Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1614
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T22:01:52.705321Z
status: published
lens: g_space
sector: health
headline: Non-medical drivers of health and pediatric no-show prediction: identifying modifiable risk factors
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: No-shows were associated with unconfirmed appointments, non-active patient portal status, transportation needs, and housing instability.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers reviewed 7,931 scheduled pediatric appointments across six general pediatrics clinics between April and August 2024, and 34.8% ended as no-shows. They tested a transformer-based AI/ML model with and without screening data on non-medical drivers of health, such as transportation and housing.

Adding those social variables left predictive accuracy essentially unchanged, so the main value of the data lay in explaining why families miss visits rather than in sharper forecasts. The authors suggest predictions are most useful when tied to outreach and barrier reduction, though the study does not test whether such interventions reduce no-shows.
limitation: 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.
tag: Evidence-backed gain
key_points: Of 7,931 appointments studied, 2,761 (34.8%) were no-shows. | AUROC was 0.708 with and without NMDOH variables; AUPRC was 0.536 versus 0.535. | Demographic and insurance variables gave a small gain, to AUROC 0.721 and AUPRC 0.542, with or without NMDOH. | Unconfirmed appointments, inactive portal status, transportation needs and housing instability were linked to no-shows.
rundown: The retrospective study drew on electronic health record data and NMDOH screening results from six general pediatrics clinics. Model performance was measured with AUROC and AUPRC, and multivariable logistic regression was used to examine which factors were associated with no-show status.

Appointment confirmation and patient portal activation are operational factors that clinics can change, alongside social barriers like transportation and housing. The authors frame these as modifiable targets for clinical and operational interventions.

The paper concludes that AI-based no-show prediction may be most effective when combined with targeted outreach and barrier-reduction strategies, rather than relying on better prediction alone.
sources:
- peer_reviewed | Pediatric Research | https://doi.org/10.1038/s41390-026-05531-1 | 2026-10-08
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

0a18601fef16dde0fd5cfb7ba17cbc4e2dc8213760beb319b62ca999ce26759e

previous

0000000000000000000000000000000000000000000000000000000000000000