Effect of a Billing Code to Reimburse <scp>Nurse‐Supported</scp> Rheumatology Care on Health Care Costs and Access: An Interrupted Time Series Analysis
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to evaluate the impact of a Multidisciplinary Care Assessment (MCA) billing code on health system costs and access to care in British Columbia (BC). METHODS: Data on all people treated by rheumatologists in BC were obtained from five linked health administrative databases held by Population Data BC from April 1, 2006, to March 31, 2020. Rheumatologists were allocated to either the intervention (ever-billers) or control groups (never-billers). For the intervention group, the index date was the month of the first MCA code billing. For the control group the index dates were imputed from intervention index dates. Our analysis focused on a 48-month period (24 months before and after the index date). We evaluated the impact on two cost (costs related to rheumatoid arthritis [RA]; total health care costs) and access outcomes (rheumatology-related visits per rheumatologist; days between rheumatology visits for patients with RA) using an interrupted time series analysis. RESULTS: A total of 46 rheumatologists (31 intervention and 15 control) met our inclusion criteria. Introduction of the MCA was associated with a small but significant increase in RA-related costs that, at 2 years, translates to a net absolute change of $9.66 per patient per month, but no statistically significant changes in total health care costs. There was no statistically significant change in the number of rheumatology-related visits, but at 2 years there was a net absolute reduction in the median days between rheumatologist visits for patients with RA (6.3 days). CONCLUSION: The introduction of the MCA code was associated with a negligible increase in the RA-related costs and an improvement in access to ongoing care for 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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".