Abstract PO-021: A hub and spoke model to improve cancer care quality: Advancing Cancer Care Together (ACCT) for Asian American Medicaid beneficiaries in Orange County, California
Bibliographic record
Abstract
Abstract Introduction: Orange County (OC) is home to the third-largest population of Asian Americans in the U.S., including the largest population of Vietnamese outside of Vietnam. While breast, lung and colorectal cancers are the top overall causes of cancer incidence and mortality in OC, unique cancers are prevalent among Asian and Pacific Islanders including liver and stomach cancers. The University of California, Irvine Chao Family Comprehensive Cancer Center (UCI CFCCC) adapted a hub-and- spoke model of care (Elrod & Fortenberry, 2017) to increase efficiency among underserved Asian Americans who continue to experience disparities in screening, early detection, and access to cancer treatment. Methods: Our hub-and-spoke model arranges service delivery assets into a network between community organizations through culturally/linguistically competent and trained community health navigators, OC medicaid primary and specialty care providers for low/moderate complexity patients, and UCI CFCCC for high-complexity cancer treatment. UCI CFCCC serves as the anchor establishment (hub) which offers a full array of services. This is complemented by community providers and care coordinators at local Federally Qualified Health Centers (spokes) which offer culturally-tailored primary prevention services. The community patient navigators (rim) located at community-based organizations, routes patients needing more tailored services to the spokes or hub for screening or treatment. Results: Patient Navigators at OC Herald Center, OC Asian Pacific Islander Community Alliance, and Vietnamese American Cancer Foundation have educated 2,246 Korean, Vietnamese, and Chinese individuals on cancer prevention and screening guidelines. Of those, 320 medicaid members have been routed to KCS Health Centers (Korean-serving FQHC lookalike), Southland Integrated Services, Inc (Vietnamese and Chinese-serving FQHC), or Medicaid community providers for cancer screening and/or follow-up. 64 community providers have been trained on NCCN guideline adherent care for Korean, Vietnamese, and Chinese. UCI CFCCC has developed an algorithm/pathway for Medicaid-serving community physicians to easily refer qualified Vietnamese, Chinese, or Korean patients to the hub for complex care or clinical trials. Conclusions: The current COVID-19 pandemic has exacerbated disparities in screening and early detection, and compounds the uncertainty about the importance of optimizing cancer care quality (i.e. access proportion and timeliness, adherence to guidelines, patient satisfaction). Disparities being highlighted in COVID-19 has shown us the power and need of community engagement models to rapidly catalyze and create unique community-based efforts that strengthen capacities and infrastructures, and promote best practices in cancer prevention and early detection designed to decrease cancer incidence and/or mortality in the communities we serve. Citation Format: Cevadne Lee, Ellen Ahn, Mary Anne Foo, Sherry Huang, Becky Nguyen, Tricia Nguyen, Jacqueline Tran, Robert Bristow, Sora Park Tanjasiri. A hub and spoke model to improve cancer care quality: Advancing Cancer Care Together (ACCT) for Asian American Medicaid beneficiaries in Orange County, California [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-021.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".