Impact of remote biometric sensing on readmission risk and mortality after hospital discharge: Insights from a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction Unplanned hospital readmissions are associated with higher morbidity, mortality, and financial burden. This study evaluated the association between the use of remote biometric sensing devices (RBS) and all‐cause readmission and mortality rates among adult patients discharged from the hospital. Methods We systematically searched MEDLINE, Embase, Scopus, and Global Health from inception to August 2023. Eligible studies assessed adult patients using RBS devices, defined as tools capable of automatically or manually measuring at least one biometric marker beyond physical activity, after hospital discharge. Studies required a comparison group and reported all‐cause readmission rates. Risk ratios (RRs) with 95% confidence intervals (CIs) were summarized using random‐effects models to account for variability. Subgroup analysis was conducted based on study design, follow‐up period postdischarge, and index discharge diagnosis. Results Out of 9363 identified studies, 39 studies (23 randomized control trials, 14 cohort studies, and two nonrandomized trials) comprising 160,857 patients met the inclusion criteria. RBS use was associated with lower risk of all‐cause readmission (RR = 0.75; 95% CI: 0.67–0.84, I 2 = 72.3%); especially within 30‐day postdischarge (RR = 0.74; 95% CI: 0.64–0.87; I 2 = 35%). Among the subgroup of postsurgical patients, RBS use was associated with an 18% lower all‐cause readmission risk (RR = 0.82; 95% CI: 0.69–0.98; I 2 = 0%). RBS use was associated with lower 30‐day mortality risk (RR = 0.63; 95% CI: 0.46–0.85), with no significant associations thereafter. Conclusion Among patients recently discharged from the hospital, RBS use is associated with improved short‐term outcomes. Future studies are needed to validate these findings.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".