Use of pharmacist integration in oncology clinics to identify and resolve moderate to major drug-drug interactions.
Bibliographic record
Abstract
71 Background: Medication reconciliation (MR) in outpatient clinics has been under-evaluated. We postulated that cancer patients would benefit from MR done by a pharmacist as these patients have many care providers, many medications, and are at high risk of drug-drug interactions (DDIs). Hence, we conducted a quality initiative evaluating the role of a pharmacist in the ambulatory clinics. Methods: One pharmacist prospectively rotated amongst four oncology clinics four days a week from June 3 to September 18, 2008. The pharmacist performed MR, and as a consultant developed therapeutic plans related to drug therapeutic problems (DTPs) including adverse reactions and DDIs. Patient medication lists were retrospectively analyzed using Micromedex and DDIs were categorized by frequency, severity and evidence level. A monthly survey (Likert scale) evaluating pharmacist contributions to each clinic team was completed by physicians and nurses. Results: A total of 158 patients were seen in 227 patient visits. The pharmacist identified 141 DTPs in 60 patients across 74 visits. The most frequently observed were no drug for a medical problem (51.1%), dose too low (12.8%), wrong drug (9.9%), and adverse drug reactions (9.2%). In response, 174 therapeutic plans were made. The most frequently recommended actions were drug added (40.8%), dose changed (13.2%), drug discontinued (9.2%), and interval/duration changed (7.5%). A total of 414 DDIs were identified in 110 patients, across 149 patient visits. On average, 2.62 DDIs were reported per patient, and 1.82 DDIs per visit. By severity, 139 (33.6%) major, 258 (62.3%) moderate, 16 (3.9%) minor and 1 (0.2%) contraindicated DDIs were documented. By level of evidence, 46 (11.1%) DDIs were excellent, and 236 (57%) were good. Survey results showed that doctors and nurses agreed/strongly agreed that pharmacist presence was valuable. The most useful contributions identified were consultation regarding DDIs, adverse drug effects, and medication efficacy decisions. Conclusions: DDI rates are high and most are moderate or major in severity. There is a clear benefit from the integration of a pharmacist to the clinics with an improvement in patient safety and quality 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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".