Earlier this month the University of Nottingham published a study in PloSOne about a new artificial intelligence model that uses machine learning to predict the risk of premature death, using banked health data (on age and lifestyle factors) from Brits aged 40 to 69. This study comes months after a joint study between UC San Francisco, Stanford, and Google, which reported results of machine-learning-based data mining of electronic health records to assess the likelihood that a patient would die in hospital. One goal of both studies was to assess how this information might help clinicians decide which patients might most benefit from intervention. Amitha Kalaichandran, M.H.S., M.D., is a resident physician based in Ottawa, Canada. Follow her on Twitter at @DrAmithaMD.
"We spent all those years adopting EHRs, and now we're wanting to get the most out of them. Now we have the digital data, so it should be more liquid and in control of patients and put to use in the care process, even if I go to multiple sites for my care." As ONC and CMS prepare to digest the voluminous public comment on their proposed interoperability rules, especially the emphasis on exchange specs such as FHIR and open APIs, he sees the future only getting brighter for these types of advances as data flows more freely. "We're in the interoperability business, and we like having data being more available and more liquid, and systems being more open to getting data out of them," Woodlock said. "A lot of customers are starting to embark on their journey with with FHIR, and they're really bullish on this as well: having a standards-based API way to interact with medical record medical record data," he added.
Artificial Intelligence (AI) has the capability to provide radiologists with tools to improve their productivity, decision making and effectiveness and will lead to quicker diagnosis and improved patient outcomes. It will initially deploy as a diverse collection of assistive tools to augment, quantify and stratify the information available to the diagnostician, and offer a major opportunity to enhance and augment the radiology reading. It will improve access to medical record information and give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular will form the foundation for decision and workflow support tools and diagnostic capabilities. Algorithms will provide software the ability to "learn" by example on how to execute a task, then automatically execute those tasks as well as interpret new data.
TEL AVIV, Israel, 16 April 2019--Israel's reams of electronic medical records –health data on its population of around 8.9 million people-- are proving fruitful for a growing number of digital health startups training algorithms to do things like early detection of diseases and produce more accurate medical diagnoses. According to a new report by Start-Up Nation Central, the growth in the number of Israeli digital health startups –537 companies, up from 327 in 2014--has drawn in new investors, including Israeli VCs who have never previously invested in healthcare. This has driven financing in the sector to a record $511M in 2018, up 32% year on year. By the first quarter of 2019 the amount raised was already at $214M. Of the $511M, over 50% ($285M) went to companies in decision support and diagnostics which rely heavily on data crunching.
Blockchain and artificial intelligence are two emerging technologies that are quickly bringing about further digitization in the business world. Here are six ways they are changing how businesses operate and will continue to do so for the foreseeable future. One of the advantages of blockchain technology for businesses is that it offers more transparency about shipments and how products move through the supply chain. People cannot edit information once it gets entered into the digital ledger. Then, businesses don't have to worry about potential records tampering.
One year after Open Bionics began selling its 3D-printed Hero Arm prosthetic in the UK, the bionic arm is available in the US. Open Bionics has made a name for itself as a start-up specializing in low-cost prosthetics, and you might remember it as the company behind arms inspired by Iron Man, Star Wars, Frozen and Deus Ex. But until now, the Hero Arm has only been available in the UK and France. The wait is over, y'all. Upper limb amputees in the states, sign up here and we'll refer you to your nearest @HangerNews clinic: https://t.co/NYvtvoul0K
FDA Commissioner Scott Gottlieb is making the most of his final week at the agency. In the month that has passed since Gottlieb rattled the medical device industry with news of his impending resignation, the commissioner has issued 18 public statements pertaining to nearly all corners of the agency's realm, from food, tobacco, and cosmetics to drugs and devices. Friday is Gottlieb's last day on the job. On Tuesday, Gottlieb said the agency will consider a new regulatory framework for reviewing medical devices that use advanced artificial intelligence algorithms. AI has been making headlines in medtech for a while now, and this is certainly not the first time FDA has turned its attention to how AI-based medical devices should be regulated.
Forward-thinking pharma companies are moving beyond pilots and focusing on how new technologies can add value. These are some of the technologies driving digital transformation in life sciences. Artificial Intelligence (AI): AI is just beginning to be applied in life sciences to help with intelligent use of data. It has the potential to revolutionize diagnoses, treatment planning, patient monitoring, and drug discovery. Internet-of-Medical-Things (IoMT): The rising number of connected medical devices--together with advances in the systems and software that support medical grade data and connectivity--have created the IoMT.
Another simple, yet incredibly useful way deep learning is impacting healthcare is through the categorization of electronic health records (EHRs). Deep learning is already used in many natural language processing (NLP) programs, but EHRs present a uniquely complex problem, even for deep learning networks. Free text notes are often completed in a rush meaning they are messy, full of medical jargon, sometimes incomplete or filled out by multiple people, rendering them inconsistent. However, deep learning still represents the best method we currently have to analyze these records. A deep learning model developed by Google in 2018 was capable of predicting clinical outcomes, such as mortality and unexpected readmissions, better than traditional models after it had analyzed 216,000 patient EHRs across two hospitals.
The future of healthcare is bright. Daily articles and news reports herald the arrival of digital technologies and artificial intelligence right into the heart of our homes and healthcare institutions. Indeed, technology is not so much an add-on but seen to be essential to the NHS as we look to future proof our precious health service. The recent Topol Review explored how to prepare the workforce for a digital future, and the release of the "Code of Conduct for Artificial Intelligence Systems used by the NHS" amongst others are both statements of intent in order to deliver this ambitious digital agenda. In addition, Matt Hancock, secretary of state for health and social care, has also identified technology as the answer to some of the challenges faced by the NHS and this demonstrates the weight of commitment to artificial intelligence and digital health.