Research networks for data sharing are growing into a large platform for pragmatic clinical trials to generate quality evidence for shared medical decision-making. Institutions partnering in the networks have made large investments in developing the infrastructure for sharing data. We investigate whether institutions partnering on Patient-Centered Outcomes Research Institute’s (PCORI) network can share clinical apps. At two different sites, we imported patient data in PCORI’s clinical data model (CDM) format into i2b2 repositories, and adapted the SMART-on-FHIR cell to perform CDM-to-FHIR translation, serving demographics, laboratory results and diagnoses. We performed manual validations and tested the platform using four apps from the SMART app gallery. Our study demonstrates an approach to extend the research infrastructure to allow the partnering institutions to run shared clinical apps, and highlights the involved challenges. Our results, tooling and publically accessible data service can potentially transform research networks into clinical app sharing networks and pave the way towards a learning health system.
Capturing high-quality survey data is an arduous process for large-scale and extensive interventional studies. This paper presents the architecture, interface design, and an innovative form generation engine of a system called RE- Form: Refactorized Electronic Web Forms. REForm provides researchers the capability to design and manage surveys and the flexibility to organize them in a customizable workflow. REForm has been designed, implemented, pilot-tested and deployed for an NCI-funded interventional study IMPACT. It enables IMPACT to design and conduct a baseline survey of 95 questions with 662 options, a post-intervention survey of 82 questions with 574 options, six Ecological Momentary Assessment (EMA) surveys including four questions and 26 options each, and a follow-up survey con- taining 15 questions and 125 options. Feedback designed in the post-intervention survey showed that more than 94 percent of IMPACT participants highly endorsed the design and usability of the surveys created using REForm.
Huntington's disease (HD) is a monogenic neurodegenerative disorder characterized by the progressive decay of motor and cognitive abilities accompanied by psychiatric episodes. Tracking and modeling the progression of the multi-faceted clinical symptoms of HD is a challenging problem that has important implications for staging of HD patients and the development of improved enrollment criteria for future HD studies and trials. In this paper, we describe the first steps towards this goal. We begin by curating data from four recent observational HD studies, each containing a diverse collection of clinical assessments. The resulting dataset is unprecedented in size and contains data from 19,269 study participants. By analyzing this large dataset, we are able to discover hidden low dimensional structure in the data that correlates well with surrogate measures of HD progression. The discovered structures are promising candidates for future consumption by downstream statistical HD progression models.
