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Record W2555955685

Developing a voluntary emergency health record for children in Ontario: a significant step towards a lifetime health record

2003· article· en· W2555955685 on OpenAlexaboutno aff
Andrew Szende

Bibliographic record

VenueElectronic Journal of e-Government · 2003
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsElectronic health recordHealth careMedical emergencyMedicineHealth recordsMedical recordEmergency departmentFamily medicineNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.502

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0080.003
Scholarly communication0.0080.006
Open science0.0030.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.041
GPT teacher head0.367
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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