Nurse-led medicines’ monitoring in care homes, implementing the Adverse Drug Reaction (ADRe) Profile improvement initiative for mental health medicines: An observational and interview study
Bibliographic record
Abstract
INTRODUCTION: Preventable adverse effects of medicines often pass unnoticed, but lead to real harm. INTERVENTION: Nurse-led monitoring using the structured Adverse Drug Reaction (ADRe) Profile identifies and addresses adverse effects of mental health medicines. OBJECTIVES: This study investigated the implementation and clinical impact of ADRe, and barriers to and facilitators of sustained utilisation in routine practice. METHODS: Administration of ADRe was observed for 30 residents prescribed mental health medicines in ten care homes. The study pharmacist reviewed completed ADRes against medication records. Policy context was explored in 30 interviews with service users, nurse managers and strategic leads in Wales. RESULTS: Residents were aged 60-95, and prescribed 1-17 (median 9 [interquartile range (IQR) 7-13]) medicines. ADRe identified a median of 18 [IQR 11.5-23] problems per resident and nurses made 2 [1-2] changes to care per resident. For example: falls were reported for 9 residents, and care was modified for 5; pain was identified in 8 residents, and alleviated for 7; all 6 residents recognised as dyspnoeic were referred to prescribers. Nurses referred 17 of 30 residents to prescribers. Pharmacists recommended review for all 30. Doubts about administering ADRe, sometimes expressed by people who had not yet used it, diminished as it became familiar. ADRe was needed to bridge communication between resident, nurses and prescribers. When barriers of time, complacency, and doctors' non-availability were overcome, reporting with ADRe made prescribers more likely to heed nurses' concerns regarding residents' welfare. Clinical gains were facilitated by one-to-one time, staff-resident relationships, and unification of documentation. IMPLICATIONS: To our knowledge, ADRe is the only instrument that brings a full account of patients' problems to medication reviews. This juxtaposition of signs and symptoms against prescriptions facilitates dose adjustments and de-prescribing and leads to: reduced pain and sedation; early identification of problems linked to ADRs, such as falls; and timely medication reviews e.g. for dyspnoea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".