Personalised eHealth solutions
This ambitious research and innovation project represents the next major step for MAGIC. The Evidence Ecosystem project is based on the need for overarching solutions to solve major challenges for patients and society.
In order for health care systems to function optimally, the best current evidence must be transferred seamlessly between the communities of people performing primary research (evidence producers), summarizing research into systematic reviews (evidence synthesizers), creating clinical practice guidelines and decisions aids (evidence processors and disseminators), and people responsible for implementing and evaluating evidence into improved health care (evidence implementers) – what we shall call the evidence ecosystem. Currently, these players are largely siloed, and information flow and utilization is inefficient and incomplete.
In short our vision and overarching objective for this project is to establish a Digital and Trustworthy Evidence Ecosystem that will explicitly link innovative eHealth solutions and platforms for digitally structured health data with processes and key actors in the value chain to increase value and reduce waste in health care.
Despite globally recognized advances in health research methods and standards and systems for evidence-based health care, even the most advances systems face major challenges in delivering high quality and safe health care to patients and, while maximizing value and reducing waste. To solve this challenge, policy-makers, clinicians and patients need trustworthy research evidence to ensure that diagnosis, treatment and follow-up is efficient and effective while allowing well-informed, personalized and shared decision-making at the point of care.
Requirements for optimal function of the health care system include seamless transfer of best current evidence between communities of people performing primary research (evidence producers), summarizing research into systematic reviews (evidence synthesizers), creating clinical practice guidelines and decisions aids (evidence processors and disseminators), and implementing the evidence (evidence implementers) – what we call the evidence ecosystem. Currently, these players are largely siloed, and information flow is inefficient and incomplete.
Compounding the problem of inefficient connections is the common lack of understanding of health research methods, a common culture for sharing data and eHealth solutions to allow digitally structured data to flow from primary research to the major players (evidence synthesizers, processors, disseminators and implementers) and to patients at the point of care, into clinical decision support systems (CDSS) in electronic health records and health registries to evaluate system performance. This poor coordination of people, structured data and tools results in a wasteful disconnect between evidence produced and consumed, undermining return on investments in health care, research and IT.
Building on our experiences with the WikiRecs we believe the most efficient way to implement the Evidence Ecosystem is to integrate the collaborative network approach to create trustworthy recommendations. This allows us to circumvent the barriers of organisations in need of innovating their processes and tools. The integration of WikiRecs into the Ecosystem is exemplified in our first Evidence Ecosystem implementation project, performed in collaboration with key Ecosystem actors in Finland and Belgium.
Following the production and publication of recommendations in MAGICapp for the use of probiotics in children taking antibiotics – to prevent diarrhoea and other adverse events- primary care physicians received decision support integrated in the electronic health record within 60 days. The journey from published recommendations to available decision-support included plugging the recommendations into EBM guidelines (an EBM textbook used in Finland and Belgium), translating and adapting content for Finland and Belgium, creating and implementing scripts for decision support in structured EHRs in primary care.
GPs who prescribe antibiotics to children are reminded about the recommendations for probiotics. The digitally structured data in the Ecosystem allows the GPs to drill down to recommendations, evidence summaries and decision aids in the MAGICapp as well as accessing the underlying Cochrane systematic review with meta-analysis of 23 studies. GPs can provide a prescription for probiotics together with patient information, directly from the EHR. Finally, the EHR allows performance measurement through the decision support system. In Belgium 2.5% of patients are currently offered probiotics, which provides a baseline for targeted quality improvement initiatives. For children below 2 years of age the strong recommendation to offer probiotics would be translated into a quality indicator (e.g. a health quality improvement initiative might set target of >80% of these children be prescribed probiotics). These data are automatically generated in the EHR, exemplifying flexible registries and performance measurement in the Evidence Ecosystem.
Our consortium represents pioneers within evidence-based health care, systematic reviews, guidelines, shared decision-making and CDSS. The Ecosystem project, initiated by Dr. Vandvik and colleagues (MAGIC/magicapp.org) have – together with international collaborators such as the DECIDE project and Cochrane Collaboration – demonstrated the feasibility of an innovative information model for digitally structured data and user-friendly tools for systematic reviews, guidelines and decision aids, currently showing increasing uptake in the marketplace. As outlined above we have completed our first pilot of implementing the Evidence Ecosystem in Belgium and Finland.
We are now ready to partner with eHealth experts, industrial actors, and health service providers across countries to develop, implement and evaluate the Ecosystem. The impacts of the Ecosystem include financial gains health funders, market-leading eHealth software solutions to support major advances in the ‘evidence-to-practice’ environments, and most importantly, proven health benefits for patients from improvements in diagnosis and treatment.