Stone Three: For the Heart from the Heart
Dirk Wagener, GM, Stone Three
For the Heart from the Heart
Cardiovascular disease or CVD is a global problem. Though typically viewed as a “rich-world disease”, it has now migrated to many developing countries. The rising prevalence of CVD, today, calls for cost-effective and intelligent patient-centric healthcare technology that empowers care teams and has a real impact on patients. This where Stone Three comes in. Harnessing the power of machine learning, the company enables and enhances virtual consults with computer assisted diagnostic solutions to help primary healthcare providers better diagnose and manage CVD. “We utilize machine learning to unlock the value of the health data related to cardiovascular health and derive insights that enable us to diagnose and prioritize patients who need urgent care. Our core technology focuses on medical visualization and precision telemedicine, built on our experience with medical device and data interoperability,” says Dirk Wagener, General Manager of Stone Three Healthcare.
Clearly understanding the needs and pain points of their customers, the company provides innovative solutions that exceed customer expectations and add value to their businesses. Their professional stethoscope streaming solution, TeleSensi™, delivers superior out-of-band audio quality that is clinically appropriate for improved diagnosis, workflow integration with existing user management systems and complete business integration. It allows a presenter to stream the patient’s live heart or lung sounds to a remote provider during a tele-consultation.
Similarly, to save, view, share, and diagnose heart sounds with ease, Stone Three provides the world’s first artificial intelligence murmur detection solution called SensiCardiac™ using electronic stethoscopes to remotely detect up to 15 underlying heart problems. This assisted diagnostic cardio solution enables healthcare providers and clinics to perform preventative screenings and patient triage. For instance, TeamCME, based in Oregon, U.S., undertakes the statutory Department of Transport (DoT) health assessments on truckers using SensiCardiac™ to detect their underlying heart conditions. This gives peace of mind to truckers that their hearts are being assessed for cardiac abnormalities during the statutory DoT examination. “Today, we are the largest clinically validated heart sound reference database which is key to developing new assisted diagnostic cardiac solutions. And our solutions are improved through ensuring strict adherence to compliances, regulatory approvals, and internal quality processes,” points out Wagener.
Furthermore, Stone Three developed an IT platform—The Intelligent Care System or ICS — with components critical to integrated patient-centered care for their partner PPO Serve.. The platform includes an intuitive EHR, data analytics, and case and care plan management for clinical workflows. Additionally, their unique consultation platform, Pathways—developed for Georgia Partnership for Telehealth (GPT)—enables complete telemedicine consults to GPT’s network of nurses, doctors, and hospitals to enhance care value and pave the road toward better clinical results.
“We are a SaaS company and our team has a unique combination of skills, ranging from clinical domain expertise to advanced machine learning, which enables us to build a scalable business in any part of the world,” says Wagener. Due to the uniqueness of their offerings, Stone Three plans to grow significant market share in the North American tele-auscultation market in the coming years and then enter into other global markets once telemedicine regulations in those markets become more conducive for their technology. Also, they are planning to combine more advanced assisted diagnostic solutions with their TeleSensi™ stack to allow the end user to have a much more realistic and thorough assessment experience. “We are also planning to involve more medical sensors relevant to cardiovascular health,” concludes Wagener.
Top 50 Healthcare Technology CEOs Of 2020
Viruses: Biological versus Computer
By Mark Webb-Johnson, Chief Technology Officer of Network Box
During this time of the COVID-19 pandemic, those of us working with computer viruses continue to be amazed at the similarities between the techniques used by the medical community to fight SARS-CoV-2 (the virus that causes the COVID-19 disease) and the processes involved in our electronic anti-virus systems. Let’s look at some of these similarities, and see how computer anti-virus researchers help protect.
By Mark Webb-Johnson, Chief Technology Officer of Network Box
What is the Virus?
SARS-CoV-2 is an RNA virus. Essentially, a strand of information wrapped up in a protective shell and a mechanism to infect cells – information, envelope, infection mechanism.
By comparison, a computer virus consists of the payload, a carrier, and an exploit mechanism. For example, the payload would be the malicious code, the carrier an email message, and the exploit mechanism something to take advantage of a vulnerability in a particular mail client. Another example would be script downloaded from a web page, taking advantage of a web browser exploit.
The virus’s primary purpose is to replicate – to make copies of itself. SARS-CoV-2 does this by infecting cells (in the lungs, stomach, and other areas of the human body), then using the cell’s own mechanisms to make copies of its RNA and make new virus particles.
Computer viruses have the same primary purpose of replication. They want to infect as many hosts as possible. Once in a computer system, they make multiple copies of themselves and transmit out to new hosts. It is this self-replication capability that defines this as a virus.
Identification and Testing
The gold standard for identifying viruses is the whole genome sequence. This maps the full sequence of the chemical components of the RNA (made up adenine, uracil, guanine, and cytosine; abbreviated as A, U, G, and C), and results in a very long string of these four letters. That is excellent for precise identification, but not so good for testing. Due to mutations, what we know of as SARS-CoV-2 is actually a collection of hundreds of different strains of the same fundamental virus, each with their own slightly different sequences (more on this later).
To test for the virus, researchers instead concentrate on relatively small portions of the full sequence. This way, they can extract samples from a potentially infected human, amplify the RNA in the sample to obtain enough to test with, and then compare that against the portion of the full sequence. That is the RT-PCR test.
Computer virus researchers work similarly. The payload of the virus itself is a sequence of computer code that can be expressed in binary, or more commonly in hexadecimal notation. Computer viruses are often intentionally self-encrypted and randomized (we call these polymorphic viruses) to avoid whole sequence detection. Nowadays, these are by far the most common form of computer virus seen.
Researchers extract portions of the sequence that don’t change and use pattern matching techniques to detect those partial sequences in suspicious samples. We call these ‘signatures,’ and they can be effectively used to detect known viruses in suspicious samples.
Immune Response and Vaccination
The human immune system has a component known as the ‘adaptive’ immune system. The system works by identifying portions of viruses already in the body, and creating antigen-specific cells designed to identify, remember, and attack that specific antigen. These cells protect against future infections of the same virus and can survive in the body for some time (months, or years, typically). This is why after you’ve had the measles once, for example, you usually don’t get it again. Vaccinations work by purposely injecting the body with antigens that will generate such an adaptive immune response, to protect you from future specific infections.
Computer anti-virus systems store databases of signatures of known viruses. When your computer receives a new file, it can scan it, look for a match against those signatures, and take action if a match is found (quarantine, etc.). Such signature-based systems are the computer equivalent to the body’s adaptive immune system.
Innate Immune Response
Another component in the human immune system is known as ‘innate.’ This system can detect what is not ‘you’ (what is ‘foreign’) and attack the invader. It relies on the antigen’s chemical properties and doesn’t need to have previously seen that specific antigen.
For computer anti-virus, this is extremely hard to achieve well. The capability to detect and block previously unknown viruses is what differentiates good anti-virus systems from the poor. Various techniques are used, but mostly revolve around a) decoding the virus code to the rawest form, b) detecting suspicious encoding or exploit behavior, and c) using emulation or sandboxing techniques to see what the virus does when executed. Looking at behavior, rather than code sequences.
Mutations and the Future
RNA viruses such as SARS-CoV-2 are very poor at accurate replication, and sometimes the copies made are not perfect. Base pairs get flipped. Portions of the sequence are lost. Parts of other viruses are incorporated. Before long, you are dealing with bad copies of bad copies of a bad copy. It is like the story of a million monkeys with a million typewriters, eventually producing the works of William Shakespeare. Sometimes these viral mutations are beneficial to the virus, but most often not. Whatever the outcome, these mutations are the way the virus adapts to further its goal of replication.
Thankfully, we do not see the same with computer viruses. A computer virus can and does make perfect copies of itself, 100% of the time. Sure, we have self-encrypting polymorphic computer viruses, and randomizing fragments are often introduced, but the core code of the virus is not changed, and certainly not randomly. Perhaps in time, we will see this, but with today’s non-forgiving computer CPU architectures, it is unlikely to be a successful approach.
Of course, given enough monkeys and enough typewriters, anything is possible. Perhaps they can even improve on Shakespeare. Before that day comes, however, make sure you are prepared. Subscribe to a Managed Security Services Provider (MSSP) that can adapt, and protect you from cyber threats.
AI to help kids struggling with ADHD, PDD-NOS
Stephane Bourles, CIO, Brain Balance
AI to help kids struggling with ADHD, Autism, Asperger Syndrome , PDD-NOS and other ASD’s
Left Brain or Right Brain?
In a properly functioning brain, both hemispheres communicate equally and at lightning speed, millions of times per minute. In a poorly functioning brain, the left and right sides of the brain only impart partial information, causing frequent miscommunication. This is called Functional Disconnection and is the root of many types of learning, behavioral and social problems found in children. The Brain Balance program puts the left and right brains back in sync using sensory motor exercises, academic skill building, and nutrition guidelines.
How does the Assessment Work?
The assessment consists of sensory, motor, and academic testing of more than 900 functions. The outcome of this assessment is a highly customized report providing parents with a complete understanding of their child’s behavioral, social, and academic skill levels.
We use AI to determine which brain hemisphere we believe to be stronger or weaker. The Machine Learning algorithm used for the assessment is not always accurate, which we know based on the feedback from our staff—yes we let them disagree with the system, which is intended as a tool to help them support their own assessment, but not to necessarily force them into a decision they don’t agree with.
But as valuable as an individual observation based on years of experience is, it still remains just the view of one individual. That is why we look at Artificial Intelligence as a new solution combining neural network architectures with massive computing power to enable our solution to learn a pattern from large datasets and make statistical predictions based on test results and feedback we already have for tens of thousands of students.
What is next?
Thanks to many product releases with different Machine Learning models we tested, we were able to improve our assessment accuracy and achieve precision, recall and F1 scores over 0.95. The limitation of this AI model is it is not 100% accurate and you don’t know for sure the source of truth. Since AI is a “black box” which can’t explain its prediction for most models, you have to trust your staff first.
Similar to clinical decision support systems helping healthcare practitioners, we believe this fast growing dataset about children, combined with new Artificial Intelligence models such as Explainable AI, will help our staff improve a child’s initial assessment, which will then improve our overall program’s results.
The importance of information to empower health-app users
Liz Ashall-Payne, CEO, ORCHA
Shining a light between the rock and the hard place: The importance of information to empower health-app users
Apps, and their use for the promotion of health and wellbeing, are the subject of increasing interest and enquiry; particularly in light of the NHS’ Long Term Plan, and ever-increasing pressures on scarce NHS resources. But finding and downloading effective, engaging, and most importantly, safe health-apps, is a significant challenge. While many have suggested that health-apps could be a ‘silver-bullet’, aimed at quashing the woes that stem from decreasing funding and increasing demand for incumbent NHS services, it is important to tread with caution.
Unlike pharmaceuticals, over the counter remedies, talking therapies, physiotherapy or surgery, health-apps can not only be sought, but readily obtained in absence of gatekeeping and safeguarding. The result is that you or I can download upwards of 200,000 health-apps today, with as little as a click of a button on the app-store. While this can be argued as the unique value-proposition of app-based health technologies, a lack of guarantees regarding the quality and content of such apps, means that this open-access feature of apps also represents the chief concern; and is likely limiting the enthusiasm with which healthcare professionals engage and promote their use.
The number of apps labelled as ‘pranks’ or for ‘illustration purposes only’ has been subject to year-on-year growth. But for those looking for convenient, easily accessible, and often cost-free support; or for those who are either vulnerable or impressionable; such technologies can pose significant health risks, beyond those that are apparent and conveyed (if at all) in the short description within the app store. An app that misleadingly claims to monitor blood pressure or blood sugar for example, may result in either (1) misleading information, (2) modifications to disease management not representative of true disease status (including the use of insulin or warfarin), or (3) serious adverse events including hypoglycaemia or stroke. One such app for monitoring blood pressure has been downloaded upwards of 1,000,000 times on the Android Google Play app store, while boasting a review rating of 4.3/5.
This raises questions regarding the utility of both the subjective user review score (which is inherently limited in only capturing both very positive or very negative views) and the number of prior downloads of an app, as potential surrogates for app quality. Recent research into apps targeted at chronic insomnia disorder conducted by the Organisation for the Review of Care and Health Applications (ORCHA) confirmed this. In this analysis, which is now available in the journal BMJ: Evidence-Based Mental Health, it was demonstrated that both user review scores, and the number of prior downloads of a health-app, were not only unreliable as proxies for quality, but in fact were inversely related; and as these metrics increased, the objectively measured quality of apps decreased (as assessed by presence of clinical evidence, data privacy policies etc.) This has left a considerable void regarding where reliable indicators of the safety and quality of app-based health technologies can be found, something which at least in part, has been tackled by the National Health Service (NHS).
Courtesy of the NHS’s digital assessment questionnaire (DAQ), and the NHS apps library (beta version), the number of ‘accredited’ and NHS-approved health-apps is increasing, albeit at a leisurely pace, with approximately 50 apps accredited so far. While a step in the right direction, the rigour with which such reviews are conducted comes at the expense of speed, and an estimated review time of 6-8 weeks, limits the possibility of such initiatives being conducted at scale. As such, the difference between the total number of health-apps available (~300,000), and those which have been quality-tested and approved by regulators or accreditation bodies (~50); is if anything, increasing; leaving little or no information regarding the validity, efficacy, and most importantly the safety, of 99.99% of health-apps available today; which to date have been downloaded by potential users upwards of 50million times.
This is where the Organisation for the Review of Care and Health Applications (ORCHA) aim to transform the process of health-app research and information provision. Positioned as an open-access, free of charge and publicly facing repository regarding the user experience, clinical efficacy, and data privacy of thousands of health-apps, ORCHA’s aim is simple; to empower and inform potential users of health-apps regarding potential risks and benefits, prior to committing to use. Through a structured, objective and peer-reviewed assessment, consisting of 160 question areas, answered on a ‘yes’ or ‘no’ basis, ORCHA provides information to the 99% of users of health-apps, which are yet to be formally assessed by health technology assessment bodies, and in doing so, ORCHA removes the information asymmetry that is currently clouding the health-app market in, distrust, disbelief, and uncertainty. Publishing a new review of a health-app every 30minutes on average, ORCHA pull in information for all 300,000+ apps available on both iOS and Android Google Play, organise these into medically approved sub-categories (including diabetes, mental health and diet and exercise), and then order these apps from the most to the least downloaded.