Board certification and billing practices of international medical graduate hematologists and oncologists.
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".