Case Studies

The past several years have seen the development of a de facto stealth campaign against screening mammography. “Vast Study Casts Doubts on Value of Mammograms,” the New York Times bullhorned in 2014. “Why Getting a Mammogram May Cause More Trouble Than It’s Worth,” a Prevention headline blared in 2016. “It’s Time to End Mammograms, Some Experts Say,” trumpeted Time this past December. All of this is fueled, of course, by the never-ending disputes over guidelines issued by numerous authoritative groups.

When Josh Gluck joined Pure Storage this past April, he arrived well-acquainted with the most pressing data-management issues affecting healthcare IT leaders today. 

In a California emergency room, a trauma patient in critical condition is wheeled in following a motorcycle accident. In Texas, a patient presenting with stroke-like symptoms is brought into the hospital by frantic family members.

To meet the latest guidelines on promptness from the American Heart Association (AHA) and American Stroke Association (ASA), providers must image suspected stroke patients within 20 minutes of their arrival. For a brain deprived of oxygen by a blood clot, every second counts.

Medical historians may one day look back on 2018 as the year having a stroke stopped bringing an inescapably bleak prognosis to victims who went a while before noticing the symptoms.

Sometimes the planets align. This time, it’s to the advantage of patients at risk of in-hospital cardiac arrest. While a study recently confirmed cardiac arrest survival rates fall significantly on nights and weekends, another study shows that a wearable defibrillator can help patients 24/7.

Building the infrastructure to support the accelerating adoption of AI in healthcare is the mission of Pure Storage and its FlashBlade technology, an all-flash scale-out object-based solution that can expand to petabytes of capacity. As Esteban Rubens says, infrastructure to power AI, machine learning and deep learning needs to be effortless, efficient and evergreen to ensure success today and into the future. Here’s how.

Mark Michalski, MD, Executive Director of the MGH/BWH Center for Clinical Data Science gets to see, touch or hear about much of what’s happening in artificial intelligence.

There are the believers in augmented medicine, with physicians and machines working hand in hand and improving care and patient outcomes. And there are the stalwarts who see machines taking over the tasks of mankind. Period.

Population health is absolutely something we want to target. To do that, we are using our archive of images that includes radiology, cardiovascular, interoperative and dermatology. For example, we’re looking at body composition—the amount of muscle, visceral fat and superficial fat. And common sense makes sense. Body composition correlates with how well patients do. In some cases, abdominal fat can even be an early biomarker of some cancers, like pancreatic cancer.

When it comes to teaching new dogs new tricks, radiology training programs need to be thinking about updating their curricula and preparing for both the short- and the long-term effects of AI and machine learning, according to “Toward Augmented Radiologists,” a new commentary published online in March in Academic Radiology.

Ever the visionary, Paul Chang sees AI as an asset to radiologists. As he sees it, “AI and deep learning doesn’t replace us. It frees us to do more valuable work.” The vice chair of radiology informatics at University of Chicago Medicine takes a quick look through the crystal ball at the four stand-out challenges facing radiology with the rise of AI.