Enhancing Patient-Dedicated Time in Clinical Encounters: A Systematic Review and Meta-analysis of Intervention Strategies
Bibliographic record
Abstract
BACKGROUND: Hospitals' institutional programs designed to protect or increase the time dedicated to interactions between patients and healthcare professionals, while growing in popularity, often lack formal evaluation. This study aims to quantify the effectiveness of programs designed to protect or enhance the quality or quantity of clinical encounter time between hospitalized patients and healthcare professionals. METHODS: A systematic literature review and random-effects meta-analysis were performed on Cochrane Library, Embase, and Web of Science databases. Studies had to include ≥ 80% adult inpatients in acute care, compare groups, and assess at least one of the following outcomes: patient satisfaction, length of stay, home discharge, or 30-day readmission. Screening, data extraction, and risk of bias assessment were performed independently and in duplicate. Risk of bias was assessed using the ROBINS-I tool for non-randomized trials, and the Cochrane 2.0 instrument for randomized trials. RESULTS: A total of 117 unique studies comprising 298,517 patients were included. Compared to their controls, interventions increased the proportion of satisfied patients (+ 8% [95% CI, + 4.7 to + 11.4%]; 26 studies, 20,456 patients), the proportion of patients discharged home (+ 2.6% [95% CI, + 0.3 to + 5.0%]; 21 studies, 61,539 patients), and reduced length of stay (- 1.07 days [95% CI, - 1.62 to - 0.52]; 58 studies, 160,080 patients) without significant difference in readmission rates (- 0.8% [95% CI - 1.8 to + 0.2%]; 49 studies, 177,677 patients). Most studies were at high risk of bias, even among randomized trials. Programs varied widely in interventions, contexts, and findings. DISCUSSION: Programs enhancing or protecting clinical encounter time in acute care may improve patient experience, care quality, and discharge processes. Higher quality randomized controlled trials evaluating such interventions are warranted. Future programs may benefit from studies that draw on multi-disciplinary knowledge and implementation sciences to identify contextual factors impacting their success. SYSTEMATIC REVIEW REGISTRATION: Prospero CRD42023453402.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".