%0 Journal Article %A Claire E.H. Barber %A J. Carter Thorne %A Vandana Ahluwalia %A Jennifer Burt %A Diane Lacaille %A Deborah A. Marshall %A Glen S. Hazlewood %A Dianne Mosher %A Lisa Denning %A Ildiko Szamko %A Ricky Chin %A Sean Hamilton %A Susanne Benseler %A Marinka Twilt %A Natalie J. Shiff %A Vivian Bykerk %A Joanne Homik %A Cheryl Barnabe %T Feasibility of Measurement and Adherence to System Performance Measures for Rheumatoid Arthritis in 5 Models of Care %D 2018 %R 10.3899/jrheum.171284 %J The Journal of Rheumatology %P 1501-1508 %V 45 %N 11 %X Objective. To test the feasibility of reporting on 4 national performance measures for patients with rheumatoid arthritis (RA) in 5 different models of care.Methods. The following performance measures were evaluated in 5 models of care: waiting time (WT) to rheumatologist consultation, percentage of patients seen in yearly followup (FU), percentage taking disease-modifying antirheumatic drugs (DMARD), and time to starting DMARD. All models aimed to improve early access and care for patients with RA.Results. A number of feasibility issues were encountered in performance measure evaluation because of differences in site data collection and/or the duration of the model of care. For example, while 4/5 programs maintained clinical or research databases, chart reviews were still required to report on WT. Median WT for care in 2015 varied by site between 21 and 75 days. Yearly FU rates could only be calculated in 2 sites (combined owing to small numbers) and varied between 83% and 100%. Percentage of patients taking a DMARD and time to DMARD could be calculated in 3 models, and rates of DMARD use were between 90% and 100%, with median time to DMARD of 0 days in each.Conclusion. Our review has shown that even in models of care designed to improve access to care and early treatment, data to document improvements are often lacking. Where data were available for measuring, deficits in WT performance were noted for some centers. Our results highlight a need to improve reporting processes to drive quality improvement. %U https://www.jrheum.org/content/jrheum/45/11/1501.full.pdf