The Application of Human-Centered Design Approaches in Health Research and Innovation: A Narrative Review of Current Practices
Bibliographic record
Abstract
BACKGROUND: Human-centered design (HCD) approaches to health care strive to support the development of innovative, effective, and person-centered solutions for health care. Although their use is increasing, there is no integral overview describing the details of HCD methods in health innovations. OBJECTIVE: This review aims to explore the current practices of HCD approaches for the development of health innovations, with the aim of providing an overview of the applied methods for participatory and HCD processes and highlighting their shortcomings for further research. METHODS: A narrative review of health research was conducted based on systematic electronic searches in the PubMed, CINAHL, Embase, Cochrane Library, Web of Science, PsycINFO, and Sociological Abstracts (2000-2020) databases using keywords related to human-centered design, design thinking (DT), and user-centered design (UCD). Abstracts and full-text articles were screened by 2 reviewers independently based on predefined inclusion criteria. Data extraction focused on the methodology used throughout the research process, the choice of methods in different phases of the innovation cycle, and the level of engagement of end users. RESULTS: This review summarizes the application of HCD practices across various areas of health innovation. All approaches prioritized the user's needs and the participatory and iterative nature of the design process. The design processes comprised several design cycles during which multiple qualitative and quantitative methods were used in combination with specific design methods. HCD- and DT-based research primarily targeted understanding the research context and defining the problem, whereas UCD-based work focused mainly on the direct generation of solutions. Although UCD approaches involved end users primarily as testers and informants, HCD and DT approaches involved end users most often as design partners. CONCLUSIONS: We have provided an overview of the currently applied methodologies and HCD guidelines to assist health care professionals and design researchers in their methodological choices. HCD-based techniques are challenging to evaluate using traditional biomedical research methods. Previously proposed reporting guidelines are a step forward but would require a level of detail that is incompatible with the current publishing landscape. Hence, further development is needed in this area. Special focus should be placed on the congruence between the chosen methods, design strategy, and achievable outcomes. Furthermore, power dimensions, agency, and intersectionality need to be considered in co-design sessions with multiple stakeholders, especially when including vulnerable groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| grok | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| opus | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.059 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 3 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".