Background The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients’ EHR and genomic data. Data interoperability is paramount to the program’s success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model. Methods and results Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites’ existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP’s visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects.

ObjectiveHealthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support diverse patient-centric data models.Materials and MethodsWe develop methodology for extending i2b2’s pre-existing API to query additional data models, using i2b2’s recent “multi-fact-table querying” feature. Our method involves developing data-model-specific i2b2 ontologies and mapping these to query non-standard table structure.ResultsWe implement this methodology to query OMOP and PCORnet models, which we validate with the i2b2 query tool. We implement the entire PCORnet data model and a five-domain subset of the OMOP model. We also demonstrate that additional, ancillary data model columns can be modeled and queried as i2b2 “modifiers.”Discussioni2b2’s REST API can be used to query multiple healthcare data models, enabling shared tooling to have a choice of backend data stores. This enables separation between data model and software tooling for some of the more popular open analytic data models in healthcare.ConclusionThis methodology immediately allows querying OMOP and PCORnet using the i2b2 API. It is released as an open-source set of Docker images, and also on the i2b2 community wiki.

Hybrid inflation models are especially interesting as they lead to a spike in the density power spectrum on small scales, compared to the CMB, while also satisfying current bounds on tensor modes.Here we study hybrid inflation with N waterfall fields sharing a global SO(N ) symmetry. The inclusion of many waterfall fields has the obvious advantage of avoiding topologically stable defects for N > 3.We find that it also has another advantage: it is easier to engineer models that can simultaneously (i) be compatible with constraints on the primordial spectral index, which tends to otherwise disfavor hybrid models, and (ii) produce a spike on astrophysically large length scales. The latter may have significant consequences, possibly seeding the formation of astrophysically large black holes. We calculate correlation functions of the time-delay, a measure of density perturbations, produced by the waterfall fields, as a convergent power series in both 1/N and the field's correlation function (x). We show that for large N , the two-point function is δt(x) δt(0) ∝ 2 (|x|)/N and the three-point function is δt(x) δt(y) δt(0) ∝ (|x − y|) (|x|) (|y|)/N 2 . In accordance with the central limit theorem, the density perturbations on the scale of the spike are Gaussian for large N and non-Gaussian for small N .