To describe experience of adoption of a clinical quality data registry at an academic neurology department. 

Axon registry is Â鶹´«Ã½Ó³»­-sponsored, CMS-approved clinical quality data registry designed to facilitate reporting and monitoring of quality metrics (QM) for Merit-based Incentive Payment System.  Implementation by academic sites remains limited.

Review of the registry enrollment process map at University of Miami (UM) Neurology department ambulatory clinic including data mapping, validation, extraction and transmission, analysis of barriers to optimal QM reporting and performance, interventions for process improvement, and trends for disease-specific QM performance.

In an initial phase (2016-2017), the UM IT team and registry vendor (FigMD) mapped data to identify EHR data elements to calculate QM, and validated data to ensure reporting accuracy. Quarterly data extraction and push to registry began in March 2017; reports became available in Q2-2017. Root cause analysis prompted by low performance identified problem areas: a) IT specific: data mapping not conforming to registry requirements, inadequate/incomplete data submission and sharing, timeliness of data pushing, and system compatibly issues; b) provider specific: missing or suboptimal documentation; c) vendor specific: incomplete code list, difficulty identifying missing/discrepant data. Process improvement included revised data mapping for accurate and adequate data capture, ensuring quarterly data submission, and periodic provider feedback.  Positive trends observed post-intervention in >50% reported QM, particularly fall risk, medication reconciliation, epilepsy, headache, Parkinson’s disease, sleep disorders, and multiple sclerosis.

The initial experience at an academic department identified barriers and limitations to optimal QM reporting and performance.  The interventions to improve quantity, quality, and timeliness of data extraction and submission yielded positive performance trends. Other proposed interventions including monthly report card to providers, adoption of structured documentation for optimal QM capture, enhanced IT support or data push by EMR vendor, and adopting “data pull” rather than “data push” method may further improve performance.