Session Title: Quality Measures and Quality of Care
Session Type: Abstract Submissions (ACR)
Background/Purpose: The default design of our institutions’ electronic health record (EHR)(Epic Systems) was not optimally structured to systematically collect quality metrics for rheumatic disease management such as disease activity and physical function measures. Our goal was to implement a Quality Improvement (QI) program for care of patients with RA that could easily be incorporated into routine clinical practice. By optimizing EHR technology to provide relevant and timely data analysis reports, we hypothesized that we would create reproducible, actionable population management solutions for providers caring for patients with RA.
Methods: With the assistance of information technology (IT) technicians at our institution, we created “documentation flowsheets” in our EHR with the following fields: Patient Global Activity score, Provider Global Activity Score, Tender Joint count, Swollen Joint count, and ESR. This allowed the automatic calculation of Clinical Disease Activity Index (CDAI) as well as DAS28-ESR. Providers and clinic staff collaborated to enter data into flowsheets for each RA patient visit. Provider could review scores in real time for individual patients and track scores over time in a flowsheet. Data was collected and analyzed on a monthly basis for individual providers as well as the entire practice and was disseminated to the Rheumatology faculty with full transparency.
Results: Over the first six months of data collection for this project we observed continuous improvement in all RA documentation metrics. There was steady improvement in documentation of disease activity measures. CDAI scores were documented in 80.9% of visits 6 months into this project, compared to 56.5% of visits at study initiation. Patient global assessment was documented in 85.4% of visits vs. 68.5% at baseline. DAS28-ESR scores were documented at a lower frequency, 40.4%, due to lack of availability of ESR results to physicians at the time of the patient visit, but rates improved compared to the start of the study (21.7%).
In the final month of data collection, among RA patients with documented disease activity, 54.1% had CDAI scores of ≤10.0 indicating low level of disease or remission, where as 19.4% had high levels of disease activity (CDAI >22). We could not determine if there was an overall improvement in RA disease activity level in our patients over the six months of data collection due to large variation in distributions of patient disease activity from month to month.
Conclusion: Effective implementation of an RA QI Program is challenging but achievable. In addition to technical tools and having the right personnel involved (e.g. physician champions, IT programmers and report writers, analysts and clinic manager and clinic support staff), successful implementation requires strong buy-in from providers. This can be accelerated by objective documentation and analysis to identify areas needing improvement, and then providing this feedback to providers. In the future we plan to assess whether systematic documentation of disease activity can lead to improved patient outcomes.
A. J. Gross,
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/integrating-collection-of-rheumatoid-arthritis-disease-activity-and-physical-function-scores-into-an-academic-rheumatology-practice-to-improve-quality-of-care/