AI News, Supporting the use of standardized nursing terminologies with ... artificial intelligence

What is Interoperability? | HIMSS

Interoperability is the ability of different information systems, devices and applications (‘systems’) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including by the individual.

Benefits of Artificial Intelligence to Radiology Workflows

“Radiological imaging data continues to grow at a disproportionate rate when compared with the number of available trained readers, and the decline in imaging reimbursements has forced health-care providers to compensate by increasing productivity,”

Industry leaders in radiology are actively working to identify opportunities for machine learning, neural networks, and natural language processing to optimize radiology workflows.

While the workgroup noted that interpretation (e.g., detection, segmentation, classification) garnered the bulk of attention, it emphasized in a Journal of the American College of Radiology article that “progress in AI research and development is occurring in all areas.”

In this context, deep learning can potentially excel by learning a hierarchical normal representation of a specific type of image from a large number of normal exams,”

“Given its ability to learn complex data representations, deep learning is also often robust against undesired variation, such as the inter-reader variability, and can hence be applied to a large variety of clinical conditions and parameters,”

“In many ways, deep learning can mirror what trained radiologists do, that is, identify image parameters but also weigh up the importance of these parameters on the basis of other factors to arrive at a clinical decision.”

“We find a widening gap between advancements in image acquisition hardware and image-reconstruction software, a gap that can potentially be addressed by new deep learning methods for suppressing artefacts and improving overall quality,”

“The choice of protocol, however, is subject to variability since it is frequently operator-dependent, and consequently, the radiation dose and the quality of the exam are subject to variability at both intra- and inter-institutional levels,”

“In this setting, AI can be an optimising tool for assisting the technologist and radiologist in choosing a personalised patient’s protocol, in tracking the patient’s dose parameters, and in providing an estimate of the radiation risks associated with cumulative dose and the patient’s susceptibility (age and other clinical parameters).”

Pure's data solutions enable SaaS companies, cloud service providers, and enterprise and public sector customers to deliver real-time, secure data to power their mission-critical production, DevOps, and modern analytics environments in a multi-cloud environment.

One of the fastest-growing enterprise IT companies in history, Pure Storage enables customers to quickly adopt next-generation technologies, including artificial intelligence and machine learning, to help maximize the value of their data for competitive advantage.

Connecting the dots of patient care, Philips Tasy EMR

An integrated EMR to achieve your goals across the healthcare continuum. Philips Tasy EMR is a comprehensive healthcare informatics solution that touches all ...

Personalizing Health Care Through Big Data, George Runger, PhD

George is the chair of the Department of Biomedical Informatics (BM I) and professor in the School of Computing, Informatics, and Decision Systems Engineering ...

NINR Big Data Boot Camp Part 3: Big Data Analytics for Healthcare - Dr. Bonnie Westra

The National Institute of Nursing Research (NINR) Big Data in Symptoms Research Boot Camp, part of the NINR Symptom Research Methodologies Series, is a ...

The Robot Will See You Now: U of T experts on the revolution of artificial intelligence in medicine

Make room, stethoscope and otoscope. Artificial intelligence (AI) applications are increasingly among the physician's standard instruments, experts at the ...

Leveraging the HL7® FHIR® Standard to Drive Improvement in Clinical Care

The open FHIR® standard is providing new avenues to build and deploy applications and services that draw on relevant clinical data. In addition to powering ...

Part 1 — 20 Years in Healthcare Analytics & Data Warehousing: What Did We Learn? What’s the Future?

Table of Contents: People - Dale Sanders 02:21 People - Lee Pierce 18:18 People - Shakeeb Akhter 29:52 Processes - Dale Sanders 40:48 Processes - Lee ...

Leveraging Randomized Clinical Trials to Generate RWE for Regulatory Purposes - Day 1

Psychological Disorders: Crash Course Psychology #28

Want more videos about psychology every Monday and Thursday? Check out our sister channel SciShow Psych at

Improving the Implementation of Risk-Based Monitoring Approaches of Clinical Investigations

PACCARB 13th Public Mtg, Day 1 Pt 1: Welc, Opening Remarks, Patient Story, Report Out & Council Vote

Thirteenth public meeting of the Presidential Advisory Council on Combating Antibiotic-Resistant Bacteria (PACCARB). The July 10-11 public meeting was ...