Linking Clinical and Administrative Data to Inform Performance Measures Regarding Access to Specialist Care for Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
IntroductionRheumatoid arthritis (RA) is the most prevalent type of chronic adult inflammatory arthritis and requires timely diagnosis and subsequent access to specialist care and treatment from a rheumatologist. We developed a set of key performance indicators (KPIs) to evaluate access, effectiveness, acceptability, appropriateness and efficiency of care.
 Objectives and ApproachThe overall objective was to measure performance of a central intake system for referral to rheumatology against the KPIs. We report on one accessibility KPIs: the percentage of patients with new onset RA with at least one visit to a rheumatologist in the first 365 days since diagnosis. We identified a cohort of RA patients using a validated case definition: >16 years, at least 1 RA related hospitalization (ICD-10-CA:M05.x-M06.x) or two RA related physician visits ≥ eight weeks apart within two years (ICD-9: 714.x). The incident case date was date of hospitalization or second physician visit (whichever came first).
 ResultsThis KPI assessed the proportion of patients seen by a rheumatologist within one year of first RA visit by patients in the RA cohort. 13,914 cases of RA were diagnosed between April 1 2010 and March 31 2016. The percentage of patients with new onset RA with at least one visit to a rheumatologist in the first 365 days since diagnosis increased between fiscal years 2011 and 2015. Of the 2851 incident RA cases in fiscal year 2011, 1490 (53%) met the performance measure compared to 1710 of 2710 (63%) who met the definition in fiscal year 2015. Other KPIs, including wait times, are being evaluated using both clinical and administrative data.
 Conclusion/ImplicationsBy linking multiple administrative datasets, we are able to measure system performance against a defined KPI and identify opportunities for system improvement. This is the first initiative in Alberta for patients with RA where data from different multi-custodial data repositories have been extracted, linked and analyzed for this purpose.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".