Evidence map›Paper›PMID 28386037›Full record

ArticleStroke2017

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

2 registry-linked trialsOpen access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Stroke, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 137 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 13 pooled it
21.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02599259 nacompletednot on this map

Using Artificial Intelligence on Mobile Devices to Automate Directly Observed Therapy (DOT), Confirm Medication Ingestion, and Optimize Treatment in Patients on Anticoagulation Therapy.

TypeinterventionalSponsorAiCureRan2015 to 2016Enrolled28ConditionsStrokeArmsMonitored (M+)
NCT05214625 completednot on this mapstarted 2020, after this paper: background citation

Questionnaire Study Concerning Artificial Intelligence and Its Application in (Gastrointestinal) Healthcare - Patients' and Physicians' Perspectives

TypeobservationalSponsorMaastricht University Medical CenterRan2020 to 2021Enrolled457ConditionsGastro-Intestinal DisordersArmsQuestionnaire
3 · Its place in the literature

Who cites it

137 citing papers in PubMed, 13 syntheses or guidelines pooled it, 312 citations in OpenAlex.

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  2. Impact of Interventions on Medication Adherence in Patients With Coexisting Diabetes and Hypertension.Health expectations : an international journal of public participation in health care and health policy · 2024
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  7. A Systematic Review on Mobile Health Applications' Education Program for Patients Taking Oral Anticoagulants.International journal of environmental research and public health · 2021
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77 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Daniel L LabovitzFrom the Montefiore Medical Center, Bronx, New York (D.L.L., M.R.G., D.V.); and AiCure, New York, NY (L.S., A.H.).
Laura ShafnerFrom the Montefiore Medical Center, Bronx, New York (D.L.L., M.R.G., D.V.); and AiCure, New York, NY (L.S., A.H.). laura.shafner@aicure.com.
Morayma Reyes GilFrom the Montefiore Medical Center, Bronx, New York (D.L.L., M.R.G., D.V.); and AiCure, New York, NY (L.S., A.H.).
Deepti VirmaniFrom the Montefiore Medical Center, Bronx, New York (D.L.L., M.R.G., D.V.); and AiCure, New York, NY (L.S., A.H.).
Adam HaninaFrom the Montefiore Medical Center, Bronx, New York (D.L.L., M.R.G., D.V.); and AiCure, New York, NY (L.S., A.H.).
Montefiore Medical Center · US

Funding

Automating Directly Observed Therapy as a Platform TechnologyR44TR000873 · NCATS · AI CURE TECHNOLOGIES, LLC · PI HANINA, ADAM · 2013 to 2014
$1.8M
NCATS NIH HHS R44 TR000873
6 · The paper itself

Abstract

background and purposeThis 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.

methodsA 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.

resultsFor 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.

conclusionsPatients, 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. CLINICAL

trial registrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT02599259.

Indexed as

Artificial IntelligenceMedical Informatics ApplicationsMedication AdherenceMobile ApplicationsAdultAgedAnticoagulantsBrain IschemiaFemaleHumansMaleMiddle AgedPatient Outcome AssessmentStrokeAnticoagulantsanticoagulantsartificial intelligencepatient compliancepatient outcome assessmentstroke

Identifiers

PMID28386037
PMCPMC5432369
OpenAlexW2604186734

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.