AID/DoM Research Collaboratory

Our Mission

The Allergy & Infectious Diseases (and Department of Medicine) Research Collaboratory (ARC) has three core aims that encapsulate our mission:

  • To advance knowledge that optimizes how we care for our patients, using data analytics.
  • To leverage the power of electronic medical record (EMR) and understand how to use these data wisely.
  • To foster a learning health system that trains junior investigators to harness these data into actionable insights.

In this era of “big data,” healthcare data are among the biggest and most complicated for the following reasons: (1) these data live in multiple places, (2) they come in diverse forms: both structured and unstructured and (3) one cannot take these data at face value. The clinical enterprise, and all the activities associated with caring for patients, is enormously complex. Clinical definitions can be dynamic and subject to an ever-changing evidence base. To manage and transform healthcare data into something usable across a population in a way that can provide actionable insights requires a special set of knowledge and tools. This is where ARC comes in. Our mission is to help investigators leverage the power of these digital data to generate meaningful evidence that closes the gap between practice and quality care.

Our Data Sources

UW Medicine is a large comprehensive health system that has since late 1990s been capturing clinical data in an electronic medical record (EMR). Since 2008, UW has been using a Microsoft data aggregation platform called Amalga to organize and coalesce these EMR data and to facilitate multiple clinical, operational and research initiatives across our organization. Amalga has permitted faster and more complete access to the UW Enterprise Data Warehouse (EDW) but requires a skilled data analyst to query and extract these data. ARC can assist in accessing the EDW and offer advice on data sources, study design, clinical phenotyping and analytic methods. To get started, please complete this data query form. NOTE: If you are requesting data as part of a COVID-19 related project, please complete this survey first.

Leaf is a self-service clinical data discovery tool designed and built by UW Institute of Translational Health Sciences (ITHS) that provides a powerful platform for users to access and explore the UW Enterprise Data Warehouse directly for research and quality improvement (QI) purposes (without an analyst). Leaf on its own is merely a large filter of raw data but with guidance from those familiar with the challenges/pitfalls of EMR data, Leaf carries the enormous potential to help us learn from clinical practice and optimize patient care and outcomes in a cost-efficient manner. To learn more, here is the link to access Leaf training and resources, as well as the JAMIA article describing its inception. Please cite this article when publishing any project that has benefited from Leaf for data/cohort discovery.

ARC Core Team

Dr. Nina Kim, Associate Professor of Medicine in the Division of Allergy & Infectious Diseases (DAID), founded ARC in January 2018 along with Dr. Anna Wald, Division head, and Dr. Robert Harrington, Harborview Chief of Medicine and section head of Infectious Diseases. Dr. Kim brings to ARC a background in clinical epidemiology, observational research and data science through her work within the Center for AIDS Research Network of Integrated Clinical Systems.

Kristine Lan is the DAID data analyst and a masters-level biostatistician who trained at the University of Michigan. Ayushi Gupta is the Department of Medicine (DoM) data analyst with a masters in biomedical and health informatics from the University of North Carolina. Both analysts work under Dr. Kim’s supervision to assist members of the DAID and DoM with data access or analysis. They work in collaboration with Nicholas Dobbins, UW Research IT analyst and architect of Leaf.

Resources & Reading

COVID-19 data query

For non-COVID-19 requests: ARC data query form

ARC Data Use Agreement

Data Management – A Brief “How to” from the UW Virology Research Center

JAMIA article – Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

Brief Primer on Quality Improvement

NIH Data Collaboratory on Electronic Health Record Data

PCORI Curriculum on Causal Inference methods

Institute for Healthcare Improvement Whiteboard: Science of Improvement

RedCap for data collection, surveys and database management

UCLA Institute for Digital Research and Education: Stata Data Management

Causal Inference when using Observational Data – Lecture by Dr. Amalia Magaret