Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

Treatment adherence is a critical component of anticoagulation therapy. The recent introduction of direct oral anticoagulants (DOACs) offers a more convenient alternative to warfarin, including a wide therapeutic window, few drug and food interactions, and fixed dosing without the need for laboratory monitoring. Although DOACs have reduced the need for regular monitoring, they have also placed pressure on patients to self-manage. At the same time, the shorter half-life of DOACs makes medication adherence a significant concern.¹ Laboratory tests currently used to monitor vitamin K antagonists are either too sensitive or too insensitive to DOACs to act as reliable measures of adherence, making dose titration and the determination of failure of therapy versus poor adherence challenging in routine clinical practice.^2,3 As a result, suboptimal rates of adherence^4–6 to DOACs go undetected, placing patients at increased risk of stroke and bleeding.

Most studies rely on claims data and patient self-reports to measure adherence: both are unreliable.^7,8 Other measures, such as electronic medication packaging, although providing a date and time stamp, are largely limited to adherence studies and have limited effect on adherence.⁹ Adherence interventions (counseling, educational, text messages, and electronic monitoring) have demonstrated mixed results.^10,11 The major limitation of these approaches is that they do not verify drug administration. Although blood levels are considered the gold standard, interperson variation, logistics, and cost make them impractical in routine clinical practice.¹²

Because of its ability to ensure treatment adherence, directly observed therapy has been used for decades to measure and maximize adherence for treatment of tuberculosis and HIV infection, and in inpatient settings. The artificial intelligence (AI) platform (AiCure, New York, NY) automates directly observed therapy using AI to visually confirm medication ingestion on smartphones (Figure I in the online-only Data Supplement).

Eligible patients diagnosed with ischemic stroke (with or without preceding transient ischemic attack and with a score between 1 and 20 on the National Institutes of Health Stroke Scale) and receiving oral anticoagulation therapy were randomized to daily monitoring by the AI platform (intervention) or to no daily monitoring (control) in this 12-week, randomized, parallel-group, controlled, single-site study. All patients were prescribed warfarin, dabigatran, rivaroxaban, or apixaban. Patients attended 4 clinic visits (baseline and weeks 4, 8, and 12); prothrombin time/international normalized ratio and the activated partial thromboplastin time were regularly measured. Medication adherence was measured by pill counts, plasma sampling, and the AI platform. Informed consent was obtained before entering the study. The study was approved by an institutional review board.

Exploratory, hypothesis-generating analyses were performed for all randomized patients who took at least 1 dose and included data through week 12.

A total of 117 patients were screened, with 28 patients randomized; 15 to the AI platform and 13 to the control group. One patient randomized to the control group withdrew from the study before the first dose taken and is excluded from further analysis. Baseline demographics were similar across both groups (Table). Mean (SD) age was 57.0 years (13.17 years), and 53.6% of patients were women. Patients receiving DOACs (n=20) outnumbered patients on warfarin (n=8). Previous smartphone usage and comfort with smartphones were comparable between the 2 groups.

This study used a novel artificial intelligence platform to assess and increase medication adherence in patients with a recently diagnosed ischemic stroke. Unlike most studies, which rely on indirect measures, to our knowledge, this study was the first randomized controlled trial to compare adherence rates of all 3 DOACs (dabigatran, rivaroxaban, and apixaban) and warfarin together based on daily real-time monitoring against a control group and verified by plasma sampling. Suboptimal adherence across all DOACs confirms previous findings although this study suggests that adherence might be lower than previously recognized; high adherence to warfarin underlines the value of routine laboratory tests to, in effect, ensure adherence. Absolute improvement of 67% in patients taking DOACs and monitored by the AI app—and confirmed by drug levels—demonstrates the potential value of daily real-time monitoring to measure and maximize adherence. Few studies have deployed smartphone apps in middle-aged or elderly populations.¹³ Consistent use and general likability of the AI app over 12 weeks underscores the possibility of harnessing new technologies to optimize adherence in patients on DOACs for whom routine laboratory tests are not good indicators of adherence. AI platforms have the potential to accurately monitor medication ingestion and change patient behavior.

We acknowledge Aubri Charboneau of Sage Scientific Writing, LLC, for editorial support and John E. Hinkle of EarlyPhase Sciences, Inc, for database support and statistical analyses.