aceNon-medical drivers of health and pediatric no-show prediction: identifying modifiable risk factors
Background No-shows to medical appointments contribute to disruptions and inefficiencies in healthcare delivery. While artificial intelligence/machine learning (AI/ML) models are increasingly used to predict no-shows, few have incorporated non-medical drivers of health (NMDOH), particularly in pediatric populations. This study evaluated the inclusion of NMDOH in pediatric AI/ML no-show prediction and identified modifiable factors associated with no-shows. Methods A retrospective study was conducted of scheduled…
Analysis of the model's inputs identified actionable factors tied to missed pediatric visits, such as transportation needs, housing instability, unconfirmed appointments and inactive portal status, which clinics can target.
The study is retrospective, covers only April to August 2024 in six general pediatrics clinics, and shows associations rather than tested interventions, so it does not show that addressing these factors reduces no-shows.
Evidence
- Peer-reviewedPediatric Research2026-10-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1614, v1: “Non-medical drivers of health and pediatric no-show prediction: identifying modifiable risk factors.” Truvace, 2026-10-16. /record/TRV-2026-1614 (accessed at citation time). sha256 0a18601fef16dde0…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1614 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.