Siemens has developed "teamplay", a software program that makes it possible to systematically utilize data from diagnostic imaging procedures. The solution collects the metadata stored with every scan, including the time, type, and duration of an examination, as well as the radiation dose in the case of X-rays.
Based on this data, large hospitals, hospital networks, and diagnostic centers can get an overview of how many devices are being used. They can then deploy them more efficiently and develop their own goals, for example with regard to capacity utilization or radiation dose. The data is stored in a secure cloud, allowing for comparisons across multiple institutions and even third-party equipment suppliers.
Helping connect healthcare experts and increasing the usability of the wealth of medical imaging data – that's the goal of "teamplay," the new solution from Siemens Healthcare.
CT scanners, MRI scanners, X-ray machines, and ultrasound devices generate huge quantities of data. Roughly one million examinations are conducted every day just with Siemens devices. The majority of data collected in this way remains unused because it is stored in different formats and in widely dispersed locations. Experts estimate that the efficient use of this data could save hundreds of billions of dollars in the U.S. healthcare system alone.1
Intelligent comparison of huge quantities of data is making it possible to use imaging devices such as CT scanners more efficiently.
Faster Exams and Better Equipment Utilization Rates
The teamplay program combines all this data and makes it accessible for analysis. Because it is based on the DICOM standard for storing and exchanging data from medical image files, it is compatible with devices from different manufacturers.
One function of teamplay is to analyze the radiation dose used in X-ray procedures. Currently, radiation dose for certain kinds of examinations is determined on the basis of legal limits or specifically collected monitoring data. Comparing the examination data of different devices generates additional information about attainable standards. This helps operators set their own goals or optimally use their devices for each kind of examination.
Information about the type, time, and duration of examinations can be used to optimize the capacity utilization of medical imaging devices. Hospital networks can determine whether the equipment in place at a given location is being used efficiently and for the right examinations. This information can also be used to improve operating procedures. For example, the body coils of MRI scanners must be reconfigured for different kinds of examinations; knowing exactly how much time that takes can help to optimize the order of patient examinations for optimal efficiency.
While it is already very useful for an institution to analyze the usage of its own infrastructure, teamplay goes a step further. Participants can compare anonymized data with that of similar organizations in order to arrive at realistic benchmarks for their own goals. For this, Siemens Healthcare utilizes a secure and certified Microsoft Azure cloud solution in which anonymized data is stored in a way that meets international data privacy standards and the legal requirements applicable to the protection of medical data.
In the next few years, teamplay will be gradually expanded to offer further possibilities for making data transparent and data networking more efficient. Among other things, a feature is planned that allows attending physicians to securely exchange examination images. In short, teamplay is a smart data solution that allows for the systematic, worldwide aggregation of participants’ device-specific medical data. This in turn opens the door to conducting analyses that either cannot be performed today, or are performed only with great difficulty.
1IBM, McKinsey Global Institute, http://bit.ly/1qaeJ9t
Please note that teamplay and future teamplay functions may differ from the statements in this text; furthermore, teamplay may not be available in all countries.
Mr. Dr. Norbert Aschenbrenner
Mr. Florian Martini
Dr. Norbert Aschenbrenner | Siemens Pictures of the Future
18.08.2017 | Albert-Ludwigs-Universität Freiburg im Breisgau
AI implications: Engineer's model lays groundwork for machine-learning device
18.08.2017 | Washington University in St. Louis
Whether you call it effervescent, fizzy, or sparkling, carbonated water is making a comeback as a beverage. Aside from quenching thirst, researchers at the University of Illinois at Urbana-Champaign have discovered a new use for these "bubbly" concoctions that will have major impact on the manufacturer of the world's thinnest, flattest, and one most useful materials -- graphene.
As graphene's popularity grows as an advanced "wonder" material, the speed and quality at which it can be manufactured will be paramount. With that in mind,...
Physicists at the University of Bonn have managed to create optical hollows and more complex patterns into which the light of a Bose-Einstein condensate flows. The creation of such highly low-loss structures for light is a prerequisite for complex light circuits, such as for quantum information processing for a new generation of computers. The researchers are now presenting their results in the journal Nature Photonics.
Light particles (photons) occur as tiny, indivisible portions. Many thousands of these light portions can be merged to form a single super-photon if they are...
For the first time, scientists have shown that circular RNA is linked to brain function. When a RNA molecule called Cdr1as was deleted from the genome of mice, the animals had problems filtering out unnecessary information – like patients suffering from neuropsychiatric disorders.
While hundreds of circular RNAs (circRNAs) are abundant in mammalian brains, one big question has remained unanswered: What are they actually good for? In the...
An experimental small satellite has successfully collected and delivered data on a key measurement for predicting changes in Earth's climate.
The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat was launched into low-Earth orbit on Nov. 11, 2016, in order to test new...
A study led by scientists of the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) at the Center for Free-Electron Laser Science in Hamburg presents evidence of the coexistence of superconductivity and “charge-density-waves” in compounds of the poorly-studied family of bismuthates. This observation opens up new perspectives for a deeper understanding of the phenomenon of high-temperature superconductivity, a topic which is at the core of condensed matter research since more than 30 years. The paper by Nicoletti et al has been published in the PNAS.
Since the beginning of the 20th century, superconductivity had been observed in some metals at temperatures only a few degrees above the absolute zero (minus...
16.08.2017 | Event News
04.08.2017 | Event News
26.07.2017 | Event News
18.08.2017 | Life Sciences
18.08.2017 | Physics and Astronomy
18.08.2017 | Materials Sciences