Digital Communication Between Patients and Health Care Professionals Across Disciplines and Sectors After Hospital Discharge: Facilitators, Barriers, and Effects
Bibliographic record
Abstract
Background Over the past decade, hospital admissions for patients undergoing orthopedic surgery have been shortened, and the time for informing and educating patients prior to discharge has been compromised. The transition of care from hospital to home poses a substantial risk of adverse events. Patients have difficulty remembering information and struggle to assess the severity of symptoms after discharge, leading to unplanned telephone contacts and clinic visits. These inquiries are frequent and pose a substantial burden on the health care system and patients. The COVID-19 pandemic showed an emerging need to implement new communication technologies. Asynchronous digital communication (DC) may provide easy access to health care and seamless communication across sectors. Objective This study aimed to investigate how DC can facilitate easy communication between patients and health care professionals (HCPs) across sectors and the effects of DC on patient-initiated telephone contacts after discharge. Methods The overall theoretical approach was inspired by Continuity of Care and the Consolidated Framework for Implementation Research. Substudy I was a scoping review on DC between patients and HCPs after hospital discharge. Substudy II explored DC in an orthopedic surgery setting and through a triangulation of qualitative data collection techniques. Substudy III investigated the effect of DC on patient-initiated telephone contacts after discharge. Results Preliminary findings from substudy I show that DC is increasingly used to support patient-provider communication after discharge. In substudy II, preliminary findings show that DC is feasible in a real-life setting, providing patients with easy access to HCPs, who accept and adapt DC to existing cross-sectoral workflows. However, barriers exist related to the technological integration between systems and individuals’ hesitation to use DC. In substudy III, DC is to be tested in a randomized controlled trial. Conclusions This study generates new knowledge of asynchronous DC that may guide future implementations across the health care system.
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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 | 0.040 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".