Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.
Aviv Y LandauAshley BlanchardNia AtkinsStephanie SalazarKenrick D CatoDesmond U PattonMaxim TopazPublished in: JMIR formative research (2023)
Our findings highlight essential considerations from primary caregivers for developing an ML-based model for detecting child abuse and neglect in ED settings. This includes how to define child abuse and neglect from a primary caregiver lens. Miscommunication between patients and health providers can potentially lead to a misdiagnosis, and therefore, have a negative impact on medical documentation. Additionally, the outcome and application of the ML-based models for detecting abuse and neglect may cause additional harm than expected to the community. Further research is needed to validate these findings and integrate them into creating an ML-based model.
Keyphrases
- mental health
- machine learning
- healthcare
- end stage renal disease
- palliative care
- emergency department
- public health
- intimate partner violence
- ejection fraction
- newly diagnosed
- chronic kidney disease
- systematic review
- peritoneal dialysis
- social media
- quality improvement
- patient reported outcomes
- big data
- cataract surgery