Digital leadership in action in a hospital through a real time dashboard system implementation and experience
Bibliographic record
Abstract
Background: Regulatory and competitive pressures and the need for cross-organizational data sharing are demanding that hospital leaders create a data-driven decision making culture to improve performance. Using an innovation assimilation strategy framework, this paper describes how a hospital used its implementation of a Real Time Dashboard System (rtDashboard) to improve performance, change its organizational culture and put it on a path towards digital leadership (DL).Objective: Implement an rtDashboard system that can support a data-driven decision making culture for performance improvement while engaging business and information technology (IT) leaders in DL practice.Results: The rtDashboard contributed significantly to monitoring hospital performance and influenced change in unit level decision making that was aligned with hospital goals. The rtDashboard implementation not only provided substantial performance improvement and quality benchmarking, but also changed the responsibility and accountability culture and helped the hospital put in practice DL principles to support future innovations.Conclusions: DL through rtDashboard is a demonstration of how a hospital can seek and strive for excellence. As much as dashboards are pivotal to organizational performance monitoring at the senior leadership level, the process used to diffuse it to every operational unit in support of a data-driven decision making culture showcases how hospital executives and IT leaders can work together to continually align and re-align their strategies to reach organizational goals – the core of DL practice.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".