Evaluating the impact on access of the introduction of nurse-supported care for people with complex rheumatic diseases in British Columbia
Bibliographic record
Abstract
Background A 2010 workforce survey revealed British Columbia was facing a shortage of rheumatologists and a consequent crisis of access to rheumatology care. Rheumatic diseases are chronic, and early intervention is crucial to prevent progression and mitigate systemic damage. Recognizing nurses may be able to perform aspects of rheumatology care and thereby “free up” rheumatologist time, the Ministry of Health introduced billing code G31060 to facilitate nurse-supported consultations for the “complex” rheumatology cases most in need of attention. The objective of this thesis is to evaluate the impact of introducing this new billing code and model of care on access to rheumatology care for the population of BC living with rheumatic disease. Methods I conducted an interrupted time series analysis with a comparator using administrative health data on outpatient visits from Population Data BC. Patients with rheumatic diseases were identified using International Classification of Diseases codes and classified as those who received the intervention (i.e. nurse-supported rheumatologist care) or ‘status quo’ (i.e. rheumatologist care alone). Access was defined as 1) number of unique patients treated per month 2) number of service units billed per month. In sensitivity analyses I explored the impact of more restrictive definitions of intervention which required more “consistent” (at least once in every year) and “high-intensity” (at least 30 per year) billing of G31060. Results The primary cohort included 128,726 patients with rheumatic disease, seen by 29 intervention and 17 comparator rheumatologists. No statistically significant effect change in level or trend of unique patients (pβ6=0.682 & pβ7=0.231) or service units (pβ6=0.744 & pβ7=0.419) attributable to the introduction of G31060 was detected in the primary analysis. Sensitivity analyses revealed statistically significant, increases in patients seen for rheumatologists billing “consistently” (62%) and with “high intensity” (168%) in April 2015 as compared to ‘status quo’. Conclusion The introduction of G31060 does not appear to impact the number of service units billed per month, nor does it necessarily increase the number of patients seen. However, consistent and high-intensity users of G31060 appear to increase the number of unique patients seen per month.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".