Participation in Electronic Patient-Reported Outcome Measures Collection as a Part of Routine Supportive Care Delivery in Oncology
Bibliographic record
Abstract
PURPOSE: The use of electronic patient-reported outcome measures (ePROMs) in supportive cancer care can lead to benefits, such as identifying at-risk patients in need of closer monitoring and treatment. Despite these benefits, most studies examining ePROMs in this area were for clinical trials rather than standard care. Since there is a need to identify which patients are more likely to participate in ePROMs, this study assessed ePROM participation rates and factors influencing greater participation among patients receiving supportive care. METHODS: This retrospective data analysis took place at a supportive care clinic within a National Cancer Institute-designated Comprehensive Cancer Center in the southeastern United States. Starting in 2017, ePROM assessments were implemented using tablets for in-person appointments at the clinic. The assessments included the Patient Health Questionnaire-9, National Comprehensive Cancer Network Distress Thermometer, and Edmonton Symptom Assessment System with additional questions added for other symptoms. Logistic regression and zero-truncated negative binomial regression models were used to analyze factors associated with ePROM assessment submission. RESULTS: The study included 4,780 patients, with 42.7% submitting at least one ePROM assessment. Higher odds of ePROM submission were observed among patients age 35-64 years, had Medicare, had nonmetastatic cancer, or had genitourinary, breast, or multiple cancers. Additionally, higher rates of ePROM submissions were observed among patients who were younger; had GI, breast, or multiple cancers; had nonmetastatic cancer; or had private insurance. CONCLUSION: This study reveals that submission rates of ePROM assessments in a cancer center's supportive care clinic may be influenced by patient demographics, cancer history, and social determinants of health. Interventions to improve ePROM submission rates may need to be tailored on the basis of cancer site, presence of metastatic cancer, and caregiver support.
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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 | 0.034 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".