Big data and outcomes: opportunities and challenges
Currently, decisions on treatments and pricing are based largely on data from clinical trials. This data is inevitably limited in scope and does not fully reflect the situation of all patients with a given disease. Yet immense amounts of data are generated daily by researchers as well as clinicians and patients themselves. If we could harness this ‘big data’, it could revolutionise research and healthcare and help us move towards more sustainable healthcare systems focused on outcomes for patients. However, delivering ‘big data for better outcomes’ is far from easy. On the big data side, bringing diverse data sources and formats together, linking them up and analysing them is far from easy due to technical, legal and ethical issues. On the outcomes side, identifying and defining outcomes for patients that are meaningful, measurable and relevant for all stakeholders in healthcare is also a challenge.
IMI’s Big Data for Better Outcomes (BD4BO) programme aims to address these challenges. It brings together all stakeholders, including patients, academic researchers, healthcare policy makers, regulators, healthcare providers, payers, and the pharmaceutical industry. Overall coordination of the programme is the responsibility of the DO>IT project, a coordination and support action set up for this task.
Leading the work on data integration is the European Health Data Network, a project which is under development and is scheduled for launch in the second half of 2018.
Meanwhile, a series of projects focused on specific therapeutic areas will put the concept of ‘big data for better outcomes’ into practice in the fields of cardiovascular disease, haematological malignancies (blood cancers), and Alzheimer’s disease. A fourth therapeutic area project, on prostate cancer, is under development and should start in the first half of 2018.
By making it easier to tap into ‘big data’, BD4BO will make research more efficient, as researchers will be able to re-use existing data instead of having to generate new data. The sheer volume of data involved will make it easier for researchers to uncover new insights into diseases and treatments, and accelerate the development of innovative, more effective medicines.
For healthcare systems, the tools and resources created by BD4BO, as well as the networks of experts in the projects, will make it easier to identify of which treatments work best (and which do not) for different groups of patients.
For patients, BD4BO will help to ensure that treatments are designed and selected on the basis of outcomes that matter to patients. It will also increase the likelihood of patients receiving a treatment that works for them.
For the pharmaceutical industry, BD4BO will supply greater knowledge of how patients experience their condition, and which outcomes really matter, allowing them to better target potential treatments and demonstrate efficacy in real-life conditions.
Ultimately, BD4BO will contribute to the evolution of healthcare systems, making them more sustainable and crucially, more focused on outcomes for patients.
The DO>IT project was launched to provide a coordination platform for the BD4BO programme, exploiting synergies across the projects and maximising its impact on healthcare systems. Specially, DO>IT will aggregate learnings and disseminate findings from the projects; develop minimum data privacy standards; engage with key stakeholders; and recommend areas for future collaborative research.
Alzheimer’s disease is on the rise in our ageing population, and new, effective treatments are urgently needed. ROADMAP aims to deliver methods and tools that will allow the scalable, transferable integration of data on patient outcomes in the real world. The tools will be developed and tested through pilot projects and will lay the foundations for a Europe-wide platform on real world evidence in Alzheimer’s disease. The project will also deliver tools for patient engagement and address the ethical, legal and social implications of adopting a real world evidence approach to Alzheimer’s disease.
Blood cancers, or haematologic cancers (e.g. leukaemia, lymphoma and myeloma), affect the production and function of blood cells and account for about one third of cancer cases in children and about one third of cancer deaths. As many blood cancers are rare, and healthcare practice varies across EU, a lack of data on relevant outcomes represents a challenge for clinicians, researchers, and decision-makers alike. HARMONY aims to use ‘big data’ to deliver information that will help to improve the care of patients with these diseases. More broadly, the project will result in a pan-European network of stakeholders with expertise in this disease area.
Cardiovascular disease (CVD) is a major killer in Europe, accounting for 45% of all deaths. BigData@Heart focuses on three types of CVD. It aims to develop new definitions of diseases and outcomes; informatics platforms that link, visualise and harmonise different data sources; data science techniques; and guidelines on the cross-border use of big data resources. In the long term, the project expects to have an impact on our understanding of heart disease, the discovery of new targets for treatments, and progress towards personalised treatments for CVD.
Projects in the pipeline
Prostate cancer is the second most common cancer in men, and accounts for 9% of all cancer deaths among men in the EU. The goal of this topic, launched under IMI2 – Call 10, is to identify and use existing real-life patient data to improve outcomes for prostate cancer patients.
European Health Data Network
Healthcare data has the potential to transform our understanding of health, disease and outcomes, yet it is currently scattered across multiple institutions and countries, stored in different formats, and subject to different rules. The European Health Data Network topic, launched under IMI2 – Call 12, aims to harmonise a large number of datasets to a common format and standard and link them to create a federated data network. This network would make it easier for researchers to find and reuse data while respecting the relevant local data privacy rules. The approach has already proven successful in other IMI projects, most notably EMIF (European Medical Information Framework).