Facilitating the representation of EORTC QoL Modules and items in common data models

In recent years, big data has become a key commodity in healthcare generating opportunities to improve patient care, outcomes, and experience. Big data refers to large amounts of data which are often produced by a high number of diverse sources. One issue which arises when working with big data relates to the alignment of potentially disparate data sources to facilitate simultaneous analysis. Common data models (CDMs), whereby disparate data sources are transformed into a common format, have emerged as a solution to this issue. One commonly known example of a CDM is the OMOP model of the OHDSI community (Observational Health and Data Science Informatics) – which is frequently used in the healthcare setting and as part of many big data initiatives funded by the European Commission. Common data models often employ existing vocabularies, such as SNOMED or LOINC to represent the data. These common vocabularies encompass a wide variety of clinical terms but often omit the inclusion of patient-reported outcome data collected within PROM instruments. This lack of representation of PROMs within existing CDMs hinders the use of well-known instruments such as the EORTC core and submodules and prevents their use in big data analyses. This project therefore aims to develop a common vocabulary (as well as supporting guidance) for the EORTC QoL vocabulary to ensure valid use of the EORTC modules in big data analyses of potentially disparate data sources. The project will initially focus on development of a vocabulary for the EORTC QLQ-C30, EORTC QLQ-PR25, and EORTC QLQ-HN43 modules before expansion into other questionnaires.

This project is currently working between WP2 and WP3 – a core ontology is being developed for the EORTC QLQ-C30 instrument which will be tested using established datasets. Once tested, the vocabulary will be expanded to include the EORTC QLQ-PR25 and EORTC QLQ-HN43 instruments.

The next stage of the study will involve development of vocabularies for the EORTC QLQ-C30, EORTC QLQ-PR25, and EORTC QLQ-HN43 instruments. The defined vocabularies will be integrated into the OHSDI OMOP Common Data Model and will be tested using established datasets of quality of life data collated by collaborators.

A lot of research projects are now based on using data about cancer patients to help improve patient care and experience. One problem which exists when using data is that different sites and hospitals collect data in different ways – for example one hospital might collect ethnicity data in 6 categories and another might collect it using 12 categories. Common data models, or standard ways of collecting data, have recently been developed so that many different datasets can be combined together into “big datasets”. These big datasets can then be analyzed together as part of big worldwide research studies. This project is looking at ways we can create a common data model for the EORTC quality of life questionnaires so that datasets of different cancer patients who have filled in the questionnaires can be combined and analyzed together. We will be able to find out a lot more about cancer patients’ quality of life if we are able to combine these datasets into one big dataset.