Background/Purpose:  Rheumatoid Arthritis (RA) quality measures evaluate performance, and thus do not by themselves result in improvement.  The ideal system to improve quality would provide real time actionable data by embedding the measures in the clinical workflow.

Methods: We designed a quality measurement system that 1) integrates with the electronic health record (EHR); 2) provides real-time recognition and closure of care gaps; and 3) allows easy recording of justifiable exceptions.  Quality measures included RA on DMARD (Disease Modifying Anti-Rheumatic Drug), RA with MDHAQ (functional assessment), RA with CDAI (disease activity measure), RA at low disease activity, TB testing (if on biologic), flu and pneumococcal vaccination, and biologic de-escalation candidate.  Color coding the measure status allowed easy recognition of an actionable item (green = measure met, red = not met/opportunity, gray = not applicable).   

Using software integrated with the EHR, the RA quality measures were programmed to appear in a specific “tasks” tab.  The tasks tab was user specific.  The nurse tasks tab showed 1) the vaccination and TB testing measures, and 2) a decision tool where the nurse could select her decision (e.g. flu shot ordered). The rheumatologist tasks tab showed 1) all of the quality measures, 2) the nurse decision (in real time), and 3) a decision tool for RA on DMARD and biologic de-escalation.  An opportunity was defined as an RA patient NOT on DMARD, or a biologic de-escalation candidate (low disease activity for at least a year).  The rheumatologist used a drop-down list to easily document medical decision making for any opportunities.  To reduce redundant work, each decision had an automatic “turn off” interval so that the decision tool did not appear at every visit. 

Results:  The tasks tabs were designed and programmed.  The measures were validated against the EHR.  Using PDSA (Plan Do Study Act) improvement methodology, the quality measurement system was implemented and the first cycle of data obtained regarding adoption of use and decisions made.  Over the course of PDSA cycle 1 (4 weeks), 18 rheumatologists used the decision tool for 62% of the opportunities (54% for RA on DMARD, 66% for Biologic De-escalation).  For RA patients NOT on DMARD, use of the tool resulted in 35% of the decisions to discuss a DMARD or add a DMARD.  For biologic de-escalation candidates, use of the tool resulted in 34% of the decisions to de-escalate biologic therapy.

Conclusion:  We designed, tested, and implemented a quality measurement system that integrates with the EHR, provides real time recognition of care gaps and cost reduction opportunities across a broad array of quality measures, and records provider decisions.  The system was well-adopted, and early data suggests that it has facilitated improving the percent of RA patients on DMARD, as well as biologic de-escalation in well-controlled RA patients.  Repeated PDSA cycles are planned to further increase tool adoption.  As we gain additional data, we will explore the system’s effect on improving the quality measures, and use the decision tool data to better understand modifiable barriers to improving these measures.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/rheumatoid-arthritis-quality-measures-automated-display-of-care-gaps-and-capture-of-physician-decision-making-at-the-clinic-visit/