e-sante

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Amazon continue d’accélérer dans l’e-santé. Cette fois-ci, le géant américain entre de plein pied dans le marché de l’analyse des données médicales en annonçant le lancement d’un nouveau service: Amazon Comprehend Medical.

Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer’s disease, according to a study published in the journal Radiology.1

Timely diagnosis of Alzheimer’s disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” said study co-author Jae Ho Sohn, M.D., from the Radiology & Biomedical Imaging Department at the University of California San Francisco (UCSF). “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

The study’s senior author, Benjamin Franc, M.D., from UCSF, approached Sohn and University of California, Berkeley, undergraduate student Yiming Ding through the Big Data in Radiology (BDRAD) research group, a multidisciplinary team of physicians and engineers focusing on radiological data science. Franc was interested in applying deep learning, a type of AI in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer’s disease.

The researchers trained the deep learning algorithm on an imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

Microsoft unveiled a new tool that allows healthcare organizations to create their own AI-powered chatbots and virtual assistants for various services.

The Microsoft Healthcare Bot is one of several new tools the company recently announced as part of its effort to transform “how healthcare is experienced and delivered.” The other tools unveiled were:

Microsoft 365 for healthcare organizations, which features new capabilities in Microsoft Teams that allow healthcare teams to communicate and collaborate in a secure hub.
Azure API for FHIR (Fast Health Interoperability Resources), which enable health system interoperability and to share data in the cloud.

The Microsoft Healthcare Bot was first introduced in 2017 as a research project. Its features include healthcare intelligence, medical content and terminology and a built-in symptom checker. In a blog post, the company said the bot can also be used to help organizations to solve business problems and can connect to health systems, like electronic health records (EHRs).

The company also plans to discuss the new tools during the HIMSS conference in February in Orlando, Florida.

14,6 milliards de dollars ont été investis dans les entreprises de l’e-santé en 2018, soit près de 3 milliards de plus que l’année précédente.

Le Conseil National de l'Ordre des Pharmaciens publie les résultats de ces travaux sur l'e-santé avec un livre vert "Pharmacie connectée et télépharmacie Le Conseil National de l'Ordre des Pharmaciens publie un livre vert "Pharmacie connectée et télépharmacie : c'est déjà demain".

Many organizations are trying to implement healthcare artificial intelligence (AI) projects using traditional infrastructure models, which isn’t working well, observed Pure Storage Global Healthcare Technology Strategy VP Josh Gluck.

By using a traditional infrastructure approach, organizations are running into scalability problems with their AI projects, Gluck told HITInfrastructure.com.

“We're finding that a lot of folks want to use AI, and they're trying to get the most out of their existing traditional infrastructures. Unfortunately, they're running into situations where it takes an inordinate amount of time to hone their algorithms. The results that come back have to be reinterpreted, or they have to rewrite the algorithm,” Gluck related.

One of the biggest AI pain points for healthcare organizations with traditional infrastructure is that they have data in isolated pockets throughout their IT environment. “Healthcare traditionally has built infrastructures for applications. It has not provided an infrastructure where the data can be accessed freely by research programs, analytics programs, or AI machine-learning programs,” he said.

A recent Gartner report backs up Gluck’s analysis. It found that the increasing complexities of AI and other advanced technologies could cause healthcare IT infrastructure to fall behind, resulting in a chaotic IT environment.

“As the advance of technology concepts continues to outpace the ability of enterprises to keep up, organizations now face the possibility that so much change will increasingly seem chaotic … The key is that CIOs will need to find their way to identifying practical actions that can be seen within the chaos,” said Gartner Fellow and Vice President Daryl Plummer.

Lors du HDI Day le 15 novembre dernier, le Healthcare Data Institute a dévoilé un sondage sur « Les données de santé sur internet et les réseaux sociaux Lors du HDI Day le 15 novembre dernier, le Healthcare Data Institute a dévoilé un sondage sur « Les données de santé sur internet et les réseaux sociaux ».