Community Research and Development Information Service - CORDIS

H2020

AtmoHealth Report Summary

Project ID: 739096

Periodic Reporting for period 1 - AtmoHealth (A pan-national collaborative analytics platform for the exploration and improvement ofpopulation health)

Reporting period: 2016-10-01 to 2017-06-30

Summary of the context and overall objectives of the project

Atmohealth: A democratised analytics platform for the improvement of health and healthcare.
The analysis of large-scale healthcare data has become an integral part of public health policy making, clinical research and medical innovation. Governments, healthcare commissioners/providers and medical researchers increasingly use population-based healthcare data to support decision-making and clinical research. The exploration of large-scale healthcare data facilitates a wide range of innovations that can lead to improved health outcome. Citizens are also increasingly keen to learn about the distribution and outcome of medical conditions.
The importance of this shift to data-driven healthcare is reflected in key strands of the modern healthcare paradigm:
1. The ‘learning health system’ – all participants in the health system (clinicians, hospitals, patients, insurers, research institutions, commissioning bodies) are producers and consumers of data in an environment where learning happens with each patient treatment.
2. The ‘integrated care system’ – recognises the systemic nature of a patient’s involvement with a ‘360-degree view’ to model optimised care pathways.
3. ‘Personalised medicine’ – interventions and treatments tailored to specific needs and attributes of individual patients, based upon phenotypic and genotypic data.
4. ‘Permissive’ clinical research model – traditional research data combined with structured/unstructured clinical data from self-report, medical device and social media.
Technology has been slow to deliver the promised major new insights and where insights have been found, translation into clinical practice is slow. Practical barriers include the dispersed nature of data sources, gaining public acceptance of the benefits of data accessibility and scepticism of the ability to translate research findings into clinical practice. The technical challenges are also significant.
First, collation of data from multiple sources is time-consuming and complex. Attempts have been made to standardise messaging within healthcare but focus on data transmission about individual patients rather than aggregation of large volumes of data for analytical purposes, ignoring the linkages and multiple vocabularies existing in real-world medical data. This lack of industry-wide standards means integrating each new dataset has to be treated as a new project, typically requiring considerable analysis and specialist IT expertise.
Secondly, data analysis is a major challenge because the contents of traditional databases are not intuitively accessible and available analytical tools require a high baseline understanding of databases and query techniques. Database analysts and informatics specialists are required to write queries to generate the required analytical outputs, a cumbersome and time-consuming process. This results in high costs and long delays, errors in ‘translation’ of requests into queries and lack of transparency of outputs, resulting in high and often prohibitive costs, in time, money and people.
Analytics is dominated by a delivery model that separates providers of analytics from consumers of outputs: strategic decisions cannot be made efficiently, frontline practitioners and managers cannot get the information they need when they need it and IT departments are stretched beyond their limit. Similarly, publicly-held data cannot easily be leveraged due to the paucity of accessible analytical tools, whilst citizens and their families cannot easily explore the implications and outcomes of conditions from which they are suffering.
There is a need for an affordable, user-friendly platform bringing together data from multiple sources that is easily accessed, ‘mined’ and utilised by all. The Atmohealth platform will meet this need. The outcome of the project will be a market-tested and market ready product that is strategically positioned to become a leading ‘next generation’ healthcare analytics platform. Our project’s key objective is to develop a disruptive population health analytics platform that:
• simplifies the integration of data from multiple sources;
• democratises access to large-scale healthcare data;
• streamlines data discovery for both specialist and non-technical end users; and
• thereby accelerates the improvements in quality, outcomes and efficiency.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

New and improved features within the Atmohealth platform:
The application now incorporates an improved AutoETL feature to transform and load data more easily into the database. New features such as multi-tenancy have been added and collaboration between users is improved with ‘snapshot’ and ‘chat’ options for results. A new ‘culture’ feature allows users to apply their own terminology to the application’s terms.
User guides and in-line help have been extended and the ‘Alpha version of the software’ released in March 2017 includes priority fixes identified during independent security testing. The application is now being prepared for release to evaluator sites following the release of the combined project initiation documents.
Commercialisation activities:
These activities have substantially increased in relation to number of conferences attended and workshops enabled, as well as numbers of Imosphere personnel involved, significantly improving the company’s visibility.
The Atmohealth website went live at the end of January and we addressed our digital presence on platforms such as Twitter and LinkedIn.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Society will benefit from enhanced access and accelerated derivation of knowledge and insight where general benefits include: Increased understanding of quality, outcomes and healthcare costs; Accelerated development of new treatments and systemic interventions; Improved targeting of resources in public policy, research and commercially; Advances in evidence-based clinical practice; Reduced cost of analysis and research; all leading to improved population health.
Healthcare providers will benefit from: Cost reduction/improved efficiency of service delivery; Improved quality/standards compliance; Speedier strategic decision-making, translation of research findings into clinical practice, data exploration and analysis; Improved clinical outcomes; Collaborative investigation; Increased transparency; Accelerated development of personalised medicine; Facilitation of evidence-based practice.
Health and healthcare researchers will benefit from: Enhanced data management across multiple research projects; Facilitation of engagement in international collaborative research; Massive increment in speed and ease of cohort identification, data exploration and analysis; Collaborative analytics; Enhanced integration of phenotypic and genotypic data; Speedier arrival at research insights; Improved publication rates.
Commissioning bodies will benefit from: More efficient targeting of commissioning resources; Early identification of population health and care delivery issues; Cost savings and improved outcomes; Real time understanding of trends and effectiveness; Commissioning decisions based on ‘real data’.
Citizens will benefit from: Democratised citizen access to data for individual citizens and user groups representing specific illnesses or conditions; Ability to explore data and present data to support individual and family understanding of health conditions; Enhanced ability to make a substantial direct contribution to the understanding of quality and outcomes of healthcare.

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