Technologies to enhance patient discharge from hospital: A scoping review
Bibliographic record
Abstract
Background: Patients are discharged from hospital every day. To inform the development of a new technology to enhance patient discharge from two wards in the Mid and South Essex NHS Foundation Trust we conducted a scoping review of the literature. Aim: To identify technologies used to enhance patient discharge from hospital. Methods: Keywords from an initial search of CINAHL were used to build our search strategy for Embase, CINAHL+ Medline, and Cochrane library. We included qualitative and quantitative studies that examined the effectiveness and experience of using technologies to enhance patient discharge from hospital. Studies published in English between 2014-2024 were included. Two authors discussed potentially eligible studies. Data on author, publication year, database, country, study aim, clinical area, patient group, sample size, research method, technology type, and key findings were entered into Excel for each eligible study. For quantitative studies, data on comparator/s, outcomes measured, and outcome measurement tools were recorded (Peters 2015). For qualitative studies, themes, method/s of data collection and analyses were recorded. Counts and descriptions of technologies, patient groups, number of participants, clinical area, outcomes measured and tools were complied. Results From 2023 abstracts, 64 eligible studies were identified from: Embase=4, CINAHL+Medline=9, Cochrane=51. Most studies were randomised controlled trial feasibility studies. Technologies identified were: mobile applications, telephone calls, websites, videos, tablets, wearable technologies, and phones. Technologies are used in Australia, Canada, China, Europe, UK, Africa and USA, in orthopaedics, cardiology, respiratory disease, cancer, eating disorders, and stroke. Outcomes measured included: quality of life, user experience and satisfaction, emergency department visits within 30 days after discharge, unplanned 30 day readmission, major adverse event/s, medication adherence, and cost effectiveness. Discussion and conclusion Technologies to enhance patient discharge from hospital have been developed and evaluated in some clinical areas during the past ten years. More research on patient and staff experiences of using these technologies is needed. References Peters, M; Marnie, C; Tricco, A; Pollock, D; Munn, Z; Alexander, L, McInerney, P; Godfrey, C; Khalil, H. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Implementation 19(1):p 3-10, March 2021.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".