The intended and unintended consequences of communication systems on general internal medicine inpatient care delivery: a prospective observational case study of five teaching hospitals
Bibliographic record
Abstract
BACKGROUND: Effective clinical communication is critical to providing high-quality patient care. Hospitals have used different types of interventions to improve communication between care teams, but there have been few studies of their effectiveness. OBJECTIVES: To describe the effects of different communication interventions and their problems. DESIGN: Prospective observational case study using a mixed methods approach of quantitative and qualitative methods. SETTING: General internal medicine (GIM) inpatient wards at five tertiary care academic teaching hospitals. PARTICIPANTS: Clinicians consisting of residents, attending physicians, nurses, and allied health (AH) staff working on the GIM wards. METHODS: Ethnographic methods and interviews with clinical staff (doctors, nurses, medical students, and AH professionals) were conducted over a 16-month period from 2009 to 2010. RESULTS: We identified four categories that described the intended and unintended consequences of communication interventions: impacts on senders, receivers, interprofessional collaboration, and the use of informal communication processes. The use of alphanumeric pagers, smartphones, and web-based communication systems had positive effects for senders and receivers, but unintended consequences were seen with all interventions in all four categories. CONCLUSIONS: Interventions that aimed to improve clinical communications solved some but not all problems, and unintended effects were seen with all systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".