[Digital health progress hubs-data integration beyond university hospitals]. / Die digitalen Fortschrittshubs Gesundheit ­ Gemeinsame Datennutzung über die Universitätsmedizin hinaus.

The digital health progress hubs pilot the extensibility of the concepts and solutions of the Medical Informatics Initiative to improve regional healthcare and research. The six funded projects address different diseases, areas in regional healthcare, and methods of cross-institutional data linking and use. Despite the diversity of the scenarios and regional conditions, the technical, regulatory, and organizational challenges and barriers that the progress hubs encounter in the actual implementation of the solutions are often similar. This results in some common approaches to solutions, but also in political demands that go beyond the Health Data Utilization Act, which is considered a welcome improvement by the progress hubs.In this article, we present the digital progress hubs and discuss achievements, challenges, and approaches to solutions that enable the shared use of data from university hospitals and non-academic institutions in the healthcare system and can make a sustainable contribution to improving medical care and research.

Machine learning in medication prescription: A systematic review.

BACKGROUND: Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. METHODS: PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. RESULTS: The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. CONCLUSIONS: By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.

Classification of Patient Portals Described in Evaluation Studies Using the TOPCOP Taxonomy.

Many patient portals have been introduced and evaluated in recent years. The results of evaluation studies are difficult to compare, however, as the evaluated patient portal is often not clearly or only incompletely described in the publication. This problem is common to evaluations in health informatics. We evaluated the completeness of descriptions of patient portals in 15 exemplary evaluation publications using the TOPCOP taxonomy. Our results show that core functionalities such as portal design, patient communication, educational features, or system notifications were quite clearly described in all 15 evaluation studies. Other descriptions, such as web accessibility or data management, were often not provided. We conclude that taxonomies such as TOPCOP should be used and even required for describing interventions in evaluation papers.

Health Economic Impact of a Pulmonary Artery Pressure Sensor for Heart Failure Telemonitoring: A Dynamic Simulation.

AIMS: Recently, a permanently implantable wireless system, designed to monitor and manage pulmonary artery (PA) pressures remotely, demonstrated significant reductions in heart failure (HF) hospitalizations in high-risk symptomatic patients, regardless of ejection fraction. The objectives of this study were to simulate the estimated clinical and economic impact in Germany of generalized use of this PA pressure monitoring system considering reductions of HF hospitalizations and the improvement in Quality of Life. MATERIALS AND METHODS: Based on the Prospective Health Technology Assessment approach, we simulated the potential of the widespread application of PA pressure monitoring on the German healthcare system for the period 2009-2021. RESULTS: This healthcare economic simulation formulated input assumptions based on results from the CHAMPION Trial, a multicenter, prospective, randomized controlled U.S. trial that demonstrated a 37% reduction of hospitalizations in persistently symptomatic previous HF patients. Based on these results, an estimated 114,800 hospitalizations would expected to be avoided. This effect would potentially save an estimated €522 million, an equivalent of $575 million, during the entire simulation period. CONCLUSION: This healthcare economic modeling of the PA pressure monitoring system's impact demonstrates substantial clinical and economic benefits in the German healthcare system.

[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC]. / Strategien zur Vernetzung von Biobanken. Klassifizierung verschiedener Ansätze zur Probensuche und Ausblick auf die Zukunft in der BBMRI-ERIC.

BACKGROUND: Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage. OBJECTIVES: Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies. MATERIALS AND METHODS: Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity. RESULTS: The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty. CONCLUSIONS: Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.

Screening for breast cancer with Breast-CT in a ProHTA simulation.

AIMS: The potential of dedicated Breast-CT is evaluated by simulating its impact onto the performance of the German breast cancer screening program. Attendance rates, cancer detection and economic implications are quantified. METHODS: Based on a prospective health technology assessment approach, we simulated screening in different scenarios. RESULTS: In the simulation, attendance rates increase from 54 to up to 72% due to reduced pain. Breast cancers will be detected earlier while nodal positives and distant recurrences decrease. Assuming no additional cost, cost savings of up to €55 million in one screening period are computed. CONCLUSION: The simulation indicates that earlier cancer detection, fewer unnecessary biopsies and less pain are potential benefits of Breast-CT resulting in cost savings and higher attendance.

A scoping review of cloud computing in healthcare.

BACKGROUND: Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain. METHODS: MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings. RESULTS: 102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated. CONCLUSIONS: Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.