Increasing the use of home medication lists in an outpatient neurorehabilitation clinic
Bibliographic record
Abstract
Medication reconciliation in ambulatory care settings helps prevent adverse drug events. Patient involvement in the process is crucial, as clinicians must verify the reported medication history with other sources such as home medication lists or brown-bagged home medications provided by patients. However, only 47.8% of brain injury and stroke adult outpatients at Toronto Rehabilitation Institute, an academic rehabilitation hospital, bring their medications/medication lists to clinic visits. In turn, missing medication information impacts the clinic by causing delays in treatment and interrupted clinic flow. This project aimed to increase the percentage of patients who bring their medications/medication lists to 80% and decrease the impact on clinic visits caused by missing medication information to 10%. This was a controlled before-after study, with the outpatient rehabilitation assessment (OPRA) clinic as the intervention and the spasticity clinic as the control. The model for improvement was used as the project framework. Process mapping, Ishikawa diagrams, driver diagrams and patient surveys generated the change ideas. Verbal reminders during confirmation phone calls, written reminders and medication list templates were implemented. Data were collected on a biweekly basis and analysed using statistical control charts. After six Plan-Do-Study-Act cycles conducted over 49 weeks, both project aims were achieved. The percentage of OPRA clinic patients who brought medications/medication lists was 81.8% and the impact on clinic visits caused by missing medication information was 9.1% of clinic visits. Special cause variation was detected on the statistical control charts. Conversely, there was no special cause variation for the spasticity clinic (the control) for either aim. Lessons learnt include the importance of prolonged data collection when implementing interventions with long lag time, and that verbal reminders may not be effective for patients with cognitive impairments. Future efforts may focus on implementing the bundle of project interventions for the spasticity clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| 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 | 0.001 |
| 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".