Collaborative team-based care implementation in ambulatory gynecological and lung cancer clinics
Bibliographic record
Abstract
IntroductionPatients with cancer are at risk for experiencing adverse health outcomes, including drug therapy problems (DTPs). Multidisciplinary approaches to team-based care (TBC) are important in improving patient health outcomes and patient safety, while reducing redundancy and increasing efficiency.ObjectivesThe primary objective was to design and test a care model that integrated a residency-trained pharmacist into an interprofessional team, including medical oncologists and nurses, in order to optimize TBC and provision of patient-centred care. Secondary objectives were to determine the numbers and types of clinical pharmacy key performance indicators (cpKPIs) and DTPs identified and resolved pre- and post-TBC pharmacist implementation in ambulatory gynecological and lung cancer clinics.MethodsThis was a prospective, non-randomized, non-blinded study, focused on implementing a collaborative TBC model in ambulatory oncology clinics, which treated patients with gynecological or lung cancers. Applicable evidence-based literature and local expertise were used to inform the processes for engaging team members and determining evaluative metrics, which highlight successes and opportunities for improvement.ResultsA residency-trained pharmacist was successfully integrated into the existing multidisciplinary ambulatory oncology team, focused on treating patients with gynecological and lung cancers. One hundred and sixty-five clinically important cpKPIs and DTPs were identified post-TBC pharmacist implementation, as compared to thirty-two interventions at baseline. The most common documented cpKPI and DTP were medication reconciliation and drug interactions, respectively.ConclusionA collaborative, multidisciplinary TBC environment works well for delivering cancer care. The addition of a pharmacist to TBC assists with identifying and resolving clinically important cpKPIs and DTPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".