Improving Hospital Care and Collaborative Communications for the 21st Century: Key Recommendations for General Internal Medicine
Bibliographic record
Abstract
BACKGROUND: Communication and collaboration failures can have negative impacts on the efficiency of both individual clinicians and health care system delivery as well as on the quality of patient care. Recognizing the problems associated with clinical and collaboration communication, health care professionals and organizations alike have begun to look at alternative communication technologies to address some of these inefficiencies and to improve interprofessional collaboration. OBJECTIVE: To develop recommendations that assist health care organizations in improving communication and collaboration in order to develop effective methods for evaluation. METHODS: An interprofessional meeting was held in a large urban city in Canada with 19 nationally and internationally renowned experts to discuss suitable recommendations for an ideal communication and collaboration system as well as a research framework for general internal medicine (GIM) environments. RESULTS: In designing an ideal GIM communication and collaboration system, attendees believed that the new system should possess attributes that aim to: a) improve workflow through prioritization of information and detection of individuals' contextual situations; b) promote stronger interprofessional relationships with adequate exchange of information; c) enhance patient-centered care by allowing greater patient autonomy over their health care information; d) enable interoperability and scalability between and within institutions; and e) function across different platforms. In terms of evaluating the effects of technology in GIM settings, participants championed the use of rigorous scientific methods that span multiple perspectives and disciplines. Specifically, participants recommended that consistent measures and definitions need to be established so that these impacts can be examined across individual, group, and organizational levels. CONCLUSIONS: Discussions from our meeting demonstrated the complexities of technological implementations in GIM settings. Recommendations on the design principles and research paradigms for an improved communication system are described.
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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 | 0.076 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".