Initial development of tools to identify child abuse and neglect in pediatric primary care.

ABSTRACT:

Background

Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to de

SUBMITTER: Hanson RF 

PROVIDER: S-EPMC10656827 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature