Ethical and practical considerations related to data sharing when collecting patient-reported outcomes in care-based child health research

Abstract

Purpose The collection and use of patient reported outcomes (PROs) in care-based child health research raises challenging ethical and logistical questions. This paper offers an analysis of two questions related to PROs in child health research: (1) Is it ethically obligatory, desirable or preferable to share PRO data collected for research with children, families, and health care providers? And if so, (2) What are the characteristics of a model best suited to guide the collection, monitoring, and sharing of these data? Methods A multidisciplinary team of researchers, providers, patient and family partners, and ethicists examined the literature and identified a need for focus on PRO sharing in pediatric care-based research. We constructed and analyzed three models for managing pediatric PRO data in care-based research, drawing on ethical principles, logistics, and opportunities to engage with children and families. Results We argue that it is preferable to share pediatric PRO data with providers, but to manage expectations and balance the risks and benefits of research, this requires a justifiable data sharing model. We argue that a successful PRO data sharing model will allow children and families to have access to and control over their own PRO data and be engaged in decision-making around how PROs collected for research may be integrated into care, but require support from providers. Conclusion We propose a PRO data sharing model that can be used across diverse research settings and contributes to improved transparency, communication, and patient-centered care and research.

Advances and Development of Electronic Neural Interfaces

“…Extensive human research is required to successfully translate existing proof-of-concept implantable and wearable devices. To examine the implantable prototypes from an analytical point of view, Vanderhout et al [24]offer pertinent ethical considerations on many elements of preliminary human research (such as validity, and reliability, subject selection, risk evaluation, data security and privacy, and informed permission) in a perspective paper. When it comes to analyzing and processing the massive amounts of physiological data produced by implantable sensors in human research, machine learning is a viable technique.…”