ArticleDigital health
Development and application of artificial intelligence follow-up system in patients with coronary heart disease.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Coronary heart disease (CHD) is a leading cause of mortality globally and poses significant public health challenges, particularly in China. Despite advancements in medical treatments, postdischarge management of patients with CHD remains inadequate, often resulting in poor medication adherence, low self-management, and increased readmission risks. Traditional manual follow-up methods are time-intensive, inefficient, and lack personalization, limiting their ability to address patients' complex healthcare needs. Objective: This study aimed to develop and evaluate an AI-assisted follow-up platform incorporating patient profiling and interactive voice response (IVR) technologies. The platform seeks to improve follow-up efficiency, enhance patient satisfaction, reduce the workload of medical staff, and provide a scalable model for intelligent follow up in clinical settings. Methods: This study utilized a prospective cohort design. A total of 4000 patients with CHD discharged from Tongji Hospital were enrolled, with 2000 patients assigned to a manual follow-up group from September 2022 to August 2023 and 2000 from September 2023 to August 2024 to an AI-assisted follow-up group. Key performance indicators, including follow-up rates, feedback collection rates, patient satisfaction, and medical staff workload, were compared between the two groups. Results: The follow-up rates were similar between the manual group (82.0%) and the AI-assisted group (81.3%) ( Conclusions: The AI-assisted follow-up platform demonstrated comparable effectiveness to manual methods while significantly improving efficiency and satisfaction for both patients and healthcare providers. By integrating patient profiling and IVR technologies, the platform offers personalized, dynamic, and cost-effective follow-up services. These findings highlight the potential of AI-enabled systems in optimizing chronic disease management and resource utilization.
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.