Improving patient-centered oral chemotherapy at an academic cancer center.
Bibliographic record
Abstract
212 Background: Despite established standards for prescribing and monitoring oral anti-cancer medications (OAC) and validated tools supporting OAC patient education, substantial quality gaps remain. Methods: To identify local gaps in care for patients receiving OAC, we used a patient survey in the GI and Endocrine medical oncology clinic. Providers were also interviewed. The survey consisted of 23 questions addressing three domains (treatment plan (T) education, self-management (S) education and health team communication (C) as well as a single question overall satisfaction score. A composite average score encompassing all 23 questions was generated. Questions were derived from ASCO chemotherapy standards and validated patient communication instruments and scored on a 5-point Likert scale. Subsequently, individualized drug-specific written care plans guiding patient education were developed and tested in iterative PDSA cycles. The aim was to improve the composite average patient survey score by 10%. The same patient survey was used to assess the impact of the change ideas. Results: We collected 32 patient surveys, 21 pre- and 11 post-intervention. Baseline surveys indicated lowest scores in the C domain. Providers reported variation in education content and communication techniques used. Care plans with standardized content for 8 OAC agents were tested in simulated and clinic settings in a stepwise fashion, with implementation beginning in Feb 2018. Providers received training on integrating the care plans into clinic workflow. The composite average score for all 23 items improved from 4.18 to 4.29. The single question overall satisfaction score improved from 4.17 to 4.45. Improvement was noted across all 3 domains (T: 4.31 → 4.41, S: 4.26 → 4.36, C: 4.00 → 4.15). For balancing measures, provider teaching time per patient initially rose following implementation before returning to baseline. Conclusions: The intervention led to improvement in the patient experience when starting OAC. Standardized content and a framework guiding provider communication were key elements of the change ideas. To meet the study aim, further PDSA cycles integrating teach back methodology and proactive phone follow up are ongoing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".