IT Technologies in Health Care Institutions in Russia: Application with a Digital Interactive Map
Bibliographic record
Abstract
IT technologies are currently one of the priority areas for the development of the healthcare sector. In the context of global information and development of the digital economy, digitalization is able to ensure the availability and decent quality of services provided without increasing the cost. The purpose of this study is to study the processes of informatization affecting medical companies and determine the role of information technologies on the base of the applications in health care institutions in Russia. The object of the study is the medical institutions of the healthcare sector, and the subject of study is the information and digital technologies used in the practice of companies. The role of information technologies in the activities of a medical institution is determined on the example of application for distribution of ambulances from a distribution center (DC). Key elements of the digital transformation of healthcare institutions (development of the author of the study) are advised in the article. The use of digital technologies in the practice of managing a medical institution is not an end in itself. The analysis made it possible to make sure that the most important tool for improving the efficiency of management, the implementation of goals and purposes, a means of adaptation in the activities of medical institutions in the context of global informatization and digitalization of the economy is the active use of information and digital technologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".