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232 Background: In a retrospective analysis at the Medical College of Wisconsin’s Cancer Center, we identified longer inpatient length of stay (LOS) for patients residing in low-socioeconomic (SES) ZIP Codes in Milwaukee County compared to their high-SES counterparts in 2020 (7.3 days compared to 7.1 days). Under the auspices of American Society of Clinical Oncology’ Quality Training Program (QTP) initiative, this study examined specific factors related to prolonged LOS for solid tumor oncology patients. Methods: This analysis includes initial CY20 LOS medical record data for select patient service areas. Supplemental data includes disease registry data, diagnostic data, and SES data determined by patient ZIP Code. We identified patients 18 years and older with a diagnosis of common oncologic malignancies from 1/1/2020-12/31/2020 (breast, gastrointestinal (GI), genitourinary (GU), gynecologic (GYN), head and neck (H&N), and lung cancers). Poisson regression models with robust standard errors were used to compare the LOS index (LOSi) between groups of patients based on race, SES group, primary payer, and BMI. Results: A total of 1,637 patients with solid tumor diagnosis admitted to hematology and oncology units were identified. The LOSi did not vary significantly by race (range 0.95 – 1.07, p = 0.40) or primary payer (range 0.99 – 1.04, p = 0.59), but lower SES groups tended to have longer LOSi, with LOSi ratio above 1 compared to high SES (low SES: 1.16, p = 0.2; medium-low SES: 1.24, p = 0.06). Among patients with breast cancer diagnosis, Black (LOSi = 1.24, p = 0.01), medium-low SES (LOSi = 1.46, p = 0.02), Medicaid (LOSi = 1.40, p = 0.00), underweight (LOSi = 1.66, p = 0.00), and overweight (LOSi = 1.23, p = 0.01) patients had slightly longer LOSi, with LOSi ratio above 1. Among patients with H&N cancer diagnosis, Black patients (LOSi = 0.77, p = 0.02) had slightly shorter LOSi, with LOSi ratio below 1. The LOSi did not vary significantly by other factors for patients with H&N cancer diagnosis or the other common oncologic malignancies evaluated. Conclusions: This study shows how patient-specific factors such as race, SES, primary payer, and BMI contribute to inpatient LOS. Healthcare systems may benefit by addressing patient-specific barriers and factors such as body mass index, SES and SDH, to reduce hospital LOS.