GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK

A new diagnostic technique that has the potential to identify opioid-addicted patients at risk for relapse could lead to better treatment and outcomes.

 

Using an algorithm that looks for patterns in brain structure and functional connectivity, researchers were able to distinguish prescription opioid users from healthy participants. If treatment is successful, their brains will resemble the brain of someone not addicted to opioids.

 

“People can say one thing, but brain patterns do not lie,” says lead researcher Suchismita Ray, an associate professor in the health informatics department at Rutgers School of Health Professions.

 

“The brain patterns that the algorithm identified from brain volume and functional connectivity biomarkers from prescription opioid users hold great promise to improve over current diagnosis.”

 

In the study in NeuroImage: Clinical, Ray and her colleagues used MRIs to look at the brain structure and function in people diagnosed with prescription opioid use disorder who were seeking treatment compared to individuals with no history of using opioids.

 

The scans looked at the brain network believed to be responsible for drug cravings and compulsive drug use. At the completion of treatment, if this brain network remains unchanged, the patient needs more treatment.

 

read the study at https://doi.org/10.1016/j.nicl.2021.102663

 

read the article at https://www.futurity.org/opioid-addiction-relapse-algorithm-2586182-2/

 

 

Via nrip

Information technology has allowed much of our economy to automate processes. We have seen transformations of the airline, banking, brokerage, entertainment, lodging, music, printing, publishing, shipping and taxi industries through the availability of massive volumes of real-time price and service data. Across America, consumer-facing retail service continues to shift to a virtual environment.

 

Healthcare is the exception. Many health information technology (health IT) products initially focused on billing.

The misalignment between billing support and the sense that these tools do not materially automate clinician work to build in efficiencies or improve workflows adds to an overall frustration with the increasing amount of time providers spend at their screens.

 

Automation is hard because it tends to require interfaces of various types – both to other machines (Internet of Things) and to humans.

 

Often automation proposals involve solutions that focus on highly structured data. But, someone or something has to put energy (physician salary, for example) into organizing much of this information, assuming it is even knowable.

 

The underlying disease or patient behavior (e.g., smoking) is also often not knowable. And, automation relying on machine to machine interfaces regularly runs into a lack of application programming interfaces (APIs) supporting complex clinical data flows.

 

I posted this week old piece now and now when it was published as #NHITWeek is this week. A lot of posts this week deal with possibilities and problems with healthcare focussed automation. 

 

The original unedited piece can be read at https://www.himss.org/news/healthcare-automation-transforming-medicine

Via nrip

• The world's largest technology companies have all hired well-known cardiologists.
• Heart disease and high blood pressure affect a large number of people, are well-understood, and there's evidence that consumer products can help.

 

Big Silicon Valley companies have often competed for talent with specialized skills, like expertise in artificial intelligence or trendy new programming languages.

 

Now they're competing for heart doctors.

 

Apple, Alphabet, and Amazon have all hired well-known cardiologists.

 

This might just be a coincidence. Cardiologists tend to be well educated and hard working, and big tech companies have a track record of recruiting such people.

 

 

Via nrip