Impact of information and communication technology on interprofessional collaboration for chronic disease management: a systematic review
Bibliographic record
Abstract
Objectives Information and communication technology is often lauded as the key to enhancing communication among health care providers. However, its impact on interprofessional collaboration is unclear. The objective of this study was to determine the extent to which it improves communication and, subsequently, enhances interprofessional collaboration in chronic disease management. Methods A systematic review of academic literature using two electronic platforms: HealthSTAR and Web of Science (core collection and MEDLINE). To be eligible for inclusion in the review, articles needed to be peer-reviewed; accessible in English and focused on how technology supports, or might support, collaboration (through enhanced communication) in chronic disease management. Studies were assessed for quality and a narrative synthesis conducted. Results The searches identified 289 articles of which six were included in the final analysis (three used qualitative methods, two were descriptive and one used mixed methods). Various forms of information and communication technology were described including electronic health records, online communities/learning resources and telehealth/telecare. Three themes emerged from the studies that may provide insights into how communication that facilitates collaboration in chronic disease management might be enhanced: professional conflict, collective engagement and continuous learning. Conclusions The success of technology in enhancing collaboration for chronic disease management depends upon supporting the social relationships and organization in which the technology will be placed. Decision-makers should take into account and work toward balancing the impact of technology together with the professional and cultural characteristics of health care teams.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".