Assessing Impact: An evaluation of CIHR’s eHealth Innovations Partnership Program
Bibliographic record
Abstract
Background:Research funders make substantial investments in health services research to support the implementation, evaluation, and adoption of equitable, evidence-based solutions in the health system. However, there is limited understanding of the impacts of these investments and their influence on policy and practice. Assessing research impact is crucial to understanding the effectiveness of research, the extent to which research informs decision-making, how research contributes to improving population health and health system performance, and the overall value of research investments. After a decade of investing in digital health research, the Canadian Institutes of Health Research (CIHR) undertook a research impact assessment on its largest digital health program, the eHealth Innovations Partnership Program (eHIPP), to understand the program impacts and outputs.The eHIPP program had a strong focus on leveraging ehealth innovations as tools to improve care coordination. The aims of eHIPP were to: ) identify patient-oriented ehealth solutions that improve health outcomes and patient experience, and lower the cost of care; and 2) foster partnerships between providers, patients, families, researchers, decision makers and health technology partners to co-design, implement, and evaluate the effectiveness of the ehealth solutions. Approach: A mixed methods approach was taken to collect and analyze data from eHIPP grant recipients and their partners. This involved a review of CIHR documents related to the program and online surveys to the lead researchers and their partners (n=22 responses). Semi-structured interviews with grantees and their project partners (n=2) were used to further explore survey findings, followed by thematic analysis, triangulation, and synthesis of data from the various sources. Two complementary impact frameworks, the Canadian Academy of Heath Science Making an Impact Framework and the Canadian Health Services and Policy Research Alliance Informing Decision-Making an Impact Framework, served as the foundation for the impact assessment. Results:The eHIPP program supported 22 teams through a total investment of $42M, including $3.9M from CIHR and $28.7M (cash and in-kind support) from 9 applicant partners. There were 36 collaborating organizations that provided support to the 22 teams, which were located in 9 research institutions across 6 provinces.eHIPP had positive impacts on the individual research programs, research teams and careers of lead investigators. A total of 92.5% of respondents agreed or strongly agreed that eHIPP funding was essential for their research and their advancements in digital health. Researchers also developed their academic profiles through the mentorship and supervision of over 27 trainees, 65 publications and 94 presentations.The research teams reported over 35 co-designed solutions focused on improving the coordination of care across the health system, with most solutions in the areas of virtual care (n= 0), home health monitoring (n= 8), and wearables/sensors (n= 4). Respondents highly valued their partnerships with healthcare providers (95%), patients (86%), and policy-makers (52%). Additionally, respondents indicated that both health providers and patients were engaged in co-design, provided input and feedback, and supported the implementation process.Respondents reported their solutions had impacts on health and the health system, including improved equitable access to care (59%), better patient experience (59%), enhanced health outcomes (59%), improved population health (45%), and enhanced health equity (4%). Respondents also indicated their solutions contributed to influencing health system practices (50%) and informing health policy (32%). Implications:This assessment explored the impacts of CIHR eHIPP program. Findings indicate that collaboration among stakeholders, strong partnerships, and co-design approaches are highly valued and instrumental for implementing patient-centred digital health solutions that address population needs, enhance equity, and shape policy and practice. This research impact assessment highlights the critical role of digital innovations in advancing integrated care across Canada and contributes to advancing the science of funding for impact.
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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 | 0.271 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".