Making eHealth a Clinical Priority for Nurse Leaders – Transforming and Integrating the Chief Nurse Executive’s Operational Agenda with the Corporate eHealth Agenda
Bibliographic record
Abstract
Today’s health system prides it’s “branding ” of providing patient focused care. However in reality, is the patient actually the focus, if and when one assesses organizational structures, frameworks and processes that deploy health care services? Do present day, traditional structures and processes support the patient or do they support the service provider? Technology driven solutions are changing the landscape of our practice settings where nurse leaders have traditionally been reluctant to incorporate eHealth mandates as a key focus in their operational agenda and planning. The omission of eHealth mandates at the senior leadership table is no longer feasible, since information and communication technologies (ICTs) are influencing practice and changing care delivery models that are inherently patient centric. This paper will highlight the activities and processes that are transforming an academic health science centre’s Chief Nurse Executive’s (CNE) operational agenda to include the corporate eHealth agenda. Specifically, through the creation of a unique corporate eHealth partnership that involves a Chief Information Officer (CIO), Chief Administrative Officer (CAO), Chief Medical Officer (CMO) and the Director of Clinical Informatics will be described. This multi-partnership model and associated structures has had positive impact and is a key strategy that St. Michael’s Hospital, Toronto, ON will leverage to meet its eHealth Agenda.
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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 | 0.050 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".