Trial reportJournal of the American Medical Informatics Association : JAMIA2024
Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.
Trial report in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04502563 (Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 Multicenter Implementation Study), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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.
Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 Multicenter Implementation Study
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Factors Influencing the Uptake of Digital Health Interventions for Cardiovascular Disease Prevention Among Healthcare Providers: A Systematic Review.Global heart · 2026Pooled it
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Neuropsychological Mechanisms Associated with the Effectiveness of AI-Delivered Health Promotion Programs: A Comprehensive Meta-Analysis.Brain sciences · 2026Review
- Noninvasive biometric monitoring technologies for patients with heart failure.Heart failure reviews · 2025Review
- Application of Telemedicine in the Management of Cardiovascular Diseases: A Focus on Heart Failure.Reviews in cardiovascular medicine · 2025Review
- Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters.BMC cardiovascular disorders · 2025Article
- Combined use of the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework with other implementation frameworks: a systematic review.Implementation science communications · 2025Review
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesWe conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). MATERIALS AND
methodsA mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected.
resultsClinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. DISCUSSION: Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved.
conclusionThe implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.
Indexed as
Identifiers
What OpenQuestion holds
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.