Related Experiment Video
Updated: May 8, 2025
Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research
Published on: February 9, 2024
Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis
Jimin Shin1, Gabrielle Caron1, Petronella Stoltz2
11School of Medicine, University of Connecticut, Farmington.
Insights
Artificial intelligence (AI) in neonatal cranial deformity care increased orthotic therapy use but did not reduce radiography. Provider bias limited AI
Area of Science:
- Pediatric craniofacial healthcare
- Neonatal care
- Artificial intelligence in medicine
Background:
- Inefficiencies exist in neonatal cranial deformity management, including overuse of radiography and underuse of conservative therapies.
- Artificial intelligence (AI) offers potential solutions for improving diagnostic accuracy and resource utilization.
Purpose of the Study:
- To evaluate the impact of AI-generated craniometrics and interpretations on resource utilization in neonatal cranial deformity evaluation.
- To determine if AI integration improves initial management patterns for neonatal head shape concerns.
Main Methods:
- Retrospective chart review of pediatric patients with head shape concerns (January 2019-June 2023).
- Comparison of resource utilization (radiology, physical therapy, orthotics, specialist follow-up) between conventional and AI-assisted encounters.
- Propensity matching used to control for confounding variables between groups.
Main Results:
- AI model review was associated with increased orthotic therapy use (31.5% vs 38.6%, p=0.0132).
- AI was more frequently reviewed during telemedicine and advanced practice provider encounters.
- Radiology ordering rates and physical therapy or specialist follow-up did not significantly change with AI review.
Conclusions:
- AI-assisted diagnostics show potential for reducing radiography reliance and improving adherence to guidelines in neonatal cranial deformity care.
- Provider biases and underutilization of AI data currently limit its potential benefits.
- Future AI adoption requires improved provider education and user interface optimization.
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.
