Landau Aviv Y, Blanchard Ashley, Atkins Nia, Salazar Stephanie, Cato Kenrick, Patton Desmond U, Topaz Maxim
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
New York Presbyterian Morgan Stanley Children's Hospital, Columbia University Irving Medical Center, New York, NY, United States.
JMIR Form Res. 2023 Jan 31;7:e40194. doi: 10.2196/40194.
Child abuse and neglect, once viewed as a social problem, is now an epidemic. Moreover, health providers agree that existing stereotypes may link racial and social class issues to child abuse. The broad adoption of electronic health records (EHRs) in clinical settings offers a new avenue for addressing this epidemic. To reduce racial bias and improve the development, implementation, and outcomes of machine learning (ML)-based models that use EHR data, it is crucial to involve marginalized members of the community in the process.
This study elicited Black and Latinx primary caregivers' viewpoints regarding child abuse and neglect while living in underserved communities to highlight considerations for designing an ML-based model for detecting child abuse and neglect in emergency departments (EDs) with implications for racial bias reduction and future interventions.
We conducted a qualitative study using in-depth interviews with 20 Black and Latinx primary caregivers whose children were cared for at a single pediatric tertiary-care ED to gain insights about child abuse and neglect and their experiences with health providers.
Three central themes were developed in the coding process: (1) primary caregivers' perspectives on the definition of child abuse and neglect, (2) primary caregivers' experiences with health providers and medical documentation, and (3) primary caregivers' perceptions of child protective services.
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.
虐待和忽视儿童问题，曾经被视为一个社会问题，如今已泛滥成灾。此外，医疗服务提供者一致认为，现有的刻板印象可能将种族和社会阶层问题与虐待儿童联系起来。临床环境中电子健康记录（EHRs）的广泛应用为解决这一泛滥问题提供了一条新途径。为了减少种族偏见，并改善使用电子健康记录数据的机器学习（ML）模型的开发、实施和结果，让社区中的边缘化成员参与这一过程至关重要。
本研究引出了黑人和拉丁裔主要照顾者对于生活在服务不足社区时遭受虐待和忽视儿童问题的观点，以突出设计基于机器学习的模型来检测急诊科（EDs）中虐待和忽视儿童情况的注意事项，这对减少种族偏见和未来干预措施具有重要意义。
我们进行了一项定性研究，对20名黑人和拉丁裔主要照顾者进行了深入访谈，他们的孩子在一家儿科三级护理急诊科接受治疗，以深入了解虐待和忽视儿童问题以及他们与医疗服务提供者的经历。
在编码过程中形成了三个核心主题：（1）主要照顾者对虐待和忽视儿童定义的看法，（2）主要照顾者与医疗服务提供者及医疗记录的经历，（3）主要照顾者对儿童保护服务的看法。
我们的研究结果突出了主要照顾者对于开发基于机器学习的模型以检测急诊科中虐待和忽视儿童情况的重要考虑因素。这包括如何从主要照顾者的角度定义虐待和忽视儿童。患者与医疗服务提供者之间的沟通不畅可能会导致误诊，从而对医疗记录产生负面影响。此外，基于机器学习的检测虐待和忽视儿童模型的结果及应用可能会给社区带来比预期更大的伤害。需要进一步的研究来验证这些发现，并将其纳入基于机器学习的模型创建中。
