Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services. OBJECTIVE: This study was conducted to integrate and analyze users' experiences along the HBD analysis journey using the human-centered design methodology and reflect them in the development of AI agents that support future HBD analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and use of HBD, which will be urgently needed in the near future. METHODS: Using human-centered design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analyzing and using HBD. We identified users' empirical characteristics, emotions, pain points, and needs related to HBD analysis and use and created personas and journey maps. RESULTS: The general characteristics of participants (n=16) were as follows: the majority were in their 40s (n=6, 38%) and held a PhD degree (n=10, 63%). Professors (n=7, 44%) and health care personnel (n=10, 63%) represented the largest professional groups. Participants' experiences with big data analysis varied, with 25% (n=4) being beginners and 38% (n=6) having extensive experience. Common analysis methods included statistical analysis (n=7, 44%) and data mining (n=6, 38%). Qualitative findings from shadowing and in-depth interviews revealed key challenges: lack of knowledge on using analytical solutions, crisis management difficulties during errors, and inadequate understanding of health care data and clinical decision-making, especially among non-health care professionals. Three types of personas and journey maps-health care professionals as big data analysis beginners, health care professionals who have experience in big data analytics, and non-health care professionals who are experts in big data analytics-were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through a navigation function, a crisis management support system, communication and sharing among users, and expert linkage service. CONCLUSIONS: The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and use. This is expected to further increase the usability of HBD by helping users perform effective use of HBD more easily.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".