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The next big things in H2020: Big data

Big Data collection, storage and analytics is a continuously evolving concept which has still not been consolidated in day-to-day healthcare, biomedical sciences and the biotech industry.

We are well capable of gathering enormous amounts of data in very intricate ways, however, it is not the challenge to gather new data, but conversely using those amounts of data for real and efficient impact. It has indeed been a major technical barrier to efficiently use data from different information sources representing the overall picture of patient experiences. This requires a.o. smart data systems and analyses that ‘use the same language’ to subsequently translate this to real applications.

The European Commission, by means of the Horizon2020 Programme, has committed itself to the interoperability of health data paired with advances in standardisation. This opens up a number of possible projects along the data pipeline (Fig 1);

Figure 1 | Data - pipeline

Data standards

To subtract intelligent knowledge from raw patient datasets a change is needed in the telemetry and high frequency data by completing the patient-image with low and high-frequency observations of e.g. therapy and imagery. For example, with imaging diagnostics, where the interpretation of x-ray images, CT and MRI scans is moving from hands-on clinician-based judgement to analytical intelligent algorithms and pattern analysis for risk or patient stratification and diagnostics.

Integration of data types, including real-world data

Once data standardisation is in place, the next step in the pipeline is to integrate all different data sources. In the near future, these data sources will increasingly be derived from direct, on-line monitoring of the individual patient, e.g. with wearables and the internet of things (IoT) in healthcare settings such as the hospital. Focussing on patient-centred care, wearable devices, home monitoring tools and mHealth apps have the potential to significantly change the healthcare industry.

In addition to convincing providers and patients alike, integration of patient generated data into products, and how to fit this to the clinical workflow, will be an important focus of future projects.

Complex statistics and informatics

Integrating information from imaging, wearables or IoT devices is not only essential for analysis but also for (adaptive) interpretation of data and representation. Cognitive computing and machine learning have the potential to change clinical decision making. Semantic computing allows the training of algorithms and enables the formation of connections and conclusions about integrated datasets.

Translation into actual useful tools towards precision medicine

Personalised healthcare requires the combination of all data pipeline steps mentioned above with genomic sequencing and drug and therapy development pipelines. This will prompt a change in multiple precision medicine tools such as decision support systems (DSS), identification of novel (drug) targets and subsequent lead generation, health technology assessment (HTA) methods and smarter clinical trials. Combining smarter devices and fluid data exchange will allow optimised clinical-trial design and outcomes and greater efficiency. In addition, trials can increasingly adapt to safety, efficacy and effect signals seen only in small but identifiable subpopulations of patients, improving the personalised aspect of clinical trials. Personalising therapies based on individual unique circumstances is a major trend and focus of the upcoming work programme.