A quantitative study on the impact of a community falls pharmacist role, on medicines optimisation in older people at risk of falls
Bibliographic record
Abstract
Abstract Background The World Falls guidance includes medication review as part of its recommended multifactorial risk assessment for those at high risk of falling. Use of Falls Risk Increasing Drugs (FRIDs) along with polypharmacy and anticholinergic burden (ACB) are known to increase the risk of falls in older people. Method The impact of a community falls pharmacist within a hospital Trust, working as part of a multi-professional community falls prevention service, was evaluated in 92 people aged 65 years or older, by analysing data before and after pharmacist review, namely: number and type of FRIDs prescribed; anticholinergic burden score using ACBcalc ® ; appropriateness of medicines prescribed; bone health review using an approved too; significance of clinical intervention; cost avoidance, drug cost savings and environmental impact. Results Following pharmacist review, there was a reduction in polypharmacy (mean number of medicines prescribed per patient reduced by 8%; p < 0.05) and anticholinergic burden score (average score per patient reduced by 33%; p < 0.05). Medicines appropriateness improved (Medicines Appropriateness Index score decreased by 56%; p < 0.05). There were 317 clinically significant interventions by the community falls pharmacist. One hundred and one FRIDs were deprescribed. Annual cost avoidance and drug cost savings were £40,689-£82,642 and avoidable carbon dioxide (CO 2 ) emissions from reducing inappropriate prescribing amounted to 941 kg CO 2 . Conclusion The community falls pharmacist role increases prescribing appropriateness in the older population at risk of falls, and is an effective and cost-efficient means to optimise medicines in this population, as well as having a positive impact on the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".