Intersection of Health Informatics Tools and Community Engagement in Health-Related Research to Reduce Health Inequities: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The exponential growth of health information technology has the potential to facilitate community engagement in research. However, little is known about the use of health information technology in community-engaged research, such as which types of health information technology are used, which populations are engaged, and what are the research outcomes. OBJECTIVE: The objectives of this scoping review were to examine studies that used health information technology for community engagement and to assess (1) the types of populations, (2) community engagement strategies, (3) types of health information technology tools, and (4) outcomes of interest. METHODS: We searched PubMed and PCORI Literature Explorer using terms related to health information technology, health informatics, community engagement, and stakeholder involvement. This search process yielded 967 papers for screening. After inclusion and exclusion criteria were applied, a total of 37 papers were analyzed for key themes and for approaches relevant to health information technology and community engagement research. RESULTS: This analysis revealed that the communities engaged were generally underrepresented populations in health-related research, including racial or ethnic minority communities such as Black/African American, American Indian/Alaska Native, Latino ethnicity, and communities from low socioeconomic backgrounds. The studies focused on various age groups, ranging from preschoolers to older adults. The studies were also geographically spread across the United States and the world. Community engagement strategies included collaborative development of health information technology tools and partnerships to promote use (encompassing collaborative development, use of community advisory boards, and focus groups for eliciting information needs) and use of health information technology to engage communities in research (eg, through citizen science). The types of technology varied across studies, with mobile or tablet-based apps being the most common platform. Outcomes measured included eliciting user needs and requirements, assessing health information technology tools and prototypes with participants, measuring knowledge, and advocating for community change. CONCLUSIONS: This study illustrates the current landscape at the intersection of health information technology tools and community-engaged research approaches. It highlights studies in which various community-engaged research approaches were used to design culturally centered health information technology tools, to promote health information technology uptake, or for engagement in health research and advocacy. Our findings can serve as a platform for generating future research upon which to expand the scope of health information technology tools and their use for meaningful stakeholder engagement. Studies that incorporate community context and needs have a greater chance of cocreating culturally centered health information technology tools and better knowledge to promote action and improve health outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.037 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".