Using Artificial Intelligence to Measure and Optimize Adherence in Patients on Anticoagulation Therapy
Bibliographic record
Abstract
Background: The introduction of direct oral anticoagulants (DOACs), while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding. Objective: To evaluate an artificial intelligence (AI) platform that visually confirms medication ingestion on smartphones in elderly stroke patients on anticoagulation therapy. Methods: A randomized, parallel-group, 12-week study was conducted in adults (N=28) with a recently diagnosed ischemic stroke. Patients were randomized to daily monitoring by the AI platform (intervention) or to no daily monitoring (control). The AI app visually identified the patient and the medication and confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results: For all patients (N=28), mean age was 57 (SD 13.2) years and 53.6% were female. Mean cumulative adherence based on the AI platform was 90.5% (SD 7.5%). Plasma drug concentration levels indicated that adherence was 100% (15 of 15) and 50% (6 of 12) in the intervention and control groups, respectively, and mean cumulative pill count adherence was 97.2% (SD 4.4%) and 90.6% (SD 5.8%), respectively. Conclusions: Patients, some with little experience using a smartphone, successfully used the technology and demonstrated a 67% absolute improvement in adherence to DOACs based on plasma drug concentration levels. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on DOAC therapy. ClinicalTrial: Clinicaltrials.gov NCT02599259; https://clinicaltrials.gov/ct2/show/NCT02599259 (Archived by WebCite at http://www.webcitation.org/6n6GS3vQ3).
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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 | 0.000 | 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".