EHR documentation of cost discussions and financial interventions for a cohort of patients with advanced cancer: Findings from a retrospective chart review-based study.
Bibliographic record
Abstract
e23150 Background: Financial Toxicity (FT), the adverse impact of high medical costs on quality of life, notably affects many oncology patients. In a prior initiative to improve quality of life for advanced cancer patients, a palliative care-trained nurse practitioner (PCNP) was embedded in an academic oncology practice. Patients completed the Edmonton Symptom Assessment System (ESAS) and Canadian Problem Checklist (CPC) to gauge physical symptoms, psychosocial issues, and financial concerns. However, the incidence of self-reported financial issues and electronic health record (EHR) documentation of cost discussions and interventions remain unclear for this patient group. Methods: The study included adult oncology patients with advanced cancer seen by the PCNP from January 1, 2020, to January 1, 2022. We assessed incidence of self-reported financial concerns via CPC checklists in this cohort. Ten charts underwent detailed manual review for evidence of cost discussions or referral to finance-related interventions for up to 1 year from the initial consult with the PCNP or until death. During the review, we identified keywords in documentation of cost discussions and finance-related interventions to create a keyword library. We iteratively compared the library to our reference standard manual chart review to ensure 100% sensitivity. We then employed this keyword library to evaluate the remaining charts. Descriptive statistics were employed to assess correlations between self-reported financial concerns, chart documentation of cost discussions or financial interventions, and patient demographics. Results: Out of 109 eligible patients, 71% (77) completed pre-visit questionnaires, with 30% (23) reporting financial issues. Of this group, 43% (10) died during the study period. Average follow-up was 5.0 months among the decedents. Among those reporting financial concerns, 65% (15) had documented cost discussions or financial assistance referrals in the EHR, involving physicians, social workers, and other clinic staff. Most interventions addressed medication costs and referrals to financial assistance as indicated. Patient demographics (age, sex, insurance type) showed no apparent association with chart documentation of cost discussions or financial assistance. Conclusions: We used retrospective chart review to evaluate the extent of documentation of cost discussions and finance-related intervention in a cohort of patients with advanced cancer. In our patient cohort, 30% self-reported financial concerns, a proportion consistent with previously reported incidence of FT among oncology patients. Notably, 35% of the patients with known financial concerns had no other relevant documentation in the EHR. More work is needed to establish best practices for systematic screening for FT and interventions to support patients.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".