Seeing it clearly: How to use graphics to guide collaborative consultation in pregnancy

Objective This study aims to examine the use of decision aids which may improve shared decision making through an exploration of risk apprehension and modes of collaborative communication. Conclusions Decision aids such as graphics have a key role in facilitating shared treatment decision making, perhaps particularly in perinatal mental health care. They are most useful within a trusting, two-way conversation.

Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation

Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings. Objectives: To identify priority areas for AI and PC in Ontario, Canada. Methods: A collaborative consultation event engaged multiple stakeholders in a nominal group technique process to generate, discuss, and rank ideas for how AI can support Ontario PC. Results: The consultation process produced nine ranked priorities: 1) preventative care and risk profiling, 2) patient self-management of condition(s), 3) management and synthesis of information, 4) improved communication between PC and AI stakeholders, 5) data sharing and interoperability, 6-tie) clinical decision support, 6-tie) administrative staff support, 8) practitioner clerical and routine task support, and 9) increased mental health care capacity and support. Themes emerging from small group discussions about barriers, implementation issues, and resources needed to support the priorities included: equity and the digital divide; system capacity and culture; data availability and quality; legal and ethical issues; user-centered design; patient-centredness; and proper evaluation of AI-driven tool implementation. Discussion: Findings provide guidance for future work on AI and PC. There are immediate opportunities to use existing resources to develop and test AI for priority areas at the patient, provider, and system level. For larger-scale, sustainable innovations, there is a need for longer-term projects that lay foundations around data and interdisciplinary work. Conclusion: Study findings can be used to inform future research and development of AI for PC, and to guide resource planning and allocation.

Nog-da-win-da-min: A Collaborative Consultation with First Nations about Children’s Well-being

The history of the relationship between child welfare agencies in Canada and First Nations has been fraught with pain, and the removal of children from their families and communities is often described as an attempted cultural genocide. The realities of colonization, residential schools, and the “60s scoop” have created a legacy of pain and distrust which can be difficult for today’s Native child welfare services to address. Nog-da-win-da-min Family and Community Services (NFCS) is an Anishinaabe agency that decided to consult with its seven member communities in order to obtain their input about future service development, but, with this legacy, were unsure how to engage the communities in meaningful dialogue. As such, they partnered with a team of researchers at NORDIK Institute to design and carry out a communitybased consultation. This article explores the collaborative process of creating and tailoring a consultation method to be an empowering and positive experience for participants, to be conducted within safe and accessible spaces throughout the communities. This required a thoughtful process development, which respected participants’ knowledge and experiences (local knowledge), accommodated intergenerational trauma with sensitivity, and that employed Indigenous language and concepts (such as the Medicine Wheel) to guide the process. This article outlines some key learnings for others undertaking similar dialogues and consultations.