Executive Summary
The portion of elderly people within the Germany population is steadily increasing. As a result, the demand for adequate elderly care services is high. Currently, capacity planning and strategic decision taking is conducted by districts individually. As of today, this federated approach is lacking standardized analysis methods to create a holistic national view on the care topic.
User groups of ERPEL: Family members, social worker and social planners, as well as care service providers (excerpt).
The Electronic Registry for Elderly Care Services in Germany (ERPEL) (German: Elektronisches Register für Pflege-Dienstleistungen in Deutschland (ERPEL)) forms a longitudinal database of care-specific measures from individual districts to form a holistic national overview. For example, it contains details about available elderly care services, the current available capacity, demand for a specific service in a specific region.
ERPEL allows the up-to-date quantification of offer and demand for elderly care services across geographical regions. Thus, it enables a standardized methodological approach for interactive data analysis and exploration to support demand planning.
Amongst others, we are addressing the requirements of the following user groups:
- Family members, who struggle to find appropriate elderly care for their relatives,
- Social workers, who want to offer guidance for elderly care services,
- Social planners, who aim to support governmental decision-making through provision of latest data, and
- Care service providers, who are interested in the current and future demand for strategic planning and investments.
Executive Summary
BigMedilytics is an international collaboration between partners from academia and industry across Europe. It aims to transform Europe’s Healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously, covering the entire Healthcare Continuum – from Prevention to Diagnosis, Treatment and Home Care throughout Europe.
In particular, we are focusing on applying latest big data and machine learning technologies to the use case nephrology to measure and analyze clinical performance indicators, integrate predicitive models, and measure their impact on clinical routine.
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Executive Summary
The documentation of patient treatments is a crucial but unloved part of psychotherapy, because it is time-consuming and error-prone due to an ineffective combination of analogue and digital media. Progress notes are usually hand-written and once official documents are requested, piles of paper documents are gone through and assembled to digital files. We developed the interactive documentation system Tele-Board MED, which allows patient and care provider to jointly take notes and freely structure them on a whiteboard-inspired graphical user interface. We found that Tele-Board MED increases the patient’s engagement in treatments. A second goal is to increase the efficiency and correctness of medical documentation, which is a matter of ongoing research. Generally, documentation panels in Tele-Board MED replace paper sheets with handwritten therapy notes. As a next step, we want to close the gap between documentation panels and clinical documents. In particular, discharge letters and medical reports shall be created semi-automatically from Tele-Board MED data.
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You can find additional details about Tele-Board MED at the project webpage.
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Executive Summary
The HiGHmed consortium aims to develop and use innovative information infrastructures to increase the efficiency of clinical research and to swiftly translate research results into validated improvements of patient care. These aims are tightly connected with challenges to integrate and further develop solutions of innovative, internationally interoperable data integration and methods, with the aim to demonstrate their added value for health research and patient care. The concepts must be designed in a way that will help to develop sustainable structures and with the perspective for an easy roll-out to other hospitals. You might want to refer to the HiGHmed consoritum website for further details.
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Back to school to understand the “Code of Life”. We are happy to invite you to “Code of Life” Massive Open Online Course (MOOC) hosted by openHPI. The course starts on Nov 14, 2016 and is designed as an interactive set of daily lectures followed by tasks. Sign-up for free to attend this unique course and to get your personal exam certificate at the end of the course.
We are proud to announce that the Smart Analysis Health Research Access (SAHRA) cooperation project had its official project kickoff on Sep 10, 2015 in Berlin.
Executive Summary
The aim of the Smart Analysis Health Research Access (SAHRA) cooperation project is to provide scientific analysis methods incorporating latest in-memory database technology for analysis of longitudinal health data. These methods support research and the development of innovative solutions and products by providing real-time analysis of longitudinal health data for the first time. Public and governmental institutes as well as small and mid-sized healthcare enterprises are the target audience of the project. Latest data protection and privacy measures are taken to protect any data on the SAHRA platform in compliance with latest German and International data protection laws.
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In the following, we assembled related content that might be of your particular interest.
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Research Publications
- Schapranow, M.-P.: Die digitale Transformation mitgestalten — Der Datenspendeausweis: Souveräner Umgang mit persönlichen Gesundheitsdaten. Plattform Life Sciences. 38--39 (2017).
- Schapranow, M.-P., Brauer, J., Plattner, H.: The Data Donation Pass: Enabling Sovereign Control of Personal Healthcare Data. Proceedings of the World Congress in Computer Science, Computer Engineering, and Applied Computing. CSCE (2017).
- Schapranow, M.-P.: Datenspendeausweis für Bürger: Ein Plädoyer für mündige Patienten, die die eigenen Gesundheitsdaten am besten verstehen. Management & Krankenhaus. (2016).
- Rueckert, L.: Real-time Exploration of Healthcare Data using In-Memory Database Technology, (2016).
- Postel, M.: Geographical Exploration of Key Performance Indicators for Elderly Care Planning, (2016).
- Horschig, F.: Prediction of Health Research Data using In-Memory Database Technology. (2016).
- Schapranow, M.-P., Uflacker, M., Sariyar, M., Semler, S., Fichte, J., Schielke, D., Ekinci, K., Zahn, T.: Towards An Integrated Health Research Process: A Cloud-based Approach. Proceedings of IEEE International Conference on Big Data. bl. 2813--2818. IEEE (2016).
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Contact
Please feel free to get directly in touch with the experts. Use the following contact form, if you have any open questions you might to share with us.
We are proud to announce that the Systems Medicine of Heart Failure (SMART) cooperation project had its official project kickoff on Mar 23, 2015 in Berlin.
2015-2018