Knowledge is power -- and in healthcare, that holds absolutely true. Yet, for an industry that is under
financial stress, increasing complexity of disease and comorbidity, and burdened by capacity constraints --
why has data not been healthcare’s savvier? Three major challenges have inhibited this:
- data is not accessible and remains in siloes;
- data is not analysed to derive meaningful clinical insights;
- insight isn’t accessible for actioning by providers or patients to self/joint manage their condition.
Our consortium of medical professionals, data scientists, ICT-infrastructure experts, machine learning
researchers and legal experts have designed Enabling Patient Interventions to liberate, analyse, and action
that data in a trustworthy way.
EPI aims to empower patients and providers through self-management, shared management, and personalization
across the full health spectrum. To do so, we will build a fuller picture of the person by linking traditional
eHealth data sets with new sources of data. Further, we will develop a platform based upon a secure and
trustworthy distributed data infrastructure, combining data analytics, including machine learning, and health
decision support algorithms to create new, actionable, and personalized insights for prevention, management,
and intervention to providers and patients. We will develop new machine learning methods for determining and
analysing optimal interventions within small patient groups.
Our insights will be applied in healthcare use cases representing a spectrum of health management challenges
ranging from common chronic to highly lethal orphan diseases, and will empower better self/joint management of
these conditions to improve cost, quality, and outcomes of care.
The overall aim of this project is to explore the use and effectiveness of data driven development of
scientific algorithms, supporting personalized self- and joint management during medical interventions /
treatments. The key objective is to use data science promoting health practically with data from various
sources to formulate lifestyle advice, prevention, diagnostics, and treatment tailored to the individual, and
to provide personalized, effective, real-time feedback via a concept referred in this proposal as a digital
health twin. The project addresses six research questions:
- Dynamically Analyzing Interventions based on Small Groups: how can we determine, based on as little data
as possible, whether an intervention does or does not work for a small group or even an individual patient?
- Dynamically Personalizing the Group: how can we identify effective intervention strategies and optimize
personalization strategies applicable for different patient and lifestyle profiles via dynamic (on-line)
clustering of patients? Can those clusters be adapted as new data about patients and results of
interventions come in and as other data may be removed or modified?
- Data and Algorithm Distribution: what are the consequences of a distributed, multi-platform, multi-domain,
multi-data-source big data infrastructure on the machine learning algorithms and what are potential
consequences on performance?
- Adaptive health diagnosis leading to optimized intervention: how can we enhance self- / joint management
by dynamically integrating updated models generated from machine learning from various data sources in state
of the art health support systems that based on personal health records, knowledge of health modes and
- Regulatory constraints and data governance: how can we create scalable solutions that meet legal
requirements and consent or medical necessity-based access to data for allowed data processing and
preventing breaches of these rules by embedded compliance, providing evidence trails and transparency, thus
building trust in a sensitive big data sharing infrastructure?
- Infrastructure: how can the various requirements from the use-cases be implemented using a single
functional ICT-infrastructure architecture?
- Leon Gommans, John Vollbrecht, Betty Gommans - de Bruijn, Cees de Laat, "The Service Provider Group
Framework; A framework for arranging trust and power to facilitate authorization of network services.",
Future Generation Computer Systems, (Accepted paper), June 2014
- Leon Gommans, "Multi-Domain Authorization for e-Infrastructures", UvA, Dec 2014.
- Internet2 2012 session: "Trust Framework for Multi-Domain Authorization".
- speakers: Leon Gommans , John Vollbrecht, chair: Cees de Laat.
- Managing Our Hub Economy, Marco Iansiti,
Karim R. Lakhani, Harvard Business review, September-October 2017 issue, [local
- NWO press
release: Enabling Personalized Interventions - EPI.