The efficacy of computer-enabled discharge communication interventions: a systematic review
Bibliographic record
Abstract
CONTEXT: Traditional manual/dictated discharge summaries are inaccurate, inconsistent and untimely. Computer-enabled discharge communications may improve information transfer by providing a standardised document that immediately links acute and community healthcare providers. OBJECTIVE: To conduct a systematic review evaluating the efficacy of computer-enabled discharge communication compared with traditional communication for patients discharged from acute care hospitals. DATA SOURCES: MEDLINE, EMBASE, Cochrane CENTRAL Register of Controlled Trials and MEDLINE In-Process. Keywords from three themes were combined: discharge communication, electronic/online/web-based and controlled interventional studies. STUDY SELECTION: Study types included: clinical trials, quasiexperimental studies with concurrent controls and controlled before--after studies. Interventions included: (1) automatic population of a discharge document by computer database(s); (2) transmission of discharge information via computer technology; or (3) computer technology providing a 'platform' for dynamic discharge communication. Controls included: no intervention or traditional manual/dictated discharge summaries. Primary outcomes included: mortality, readmission and adverse events/near misses. Secondary outcomes included: timeliness, accuracy, quality/completeness and physician/patient satisfaction. DATA EXTRACTION: Description of interventions and study outcomes were extracted by two independent reviewers. RESULTS: 12 unique studies were identified: eight randomised controlled trials and four quasi-experimental studies. Pooling/meta-analysis was not possible, given the heterogeneity of measures and outcomes reported. The primary outcomes of mortality and readmission were inconsistently reported. There was no significant difference in mortality, and one study reported reduced long-term readmission. Intervention groups experienced reductions in perceived medical errors/adverse events, and improvements in timeliness and physician/patient satisfaction. CONCLUSIONS: Computer-enabled discharge communications appear beneficial with respect to a number of important secondary outcomes. Primary outcomes of mortality and readmission are less commonly reported in this literature and require further study.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".