Understanding multidisciplinary care for people with rheumatic disease in British Columbia, Canada, through patients, nurses and physicians voices: a qualitative policy evaluation
Bibliographic record
Abstract
BACKGROUND: In 2011, the province of British Columbia (BC) moved to allow patients with complex rheumatic disease to be seen by nurses along with their rheumatologist by introducing a 'Multidisciplinary Care Assessments' (MCA) billing code (G31060). OBJECTIVE: To describe multidisciplinary care introduced as part of MCAs across BC and investigate the perceived impact of this intervention, the addition of nurses to the care team, on patient care from the perspective of patients, nurses, and rheumatologists. METHODS: We conducted semi-structured interviews, informed by a qualitative evaluation approach with patients, nurses, and rheumatologists from September 2019 - August 2020. Interviews investigated 1) the experiences of all stakeholders with adopting the multidisciplinary care billing code, 2) the perceived role of the nurse in the care team, and 3) the perceived impact of multidisciplinary care on patient experience and outcomes. We purposefully sampled practices for maximum variation of geographical location (rural vs. urban), size of practice (i.e., patient caseload), and number of nurses employed. RESULTS: We interviewed 21 patients, 13 nurses, and 12 rheumatologists from across BC. Our analysis identified variation in the way rheumatologists adopted multidisciplinary care across BC. Our analysis showed some heterogeneity in the way the MCA was delivered in rheumatology practices; however, patient education was identified as the core role of nurses across practices. We identified six core themes describing the impact of this model of care, all representing improvements in the way practices functioned, from improved efficiency to access, patient experience, time management, clinician experience, and patient health outcomes. Contextual factors that influenced the presence of these themes were related to the time the nurses spent with patients and the professional roles they performed. CONCLUSION: Our results suggest nurse care can complement physician care by extending contact time for patients and promoting the efficient use of health care professionals' skills, time, and resources. These data may encourage future uptake of the billing code to help ensure the policy delivers maximum benefits to patients given the wide range of perceived benefits described by clinicians and patients.
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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 | 0.042 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".