The adoption of networked care models is set to revolutionize health-care delivery. As we move forward, expect to see health-care organizations increasingly investing in technologies that enable seamless data sharing and interoperability. Imagine a scenario where a patient's entire medical history, including imaging data from various specialists, is instantly accessible to any authorized health-care provider. This level of connectivity not only improves diagnosis and treatment but also enhances the overall patient experience. True data integration is becoming the norm in health care.

Researchers out of the University of British Columbia have found that artificial intelligence can detect sepsis quicker. According to a UBC news release Tuesday, sepsis is responsible for at least one in five deaths worldwide, including those from severe cases of COVID-19. But experts warn, the life-threatening condition is difficult to detect early. UBC researchers say because sepsis is defined as the body's dysfunctional response to an infection and has a variety of symptoms – including fever, fatigue, hyperventilation and a fast heart rate – it can often appear at first to be from other diseases. "This new technique dissects the dysfunctional immune responses involved in sepsis like never before, providing new insights into the biological processes involved in sepsis of any type, including that from COVID-19," says Arjun Baghela, a UBC graduate student who led the analysis.

A new research collaboration between researchers at the University of Alberta and the University of Glasgow is exploring whether interaction with an AI-enhanced, socially intelligent robot can effectively distract children during painful clinical procedures, reducing their pain and distress. "Pain is much more than just a physical response; we also want to manage a child's stress, anxiety and distress," said U of A medical researcher and pediatric emergency physician Samina Ali. "We want to know if integrating a robot into the clinical setting can create a more positive, meaningful and less traumatic experience for children and their families." The three-year project builds on a series of smaller studies, supported by funding from the Stollery Children's Hospital Foundation, that used programmable humanoid robots named MEDi to deliver cognitive behavioural therapy-based interventions to children as they went through procedures involving needles. In those studies, the MEDi robot was remotely operated and followed a limited script.

Artificial intelligence (AI) is the enabling of machines to "think" like humans. The ability of AI to make data-driven decisions (pattern recognition) and to identify shared characteristics among data points are especially relevant to medicine (data mining). Spurred on by the explosive expansion of the Internet of Things (IoT) and the decreasing cost of cloud storage and computing, the AI health-care market is likely to exceed $34 billion by 2025, according to a report by Tractica. In this article, we will explore the ever-expanding application of AI in medical diagnosis, drug and device development, and operational improvement. IBM's Watson was the first AI platform to enter the field of medical research.

My stomach is killing me!

My stomach is killing me!

SAN FRANCISCO, CA – Late one evening, Gunjan Bhardwaj got a phone call from his friend and mentor -- a call that would change his career. Not long after, Bhardwaj spent days in the hospital, as his friend underwent painful chemotherapy. As a former consultant at Ernst and Young and Boston Consulting Group, Bhardwaj understood the flow of information in the Internet age. He knew artificial intelligence (AI) and machine learning were capable of digesting large data sets and arriving at powerful connections and insights. What Bhardwaj didn't understand was, why health-care information to help his friend wasn't available.

To cite this article: Gina Neff. The biggest challenge for the use of "big data" in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. "I FOUND THE BUZZ AS FEVERISHLY LOUD AROUND HEALTH INFORMATION INNOVATION AS IT WAS DURING MY RESEARCH ON THE FIRST DOT-COM BOOM." To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies.