656: THE ROLE AND IMPACT OF PHARMACISTS IN A VIRTUAL POST-ICU RECOVERY CLINIC
Bibliographic record
Abstract
Introduction: ICU survivors currently have unmet care needs in their transition from the ICU back to recovery. Many survivors struggle with functional disability, chronic health problems, and mental health concerns. In our province, >50% of the population lives in a rural setting with 32% of households living >200 km away from specialist care. We designed our pilot program in consultation with ICU clinicians, allied healthcare providers, primary care providers, patients, and families. The primary goal of the Virtual Post ICU Recovery Clinic (VIRC) pilot project is to provide comprehensive, multidisciplinary care to ICU survivors, in their journey to recovery, out to 180 days from hospital discharge. Methods: This is an interim analysis from June 2022 to June 2023. Consecutive patients who attended the VIRC were included in this single centre service evaluation of the pharmacist actions and impacts on patient care. Results: Our ongoing pilot project has recruited a total of 68 patients, median age 60.5 years, 50% female, clinical frailty score 4, SOFA score on ICU admission 11, median ICU and hospital length of stay 5.9 and 19.2 days, respectively, and 74% of our patients met criteria for polypharmacy (>5 medications). The virtual nature of the clinic lowered barriers for patient attendance, where we could meet those patients in their own home with 49% living >50 km away from the primary ICU site. As of June 2023, 49% of our recruited patient have thus far been seen by the sole pharmacist, with a median time per patient of 1.2 hours spent including chart review, interview, assessment, treatment provision/education, and documentation. In clinic, 32% of patients had a medication discontinued, 12% of patients had a chronic medication restarted, and 20% had medications optimized. The pharmacist also recommended vaccination optimization in 27% of patients, suggested referral to other providers in 43%, and conducted medication education for 63% for areas including sleep hygiene, puffer use optimization, adherence, and avoidance of drug interactions. Conclusions: A pharmacist in a virtual post-ICU clinic could provide many services to ICU survivors, including recommendations for medication optimization, patient education, and managing drug interactions, meeting them on their terms instead of a physical clinic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".