Opportunities and Challenges in the Adoption of Digital Health Technologies in Kazakhstan
Bibliographic record
Abstract
The study aimed to address the effectiveness of digital technologies in the medical system. The experience of digital technology introduction into the medical system of such countries as Canada, Israel, Germany, the UK and others was analysed. Artificial intelligence used in Israel with the C-Pi and AutoML platforms has improved diagnostic and prognostic accuracy by analysing big data on patients. The introduction of such solutions in the healthcare system can improve the accuracy of disease identification and develop personalised treatment regimens, which are highly relevant for managing chronic diseases. Telemedicine is an important area, especially in remote regions. The use of telemedicine devices for patient monitoring can reduce the burden on healthcare facilities and speed up the response to changes in health status. Mobile health monitoring apps, such as Israelэs Healthy.io, which allows for urine tests at home, can help patients monitor their condition without visiting a doctor. This approach is particularly useful during pandemics and limited access to healthcare. Electronic health applications, such as in Germany with Digital Health Applications, aimed at improving mental and physical health, could significantly help in the treatment of mental disorders and chronic diseases. However, this would require additional training for healthcare professionals to use the new technologies effectively. Canadian experience with a hybrid approach to palliative care has shown that remote consultations can improve the quality of care for seriously ill patients. Television rehabilitation programmes used in Norway and Australia for patients with heart failure could expand access to rehabilitation services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".