ArticleJMIR medical informatics2026
Design and Preliminary Testing of the CardioCare System in Health Checkup Centers: Implementation Report.
Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Health checkup programs in China reach millions of older adults for cardiovascular risk screening, but few effective mechanisms exist to ensure high-risk individuals receive follow-up preventive care. Objective: We developed the CardioCare system, a digital tool integrating a 10-year cardiovascular disease (CVD) risk prediction model with personalized health management features, to address this gap. Methods: The CardioCare system was used in a hospital-based health checkup center. The system incorporates an established 10-year CVD risk model to estimate risk. We conducted preliminary usability testing at a health checkup center in Guangzhou. Older adults identified as high risk (≥10% 10-year CVD risk) were given personalized feedback and invited to a cardiovascular risk management clinic. Unlabelled: The CardioCare system was successfully implemented, automatically stratifying CVD risk for each checkup attendee and generating patient-specific recommendations. The CVD risk model integration functioned without major technical issues, although minor performance delays were identified and resolved, and physicians and nurses reported that the tool was user-friendly and fit smoothly into the clinic workflow. However, patient engagement was low: of the 2069 high-risk individuals invited for follow-up care, 181 (8.7%) attended at least 1 in-person visit at the clinic. Feedback from the patients who engaged was positive regarding the clarity of risk information and advice, but the low response rate indicated significant barriers to uptake. Conclusions: This study demonstrated the technical feasibility of integrating a digital CVD risk assessment and management system into a health checkup setting. The system was successfully embedded into the clinical workflow and was acceptable to physicians and nurses. However, the low follow-up attendance among high-risk individuals highlights a major implementation limitation, indicating that risk identification alone is insufficient to ensure patient engagement in preventive care. Future development should focus on structured, multichannel engagement strategies and more convenient follow-up models.
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.