2024 Watch List: Top 10 Technologies and Issues Related to Caring for Children and Youth With Medical Complexity
Bibliographic record
Abstract
What Is the 2024 Watch List? The Watch List annual Horizon Scan report presents emerging technologies and issues that have the potential to shape the future of health care in Canada. CADTH’s 2024 Watch List focuses on care for children and youth with medical complexity. This top 10 has been divided in 2 parts — the top 5 technologies and top 5 issues related to children and youth with medical complexity that have the potential to make a significant and meaningful impact in transforming health systems in Canada over the next 5 years. These technologies and issues could shape the future of health care for not only children and youth with medical complexity, but also for others with chronic health conditions. Why Is This an Issue? Children and youth (people aged 24 years and younger) with medical complexity are a diverse group with a range of needs (e.g., single or multiple conditions, rare diseases). Although there is no single definition, common characteristics include significant functional limitations often causing the child or youth to be reliant on technology; high health care utilization, often requiring specialized care and services from different providers in multiple settings; and high health care service needs, such as care provision in the home and care coordination, which can have significant social and financial impacts on caregivers and the family. Children and youth with medical complexity account for less than 1% of all children and youth in Canada, but they account for 37% of hospital stays and 17% of emergency department visits. Due in part to the high number of health care interactions, this group experiences the effects of challenges within the health care system more acutely than their less medically complex peers. What Is the Potential Impact? The Watch List highlights areas for innovation, systems change, and investment. Advances in medical care have resulted in more children living with conditions that previously would not have been survivable in infancy and childhood. Now there are more children and youth with medical complexity, and they are living longer. Our current health systems were not designed to meet the complex needs of this group of people and their caregivers, including that many of them face challenges in accessing needed care. This year’s Watch List spotlights new and emerging technologies and key issues that could have a major impact on how patient care is provided to children and youth with medical complexity. What Else Do We Need to Know? In the 2024 Watch List, we identify and describe the top 5 new and emerging technologies that could shape the future of health care for children and youth with medical complexity in Canada, including new models of care and technologies and systems to improve communication. We also explore some considerations for health care decision-makers about the potential impact of these technologies on care pathways, health care human resources, health care infrastructure, and health equity. The 2024 Watch List also identifies the top 5 issues that limit health systems from providing optimal quality of care for children and youth with medical complexity. Key issues such as the need for increased interoperability between health systems and the need for strategies to improve sustainability warrant more attention and will influence the wider adoption, diffusion, and implementation of new and emerging technologies for children and youth with medical complexity. Monitoring ongoing developments and evidence related to the top technologies and issues highlighted in the 2024 Watch List can help guide health system planning in Canada and improve access to high-quality care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".