Financial Toxicity in the Clinical Encounter: A Paired Survey of Patient and Clinician Perceptions
Bibliographic record
Abstract
Objective: To compare the agreement between patient and clinician perceptions of care-related financial issues. Patients and Methods: We surveyed patient-clinician dyads immediately after an outpatient medical encounter between September 2019 and May 2021. They were asked to separately rate (1-10) patient's level of difficulty in paying medical bills and the importance of discussing cost issues with that patient during clinical encounters. We calculated agreement between patient-clinician ratings using the intraclass correlation coefficient and used random effects regression models to identify patient predictors of paired score differences in difficulty and importance of ratings. Results: 58 pairs of patients (n=58) and clinicians (n=40) completed the survey. Patient-clinician agreement was poor for both measures, but higher for difficulty in paying medical bills (intraclass correlation coefficient=0.375; 95% CI, 0.13-0.57) than for the importance of discussing cost (-0.051; 95% CI, -0.31 to 0.21). Agreement on difficulty in paying medical bills was not lower in encounters with conversations about the cost of care. In adjusted models, poor patient-clinician agreement on difficulty in paying medical bills was associated with lower patient socioeconomic status and education level, whereas poor agreement on patient-perceived importance of discussing cost was significant for patients who were White, married, reported 1 or more long-term conditions, and had higher education and income levels. Conclusion: Even in encounters where cost conversations occurred, there was poor patient-clinician agreement on ratings of the patient's difficulty in paying medical bills and perceived importance of discussing cost issues. Clinicians need more training and support in detecting the level of financial burden and tailoring cost conversations to the needs of individual patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".