Open Source Learning Environment in R for Individualized Health


Platform Website:


OSLER-inHealth is dedicated to the rapid dissemination of the statistical tools and methodologies developed by the Learning Health Community (LHC). It is an R-based statistical package collection that will allow researchers to adapt the tools developed by the LHC investigators for their own research purposes, enabling the faster adoption of innovative inHealth solutions by the wider healthcare community.

Based on the programming language R, OSLER-inHealth starts with 2 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. OSLER-inHealth accepts new R package submissions, which are subject to a formal review and continuous automated testing.

Core Team



Open-Source Software Platform for Learning Health Communities. The overarching goal of the software platform is to provide an R-based environment comprising software tools to support the generation, management, analysis, and visualization of complex health data to support health decisions. I am currently working with colleagues at Johns Hopkins Individualized Health Initiative (Dr. Scott Zeger;, International Vaccine Access Center (Dr. Katherine O’Brien, Johns Hopkins University), Harvard Brigham and Women’s Hospital (Dr. Vincent Carey) and Kaiser Permanente Washington Health Research Institute (Dr. Yates Coley) on a Patient Centered Outcome Research Institute (PCORI) grant titled Bayesian Hierarchical Models for Design and Analysis of Studies to Individualize Healthcare.

For many health decisions, the intelligent acquisition and use of data can improve the chance of a successful outcome. One example is whether and how often to screen for common cancers where the potential harms should be considered along with the potential benefits, and the relevant information is increasingly complex, including digitalized images, DNA sequences, novel biomarkers, and multivariate time series from wearable devices, in addition to the more traditional clinical indicators of phenotype. Electronic health records (EHR) have made it possible to acquire and manage health information more effectively. They also enable Boolean-style (“if, then, else”) analyses. For example, if a newly recorded lab value is above a particular level, an EHR can automatically schedule a follow-up visit.

But in today’s information-rich environment, there is heightened need to define, measure, and track health state, to integrate traditional with more complex health measures, and to develop and use appropriate tools for analysis. EHR systems are an essential component to a health information system. However, for an EHR to benefit patients fully, it must be a component in a system that is designed to generate and then use health data to improve individual and population health: to frame key questions, to generate and integrate the relevant evidence, and to build, test, and continuously refine mechanistic or empirical (statistical) models that evaluate and communicate the evidence from the available data as evidence in health decisions.


Patient-Centered Outcomes Research Institute (PCORI) PCORI/ME-1408-20318