In this project, we will extend and rigorously evaluate past investments made by the NIH, ACR, and AHRQ in novel health information technology developed by the project team to: enable the systematic collection and integration of Patient Reported Outcome (PRO) and healthcare provider data in routine clinical practice;make use of this data to facilitate patient-provider interaction around optimal use of rheumatoid arthritis (RA) therapies;integrate this data with information in Electronic Health Record (EHR) systems;and demonstrate benefit for both process and outcomes among patients with RA.
Our specific aims are:
Aim 1 : To refine and integrate a novel approach to the electronic collection and use of PRO data from RA patients to facilitate better patient-provider communication, and achievement of Treat to Target (T2T) goals. We will further pilot-test novel and recently-completed technologies developed by our research team: 1) the RhEumatoid Arthritis Disease activitY (READY) electronic measurement tool that will collect data from patients using multiple existing, validated PRO instruments at physician offices and patients'homes via the Internet and smari:phones (e.g. iPhone);2) a risk communication tool focused on optimal use of biologic agents for RA patients considering changes in therapy;and 3) linked EHR-based data available through the ACR's new national registry, the Rheumatology Informatics System for Effectiveness (RISE). With critical input from many key stakeholders, including patients, we will refine this integrated tool in a variety of clinical practices using commonly available computing devices (e.g. iPad) to create a highly generalizable resource that can be deployed across both community and academic practice settings nationally.
Aim 2 : To conduct a cluster-randomized study to examine the effect of the integrated electronic tool to optimize RA patient care. We will test the hypothesis that RA patients receiving care in the physician practices randomized to receive the intervention tool will attain better RA outcomes as quantified by the proportion of patients in each physicians'practice that have achieved a T2T goal of low disease activity or remission one year after randomization.

Public Health Relevance

Past treat to target studies have important problems with respect to generalizability, physician autonomy and acceptability (at least in the U.S.), and practicality for implementation in routine clinical care. Therefore the proposed study will incorporate the salubrious elements of published treat-to-target trials but will avoid their limitations by employing a practical, real-worid design that emphasizes a patient-centered treatment approach supported by innovative health information technology tools that will be rigorously tested.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Comprehensive Center (P60)
Project #
Application #
Study Section
Special Emphasis Panel (ZAR1-KM)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Alabama Birmingham
United States
Zip Code
Stoll, M L; Kumar, R; Lefkowitz, E J et al. (2016) Fecal metabolomics in pediatric spondyloarthritis implicate decreased metabolic diversity and altered tryptophan metabolism as pathogenic factors. Genes Immun 17:400-405
Stoll, Matthew L; Cron, Randy Q (2016) The microbiota in pediatric rheumatic disease: epiphenomenon or therapeutic target? Curr Opin Rheumatol 28:537-43
Yang, Celeste; Bartolucci, Alfred A; Cui, Xiangqin (2015) Multigroup Equivalence Analysis for High-Dimensional Expression Data. Cancer Inform 14:253-63
Curtis, J R; Yang, S; Chen, L et al. (2015) Determining the Minimally Important Difference in the Clinical Disease Activity Index for Improvement and Worsening in Early Rheumatoid Arthritis Patients. Arthritis Care Res (Hoboken) 67:1345-53
Li, Peng; Redden, David T (2015) Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat Med 34:281-96
Yan, Qi; Weeks, Daniel E; Celedón, Juan C et al. (2015) Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method. Genetics 201:1329-39
Li, Peng; Redden, David T (2015) Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Med Res Methodol 15:38
Liu, Nianjun (2015) QTL mapping - Current status and challenges: Comment on ""Mapping complex traits as a dynamic system"" by L. Sun and R. Wu. Phys Life Rev 13:194-5
Cui, Xiangqin; Yu, Shaohua; Tamhane, Ashutosh et al. (2015) Simple regression for correcting ΔCt bias in RT-qPCR low-density array data normalization. BMC Genomics 16:82
Yan, Qi; Weeks, Daniel E; Tiwari, Hemant K et al. (2015) Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples. Hum Hered 80:126-38

Showing the most recent 10 out of 19 publications