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clinical data, RISE is attempting to address some of these previous limitations. In addition, as payers, particularly Medicare, increasingly tie payments to value assessments, there is an urgent need to develop measures to define “value” in rheumatology. The Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015 has put into place an aggressive timeline for a merit-based incentive payment system and for alternative payment models. For rheumatologists to be successful under these payment reforms, it will be critical to generate both the measures and tools to capture the value of rheumatologic care in a meaningful way. Being able to develop, test, and rapidly implement novel measures in RISE that define quality and efficiency will be critical. Understanding the scientific validity, feasibility, usefulness, and both intended and unintended consequences of these measures are important strategic goals of RISE. For researchers, RISE aims to generate data that can advance our understanding of the natural history, outcomes, and treatment of rheumatologic disorders. Aggregated population-wide EHR data are a relatively new data source in the US, and both management and analyses of these data will require rapid innovation in research methods and practices. First, RISE will include new types of data, including the text of clinical notes. Extracting information from this unstructured data will require developing new algorithms to identify variables of interest, using techniques such as text mining, natural language processing, and machine learning. Second, although RISE’s privacy and security framework is HIPAA compliant and research procedures have been approved by a national IRB, addressing future threats to data security will require continuous vigilance and innovation. Third, data in RISE are observational in nature, and will accumulate rapidly. Analysis of “big data” will require new methods, including approaches to deal with missingness, censoring, nonuniform variables across a population, and evolving computational methods to recognize patterns in data such as machine learning. Moreover, changes in health data standards (e.g., implementation of ICD-10) will require ongoing data integration and validation. Fourth, we anticipate that there will be interest in integrating RISE with other data sources and platforms, such as patient-powered networks, clinical trials networks, administrative claims data, or disease-specific registries. Building the scientific and privacy frameworks to facilitate such integrations will also require significant collaboration, funding, and innovation. In the near term, we hope that RISE will facilitate creating learning networks to share best practices and close gaps in quality of care. In summary, the ACR has developed RISE to help rheumatologists leverage the new wave of big data from EHRs. By aggregating, analyzing, and continuously feeding data back to rheumatology practices, RISE aims to advance our shared goals of improving knowledge about rheumatologic conditions, refining treatment strategies and outcomes, and improving the quality and safety of care. AUTHOR CONTRIBUTIONS All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Yazdany had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design. Yazdany, Bansback, Clowse, Collier, Law, Liao, Michaud, Morgan, Oates, Orozco, Reimold, Simard, Myslinski, Kazi. Analysis and interpretation of data. Yazdany. REFERENCES 1. Centers for
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