Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - BigMedilytics (Big Data for Medical Analytics)

Teaser

The healthcare sector currently accounts for 10% of the EU’s GDP. In 2014 the EU-28’s total healthcareTo improve productivity of the healthcare sector, it is necessary to reduce cost while maintaining or improving the quality of care provided. The fastest, least costly and...

Summary

The healthcare sector currently accounts for 10% of the EU’s GDP. In 2014 the EU-28’s total healthcare
To improve productivity of the healthcare sector, it is necessary to reduce cost while maintaining or improving the quality of care provided. The fastest, least costly and most effective way to achieve this, is to use the knowledge that is hiding within the already existing large amounts of generated medical data.
The BigMedilytics project aims to use state-of-the-art Big Data technologies in order to improve the productivity of the Healthcare sector by at least 20%, by reducing cost to the patient, improving quality through better patient outcomes and delivering better access – simultaneously.
BigMedilytics aims to transform Europe’s Healthcare sector by executing 12 pilots that are distributed over three themes: (1) Population Health and Chronic Disease Management, (2) Oncology and (3) Industrialization of Healthcare Services. The first two themes together cover 78% of all deaths in from non-communicable diseases in Europe. The third theme focuses on optimizing workflows with a hospital. This theme represents hospital operations and equipment cost, thus covering around 33% of the total cost of healthcare spending.
The list below provides an overview of the pilots within each theme:
• Theme 1: Includes the following pilots: Comorbidities, Kidney disease, Diabetes, COPD/Asthma and Heart failure
• Theme 2: Prostate cancer, Lung cancer and Breast cancer
• Theme 3: Stroke workflows, Sepsis workflows, Asset management and Radiology workflows

In order to scale the Big Data Healthcare concepts across Europe, the project defines the best “Big Data technology and Healthcare policy” Practices taking into account aspects related to (i) Big Data technologies, (ii) new business models and (iii) European and national healthcare data policies and regulations.
The overall objectives of the BigMedilytics project are:
• Improve chronic disease and cancer outcomes using Big Data
• Optimize workflows through industrializing healthcare services using Big Data
• Guarantee replicability of Big Data concepts for healthcare
• Increase market share through data integration
• Establish secure and privacy preserving cross-border and cross-organisation healthcare services thus strengthening EU’s Digital Market Strategy
• Define Best “Big Data” practices
• Enable knowledge transfer

Work performed

1. WP 1: Customization and Deployment of Core Technologies and Platforms
This work package monitors the technological developments within all BigMedilytics pilots. It oversees the transfer of mature Big Data technologies into the BigMedilytics use cases. Deliverables have been written cover: a) technical requirements, b) initial prototypes and c) updated prototypes. The final outcome of WP1 at the end of the project will be the BigMedilytics-BigMatrix, a blueprint for Big Data Healthcare Analytics.
2. WP 2: Big Data Solutions for Chronic Disease Management
The main objective is to assess both: the potential impact of Big data technologies in improving the outcomes in health care; as well as, the reducing costs in different non-communicable diseases. WP2 aims to: improve the stratification of risk based on the electronic health records of a large population, propose advanced design of clinical interventions beyond the current state-of-the-art, improve the efficacy of health-care delivery, and scale up the big data technologies across the entire health care continuum.
3. WP 3: Big Data Solutions for Oncology
For all pilots the requirements from a clinical and technology perspective were defined between the technology partner(s) and the clinical partner(s). The requirements were translated into an implementation plan and the respective pilot technology was implemented by the technology partners. After verification of the technical implementation with the clinical partner the technology was updated if required. The updated prototypes were deployed for clinical use and clinical staff was trained if applicable. Key Performance Indicators (KPIs) relevant to the individual pilots were defined by the clinical teams and a baseline for t=0 was established per KPI.

4. WP4: Industrializing Healthcare Services
This work package consists of 4 pilots which focus on optimizing operational workflows within hospitals. Progress made includes “Refinement of the KPI’s”, “Definition of the data governance structures” and “Development of the study protocols”. “Customizing the initial prototype technologies” and “Preparing the RTLS systems for pilot sites ETZ, INC and OLV” have been completed. “Deployment of the initial prototypes” has been completed for pilots 10-12, and is close to completion for pilot 9. “Interventions for KPI improvement” have been decided. “Collecting baseline Radiology, EMR and RTLS data” is on track. Four posters and three live demonstrations were presented at the midterm BigMedilytics EEP event.

5. WP5: Business Impact
This workpackage shows how the use of big data applications of the pilots can lead to an increase in performance in the healthcare sector. To measure performance we developed a Balanced Score Card and defined KPIs. In addition, this workpackage supports pilot to develop a business model that enables implementation and replication of the pilot in other countries across Europe and across other diseases. Parallel to business modelling, we mapped the relevant European and National regulations for the collection, management and use of big data (technologies) in healthcare. This workpackage will result in a guide to develop big data technology and healthcare policy in Europe.

6. WP6: Dissemination, Communication & Standardisation
The following dissemination tasks have been completed: the development of the communication plan and tools (e.g. project image, project website, newsletter, brochure and social media content), as well as a specific protocol for the generation of high-impact press releases and policies for external communication. Dissemination procedures have been created and distributed among the consortium. The consortium has been contributing to the communication with contents and presenting the project in different events. The project has also involved External Exploitation Partners in the first workshop organized in Valencia in September 2019.

7. WP7: Project Mana

Final results

BigMedilytics focuses on how the latest Big Data technologies can be adapted and applied in novel ways in various healthcare applications. Some examples include:
• Deep Learning algorithms can be used to automatically identify and label tumours.
• Real-time complex event detection enables better understanding of the situational context due to efficient integration of different real-time data and signals.
It demonstrate how to achieve an increase in healthcare productivity between 20% and 63% across 12 different pilots covering the most prevalent and expensive disease groups across Europe.

Website & more info

More info: http://www.bigmedilytics.eu.