Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches.

Abstract

Objective: Overuse of radiography studies and underuse of conservative therapies for cranial deformities in neonates is a known inefficiency in pediatric craniofacial healthcare. This study sought to establish whether the introduction of artificial intelligence (AI)-generated craniometrics and craniometric interpretations into craniofacial clinical workflow improved resource utilization patterns in the initial evaluation and management of neonatal cranial deformities.

Methods: A retrospective chart review of pediatric patients referred for head shape concerns between January 2019 and June 2023 was conducted. Patient demographics, final encounter diagnosis, review of an AI analysis, and provider orders were documented. Patients were divided based on whether an AI cranial deformity analysis was documented as reviewed during the index evaluation, then both groups were propensity matched. Rates of index-encounter radiology studies, physical therapy (PT), orthotic therapy, and craniofacial specialist follow-up evaluations were compared using logistic regression and ANOVA analyses.

Results: One thousand patient charts were reviewed (663 conventional encounters, 337 AI-assisted encounters). One-to-one propensity matching was performed between these groups. AI models were significantly more likely to be reviewed during telemedicine encounters and advanced practice provider (APP) visits (54.8% telemedicine vs 11.4% in-person, p < 0.0001; 12.3% physician vs 44.4% APP, p < 0.0001). All AI diagnoses of craniosynostosis versus benign deformities were congruent with final diagnoses. AI model review was associated with a significant increase in the use of orthotic therapies for neonatal cranial deformities (31.5% vs 38.6%, p = 0.0132) but not PT or specialist follow-up evaluations. Radiology ordering rates did not correlate with AI-interpreted data review.

Conclusions: As neurosurgeons and pediatricians continue to work to limit neonatal radiation exposure and contain healthcare costs, AI-assisted clinical care could be a cheap and easily scalable diagnostic adjunct for reducing reliance on radiography and encouraging adherence to established clinical guidelines. In practice, however, providers appear to default to preexisting diagnostic biases and underweight AI-generated data and interpretations, ultimately negating any potential advantages offered by AI. AI engineers and specialty leadership should prioritize provider education and user interface optimization to improve future adoption of validated AI diagnostic tools.