ArticleNPJ digital medicine2026
Randomized trial of electronic health record implemented AI risk prediction in kidney transplant care.
Article in NPJ digital medicine, 2026. 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.
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 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
14 authors.
Funding
Abstract
Artificial intelligence (AI)-based risk prediction is increasingly implemented in clinical care, but randomized evidence on communication and shared decision-making (SDM) outcomes is limited. In the single-center PRIMA-AI trial, 76 kidney transplant recipients with estimated glomerular filtration rate <30 mL/min/1.73 m² were randomized 1:1 to usual care or usual care plus an electronic health record (EHR)-integrated machine-learning model predicting 1-year graft loss risk. The primary outcome was patient-reported conversations about treatment options after graft loss during 12 months. Conversation frequency did not differ between groups (intervention 14/36 [39%] vs control 16/40 [40%]; chi-square p = 1.00). No significant between-group differences were observed for secondary clinical, SDM-related, relationship, or distress outcomes. Post-study user feedback suggested low and variable tool uptake with workflow barriers. Passive EHR availability of AI risk estimates did not improve communication or SDM-related outcomes. Future interventions should strengthen workflow integration and directly support SDM. Trial Registration: ClinicalTrials.gov number, NCT0605651, registered 2023-09-21.
Identifiers
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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.