Usability of a computerised drug monitoring programme to detect adverse drug events and non-compliance in outpatient ambulatory care
Bibliographic record
Abstract
OBJECTIVE: To assess the usability of a computerised drug monitoring programme for ambulatory patients receiving outpatient prescriptions. MATERIALS AND METHODS: A prospective cohort of 200 patients received two automated calls after a new drug prescription (day 3 and day 17) to screen for unfilled prescriptions and medication problems. Usability was assessed objectively and subjectively with the coding of technical (eg, voice recognition problems) and respondent burden (eg, failing to follow instructions) problems observed during the calls, and with an interview 21 days after the prescription. Associations between personal factors, usability and call outcome were examined with logistic regression models. RESULTS: The automated calls successfully reached 70.0% of enrolled patients. Older age increased the likelihood of experiencing technical (OR 2.18, 95% CI 1.22 to 3.88) and respondent burden problems (OR 3.32, 95% CI 1.88 to 5.87), as well as unsuccessful calls (OR 2.16, 95% CI 1.19 to 3.91). Patients with higher education experienced less respondent burden problems (OR 0.44, 95% CI 0.21 to 0.91), but they were more prone to have unsuccessful calls (OR 2.65, 95% CI 1.07 to 6.56) and less likely to find them useful (OR 0.23 95% CI 0.08 to 0.68). Older adults perceived the calls as easy to use and useful, although they reported lower intention to use the automated calls in the future (OR 0.32, 95% CI 0.15 to 0.70). DISCUSSION: As reported in previous studies, we found that older adults tend to have more difficulty when interacting with automated calls. Evidence about the association between education and usability was mixed. CONCLUSIONS: Our results highlight practical suggestions to improve the feasibility and usability of automated calls in primary care screening programmes.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".