Healthcare Organizations and Enterprise Architecture: A Case Study in Canada
Bibliographic record
Abstract
The paper focuses on exploring the perceptions of stakeholders (medical doctors, nurses, pharmacists, IT staff and other employees) in healthcare organizations in Canada on how they developed Enterprise Architecture (EA) to improve managerial decision making and align business activities and Information Technology (IT). Both quantitative and qualitative methods were adopted for this research. A total of 120 questionnaires were sent out but only 72 responses were received. Participants included industry professionals involved in implementing information systems (IS) within healthcare organizations. Data was collected physically and through emails. Also 3 subject matter experts (experts) were interviewed for the study. These experts each have over ten years’ experience in EA practice and are doctorate degree holders (PhDs). The results of the study showed that stakeholders see the potential for EA to be a tool for planning IT/IS projects, breaking down organizational silos, creating digital transformation, and proactively responding to disruptive forces. They do not see EA as the necessary tool for integrating IT solutions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".