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

Daniel L. Labovitz, Laura Shafner, Morayma Reyes Gil, Deepti Virmani, Adam Hanina

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Background and Purpose - This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, 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 artificial intelligence platform (intervention) or to no daily monitoring (control). The artificial intelligence application visually identified the patient, the medication, and the confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results - For all patients (n=28), mean (SD) age was 57 years (13.2 years) and 53.6% were women. Mean (SD) cumulative adherence based on the artificial intelligence 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 direct oral anticoagulants, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on direct oral anticoagulant therapy.

Original languageEnglish (US)
Pages (from-to)1416-1419
Number of pages4
JournalStroke
Volume48
Issue number5
DOIs
StatePublished - May 1 2017

Fingerprint

Artificial Intelligence
Patient Compliance
Anticoagulants
Stroke
Therapeutics
Medication Adherence
Pharmaceutical Preparations
Eating
Hemorrhage
Technology
Pressure
Equipment and Supplies
Control Groups

Keywords

  • anticoagulants
  • artificial intelligence
  • patient compliance
  • patient outcome assessment
  • stroke

ASJC Scopus subject areas

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialized Nursing

Cite this

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. / Labovitz, Daniel L.; Shafner, Laura; Reyes Gil, Morayma; Virmani, Deepti; Hanina, Adam.

In: Stroke, Vol. 48, No. 5, 01.05.2017, p. 1416-1419.

Research output: Contribution to journalArticle

@article{817435313f8f4ac4840156d80924ccf1,
title = "Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy",
abstract = "Background and Purpose - This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, 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 artificial intelligence platform (intervention) or to no daily monitoring (control). The artificial intelligence application visually identified the patient, the medication, and the confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results - For all patients (n=28), mean (SD) age was 57 years (13.2 years) and 53.6{\%} were women. Mean (SD) cumulative adherence based on the artificial intelligence 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 direct oral anticoagulants, absolute improvement increased to 67{\%}. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on direct oral anticoagulant therapy.",
keywords = "anticoagulants, artificial intelligence, patient compliance, patient outcome assessment, stroke",
author = "Labovitz, {Daniel L.} and Laura Shafner and {Reyes Gil}, Morayma and Deepti Virmani and Adam Hanina",
year = "2017",
month = "5",
day = "1",
doi = "10.1161/STROKEAHA.116.016281",
language = "English (US)",
volume = "48",
pages = "1416--1419",
journal = "Stroke",
issn = "0039-2499",
publisher = "Lippincott Williams and Wilkins",
number = "5",

}

TY - JOUR

T1 - Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

AU - Labovitz, Daniel L.

AU - Shafner, Laura

AU - Reyes Gil, Morayma

AU - Virmani, Deepti

AU - Hanina, Adam

PY - 2017/5/1

Y1 - 2017/5/1

N2 - Background and Purpose - This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, 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 artificial intelligence platform (intervention) or to no daily monitoring (control). The artificial intelligence application visually identified the patient, the medication, and the confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results - For all patients (n=28), mean (SD) age was 57 years (13.2 years) and 53.6% were women. Mean (SD) cumulative adherence based on the artificial intelligence 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 direct oral anticoagulants, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on direct oral anticoagulant therapy.

AB - Background and Purpose - This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, 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 artificial intelligence platform (intervention) or to no daily monitoring (control). The artificial intelligence application visually identified the patient, the medication, and the confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results - For all patients (n=28), mean (SD) age was 57 years (13.2 years) and 53.6% were women. Mean (SD) cumulative adherence based on the artificial intelligence 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 direct oral anticoagulants, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on direct oral anticoagulant therapy.

KW - anticoagulants

KW - artificial intelligence

KW - patient compliance

KW - patient outcome assessment

KW - stroke

UR - http://www.scopus.com/inward/record.url?scp=85018673056&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85018673056&partnerID=8YFLogxK

U2 - 10.1161/STROKEAHA.116.016281

DO - 10.1161/STROKEAHA.116.016281

M3 - Article

C2 - 28386037

AN - SCOPUS:85018673056

VL - 48

SP - 1416

EP - 1419

JO - Stroke

JF - Stroke

SN - 0039-2499

IS - 5

ER -