Impact of a “Pharmacist First” innovative workflow plan in patients with hypertension and/or diabetes
Bibliographic record
Abstract
Usual community pharmacy workflow, whereby patients might see a pharmacist at the end of the dispensing process, is not conducive to proactive patient-centred care. The objective of this study was to evaluate the impact of the “Pharmacist First” (P1st) workflow model on blood pressure and glycemic control in patients with hypertension and/or diabetes. This retrospective review was set in 2 community pharmacies that use the P1st model in the Greater Edmonton Region. The population entailed patients with hypertension and/or type 1 or 2 diabetes who received care via the P1st workflow model. The P1st workflow model places the patient in immediate contact with the pharmacist. The pharmacist first assesses prescription appropriateness, reviews relevant laboratory tests, discusses chronic disease control and addresses any questions or concerns the patient has before passing the prescription to be filled by a technician. This allows issues or concerns to be identified and addressed up front, rather than waiting until the prescription is filled and the patient is ready to leave the pharmacy. The primary outcome assessed in this study was change in blood pressure and/or A1C from baseline to the last follow-up visit. We reviewed 215 patient records. The mean age was 69.4 years (standard deviation 12.5), 51.2% of patients were male, 57.7% had hypertension, 5.6% had diabetes, and 36.7% had both. Median follow-up time was 4.2 months (interquartile range 2.5 -9.3). In 203 patients with hypertension, systolic blood pressure was reduced from 139.83 mmHg to 131.26 mmHg ( p < 0.001) and diastolic blood pressure from 80.26 mmHg to 76.86 mmHg ( p < 0.001). In 87 patients with diabetes, A1C changed from 7.4% to 7.2% ( p = ns). The P1st workflow model demonstrated significant improvements in blood pressure. Further investigation is needed to evaluate the effectiveness of this model with a control group, longer follow-up and evaluation of the patient experience.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".