Assessment of Digital Capabilities by 9 Countries in the Alliance for Healthy Cities Using AI: Cross-Sectional Analysis
Bibliographic record
Abstract
Background: The Alma-Ata Declaration of 1978 initiated a global focus on universal health, supported by the World Health Organization (WHO) through healthy cities policies. The concept emerged at the 1984 Toronto "Beyond Health Care" conference, leading to WHO's first pilot project in Lisbon in 1986. The WHO continues to support regional healthy city networks, emphasizing digital transformation and data-driven health management in the digital era. Objective: This study explored the capabilities of digital healthy cities within the framework of digital transformation, focusing on member countries of the Asian Forum of Healthy Cities. It examined the cities' preparedness and policy needs for transitioning to digital health. Methods: A cross-sectional survey was conducted of 9 countries-Australia, Cambodia, China, Japan, South Korea, Malaysia, Mongolia, the Philippines, and Vietnam-from August 1 to September 21, 2023. The 6-section SPIRIT (setting approach and sustainability; political commitment, policy, and community participation; information and innovation; resources and research; infrastructure and intersectoral; and training) checklist was modified to assess healthy cities' digital capabilities. With input from 3 healthy city experts, the checklist was revised for digital capabilities, renaming "healthy city" to "digital healthy city." The revised tool comprises 8 sections with 33 items. The survey leveraged ChatGPT (version 4.0; OpenAI, Microsoft), accessed via Python (Python Software Foundation) application programming interface. The openai library was installed, and an application programming interface key was entered to use ChatGPT (version 4.0). The "GPT-4 Turbo" model command was applied. A qualitative analysis of the collected data was conducted by 5 healthy city experts through group deep-discussions. Results: The results indicate that these countries should establish networks and committees for sustainable digital healthy cities. Cambodia showed the lowest access to electricity (70%) and significant digital infrastructure disparities. Efforts to sustain digital health initiatives varied, with countries such as Korea focusing on telemedicine, while China aimed to build a comprehensive digital health database, highlighting the need for tailored strategies in promoting digital healthy cities. Life expectancy was the highest in the Republic of Korea and Japan (both 84 y). Access to electricity was the lowest in Cambodia (70%) with the remaining countries having had 95% or higher access. The internet use rate was the highest in Malaysia (97.4%), followed by the Republic of Korea (97.2%), Australia (96.2%), and Japan (82.9%). Conclusions: This study highlights the importance of big data-driven policies and personal information protection systems. Collaborative efforts across sectors for effective implementation of digital healthy cities. The findings suggest that the effectiveness of digital healthy cities is diminished without adequate digital literacy among managers and users, suggesting the need for policies to improve digital literacy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".