Digital Health Interventions and Patient Safety in Abdominal Surgery
Bibliographic record
Abstract
Importance: Over the past 2 decades, several digital technology applications have been used to improve clinical outcomes after abdominal surgery. The extent to which these telemedicine interventions are associated with improved patient safety outcomes has not been assessed in systematic and meta-analytic reviews. Objective: To estimate the implications of telemedicine interventions for complication and readmission rates in a population of patients with abdominal surgery. Data Sources: PubMed, Cochrane Library, and Web of Science databases were queried to identify relevant randomized clinical trials (RCTs) and nonrandomized studies published from inception through February 2023 that compared perioperative telemedicine interventions with conventional care and reported at least 1 patient safety outcome. Study Selection: Two reviewers independently screened the titles and abstracts to exclude irrelevant studies as well as assessed the full-text articles for eligibility. After exclusions, 11 RCTs and 8 cohort studies were included in the systematic review and meta-analysis and 7 were included in the narrative review. Data Extraction and Synthesis: Data were extracted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline and assessed for risk of bias by 2 reviewers. Meta-analytic estimates were obtained in random-effects models. Main Outcomes and Measures: Number of complications, emergency department (ED) visits, and readmissions. Results: A total of 19 studies (11 RCTs and 8 cohort studies) with 10 536 patients were included. The pooled risk ratio (RR) estimates associated with ED visits (RR, 0.78; 95% CI, 0.65-0.94) and readmissions (RR, 0.67; 95% CI, 0.58-0.78) favored the telemedicine group. There was no significant difference in the risk of complications between patients in the telemedicine and conventional care groups (RR, 1.05; 95% CI, 0.77-1.43). Conclusions and Relevance: Findings of this systematic review and meta-analysis suggest that perioperative telehealth interventions are associated with reduced risk of readmissions and ED visits after abdominal surgery. However, the mechanisms of action for specific types of abdominal surgery are still largely unknown and warrant further research.
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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 | 0.020 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".