Unintentional Discontinuation of Chronic Medications for Seniors in Nursing Homes
Bibliographic record
Abstract
Transitions of care leave patients vulnerable to the unintentional discontinuation of medications with proven efficacy for treating chronic diseases. Older adults residing in nursing homes may be especially susceptible to this preventable adverse event. The effect of large-scale policy changes on improving this practice is unknown.The objective of this study was to analyze the effect of a national medication reconciliation accreditation requirement for nursing homes on rates of unintentional medication discontinuation after hospital discharge.It was a population-based retrospective cohort study that used linked administrative records between 2003 and 2012 of all hospitalizations in Ontario, Canada. We identified nursing home residents aged ≥66 years who had continuous use of ≥1 of the 3 selected medications for chronic disease: levothyroxine, HMG-CoA reductase inhibitors (statins), and proton pump inhibitors (PPIs).In 2008 medication reconciliation became a required practice for accreditation of Canadian nursing homes.The main outcome measures included the proportion of patients who restarted the medication of interest after hospital discharge at 7 days. We also performed a time series analysis to examine the impact of the accreditation requirement on rates of unintentional medication discontinuation.The study included 113,088 adults aged ≥66 years who were nursing home residents, had an acute hospitalization, and were discharged alive to the same nursing home. Overall rates of discontinuation at 7-days after hospital discharge were highest in 2003-2004 for all nursing homes: 23.9% for thyroxine, 26.4% for statins, and 23.9% for PPIs. In most of the cases, these overall rates decreased annually and were lowest in 2011-2012: 4.0% for thyroxine, 10.6% for statins, and 8.3% for PPIs. The time series analysis found that nursing home accreditation did not significantly lower medication discontinuation rates for any of the 3 drug groups.From 2003 to 2012, there were marked improvements in rates of unintentional medication discontinuation among hospitalized older adults who were admitted from and discharged to nursing homes. This change was not directly associated with the new medication reconciliation accreditation requirement, but the overall improvements observed may have been reflective of multiple processes and not 1 individual intervention.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".