Framework for development of Information Technology Infrastructure for Health (ITIH) care in India – a critical study
Bibliographic record
Abstract
Health care in India is undertaken by huge numbers of providers - Government, Corporate and Private. Most of the providers do not maintain the medical records systematically and properly following international standards and guidelines. Paper-based health records and unavailability of right information at right time prevents better health care. Here comes the importance of health informatics. Developmental origins of Health and Disease (DOHaD), has proven the importance of records of individual in predicting/explaining the diseases. Dept. of Information Technology, Govt. of India, has taken initiative to develop Information Technology Infrastructure for Healthcare (ITIH) in India. ITIH provides the modalities and procedures to be undertaken for better health care of vast population of India. The framework is a guideline document and comprehensive roadmap that prescribes IT standards and guidelines for each stakeholder across diverse healthcare settings in India with the goal of building an Integrated Healthcare Information Network. The paper highlights the formation of expert group and terms of reference, defining the standards and guidelines, identified the nodal agencies, R&D organizations and healthcare applications. The importance of tele-medicine are also discussed. The paper has discussed the main challenges, namely, funding, computer literacy, infrastructure and coordination, retro conversion of manual system of records to electronic system, standards and guidelines, interoperability, privacy, information overload.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".