Digital leadership in action in a hospital through a real time dashboard system implementation and experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it