Barriers to Communication and Information Exchange in Patient Transfer and Its Consequences
Bibliographic record
Abstract
<p><strong>BACKGROUND &amp; OBJECTIVES:</strong> The patient transfer is multidisciplinary process and communication and information exchange are crucial for its successful accomplishment. The purpose of this study was to identify barriers that negatively influence communication and information exchange during the patient transfer between hospitals and the Center for Treatment Guidance and Information (CTGI) and to describe their consequences.</p><p><strong>METHODS:</strong> A qualitative study based on Focus Group Discussion (FGD) and semi-structured interviews were conducted. Three FGDs were carried out with 5 experts with years of experience working in the CTGI and 25 interviews with individuals involved in patient transfer process. Data were analyzed using content analysis method.</p><p><strong>FINDINGS: </strong>Three major themes including poor communication and information exchange at the CTGI, referring hospital, and receiving hospital were identified. The most important sub-themes at the level of CTGI were: the unavailability of accurate patient medical history and lack of confidence and different working process for patient admission in hospitals. At the level of referring hospital they were incomplete medical history, medical documents, vague patient transfer indications and lack of effective communication. At the level of receiving hospital they were lack of providing feedback, lack of mutual communication and incorrect report of available beds. Also four major consequences of poor communication and information exchange were identified which are managerial, clinical, economic and social consequences.<strong></strong></p><p><strong>CONCLUSIONS:</strong> To overcome the barriers, there is a need for proper monitoring by accountable organizations, reviewing the protocols for patient transfer, an increase in inter-sector collaboration and improvement in communications infrastructure and collection of data.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".