Global eHealth capacity: secondary analysis of WHO data on eHealth and implications for kidney care delivery in low-resource settings
Bibliographic record
Abstract
OBJECTIVE: To describe the use of electronic health (eHealth) in support of health coverage for kidney care across International Society of Nephrology (ISN) regions. DESIGN: Secondary analysis of WHO survey on eHealth as well as use of data from the World Bank, and Internet World Stats on global eHealth services. SETTING: A web-based survey on the use of eHealth in support of universal health coverage. PARTICIPANTS: 125 WHO member states provided response. PRIMARY OUTCOME MEASURES: Availability of eHealth services (eg, electronic health records, telehealth, etc) and governance frameworks (policies) for kidney care across ISN regions. RESULTS: The survey conducted by the WHO received responses from 125 (64.4%) member states, representing 4.4 billion people globally. The number of mobile cellular subscriptions was <100% of the population in Africa, South Asia, North America and North East Asia; the percentage of internet users increased from 2015 to 2020 in all regions. Western Europe had the highest percentage of internet users in all the periods: 2015 (82.0%), 2019 (90.7%) and 2020 (93.9%); Africa had the least: 9.8%, 21.8% and 31.4%, respectively. The North East Asia region had the highest availability of national electronic health record system (75%) and electronic learning access in medical schools (100%), with the lowest in Africa (27% and 39%, respectively). Policies concerning governance aspects of eHealth (eg, privacy, liability, data sharing) were more widely available in high-income countries (55%-93%) than in low-income countries (0%-47%), while access to mobile health for treatment adherence was more available in low-income countries (21%) than in high-income countries (7%). CONCLUSION: The penetration of eHealth services across ISN regions is suboptimal, particularly in low-income countries. Increasing utilisation of internet communication technologies provides an opportunity to improve access to kidney education and care globally, especially in low-income countries.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".