Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture

Abstract

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Keywords: Clinical Trials; Distributed Computing; Electronic Health Record; Learning Health System; Patient Engagement.

Figure 1

Each site will install the Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS) Sidecar, for identifying and reviewing cohorts and the mySCILHS suite to: (a) manage linkage of contact data to the de-identified Patient Cohort list produced by the multisite Shared Health Research Information Network (SHRINE) query; (b) administer and store consent documents; (c) outreach to patients through web-based survey and telephony; and (d) promote ongoing patient engagement through outgoing messaging, including (in the future) return of research results to patients. The web-based survey will be administered using REDCap and Indivo technologies, and will be accessible either by patients at home, or at the point of care, through tablet/kiosk-based interaction. Once completed, patient-reported data will have subject identifiers encoded; its standardized survey metadata will then be loaded into the corresponding SCILHS sidecar (i2b2 node), enabling semantic data linkage with electronic health record data via SHRINE/i2b2, while preserving subject confidentiality. These software platforms will be provided to sites as self-contained, pre-configured virtual machines, enabling rapid dissemination of these technologies while minimizing administrative and software development overhead at each site.

Figure 2

The Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS) data workflow. We present here a general workflow. There will be important variations depending on the nature of the study, whether in-person consent is required, and whether patient identifiers are needed. Shared Health Research Information Network (SHRINE) architecture implemented as a modular framework. Using a mapper toolkit, each site exposes a common queryable data model, implemented in the ontology (ONT) cell. The ONT cell manages the vocabulary of the data model and is one of several cells in the i2b2 architecture, including the broadcaster-aggregator cell (AGG, broadcasts the query across all i2b2 nodes in the SHRINE peer-to-peer network and aggregates the results), the Identity Management Cell (IM, used for authentication), the Clinical Research Chart (CRC, manages the clinical data), the Workplace Cell (WORK, manages the workflow), and the Substitutable Medical Applications, Reusable Technologies (SMART) Cell (manages the SMART API). We implement the following workflow. A query from a Patient Centered Outcomes Research Institute (PCORI) approved study is translated to a SHRINE central node query either manually, or by a PCORNet adaptor, the specifications for which are still to be determined. The SHRINE Central Node broadcasts the query across the true peer-to-peer network (ARROW 1). i2b2 nodes containing coded data are queried at each site to identify appropriate patients returning obfuscated, aggregate patient counts (ARROW 2). Patient identifiable data remains at each site where investigators can use SMART Apps to review records prior to aggregation (ARROW 3). Also, see figure 3. The patient list is passed to mySCILHS for outreach to patients via apps, survey, or telephony (ARROW 4). Patient generated data are imported into i2b2 via simple input formats (CSV, for example) and placed into the i2b2 data model in a flexible schema that allows these to become first-class queryable data objects (ARROW 5). The adjudicated patient data (reviewed by investigators using SMART Apps and confirmed as valid) from each site, including patient-reported data can be added (ARROW 6) to a research data mart in one of several analytic data models (including the PCORI Common Data Model) with a level of identifiers appropriate to the level of consent obtained. Additional, outside data such as Centers for Medicare and Medicaid claims can be added in this step.

Figure 3

A Substitutable Medical Applications, Reusable Technologies (SMART) Platforms HTML5 App running on i2b2, providing a richly featured electronic health record-like view of the data.