The healthcare sector has many stakeholders, including the pharmaceutical and medical products industries, healthcare providers, health insurers, clinicians and patients. Each stakeholder generates pools of textual data, which have typically remained disconnected. The amount of information to analyse in the health sector is growing rapidly. The two types of textual information in the medical domain that are of particular interest in KConnect are published scientific papers in the medical domain, and Electronic Health Records (EHR). According to Medline Trend, 1.120.070 papers were published in Medline in 2013, almost double the number of papers in 2003 (591.637). Making sense of the knowledge contained in this amount of complex unstructured text can only be done rapidly enough through the use of (semi-)automated text analysis techniques. A hospital with 250.000 active patients generates one Terabyte of text data per year. It is essential to process this data for Comparative Effectiveness Research to predict which treatments work best for which patients; for Predictive Modeling to flag patients with potential negative developments (e.g. potentially suicidal psychiatric patients); as well as for Quality Control of the healthcare system. As increasing numbers of medical establishments are realising the potential of EHR analysis, and also the cost of not doing this analysis in terms of inefficiency and unnecessary loss of life, the demand for such solutions will increase significantly in the next years.
The overall objective of the KConnect project was to create a medical text Data-Value Chain with a critical mass of participating companies using cutting-edge commercial cloud-based services for multilingual Semantic Annotation, Semantic Search and Machine Translation of Electronic Health Records and medical publications.
To achieve this overall objective, the KConnect project achieved six sub-objectives:
1. Facilitate straightforward end-user adaptation of KConnect’s multilingual medicine-specific Semantic Annotation, Semantic Search and Machine Translation technologies to new languages, by making available language adaptation toolkits.
2. Productise multilingual medicine-specific Semantic Annotation, Semantic Search and Machine Translation services through a cloud-based market and as installable packages on private clouds.
3. Facilitate integration of multilingual medicine-specific Semantic Annotation, Semantic Search and Machine Translation technologies into online health portals and vertical search solutions through two routes: the cloud-based market and locally installed as part of a private cloud solution.
4. Expand the multilingual medicine-specific Semantic Annotation, Semantic Search and Machine Translation technologies to the analysis of patient records, to allow straightforward implementation of innovative solutions within hospitals.
5. Develop pricing models and business models to exploit both the cloud-based market and customised vertical search solution approaches.
6. Ensure impact and take-up through the effective dissemination and communication of project results, in particular through the creation of a KConnect Professional Services Community.
