OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology

Abstract

PURPOSE Many institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology. MATERIALS AND METHODS For 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set. RESULTS The resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients. CONCLUSION Using a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.

A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry

“…A system that can incorporate standardised structured data and common data elements as easily as new input. Standardising takes up a lot of time and effort; for the identification of a set of clinical and genomic data elements the OSIRIS group needed a year of weekly multidisciplinary meetings [18] . For this reason standardising will always lag behind innovation and research.…”

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

“…Of the 49 studies, the majority were conducted in Germany (47%, n=23) [12,26,[28][29][30][31]34,35,[40][41][42][45][46][47]52,53,[56][57][58]60,62,63,69], the United States (27%, n=13) [22,25,36,44,[48][49][50]61,[64][65][66]68,70], and Australia (6%, n=3) [1,43,67]. The remaining studies were performed in Austria (2%, n=1) [32], Canada (2%, n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan (2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55], Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and the United Kingdom (2%, n=1)…”

Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality

“…7,8 First, we designed data elements applicable to prostate cancer research, led by a board-certified medical oncologist and adapting Prostate Cancer Working Group 3 recommendations 1 as much as necessary for data retrieval from the medical record. Data elements were designed to be useful for prostate cancer research, without reference to data capture models 3,[9][10][11] existing in late 2017, and they were not intended for interoperability across other tumor types.…”