The impact of introducing multidisciplinary care assessments on access to rheumatology care in British Columbia: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: In 2011 the British Columbia (BC) Ministry of Health introduced a new fee-for-service billing code that allowed "Multidisciplinary Care Assessment" (MCA). This change has the potential to change access to and quality of care for patients. This study aimed to explore the impact on access to rheumatology services in the province. METHODS: Fee-for-service rheumatology billings were evaluated for each rheumatologist 2 years before and after use of the MCA code. Numbers of 1) unique patients and 2) services provided per month were used as proxy measures of access to care. A multiple-baseline interrupted time series model assessed the impact of the MCA on levels and trends of the access outcomes. RESULTS: Our analysis consisted of 82,360 patients cared for by 26 rheumatologists who billed for an MCA. In our primary analysis we observed a sustained increase in the mean number of unique patients of 4.9% (95% CI: 0.0% to 9.9%, p = 0.049) and the mean number of services of 7.1% (95% CI: 1.0% to 13.6%, (p = 0.021), per month provided by a rheumatologist, corresponding to the initial use of MCA. CONCLUSION: The introduction of the MCA code was associated with an initial increase in the measures of access, which was maintained but did not increase over time. Our study suggests that the use of Multidisciplinary Care Assessment can contribute to expanding and/or sustaining access to care for people with complex chronic conditions, like rheumatic diseases.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".