Research impact assessment of a Canadian digital health funding program: a case study
Bibliographic record
Abstract
BACKGROUND: Digital innovations have the potential to enhance equitable access to health systems, improve care integration and support learning health systems. Research funders make substantial investments in digital health research to advance the uptake of evidence-informed digital solutions within health systems, yet their impacts on health and health system outcomes, health equity, policy and practice remain poorly understood. Research impact assessments (RIAs) serve as a vital tool for funders to examine the links between research investments and real-world change. The Canadian Institutes of Health Research commissioned an RIA on its largest digital health program, the eHealth Innovations Partnership Program (eHIPP), to understand the program's outputs and impacts. METHODS: This study applied two complementary frameworks, the Canadian Academy of Heath Science's (CAHS) Making an Impact Framework and the Canadian Health Services and Policy Research Alliance's (CHSPRA) Informing Decision-Making Framework, to assess the research impact of the eHIPP program, funded from 2015 to 2021. A mixed-methods approach was taken to collect and analyse data from eHIPP grant recipients and their partners. RESULTS: The eHIPP program supported 22 research teams through a total investment of CAD$ 42M. The RIA revealed impacts in the areas of capacity development, knowledge creation, informing decision-making and health outcomes. The teams generated 36 co-designed, evidenced-informed solutions, 79 publications, 194 presentations and 38 media interviews or articles. Solutions were reported to influence health system practice (52%) and policy (33%), improve health outcomes (62%), enhance equitable access to care (62%), improve patient (62%) and provider experience (52%), increase cost-effectiveness (52%), enhance population health (48%) and improve health equity (43%). CONCLUSIONS: This RIA study highlights the importance of stakeholder collaboration, robust partnerships and co-design approaches in effectively integrating patient-centred digital health solutions into health systems. These elements are key to advancing the Quintuple Aim (improved cost, population health and equity and experience of patients and providers) and supporting evidence-informed decisions. This paper presents a first case study applying the CAHS and CHSPRA frameworks to assess the impacts of a large digital health funding program. Further, it explores the program's outcomes and impacts and highlights considerations, successes and challenges for funders when applying RIA.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".