Platform-Based Patient-Clinician Digital Health Interventions for Care Transitions: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: With the increased adoption of technology, the use of digital health interventions in health care settings has increased. Patient-clinician digital health interventions have the potential to improve patient care, especially during important transitions between hospital and home. Digital health interventions can provide support to patients during these transitions, thereby leading to better patient outcomes. OBJECTIVE: This scoping review aims to explore the available literature, specifically (1) to examine the impact of platform-based digital health interventions focused on care transitions on patient outcomes, and (2) to identify the barriers to and enablers for the uptake and implementation of these digital health interventions. METHODS: This protocol was developed based on Arksey and O'Malley's, Levac and colleagues', and JBI scoping review methodologies, and it has been reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement for the Scoping Reviews) format. The search strategies were developed for 4 databases: MEDLINE, CINAHL, EMBASE, and the Cochrane Central Register of Controlled Trials by using key words such as "hospital to home transition" and "platform-based digital health." Studies involving patients 16 years or older that used a platform-based digital health intervention during their hospital to home transition will be included in this review. Two reviewers will independently screen articles for eligibility by using a 2-stage process (ie, title and abstract screening and full-text screening). We expect to refine the eligibility criteria during the title and abstract screening process as we anticipate retrieving a significant number of articles. In addition, we will also perform a targeted search of the grey literature, as well as data extraction. Data analysis will consist of a narrative and descriptive synthesis. RESULTS: The review is expected to identify research gaps that will inform the development of future patient-clinician digital health interventions. We have identified a total of 8333 articles. Screening began in September 2022, and data extraction is expected to commence in February 2023 and end by April 2023. Data analyses and final results will be submitted to a peer-reviewed journal in August 2023. CONCLUSIONS: We expect to find a wide variety of postcare interventions, some gaps in the quality of research evidence, as well as a lack of detailed information on digital health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42056.
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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 | 0.113 | 0.118 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.083 | 0.015 |
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".