Designing an AI-Enhanced Public Health Care Platform for the Rapidly Aging Population in South Korea: Protocol for a Mixed Methods Study Based on the Design Thinking Approach
Bibliographic record
Abstract
Background: South Korea is undergoing one of the world's fastest demographic shifts toward an aging society, with projections indicating that by 2047, half of all households will be led by older adults. While digital health technologies such as mobile health apps and telemedicine offer promising solutions for promoting healthy aging and reducing health care expenditures, their adoption among older Koreans remains limited. To address these challenges, this study will use a user-centered design thinking approach to develop a public health care platform tailored to the needs of South Korea's older adults. Objective: The primary objective of this study is to develop and evaluate a user-centered digital health care platform tailored to older adults in South Korea, with the aim of overcoming key barriers, such as low digital literacy, interface complexity, and mistrust in artificial intelligence-driven systems, and ultimately bridging the digital divide in health. Methods: This mixed methods study will integrate qualitative and quantitative research within a design thinking framework, progressing through 3 operational phases: empathize and define, ideate, and prototype and test. In phase 1, a scoping literature review, field observations at 5 community centers for older people and in-depth interviews with 29 older adults and 15 stakeholders were conducted to identify behavioral barriers and user needs. In phase 2, an open idea contest and expert focus groups were used to generate and prioritize innovative features for the platform. Phase 3 involves co-design workshops, minimum viable product development using a no-code platform, and usability testing with 8 to 10 older adults. Results: The research received a grant (HI22C1477) from the Korea Health Industry Development Institute (2023-2025) and ethical approval (2023-01-014) from Sungkyunkwan University. As of July 2025, data collection for phase 2 is ongoing, and preliminary findings are expected by late 2025. Conclusions: By adopting an inclusive design thinking approach, this study aims to produce a practical and user-centered platform for older adults. The findings will contribute to research on digital health equity and offer actionable insights for community-centered technology design.
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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 | 0.072 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".