SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies

Abstract

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

“…In the USA, a patient research system allows aggregating patient observations from a large number of hospitals in a uniform way. [52] …”

Information Technology for Clinical, Translational and Comparative Effectiveness Research

“…Utilizing the availability of patient data from federated EHR systems in many different sites, as well as in international multilingual settings is still challenging [2]. Although promising clinical data re-use for research is being enabled through the building of major emerging research infrastructures such as SHARPn [16], i2b2-SHRINE [6,11], EHR4CR [2], limitations and new issues arise [5,8].…”

Patient Engagement and Attitudes Toward Using the Electronic Medical Record for Medical Research: The 2015 Greater Plains Collaborative Health and Medical Research Family Survey (Preprint)

“…[5] The inclusion criteria were manually translated at each site to the equivalent local codes. Each site then ran the resulting query and their local i2b2 server generated a "patient set"-a list of de-identified patient numbers.…”