A83 BEST PRACTICES FOR THE PROVISION OF VIRTUAL CARE IN IBD AND BEYOND: A SYSTEMATIC REVIEW OF CURRENT GUIDELINES
Bibliographic record
Abstract
Abstract Background Telemedicine has emerged as a feasible adjunct to in-person care in multiple clinical contexts, including inflammatory bowel disease (IBD), and its role has expanded in the context of the COVID-19 pandemic. However, there exists a general paucity of information surrounding best practice recommendations for conducting specialty or disease-specific virtual care. Aims The purpose of this study was to systematically review existing best practice guidelines for conducting telemedicine encounters, both in general and specific to patients with IBD. Methods A systematic review of MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) of existing guidelines for the provision of virtual care was performed. Data was synthesized using the Synthesis Without Meta-Analysis (SWiM) guideline, and the AGREE II tool was used to evaluate quality of evidence Results A total of 60 studies providing guidance for virtual care encounters were included; 52% of these were published during the COVID-19 pandemic. No gastroenterology-specific guidelines were found. The majority (95%) of provider guidelines specified a type of virtual encounter to which their guidelines applied. Of included studies, 65% provided guidance regarding confidentiality/security, 58% discussed technology/setup, and 56% commented on patient consent. 31 studies also provided guidance to patients or caregivers. Overall guideline quality was poor. Conclusions General best practices for successful telemedicine encounters include ensuring confidentiality and consent, preparation prior to a visit, and clear patient communication. Future studies should aim to objectively assess the efficacy of existing clinician practices in order to further optimize the provision of virtual care for specific populations, such as patients with IBD. Funding Agencies None
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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 | 0.043 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".