Business Intelligence Dashboards for Patient Safety and Quality: A Narrative Literature Review
Bibliographic record
Abstract
Business Intelligence (BI) dashboards are interactive data visualization displays identifying key patient quality and safety trends and metrics. Yet, it remains unclear whether dashboards are impacting clinical care for desired organizational outcomes. In this paper we summarize the positive and negative impacts of dashboards on safety and quality from the literature and those insights are used to develop a dashboard checklist tool. The research involved 3 phases. In Phase 1 a narrative literature review used "Dashboards AND ("Patient Safety" OR "Quality")" as primary search terms. In Phase 2, A SWOT (strengths, weaknesses, opportunities, threats) analysis was conducted based on the findings from the previous phase. Strengths and opportunities included focusing on metrics, clear goals, routine data review processes, transparency, quality improvement interventions and centralized monitoring. Weaknesses and threats included usability issues, cultural barriers, wrong metrics, tunnel vision and siloed development. Phase 3 involves translating the SWOT analysis to a checklist for evidence informed dashboard development and deployment.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".