The state has suspended Boston-based COVID-19 testing lab Orig3n Laboratory after it produced nearly 400 false positive results. Public health officials became aware in early August of an "unusually high positivity rate" among the lab's test results and requested that Orig3n stop testing for the virus as of Aug. 8. Specimens were sent to an independent lab to be retested as part of a state Department of Public Health investigation, and the results showed at least 383 false positives. On Aug. 27, the state Department of Public Health notified Orig3n of "three significant certification deficiencies that put patients at immediate risk of harm," according to a DPH spokeswoman. They included the failure of the lab's director to provide overall management, issues with the extraction phase of testing, and a failure to meet analytic requirements such as documenting the daily sanitizing of equipment used for coronavirus testing. A statement of deficiency was issued on Sept. 4. The lab must now respond with a written plan of correction by Sept. 14, "and if action is not taken it can face sanctions," DPH said.
Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as " training data," in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML related tasks were translated to the cloud, creating latency, consuming scarce power, and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the Edge slower, more expensive, and less predictable. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into an almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain.
In a groundbreaking ruling, CMS has granted Viz.ai the first New Technology Add-on Payment (NTAP) for artificial intelligence software. NTAP, part of the CMS Inpatient Prospective Payment System (IPPS), was set up to support the adoption of cutting-edge technologies that have demonstrated substantial clinical improvement and ensure early availability to Medicare patients. In the US, stroke is the number one cause of long term disability, but is a treatable condition if identified early enough. Viz.ai has been recognized by Forbes, Fast Company, and AuntMinnie as one of the leading AI healthcare companies in the US. The company provides software that improves clinical and financial outcomes1,2 by streamlining acute care, leading to shorter time to treatment, improved patient outcomes, reduced length of stay, and increased number of procedures.
Patients will benefit from major improvements in technology to speed up the diagnosis of deadly diseases like cancer thanks to further investment in the use of artificial intelligence across the NHS. A £50 million funding boost will scale up the work of existing Digital Pathology and Imaging Artificial Intelligence Centres of Excellence, which were launched in 2018 to develop cutting-edge digital tools to improve the diagnosis of disease. The 3 centres set to receive a share of the funding, based in Coventry, Leeds and London, will deliver digital upgrades to pathology and imaging services across an additional 38 NHS trusts, benefiting 26.5 million patients across England. Pathology and imaging services, including radiology, play a crucial role in the diagnosis of diseases and the funding will lead to faster and more accurate diagnosis and more personalised treatments for patients, freeing up clinicians' time and ultimately saving lives. Technology is a force for good in our fight against the deadliest diseases – it can transform and save lives through faster diagnosis, free up clinicians to spend time with their patients and make every pound in the NHS go further.
Nowadays the term computing is very broad. The definition covers everything that is necessary to handle information with computers, e.g. Whole disciplines like Computer Engineering, Information Technology or Cybersecurity also belong to this term. I want to start this article with a very brief history of computing and based on this extrapolate where the journey could go. Computing is as old as mankind.
The precise figure is unknowable because only 3 to 10% of this fraud is ever detected. With more data in health care than ever before, what is the opportunity to use artificial intelligence (AI) and other advanced analytics techniques to improve fraud detection? That was the topic of a recent webinar featuring Prime Therapeutics, the pharmacy benefit manager that Fast Company named among the world's most innovative companies for 2020 for their use of SAS Detection and Investigation for Health Care to fight fraud, waste and abuse. SAS Medical Director Steve Kearney, PharmD, hosted the webinar. Prime Therapeutics integrates pharmacy and medical claims into an advanced analytic engine to identify cases for their investigators.
Editor's note: This article is based on a roundtable discussion sponsored by Optum. The full report of the roundtable discussion, Strategy: The Key Factor in the Future of Healthcare Innovation, is available as a free download. Innovation is paving the way for hospitals and healthcare systems to move into the future and truly change healthcare delivery. While solutions featuring artificial intelligence, robotic process automation, and natural language processing are fueling advances, healthcare innovation is more than a technology play. Effective transformation requires formulating new strategies for payment, reimagining models of care, applying real-time data, and addressing social determinants of health.
The healthcare industry is increasingly focusing on niche patient populations. Around half of FDA approvals in the past two years were for rare or orphan drugs that serve fewer than 200,000 patients in total in the US and 1 in 2,000 patients in Europe. By 2024, orphan drug sales are expected to capture one-fifth of worldwide prescription sales. However, finding these hard-to-reach patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?
The Food and Drug Administration (FDA) has cleared two additional Siemens Healthineers artificial intelligence-based software assistants in the AI-Rad Companion family. The AI-Rad Companion Prostate MR for Biopsy Support automatically segments the prostate on MRI images and enables radiologists to mark lesions, facilitating targeted prostate biopsies. "These new AI-Rad Companion applications for MR exams in the brain and prostate regions will help physicians manage their workloads and achieve a patient-focused decision-making process to increase efficiency and improve the quality of care," said Peter Shen, Vice President of Innovation and Digital Business at Siemens Healthineers North America. The AI-Rad Companion Brain MR for Morphometry Analysis supports brain volumetry, which involves measuring the volume of gray matter (nerve cells), white matter (nerve cell connections), and cerebrospinal fluid in various segments of the brain and comparing the results to normal volumes. In typical clinical presentation and when combined with independent confirmation, reduced volume may indicate Parkinson's disease, Alzheimer's disease, or other forms of dementia.¹