Impact of a Dedicated Inpatient Radiation Oncology Consult Service on Goal-Concordant Care
Bibliographic record
Abstract
Purpose Radiotherapy has an increasing role in the management of metastatic cancers. Integrating radiation with surgical and systemic approaches is complex, and inappropriate management can lead to prolonged hospitalizations inconsistent with palliative goals. An Inpatient Radiation Oncology Consult (IROC) service was created in January 2020 to provide rapid access to specialized care for hospitalized patients. Here we report outcomes of the IROC service, focusing on quality-of-care metrics including hospital length-of-stay (LOS), use of hypofractionated approaches, and treatments for patients discharged to hospice. Methods and Materials We conducted a pre-post observational study to compare inpatient consults placed before ( N = 1,507) and after ( N = 1,509) IROC, from 2019-2021. Continuous variables were analyzed using the Mann-Whitney test and categorical variables using the Fisher's exact test. Results We found that IROC was associated with reductions in hospital length of stay (LOS, mean difference 1.0 days, P = 0.045). Under IROC, inpatient treatment courses were shorter (5.8 v 5.0 days, P = 0.007), in part driven by increased adoption of palliative hypofractionated radiotherapy approaches (74% v 82%, P = 0.001). The reduction in LOS was greatest for patients discharged to hospice (5.1 v 3.7 days, P = 0.036). Conclusions The IROC service was associated with reduced hospital LOS, increased use of hypofractionated approaches and decreased treatments for patients discharged to hospice. Our findings demonstrate the value of a dedicated program addressing radiation delivery to hospitalized patients to improve goal-concordant treatments. The financial impact of reducing low value care is an important subject for future investigations.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".