REVIEW AND ANALYSIS OF INDIAN ELECTRONIC HEALTH RECORD (EHR) SYSTEM ALONG WITH DENMARK, CANADA AND AUSTRALIA
Bibliographic record
Abstract
Cloud-based information technology has a major impact on the improvement of service quality in many sectors, quality of service in health sectors being one of the most important out of these. In the heath sector as well, information technology may play a crucial role to keep the patient record in the digital version to improve the quality of health services, more importantly, emergency health services. Cloud-based secure storage of patients' records and appropriate authentication mechanism not only saves time for access to data, but less storage space will be required removing data redundancy and ambiguities. The cost of cloud-based centrally controlled health records will also be highly cost-effective relatively, as compared to conventional paper-based records or offline digital records. Electronic Health Record (EHR) is the standard terminology used for clouds based storage of health data. So EHR may not only provide easy access to data globally but also access mechanisms may be effectively controlled as per policies drawn by the governments. Electronic Health Records controlled and authenticated by government policies is also reliable for other health providers like medical specialists, physicians, nurses, and doctors, etc. EHR is an electronic record of a patient that reports on an individual's lifetime health. EHR is used to enhance the facility of the health services for a patient by using cloud-based computing applications. So, EHR provides all the information in an integrated care system inexpensively and flexibly along with the security of data that gives the authentic data for a patient. This paper aims to compare and analyze the health architecture of EHR of INDIA with that of to follow Denmark, Canada, and Australia. India is a country heavily populated and low per capita income and consequently, expenditure, has relatively behind in usage of digital technology in the fields of public services, particularly health services. Even on the part policy framework, India needs to improve. Only limited reference EHR guidelines have been framed by the government, however many developed countries including the ones mentioned in the analysis have already functional cloud-based EHR systems in place. The EHR policy framework proposed by the Indian government is also described in this paper. Analysis of policies, framework architecture and working functionality of EHR systems adopted by the mentioned countries may help in the effectively functional implementation of the EHR system in India as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".