Digital Applications Targeting Medication Safety in Ambulatory High-Risk CKD Patients
Bibliographic record
Abstract
Background and objectives Patients with CKD are at risk for adverse drug reactions, but effective community-based preventive programs remain elusive. In this study, we compared the effectiveness of two digital applications designed to improve outpatient medication safety. Design, setting, participants, & measurements In a 1-year randomized controlled trial, 182 outpatients with advanced CKD were randomly assigned to receive a smartphone preloaded with either eKidneyCare ( n =89) or MyMedRec ( n =93). The experimental intervention, eKidneyCare, includes a medication feature that prompted patients to review medications monthly and report changes, additions, or medication problems to clinicians for reconciliation and early intervention. The active comparator was MyMedRec, a commercially available, standalone application for storing medication and other health information that can be shared with patients' providers. The primary outcome was the rate of medication discrepancy, defined as differences between the patient’s reported history and the clinic’s medication record, at exit. Results At exit, the eKidneyCare group had fewer total medication discrepancies compared with MyMedRec (median, 0.45; interquartile range, 0.33–0.63 versus 0.67; interquartile range, 0.40–1.00; P =0.001), and the change from baseline was 0.13±0.27 in eKidneyCare and 0.30±0.41 in MyMedRec ( P =0.007). eKidneyCare use also reduced the severity of clinically relevant medication discrepancies in all categories, including those with the potential to cause serious harm (estimated rate ratio, 0.40; 95% confidence interval, 0.27 to 0.63). Usage data revealed that 72% of patients randomized to eKidneyCare completed one or more medication reviews per month, whereas only 30% of patients in the MyMedRec group (adjusted for dropouts) kept their medication profile on their phone. Conclusions In patients who are high risk and have CKD, eKidneyCare significantly reduced the rate and severity of medication discrepancies, the proximal cause of medication errors, compared with the active comparator. Clinical Trial registry name and registration number: www.ClinicalTrials.gov, NCT02905474.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".