The overall goals of the Washington Heights/Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) were to gain a comprehensive understanding of the Washington Heights/Inwood (WHI) population, to facilitate research with this population, and to demonstrate WICER infrastructure capabilities for comparative effectiveness research/patient-centered outcomes research (CER/PCOR) for the significant clinical problem of hypertension. We engaged with WHI community member and CER/PCOR investigator stakeholders to accomplish these goals through collection of community-based surveys from more than 6,000 WHI community members;characterization of the WHI community in the New York-Presbyterian Columbia University Medical Center clinical data warehouse (NYP-CUMC CDW) and overlap among survey, NYP-CUMC CDW, and Visiting Nurse Service of New York data sources;design and implementation of a de-identified research data warehouse (RDW) and a user interface (Research Data Explorer [RedX]) for browsing RDW data;design and implementation of a scheduling system that integrates research and clinical tasks;conduct of three CER/PCOR studies, and establishment of data governance policies, structures, and processes to facilitate data access for CER/PCOR. Building upon the accomplishments of WICER, the specific aims of WICER 4 U are: 1. To apply principles of stakeholder engagement and a variety of methods to understand the needs of diverse stakeholders (CER/PCOR investigators, clinical transformation leaders, WHI community members, and WHI community-based organizations) and use this understanding to enhance the existing WICER data infrastructure. 2. To extend the existing WICER data infrastructure through a) collection of additional WICER survey data through the GetHealthyHeights web portal;b) linkage of biomarker data for a sub-group of WICER survey participants;c) linkage of DNA sample data for a sub-group of WICER survey participants;d) integration of population measures for WHI from population-based behavioral and environmental surveys;e) refinement and extension of RedX;and f) refinement and integration of a set of novel algorithms for the identification of matched controls for a given set of study cases, the quantification of potential confounding bias, and the inference of missing signals in the linked WICER data sets. 3. To assess the capability of the extended WICER data infrastructure to meet stakeholder needs through a) the conduct of pilot studies to assess bias, completeness, accuracy, and analytic utility of data in the linked WICER datasets for the clinical topics of hypertension and mental health;and b) the enhancement of the data governance policies, structures, and processes developed in WICER.

Public Health Relevance

WICER 4 U applies innovative methods of stakeholder engagement to refine and extend the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) to meet the needs of diverse stakeholders and address critical barriers to comparative effectiveness research/patient-centered outcomes research and to support real-world decision making by the public, patients, clinicians, and policy makers.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Project (R01)
Project #
1R01HS022961-01
Application #
8691256
Study Section
Special Emphasis Panel (ZHS1-HSR-W (01))
Program Officer
Randhawa, Gurvaneet
Project Start
2013-09-30
Project End
2015-03-31
Budget Start
2013-09-30
Budget End
2015-03-31
Support Year
1
Fiscal Year
2013
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
None
Type
Schools of Nursing
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Yoon, Sunmoo; Taha, Basirah; Bakken, Suzanne (2014) Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression. Stud Health Technol Inform 201:71-8
Weng, Chunhua; Payne, Philip R O; Velez, Mark et al. (2014) Towards symbiosis in knowledge representation and natural language processing for structuring clinical practice guidelines. Stud Health Technol Inform 201:461-9
Lee, Young Ji; Boden-Albala, Bernadette; Larson, Elaine et al. (2014) Online health information seeking behaviors of Hispanics in New York City: a community-based cross-sectional study. J Med Internet Res 16:e176
Karczewski, Konrad J; Snyder, Michael; Altman, Russ B et al. (2014) Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. PLoS Genet 10:e1004122