GM-023 Performance indicators in hospital pharmacy: experience of a teaching hospital with a documentation tool
Bibliographic record
Abstract
Background Most professional pharmacy associations recognise the importance of documenting pharmaceutical activities. Such documentation is usually a hospital based decision and relies on local consensus of indicators and tools. Pharmacy practice includes pharmaceutical services, pharmaceutical care, teaching, research and management. Purpose To describe the pharmacy indicators collected and used by a teaching hospital. Material and methods This was a descriptive retrospective study. A documentation tool was used by pharmacists to collect and describe their workload since 1998. The tool is available on the hospital intranet and is completed by each pharmacist at the end of the day. Data were extracted from the SQL database for all 27 indicators for 2 fiscal years from 1 April 2014 to 31 March 2016. Only descriptive statistics were performed. Results Data extracted represented a total of 125 520 hours worked. The proportion of pharmacist time per axis was: pharmaceutical care (43.1%), pharmacy services (35.8%), management (10.5%), teaching (6.7%) and research (3.9%). A total of 253 532 pharmaceutical interventions were found. The proportion of pharmaceutical activities were, in decreasing order: drug therapy adjustment (54.2%), medication reconciliation at admission (10.0%), continuity of care (9.3%), patient counselling (5.4%), medical rounds (4.1%), other interventions (3.9%), laboratory orders (2.9%), medication error management (2.7%), pharmacovigilance (2.6%), pharmacokinetics (1.9%), medication reconciliation at discharge (1.6%), drug interactions (1.1%) and medication reconciliation at the point of transition of care (0.3%). 21.7% of pharmaceutical interventions were written in the patient’s file. Ratios of interventions per patient days were calculated per clientele. Decentralised pharmacists at the bedside or in outpatient clinics provided a total of 136 676 patient follow-ups. A total of 94 865 information requests were addressed to pharmacists (71.5% from other clinicians and 28.5% from external stakeholders). Pharmacists supervised pharmacy students for a total of 5545 student days. These data were used to benchmark current practice between years and with other hospitals. Data were shared with pharmacists and administrators to describe and evaluate the current contribution of pharmacists within the hospital. Conclusion This study has described the activity of pharmacists within a teaching hospital. The use of a documentation tool is feasible and useful to support the evaluation and benchmarking of pharmacists in the healthcare sector. No conflict of interest
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".