Team-based care in specialist practice: a path to improved physician experience in British Columbia
Bibliographic record
Abstract
BACKGROUND: Specialist physicians in the province of British Columbia commonly work on teams in acute care settings such as operating rooms or inpatient hospital units. However, while the implementation of team-based care (TBC) has been supported in primary care clinics, no formal mechanisms have supported specialist physicians in adopting TBC in their private outpatient offices. Adopting TBC models is associated with improving physician experience, efficiency, and patient experience. METHODS: The Institute for Healthcare Improvement Breakthrough Series guided a program to support 11 specialist physicians, representing nine different specialties, to develop and implement TBC in outpatient offices. Participants were supported through resources including funding, mentorship, and learning opportunities. To determine whether the program improved physician experience, quantitative data were collected using the validated Mini Z survey and qualitative data were collected through monthly reports, semi-structured interviews, and focus groups. Patient experience data were collected through surveys and follow-up calls. RESULTS: The fifteen-month program was successful, with 10 of the 11 specialists implementing TBC in their offices. The Mini Z results demonstrated that physician experience improved over the course of the program, with scores on job satisfaction, work pace, and time spent on the electronic medical record improving the most. Interviews with specialists and focus groups with specialists' team members support these findings, with participants stating that TBC modulates workloads, begins to affect burnout, improves work-life balance, and increases the efficiency of care. Patients reported positive experiences while receiving TBC. Patients were less likely to visit the emergency department after consultations with specialist teams, and providers agreed that their patients would be less likely to seek acute care because of the new practice models. CONCLUSION: TBC is a viable model for specialist physicians and their health care teams practicing in British Columbia to foster well-being, job satisfaction, and efficiency, and to improve patient experience. These findings may be of interest to specialists, health care providers, policymakers, and administrators looking to better support and retain specialist practices that are integral to patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".