Multisite Quality Improvement Initiative to Identify and Address Racial Disparities and Deficiencies in Delivering Equitable, Patient-Centered Care for Multiple Myeloma—Exploring the Differences between Academic and Community Oncology Centers
Bibliographic record
Abstract
Treatment of multiple myeloma (MM) is complex; however, with equal access to care, clinical outcomes for Black patients match those in other patient groups. To reveal and begin to address clinical practice barriers to equitable, patient-centered MM care, this quality improvement (QI) initiative assessed patient electronic medical records (EMRs) and surveyed patients and providers at two large hospital systems and four community-based practices. For the educational intervention, providers participated in feedback-focused grand rounds sessions to reflect on system barriers and develop action plans to improve MM care. EMR reviews revealed infrequent documentation of cytogenetics and disease staging at community-based practices compared to large hospital systems. In surveys, providers from each care setting reported different challenges in MM care. Notably, the goals of treatment for patients and providers aligned at community clinics while providers and patients from large hospital systems had discordant perspectives. However, providers in community settings underreported race-associated barriers to care and identified different factors impacting treatment decision-making than Black patients. Relative to pre-session responses, providers were more likely to report high confidence after the educational sessions in aligning treatment decisions with guidelines and clinical evidence and shared decision-making (SDM). This QI study identified discordant perceptions among providers at large hospital systems and community-based practices in providing quality MM care. Provider education yielded increased confidence in and commitment to patient-centered care.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".