FDA Approvals For Smart Algorithms In Medicine In One Giant Infographic

We strongly believe that only digital health can bring healthcare into the 21st century and make patients the point-of-care. Mental health algorithms mimicking empathy? A.I. outsmarting human doctors? Simple big data analytical software presented with clever marketing tactics? It’s difficult to assess the actual state of play when it comes to artificial intelligence in healthcare. Moreover, there’s no database that contains all the smart algorithms worth applying to medical processes. That’s the reason why we decided to collect every artificial intelligence-based algorithm that already received FDA approval – meaning that they are proven, reliable, and accurate solutions enabled by an official regulator for medical use. Let’s see the infographic in details!

The factors of algorithmic healing When we started to assess the universe of smart algorithms in healthcare, we took into account temporal and spatial factors, accuracy and credibility, as well as medical specialties where A.I. algorithms have a chance to make the care process better.

Concerning the timeline, we noticed an uptake in the appearance of new solutions in the last years, and you can see that also in the infographic. In 2014, only AliveCor’s algorithm for the detection of atrial fibrillation was approved. Two years later, the FDA found further four solutions ready for clinical use, while in 2017, six new algorithms were approved by the US regulator. This exponential growth just accelerated last year, when the FDA endorsed 23 algorithms in medicine. As the first approvals in 2019 also show, we do not expect the trend to slow down. On the contrary, we will most likely see dozens of new medical A.I. solutions on the market.

Looking at the spatial factors, while the most important hubs for A.I. development are the Silicon Valley, the Boston-New York area, Montréal, London, Bangalore, and Beijing, and the same can be applied in medicine and healthcare, the most decisive factor for compiling FDA approved algorithms was that it is the only yardstick for credible and accurate medical software. Although in Europe, the European Medicine Agency has guidelines and statements about artificial intelligence, the FDA is the only regulator with efficient instruments in its toolkit to access the credibility and accuracy of algorithms for medical purposes in detail. It also means that we had to stay on the U.S. market and consider the developments within the FDA’s jurisdiction.

Mayo Clinic CIO: 'This artificial intelligence stuff is real'

“And it is coming quickly to a care setting near you,” said Cris Ross at Health 2.0 on Tuesday, touting “small AI and big AI” tools that can help revamp IT systems to improve the experience of clinicians and patients alike. SANTA CLARA – Mayo Clinic Chief Information Officer Cris Ross put it plainly during his keynote speech at Health 2.0 this week: “Our systems are not adequately supporting our doctors, in lots and lots of ways.”

And he counts his own world-class health system as one of them. Mayo Clinic completed a landmark four-year, 90-hospital, $1.5 billion Epic implementation in 2018. But while it was “an enormous project and by all objective measures we did just fine,” said Ross, “we’re also still at place where our doctors are frustrated and our patients are not seeing a particular difference by us doing that.”

Providers want to know that they have meaningful work, where they are operating in an efficient and effective way and that they’re delivering the best treatment that’s appropriate, he explained.

“But they’re also looking for joy in practice,” said Ross. “Being a provider is hard. And we make the bar even harder by layering on unbelievable levels of complexity and regulation, which makes their work incredibly hard. We have to help them with that and try to find a way to bring some joy back to their work.”

At Mayo Clinic, he said, “part of what we are trying to do is to pursue the next generation of care.” And to do that, the health system is embracing a wide array of future-looking initiatives such as its just-announced 10-year partnership with Google Cloud, which will offer security and agility – and will enable Google’s AI scientists to work shoulder to shoulder with Mayo’s own researchers, developing new models of care.

Are oncologists ready to adopt AI tools?

Healthcare technology continues to be a sector of significant spend, with Forbes reporting that public and private investment in healthcare artificial intelligence (AI) is expected to reach $6.6 billion by 2021. An array of new technologies ranging from AI and machine learning to wearables and microchipped drug capsules, once deemed science fiction, have the potential to transform the diagnosis and treatment of disease. Yet historically adoption of technology in health care has been decidedly slower than in other industries, leading to a question of whether these investments will deliver their expected return.

The reasons behind the healthcare industry’s slow adoption of new technology are multifaceted and include everything from regulatory hurdles to cost barriers. But the willingness of healthcare providers to embrace technological innovations at the point of care is certainly a critical factor.

While AI has applications in many disease categories, the potential to apply it in oncology is particularly exciting given the rapidly increasing complexity of cancer treatment in this era of precision medicine and value-based care. To better understand how providers feel about using AI in oncology, a survey was done of 180 oncologists from across the United States, including hospital- and community-based practices. Its purpose was to gauge their views on the potential of AI to improve care, where they see opportunities to leverage it in their practices, and possible barriers to adoption.

How Handheld Computers Can Streamline and Improve Healthcare

As more hospitals adopt a mobile strategy, multipurpose tools are becoming an increasingly popular option. Workstations on wheels still serve a purpose in healthcare. The carts help clinicians tote around all of the devices and equipment they need and connect to systems at a patient’s bedside.

But they’re hardly as nimble or convenient as the current generation of clinical mobile computers that have a battery life that lasts for an entire shift, are built to withstand drops and can easily be sanitized.

Small yet mighty, these tools help ease workflows and improve communication among clinicians. And they can perform an array of critical functions that once required additional or cumbersome technologies to execute.

“Today’s caregivers are being asked to do more with less. How do you do that? You need to make some part of their jobs easier,” says John Barr, a consulting systems architect for Memorial Hermann Health System in southeast Texas, where the company’s stable of Honeywell Dolphin CT40 and CT50 models has grown to nearly 400. (He expects that number “to grow significantly, by fourfold to fivefold” in the near future.)

Although Memorial Hermann still deploys more than 6,500 workstations on wheels, response to the new computers has been positive, with users citing security and portability as perks. The equipment also supports a systemwide initiative to untether staff from stationary command posts and bulkier tools.

“I think nobody would argue that access to information on a mobile handheld device is easier than dragging around a computer on wheels,” Barr says.

That sentiment is growing: Ninety percent of healthcare organizations plan to implement (or are currently implementing) a mobile device initiative, a 2018 Jamf survey found. And 47 percent of respondents said they plan to increase mobile device usage by 2020.

Healthcare cybersecurity – the impact of AI, IoT-related threats and recommended approaches

An interview with Richard Staynings, Chief Security Strategist, Cylera. Currently leading healthcare security strategy at Cylera, a biomedical HIoT security startup, Richard Staynings has more than two decades of experience in both cybersecurity leadership and client consulting in healthcare. Last year, he served on the Committee of Inquiry into the SingHealth breach in Singapore as an Expert Witness. He recently spoke to Healthcare IT News on some of the current developments in healthcare cybersecurity.

Q. Artificial Intelligence (AI) applications in healthcare are all the rage now, and so are cybersecurity threats, given the frequency and intensity of healthcare-related incidents. In particular, some of the cyberattacks have become more sophisticated through the use of AI to get past cyber defenses. On the medical devices front, AI is also being used to constantly manage and secure the rising number of healthcare IoT devices as they connect and disconnect from hospital networks. How do you think the application of AI in healthcare cybersecurity will be like in the next few years?

A. Healthcare is widely considered to be an easy and soft target because “who in their right mind would attack the weak and defenseless?” …. or so the thought goes! The fact is that healthcare presents a rich target for cyber criminals because of the value of the data hosted and processed. When you couple that with a chronic historical underinvestment in the development of capable cybersecurity teams and tools across healthcare, you can see why perpetrators are so keen to break walk in. But it’s no longer the theft of medical records, or PII that concerns me, it’s the wholesale theft of intellectual property from research universities and pharmaceuticals by rogue nation states, (one in particular) and the potential to hold both hospitals and their patients to ransom by just about anyone. That’s what really worries me most.

I believe we are on the cusp of an AI arms race. Attackers are busy designing new attack vectors and methods to get by cyber defences that heavily leverage AI and Machine Learning (ML). Advanced persistent threats (APTs) that hide unnoticed on the network for years sometimes, while gathering vital information and gradually expanding their footprint till they own the entire network, just as the attack on SingHealth in 2018 demonstrated. AI that perfectly emulates the normal acceptable behavior of users and systems on the network and as such goes undetected by even the best cyber defences.

Digitalising internal and external hospital processes for better healthcare delivery

“Most hospitals tend to make the mistake of selecting infeasible EHR options despite knowing their constraints. Sometimes, seeking a third party’s advice who is in a neutral position would be useful,” said Heungro Lee, Partner, VAIIM Consulting Group. The Electronic Health Record (EHR) is defined as a longitudinal electronic record of patient health information generated by one or more encounters in any care delivery setting, according to the HIMSS Health Information and Technology Resource Library. Included in this information are patient demographics, progress notes, problems, medications, vital signs, past medical history, immunisations, laboratory data and radiology reports.

The EHR automates and streamlines the clinician’s workflow and its adoption can be a means in which hospitals and healthcare organisations tap on to improve healthcare delivery by capturing structured healthcare information. However, different hospitals or healthcare organisations can have very varied budgets and approaches to EHR adoption or even improvement. Mr Heungro Lee, who is in charge of healthcare strategy as a partner at VAIIM Consulting Group, highlighted some key considerations for healthcare organisations and hospitals in their approaches to EHR adoption:

“Decision making is always difficult. But with a well-designed decision making process, the journey might be easier. The first step for EHR adoption for these organisations is to define what their constraints are, be it availability of budget, timelines to meet or the internal manpower resources required.

The next step is to prioritise the goals to achieve and these could be process standardisation, improving patient care and monitoring and managing hospital’s performance, etc. The final step is to source out feasible options based on the previous two steps. From my experience, most hospitals tend to make the mistake of selecting infeasible options despite knowing their constraints. Sometimes, seeking a third party’s advice who is in a neutral position would be useful.”