Background and Purpose This study evaluated the use of an artificial intelligence (AI) platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. 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. Methods A randomized, parallel-group, 12-week study was conducted in adults (n = 28) with recently diagnosed ischemic stroke receiving any anticoagulation. Patients were randomized to daily monitoring by the AI Platform (intervention) or to no daily monitoring (control). The AI application visually identified the patient, the medication and confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results For all patients (n = 28), mean (standard deviation [SD]) age was 57 (13.2) years and 53.6% were female. Mean (SD) cumulative adherence based on the AI Platform was 90.5% (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. Conclusions Patients, some with little experience using a smartphone, successfully used the technology and demonstrated a 50% improvement in adherence based on plasma drug concentration levels. For patients receiving DOACs, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on DOAC therapy. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT02599259.

BackgroundAccurately monitoring and collecting drug adherence data can allow for better understanding and interpretation of the outcomes of clinical trials. Most clinical trials use a combination of pill counts and self-reported data to measure drug adherence, despite the drawbacks of relying on these types of indirect measures. It is assumed that doses are taken, but the exact timing of these events is often incomplete and imprecise.ObjectiveThe objective of this pilot study was to evaluate the use of a novel artificial intelligence (AI) platform (AiCure) on mobile devices for measuring medication adherence, compared with modified directly observed therapy (mDOT) in a substudy of a Phase 2 trial of the α7 nicotinic receptor agonist (ABT-126) in subjects with schizophrenia.MethodsAI platform generated adherence measures were compared with adherence inferred from drug concentration measurements.ResultsThe mean cumulative pharmacokinetic adherence over 24 weeks was 89.7% (standard deviation [SD] 24.92) for subjects receiving ABT-126 who were monitored using the AI platform, compared with 71.9% (SD 39.81) for subjects receiving ABT-126 who were monitored by mDOT. The difference was 17.9% (95% CI -2 to 37.7; P=.08).ConclusionsUsing drug levels, this substudy demonstrates the potential of AI platforms to increase adherence, rapidly detect nonadherence, and predict future nonadherence. Subjects monitored using the AI platform demonstrated a percentage change in adherence of 25% over the mDOT group. Subjects were able to use the technology successfully for up to 6 months in an ambulatory setting with early termination rates that are comparable to subjects outside of the substudy.Trial RegistrationClinicalTrials.gov NCT01655680 https://clinicaltrials.gov/ct2/show/NCT01655680?term=NCT01655680

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This study evaluated health outcomes among people who inject drugs infected with hepatitis C virus using an artificial intelligence platform. Mean (SD) cumulative adherence (visual confirmation of administration) was 91.3% (SD=10.5%). Most subjects (88.2%) achieved ≥ 80% adherence to treatment and 88.2% (15 of 17) achieved a sustained virologic response.