Integration of a Clinical Pharmacist into an Interdisciplinary Palliative Care Outpatient Clinic
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this quality improvement (QI) project was to determine if the Interdisciplinary Palliative Care Outpatient Clinic (IPCOC) at the West Palm Beach Veterans Affairs Medical Center offered improved symptom assessment and palliative care treatment outcomes. Secondary objectives were to identify, classify, and resolve medication problems and calculate the number of pharmacist recommendations accepted by prescribing providers. METHODS: An IPCOC was created by selecting disciplines for a core group including a nurse practitioner, clinical pharmacist, social worker, chaplain, and physician. Consult referrals were recruited by providing educational sessions. The patient assessments were completed using the Edmonton Symptom Assessment System: (revised version; ESAS-R). The clinical pharmacist classified and resolved drug-related problems. The pharmacy resident telephoned veterans for completion of the "Patient Assessment: Overall Satisfaction with Outpatient Palliative Care Clinic." RESULTS: Seventeen consults were received, 6 patients were excluded, and 11 were seen in clinic. One (9%) of 11 patients met the outcomes measure of system assessment documentation in the past year. At completion, 11 (100%) of 11 patients met the outcomes data measure. The Patient Satisfaction Assessment revealed veterans strongly agree to recommend the IPCOC. The clinical pharmacist identified 20 drug-related problems, made 16 recommendations, had a 93.7% implementation rate, and facilitated implementation of medication changes. CONCLUSION: This QI project demonstrates that an IPCOC improved symptom assessment and palliative care outcomes in addition to resolution of medication prescribing issues in veterans with advanced cancer by integration of a clinical pharmacist into the core 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".