Improving prescribing in the elderly: a study in the long term care setting.
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence and predictors of potentially inappropriate prescribing of medications in the long term care setting, and to determine the effectiveness of follow-up pharmacist letters to the prescribing physicians in improving prescribing. PATIENTS AND METHODS: The Improving Prescribing in the Elderly Tool was applied to the charts of all long term care patients aged 65 years and over at Parkwood Hospital, a rehabilitation hospital/long term care facility in London, Ontario. All potentially inappropriate prescriptions were verified by a consensus panel consisting of a family physician, a geriatric medicine specialist and a geriatric pharmacist. Follow-up letters to the prescribing physicians were developed that briefly described the concerns with the potentially inappropriate prescriptions and suggested safer alternatives. These letters were sent to the prescribing physicians, accompanied by a brief survey. Patient charts in which a potentially inappropriate prescription had been noted were reviewed for prescription changes two months after the prescribing physicians had received the follow-up letters. RESULTS: A total of 69 potentially inappropriate prescriptions were found in 65 of 355 long term care patients (18.3%). The most common types of potentially inappropriate prescriptions were anticholinergic drugs to manage antipsychotic side effects (17 cases), tricyclic antidepressants with active metabolites (16 cases), and long-acting benzodiazepines (14 cases). The total number of prescription medications (P<0.001), a history of mental illness (P=0.002) and a high minimum data set (MDS) score for depression (P=0.002) were all highly associated with potentially inappropriate prescribing. Variables that were not correlated with increased rates of potentially inappropriate prescribing included age, sex, code status, a diagnosis of dementia (as documented explicitly in the chart), high MDS scores for delirium or cognitive impairment, the date of the prescribing physician's graduation and the total Charlson comorbidity index score. Potentially inappropriate prescriptions were significantly less common in patients seen by a geriatric medicine specialist (P<0.001). In response to the follow-up letter suggesting safer alternatives, 37.9% of potentially inappropriate prescriptions were changed by the prescribing physician. Ninety-two per cent of responding physicians rated the follow-up letter as a "somewhat" or "very" helpful method for improving prescribing in elderly patients. CONCLUSIONS: Potentially inappropriate prescribing in the long term care setting is common and can be improved by the provision of a follow-up letter suggesting safer alternatives.
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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".