Electronic Health (eHealth) and Artificial Intelligence-based Tools to Optimize In-hospital Patient Flow: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Congested hospitals are increasingly common. Electronic health (eHealth) and artificial intelligence (AI)-based tools may improve in-hospital patient flow, however their implementation into practice varies. This study aims to identify and synthesize evidence on implementing eHealth and AI-based tools to manage in-hospital patient flow. METHODS: Structured language and keywords related to patient flow and eHealth or AI-based tools were searched in five databases. Studies were eligible if they reported barriers or facilitators (determinants) to implementing eHealth and/or AI-based tools, and/or key metrics for patient flow. Study characteristics, tool characteristics, study population, setting, and outcome measures were abstracted. Information related to determinants of implementation were categorized using the Theoretical Domains Framework and interventions were mapped to the Expert Recommendations for Implementing Change Taxonomy. RESULTS: Twenty-five studies were included; 40% were quasiexperimental studies and most (n=19) were conducted in the United States. Four categories of tools were identified with imbedding eHealth or AI-based tools into an existing electronic medical or health record being the most common. Barriers to tool implementation were commonly linked to the environmental context and resources (n=5), while facilitators were linked to social influence (n=4). CONCLUSIONS: This scoping review classified the reported barriers and facilitators to implementing eHealth and AI-based tools to improve in-hospital patient flow. Future research on in-hospital patient flow should adopt the identified measures when reporting tool effectiveness. To improve implementation efforts, more consistent reporting of determinants of tool implementation is needed.
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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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".