Digital Information Sharing Before Consultations in General Practice: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital tools that enable patients to submit information before consultations, such as Accurx and eConsult, are increasingly used in general practice. These systems aim to streamline workflows, improve documentation, and optimize consultation efficiency. However, evidence about their implementation, impact on health inequalities, and health care outcomes remains limited and fragmented. OBJECTIVE: This study aims to map and synthesize the evidence on digital tools used for preconsultation information sharing in family or general practice. METHODS: This scoping review will follow the Joanna Briggs Institute framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted on May 12, 2025, in MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), and the Cochrane Library. Gray literature will be identified via Google Scholar and the National Health Service or government websites. Eligible studies will describe or evaluate digital tools used to collect information from patients before general practice consultations. Two independent reviewers will conduct screening and data extraction. Data will be analyzed using narrative synthesis. RESULTS: Database searches identified 6991 records, with 4536 (64.88%) remaining after deduplication. Screening began in June 2025. Full-text screening was completed in November 2025, with data extraction and synthesis planned for completion by February 2026. Results will be submitted for publication in early 2026. CONCLUSIONS: This review will summarize evidence concerning the use of digital tools for preconsultation information sharing in general practice. Findings will inform implementation, research priorities, and service improvement in digitally supported care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/82649.
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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 | 0.104 | 0.098 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.019 |
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".