Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcare costs. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions and are not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centric application landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic. In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which will allow for better interoperability and innovative healthcare solutions.In the current project the research group Digital Transformation at Hanze University of Applied Sciences works together with industry partners to build an openEHR implementation for a Groningen-based mental healthcare provider.
DOCUMENT
Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcarecosts. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions andare not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centricapplication landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic.In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which willallow for better interoperability and innovative healthcare solutions.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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To reach the European Green Deal by 2050, the target for the road transport sector is set at 30% less CO2 emissions by 2030. Given the fact that heavy-duty commercial vehicles throughout Europe are driven nowadays almost exclusively on fossil fuels it is obvious that transition towards reduced emission targets needs to happen seamlessly by hybridization of the existing fleet, with a continuously increasing share of Zero Emission vehicle units. At present, trailing units such as semitrailers do not possess any form of powertrain, being a missed opportunity. By introduction of electrically driven axles into these units the fuel consumption as well as amount of emissions may be reduced substantially while part of the propulsion forces is being supplied on emission-free basis. Furthermore, the electrification of trailing units enables partial recuperation of kinetic energy while braking. Nevertheless, a number of challenges still exist preventing swift integration of these vehicles to daily operation. One of the dominating ones is the intelligent control of the e-axle so it delivers right amount of propulsion/braking power at the right time without receiving detailed information from the towing vehicle (such as e.g. driver control, engine speed, engine torque, or brake pressure, …etc.). This is required mainly to ensure interoperability of e-Trailers in the fleets, which is a must in the logistics nowadays. Therefore the main mission of CHANGE is to generate a chain of knowledge in developing and implementing data driven AI-based applications enabling SMEs of the Dutch trailer industry to contribute to seamless energetic transition towards zero emission road freight transport. In specific, CHANGE will employ e-Trailers (trailers with electrically driven axle(s) enabling energy recuperation) connected to conventional hauling units as well as trailers for high volume and extreme payload as focal platforms (demonstrators) for deployment of these applications.
The DPP4CD project, “Digital Product Passport(s) for Circular Denim: From Pilot to Practice,” focuses on delivering pilot and scalable Digital Product Passports (DPPs) in the circular denim industry. This aligns with the upcoming European Ecodesign for Sustainable Products Regulation (ESPR), making DPPs mandatory for textiles from 2027. A DPP for circular denim should clearly detail material composition, production methods, repair records, and recycling options to meet EU rules like ESPR, Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS). It combines dynamic lifecycle data into a standard, interoperable system that boosts traceability, cuts SME admin burdens, and supports sustainable, circular practices. Led by Saxion and HvA, the multidisciplinary project is based on a real-world Dutch use case with MUD Jeans, a leader in circular denim. The project combines circular economy principles with existing digital technologies, working with partners such as tex.tracer, Tejidos Royo, bAwear, Denim Deal, MODINT, EuFSI and, GS1 Netherlands. Instead of developing new tools, the project applies scalable technologies (augmented DPP extension) and methods e.g. blockchain, life cycle assessments, and traceability standards to denim supply chains. The project defines legal, environmental, technical, and user requirements for DPPs in circular denim and designs a modular, data-driven, and ESPR-compliant system that integrates offline and online components while ensuring interoperability, affordability, reliability, accountability, and scalability. It develops a data framework for material tracking, supported by interoperable digital solutions to improve data-sharing and transparency. A pilot DPP with MUD Jeans will cover the full lifecycle from production to recycling, enabling scalable DPP. The project aims to address societal challenges related to circularity, ensure scalable and implementable solutions, and create a digital platform where knowledge can be developed, shared, and utilised. By combining circular practices with digital technologies, DPP4CD will help textile businesses transition towards sustainable, transparent, and future-proof supply chains.
