Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.

OBJECTIVES: Medically minor but clinically important findings associated with physical child abuse, such as bruises in pre-mobile infants, may be identified by frontline clinicians yet the association of these injuries with child abuse is often not recognized, potentially allowing the abuse to continue and even to escalate. An accurate natural language processing (NLP) algorithm to identify high-risk injuries in electronic health record notes could improve detection and awareness of abuse. The objectives were to: 1) develop an NLP algorithm that accurately identifies injuries in infants associated with abuse and 2) determine the accuracy of this algorithm. METHODS: An NLP algorithm was designed to identify ten specific injuries known to be associated with physical abuse in infants. Iterative cycles of review identified inaccurate triggers, and coding of the algorithm was adjusted. The optimized NLP algorithm was applied to emergency department (ED) providers' notes on 1344 consecutive sample of infants seen in 9 EDs over 3.5 months. Results were compared with review of the same notes conducted by a trained reviewer blind to the NLP results with discrepancies adjudicated by a child abuse expert. RESULTS: Among the 1344 encounters, 41 (3.1%) had one of the high-risk injuries. The NLP algorithm had a sensitivity and specificity of 92.7% (95% confidence interval [CI]: 79.0%-98.1%) and 98.1% (95% CI: 97.1%-98.7%), respectively, and positive and negative predictive values were 60.3% and 99.8%, respectively, for identifying high-risk injuries. CONCLUSIONS: An NLP algorithm to identify infants with high-risk injuries in EDs has good accuracy and may be useful to aid clinicians in the identification of infants with injuries associated with child abuse.

Feasibility of Using Mobile Health to Promote Self-Management in Spina Bifida.

OBJECTIVE: To determine feasibility of using the interactive Mobile Health and Rehabilitation (iMHere) system in spina bifida and its effects on psychosocial and medical outcomes. DESIGN: In a randomized controlled trial, 13 intervention participants using the iMHere system and receiving usual care and 10 control participants receiving usual care were followed for 1 year. RESULTS: Feasibility of use of the system was demonstrated by participants using a customized smartphone system for reminders to conduct various self-care tasks, upload photos of wounds, manage medications, complete mood surveys, and for secure messaging. High usage of the system was associated with positive changes in the subscales of the Adolescent Self-Management and Independence Scale II. CONCLUSION: Use of the iMHere system in spina bifida is feasible and was associated with short-term self-reported improvements in self-management skill. This system holds promise for use in many diverse chronic care models to support and increase self-management skills.