Developing a voluntary emergency health record for children in Ontario: a significant step towards a lifetime health record
Bibliographic record
Abstract
The province of Ontario in Canada is planning to create a longitudinal electronic health record (EHR is a lifetime record of an individual's key health history and care within the health system) for each of its 12 million inhabitants. The province has funded the development of the electronic Child Health Network (eCHN), an advanced example of an integrated and shared EHR from multiple systems at multiple sites for the benefit of patients and clinicians. eCHN offers the province the foundation of a voluntary emergency health record for 1. Background The purpose of this paper is to examine the possible steps that can be taken in the Canadian province of Ontario towards the development of an emergency health record for and adults by building on the early success of a partial shared and integrated health record for This paper will first outline the history of the current record and then propose a possible approach to using that record as a platform for a province-wide emergency health record. The current record is only a partial but a useful one. It provides four of the key data domains that health care providers consider to be amongst the most essential: first, admission, discharge and transfer data; second, transcribed reports, such as clinic notes, operative notes and discharge summaries; third, laboratory reports; and, fourth, radiology reports and images. These records originate in various health information systems that are located at various hospitals throughout the province and are sent, in real time, to a central repository, from which they can be viewed by authorised health care providers. The stated aim of the provincial government is to build an emergency health record for and adults. This record, eventually, should be available to any physician or other health care provider who may be called upon to look after a patient whose health record is not immediately available at that location. It is believed that often the history of the patient is crucial for the provision of proper care and the avoidance of errors that could hurt, rather than help, the patient. Building such a record is expected to be a lengthy and costly enterprise. So far, the government has funded the establishment of a network that is providing a partial record and is moving in the direction of the province-wide, comprehensive record. The network is called the electronic Child Health Network (eCHN). It links 10 health care provider centres (six hospitals on eight sites, including a tertiary acute care hospital, a tertiary chronic and rehabilitation hospital, four community hospitals that serve as regional paediatric centres, a children's treatment centre, and a home nursing agency). It is currently funded to add at least 16 more hospitals and several other health care provider organisations in the coming year. The Province of Ontario is often called Canada's engine of growth. It is by far the largest of the country's 10 provinces, with a population of about 12 million people. It is estimated that about 25 per cent of the total population or about 3 million people are under 19 years of age and are, therefore, considered, by definition, to be paediatric patients or children. (Few of those between the ages of 14 and 18 would consider themselves to be children but, as far as the healthcare system is concerned, they are.)
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".