Background: International medical graduates (IMGs) comprise a substantial portion of the oncology workforce in the United States (US). IMGs may help address oncology workforce shortages with an aging US population, but IMGs face considerable barriers to becoming practicing oncologists in the US. We analyzed the credentialing and billing practices of IMG hematologists and oncologists (HO) to better describe the IMG workforce. Methods: We linked publicly available data from the Centers for Medicare & Medicaid Services (CMS) and the American Board of Internal Medicine to describe credentialing and billing practices of all HO who billed Medicare Part B in 2022 and whose medical school was specified in CMS data. Physicians were dichotomized as IMGs versus graduates of a US, Canadian, or Puerto Rican medical school (USMGs). We defined academic as working in a teaching hospital and research as having non-federal research funds. Results: Of 12,019 HO identified, 48% were IMGs. Even though they had a similar median number of years since medical school graduation, IMGs more frequently obtained initial hematology and medical oncology board certification (72% vs 58%, p<0.001) and maintained certification (79% vs 75%, p<0.001) than USMGs (Table). On average, IMGs billed Medicare more and had more outpatient visits and inpatient days with Medicare beneficiaries than USMGs. Most (55%) Medicare inpatient days were billed by IMGs. While USMGs were more frequently academic researchers than IMGs (35% vs 31%, p<0.001), IMGs were more frequently community clinicians than USMGs (13% vs 11%, p<0.001); there was no difference in IMG versus USMG representation for academic clinicians or community researchers. Conclusions: IMGs make up almost half of the US oncology workforce. Compared to USMGs, IMGs are more frequently double-boarded and maintaining board certification. Plus, they have more clinical productivity and higher representation in community-based oncology care than USMGs. Additional efforts should be instituted at a national level to eliminate training barriers and mitigate the biases faced by IMGs so that we can meet the growing demand for oncology care in the US. Hematologist & oncologist credentialing and billing by medical school location. Characteristic USMG, n=6,288 IMG, n=5,731 p-value Female gender 2,267 (36%) 2,005 (35%) 0.22 Median years since medical school graduation (IQR) 24 (16-36) 25 (17-34) 0.19 Oncology Single-Boarded 2,415 (38%) 1,485 (26%) <0.001 Hematology Single-Boarded 241 (4%) 148 (3%) <0.001 Hem/Onc Double-Boarded 3,632 (58%) 4,098 (72%) <0.001 Maintenance of Certification 4,704 (75%) 4,537 (79%) <0.001 Median Medicare Payments in 2022 (IQR) $78,938($35,507-$234,627) $88,401($41,329-$240,705) <0.001 Median Medicare Outpatient Visits in 2022 (IQR) 480(216.5-902) 506(237-925) 0.004 Median Medicare Inpatient Patient-Days in 2022 (IQR) 45(0-138) 62(0-171) <0.001
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 | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».