Outcomes Assessment of a Pharmacist-Directed Seamless Care Program in an Ambulatory Oncology Clinic
Bibliographic record
Abstract
The primary goal of seamless care is improved patient outcomes and improved standards of care for patients with cancer. The pharmacy service of the Newfoundland Cancer Treatment and Research Foundation conducted a randomized control study that measured clinical and humanistic outcomes of a pharmacist-directed seamless care program in an ambulatory oncology clinic. This article focuses on the intervention group, particularly the identification of drug-related problems (DRPs) and utilization of health care services as well the satisfaction of 3 types of health professionals with the services provided by the pharmacist-directed seamless care program. Overall, the seamless care pharmacist (SCP) identified an average of 3.7 DRPs per intervention patient; the most common DRP reported was a patient not receiving or taking a drug therapy for which there is an indication. The SCP identified more DRPs in patients receiving adjuvant treatment compared to those receiving palliative treatment. On average, family physicians, oncology nurses, and hospital pharmacists were satisfied with the SCP intervention indicating that they agreed the information collected and distributed by the SCP was useful to them. Pharmacist-directed seamless care services in an ambulatory oncology clinic have a significant impact on clinical outcomes and processes of patient care. The presence of a SCP can help identify and resolve DRPs experienced by patients in an outpatient oncology clinic, ensuring that patients are receiving the highest standard of 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".