Disengaged: a qualitative study of communication and collaboration between physicians and other professions on general internal medicine wards
Bibliographic record
Abstract
BACKGROUND: Poor interprofessional communication in hospital is deemed to cause significant patient harm. Although recognition of this issue is growing, protocols are being implemented to solve this problem without empirical research on the interprofessional communication interactions that directly underpin patient care. We report here the first large qualitative study of directly-observed talk amongst professions in general internal medicine wards, describing the content and usual conversation partners, with the aim of understanding the mechanisms by which current patterns of interprofessional communications may impact on patient care. METHODS: Qualitative study with 155 hours of data-collection, including observation and one-on-one shadowing, ethnographic and semi-structured interviews with physicians, nurses, and allied health professionals in the General Internal Medicine (GIM) wards of two urban teaching hospitals in Canada. Data were coded and analysed thematically with a focus on collaborative interactions between health professionals in both interprofessional and intraprofessional contexts. RESULTS: Physicians in GIM wards communicated with other professions mainly in structured rounds. Physicians' communications were terse, consisting of reports, requests for information, or patient-related orders. Non-physician observations were often overlooked and interprofessional discussion was rare. Intraprofessional interactions among allied health professions, and between nursing, as well as interprofessional interactions between nursing and allied health were frequent and deliberative in character, but very few such discussions involved physicians, whose deliberative interactions were almost entirely with other physicians. CONCLUSION: Without interprofessional problem identification and discussion, physician decisions take place in isolation. While this might be suited to protocol-driven care for patients whose conditions were simple and courses predictable, it may fail complex patients in GIM who often need tailored, interprofessional decisions on their care.Interpersonal communication training to increase interprofessional deliberation may improve efficiency, patient-centredness and outcomes of care in hospitals. Also, electronic communications tools which reduce cognitive burden and facilitate the sharing of clinical observations and orders could help physicians to engage more in non-medical deliberation. Such interventions should take into account real-world power differentials between physicians and other health professions.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".