Improved team cohesion and experience following geographical cohorting of clinician teams
Bibliographic record
Abstract
BACKGROUND: Hospitalists frequently provide care to inpatients situated across numerous medical units, resulting in inefficiency, poor clinician experience and disjointed teamwork. We implemented geographical cohorting of clinician teams to improve team cohesion, efficiency and interprofessional team experience. METHODS: We conducted an interrupted time series study of medical inpatients at a single academic medical centre. Preintervention: July 2018-April 2019, intervention development: April 2019-May 2019 and the postintervention: June 2019-June 2020. The intervention included geographical cohorting of clinician teams onto dedicated inpatient medical wards, standardisation of unit-based interprofessional rounds and end-of-day unit-based huddles. The primary outcome was surveys of team experience and the secondary outcome was the number of pages to physicians (efficiency measure). RESULTS: A total of 6043 patients were included in the study: 2668 preintervention, 386 intervention development and 2989 postintervention. 3240 (53.6%) were female and two (<1.0%) were transgender. Postintervention versus preintervention team experience improved in: awareness of healthcare workers (HCWs) method to contact physicians (56.1% vs 19.0%, p<0.001), ease of contact of physician (82.5% vs 59.5%, p=0.001), timeliness of physician response (78.9% vs 61.9%, p=0.020), agreement of team on care plan (80.7% vs 73.8%, p=0.018) and care plan is communicated efficiently (71.9% postintervention vs 45.2% preintervention, p=0.005) and timely (68.4% postintervention vs 45.2% preintervention, p=0.003). Mean physician pages reduced by a postintervention estimate (factor) of -5.80 (95% CI: -6.30 to -5.29, p<0.001). Linear mixed-effects models of clinical patient outcomes demonstrated no significant changes. CONCLUSIONS: Geographical cohorting of inpatient teams was associated with improved efficiency and team experience outcomes.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".