Quality of care improvements resulting from a model of care that incorporates the primary clerk into the care team.
Bibliographic record
Abstract
122 Background: One common model of care within the oncology outpatient clinic setting is composed of the physician and primary nurse. We propose that the quality of care provided to oncology patients can be improved in this setting by incorporating the primary clerk into the care team, working in the same office space with the physician and nurse. Methods: Three care teams operating under the new model of care were observed during oncology outpatient clinics periodically from February 2016 to May 2016. The primary clerk’s interactions with the other team members were recorded, along with other tasks completed by the clerk that did not require team interactions but impacted quality of care. Data was later complied and organized into four domains that impacted the quality of care provided to patients. Results: The contributions to the care team by the primary clerk include improved clinic flow (e.g., ensuring treatment orders are inputted by the physician), patient convenience (e.g., identifying regularly scheduled blood work that is no longer necessary), patient safety (e.g., identifying patients scheduled for treatment with rituximab that have not had the required Hepatitis B & HIV screening), and hospital flow (e.g., preventing additional workload in the hospital laboratory by identifying when lab work can be combined in already scheduled appointments, and rescheduling clinic visits when results are not yet ready, which translates into time and cost savings to the hospital). Conclusions: As a result of the enhanced quality of care delivered, it is recommended that this model of care be adopted in the place of the traditional model, which lacks the essential element of interaction between the primary clerk and the rest of the care team.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".