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Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making. Objective: To compare the performance of 3 machine learning approaches with the commonly used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related GIB. Design, setting, and participants: This retrospective cross-sectional study used data from the OptumLabs Data Warehouse, which contains medical and pharmacy claims on privately insured patients and Medicare Advantage enrollees in the US. The study cohort included patients 18 years or older with a history of atrial fibrillation, ischemic heart disease, or venous thromboembolism who were prescribed oral anticoagulant and/or thienopyridine antiplatelet agents between January 1, 2016, and December 31, 2019. In this cross-sectional study, the machine learning models examined showed similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance. link to the original investigation paper https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780274 read the pubmed article at https://pubmed.ncbi.nlm.nih.gov/34019087/
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The world's smallest computer chip is the size of a dust mite! Check out the future of medical tech. Electronics are getting imperceptibly small, opening new avenues for medical technology to place advanced monitoring and treatment devices inside our bodies. And Columbia University engineers just demonstrated a new and revolutionary version of this, creating the world's smallest single-chip system ever developed, according to a recent study published in the journal Science Advances. And, critically, the tiny new chip can be implanted via a hypodermic needle to measure internal body temperature, and potentially much more. A tiny computer chip was implanted into seven mice at once The implant created by the engineers at Columbia is record-breakingly small, but it's also breaking new ground in simply existing as a wholly functional, electronic circuit whose total volume is less than 0.1 cubic millimeter. In other words, it's the size of a dust mite, not to mention far more compact than the world's smallest computer, which is a cube-shaped device precisely 0.01-inches (0.3 mm) on each side. The smaller, new chip is only visible with a microscope, and pushed the envelope in power-sourcing and communications ingenuity design. The injectable chip could serve as an 'early warning' system against future outbreaks Such tiny chips could also be implanted in the human body, and then communicate measured information and data wirelessly through ultrasound. As the device stands, it can only measure body temperature, but it could eventually also monitor respiratory function, glucose levels, and blood pressure. "We wanted to see how far we could push the limits on how small a functioning chip we could make," said Ken Shepard, leader of the Columbia study, in a report from New Atlas. "This is a new idea of 'chip as system' — this is a chip that alone, with nothing else, is a complete functioning electronic system." more at https://interestingengineering.com/smallest-ever-injectable-chip-hints-at-cybernetic-medicine
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Commercial soft contact lenses have been on researchers' radar to help diagnose and monitor ocular diseases for a while, but they have proven tricky to use as typical sensors and electronics used for such uses normally require a hard, planar surface to function. Something a soft, curved, thin contact lens can't offer. A multidisciplinary team of researchers from Purdue University in the U.S. has created a soft contact lens that's capable of diagnosing and monitoring ocular diseases painlessly. How? The way the team managed to develop a soft contact lens for this purpose was by integrating ultrathin, stretchable biosensors with soft commercial contact lenses using wet adhesive bonding. The biosensors embedded within the contact lenses record retinal activity from the surface of the eye. As these are regular contact lenses, no topical anesthesia to manage pain and safety, as is typical with current clinical diagnosis and monitoring settings, is required. "This technology will allow doctors and scientists to better understand spontaneous retinal activity with significantly improved accuracy, reliability, and user comfort" Read the press release about the lens at https://www.purdue.edu/newsroom/releases/2021/Q1/soft-contact-lenses-eyed-as-new-solutions-to-monitor-ocular-diseases.html Read the original completed unedited story at https://interestingengineering.com/new-soft-contact-lens-diagnoses-and-monitors-eye-diseases
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Everyone loves the idea of strapping a smartwatch to their wrist and using it to get a bit healthier, but there's a fine line between casual wearables and serious medical devices. It's an important distinction, since while the former can be sold without any sort of oversight, the latter is rightly covered by the FDA's regulations. Since the most recent batch of fitness wearables could be blurring the borders somewhat, the agency has decided to make its thinking on the subject a little clearer.
The agency has published a draft guidance note that, as yet, isn't legally binding and requests opinions from the public. As far as it's concerned, "general wellness devices," i.e. watches that vaguely encourage people to get fitter, aren't any sort of risk to the public. This means that your Fitbit is okay to tell you to go for a walk, your Aura can coach your sleeping and Lumosity can pretend to make you smarter without any worries. Mostly the FDA is concerned with risk, and there isn't much risk if your smartphone tells you to lay off the burgers one every now and again.
more at http://www.engadget.com/2015/01/20/fda-wearables-guidance-note/
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Wearables, devices used to sense data and process it into information, are generating quite the buzz in healthcare these days. But down the line, does that buzz come with a sting?
In Wearable Tech News, Tony Rizzo reports wearable technology spending predictions of $50 billion by 2018. He also reports on a ground-breaking, glucose-sensing contact lens for diabetics that will be a “true solution for a very real medical problem that affects hundreds of millions of people.”
By 2016, wearable wireless medical device sales will reach more than 100 million devices, according to a Cisco blog on the future of mobility in healthcare. The importance of these devices is that healthcare professionals can access critical data via mobile apps before, during and after a patient’s hospitalization, thus boosting the speed and accuracy of patient care, the blog says.
The Age of Wearables has a few caveats, though – note that a doctor “can,” “could,” “may” or “potentially” be able to monitor a patient from a wearable, as the products are still under development. One product cites unpublished research as support, and another uses a modality, thermography, that the National Cancer Institute states has no additional benefit for breast cancer screening.
The new, intense focus on wearables is the engagement of the general public, both the ill and the well, and how they collect and transmit patient information to physicians and EHRs. This presents two challenges:
1. Are physicians prepared for this tidal wave of data and information? 2. What is the true cost of the data surge versus its benefits?
Like all healthcare information technology, wearables have huge potential – married to massive challenges.
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As the number of self-tracking health and fitness tools available to consumers continues to climb, a persistent question has been whether the data they collect might be useful to health researchers. Along with that: Are people who self-track comfortable sharing their data with researchers?
A new, must-read report from San Diego’s California Institute for Telecommunications and Information Technology (Calit2), funded by the Robert Wood Johnson Foundation, explores these and other questions.
Based on a survey with hundreds of self-trackers, a majority — 57 percent — said one critical assurance they would need before agreeing to make their self-tracked, personal health data available to researchers was that their privacy would be protected. More than 90 percent also said it was important that their data remained anonymous. Respondents said they’d be more comfortable sharing data if they knew it was only going to be used for “public good” research.
One open-ended survey that the report’s researchers posed to self-trackers found that 13 percent of respondents specifically mentioned an aversion to commercial or profit-making use of their data, according to the report. One respondent wrote: “It depends who gets it. Research using these data will be instrumental in the future of personal predictive services, but also for that reason are likely to be exploited by marketers and the politically short-sighted. Thus I would like transparency for who has access to my data.”
Among the almost 100 health researchers interviewed for the report, 46 percent said that they had already used self-tracking data in their research previously. Some 23 percent reported that they had already worked with digital health companies that offer apps or devices to consumers to track their health.
Overall, the researchers interviewed for the report were “generally enthusiastic” about the prospect of using self-tracking data in the future — 89 percent agreed or strongly agreed that such data would prove useful to their research efforts. Almost all of those researchers surveyed said that kind of data could answer questions that other data could not.
more at http://mobihealthnews.com/30979/health-researchers-see-unique-opportunity-in-self-tracker-data/
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Wearables may be the tech du jour, but the next generation of devices and services needs to focus more on keeping users engaged in the long-term. These three factors, based on behavioral science, can help them do just that.
1. Habit formation. Sustained engagement depends on a device or service’s ability to help the user form and stick with new habits. Wearable devices have the potential, all too often unrealized, to make the process of habit formation more effective and efficient than ever before. The best engagement strategies for wearables move beyond just presenting data (steps, calories, stairs) and directly address the elements of the habit loop (cue, routine, reward), triggering the deep-seated psychological sequences that lead to the establishment of new habits.
2. Social motivation. To sustain engagement beyond the initial habit formation, a device or service must be able to motivate users effectively. Social connections are a particularly powerful source of motivation that can be leveraged in many creative ways. In addition to using social connections to influence behavior, social media and networking sites can be exploited to alter habits for positive outcomes.
3. Goal reinforcement. To achieve sustained engagement, a user also needs to experience a feeling of progress toward defined goals. Research shows that achieving several smaller goals provides the positive momentum necessary for achieving bigger goals. Wearable products and services that help people experience continuous progress can do so, for example, through real-time updates that are powered by big data and insights. Facilitating personal progress in this way leads to improved health, user satisfaction and long-term sustained engagement.
more at http://gigaom.com/2014/02/22/the-three-critical-factors-wearable-devices-need-to-succeed/
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LAPTOP-HF is designed to investigate a new way to help treat heart failure. Since many heart failure patients are frequently hospitalized and often feel poorly, the hope is that this system may help your doctor adjust your medications before you develop symptoms or require hospitalization.
This is accomplished by measuring pressure in the heart and then each day providing you with your physician’s updated recommended medications and dosages. These may change daily depending on your condition. This is very similar to how diabetics manage their glucose levels. -
See more at: http://www.kumed.com/heart-care/clinical-services/heart-care-clinical-trials/laptop-hf#sthash.EfEC2fvM.dpuf
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Health Risk Assessments (HRAs) are a powerful component of population health management strategies for healthcare organizations.
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The pandemic has accelerated some trends here, notably telemedicine. That’s given a healthy push to an emerging field – remote diagnostics. Your phone is gradually – with some extensions – turning into a remote diagnostic tool to replace doctor visits and expensive tests. That’s fairly well known, even if it is extraordinary: blood oxygen levels are captured by a device costing less than $20, while an always-on cardiac monitor tracks heart activity, for example. But that’s just the very tip of the iceberg. Remote diagnosis can transform the entire scientific basis of modern medicine. Currently, the gold standard for testing the safety and efficacy of treatments is the randomized control trial (RCT), in which some part of the trial group is treated while another part is not. Both are tracked to see whether the treatment worked, and to look for adverse events like additional illness or even deaths. Outcomes are assessed using standard statistical tools to compare the two groups. This is the gold standard. But it is based on a single core assumption: that humans by and large react similarly to treatments, and hence that the best way to address disease is to identify treatments that work effectively for large numbers of patients. Ideally, treatments work for everyone, although sometimes RCTs and subsequent tracking find groups for which a treatment doesn’t work, or another for which it works especially well – maybe the old (or young), men (or women), or people with specific pre-existing conditions. Still, this is definitely mass-oriented medicine: it’s based on the impact of treatments on what might be called the median patient. Amazon is about to enter healthcare in a big way. It is already planning to offer Amazon Care (its primary care system organized around telehealth) not just to its 1.2 million employees, but to other employers as a service, much like it offered Amazon Web Services 15 years ago (and AWS is now the leading provider of internet infrastructure in the world). It also purchased Pillpack and set up Amazon Pharmacy to deliver medications and other health products online for delivery. But the real revolution is coming inside the home. The Amazon Halo is a new health monitoring device (with some admittedly creepy privacy-related features). It is designed to apply the capabilities of AI to the needs of individual patients. And then there is Alexa, which is clearly going to be Amazon’s device of choice for wellness in the home. It’s already partnered with Sharecare to provide automated advice on 80,000 wellness and health questions, as a first line response to health concerns. Alexa will likely expand, gaining the capacity to integrate remote diagnostics and escalation into Amazon’s wider telehealth network. Still, this mostly sounds like more of the same: using telehealth and the telemedicine capabilities, but mainly just extending what we do now, making it all a bit more convenient – possibly, a lot more convenient. But all these new tools, and especially diagnostics tools, make truly personal medicine possible – like Amazon, all this will be individual-specific: a market segment of you. read the entire unedited article at https://www.healthitanswers.net/into-the-future-amazon-and-the-coming-rise-of-personal-healthcare/
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People often turn to technology to manage their health and wellbeing, whether it is - to record their daily exercise,
- measure their heart rate, or increasingly,
- to understand their sleep patterns.
Sleep is foundational to a person’s everyday wellbeing and can be impacted by (and in turn, have an impact on) other aspects of one’s life — mood, energy, diet, productivity, and more. As part of Google's ongoing efforts to support people’s health and happiness, Google has announced Sleep Sensing in the new Nest Hub, which uses radar-based sleep tracking in addition to an algorithm for cough and snore detection. The new Nest Hub, with its underlying Sleep Sensing features, is the first step in empowering users to understand their nighttime wellness using privacy-preserving radar and audio signals. Understanding Sleep Quality with Audio Sensing The Soli-based sleep tracking algorithm gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur. However, to understand and improve their sleep, users also need to understand why their sleep is disrupted. To assist with this, Nest Hub uses its array of sensors to track common sleep disturbances, such as light level changes or uncomfortable room temperature. In addition to these, respiratory events like coughing and snoring are also frequent sources of disturbance, but people are often unaware of these events. As with other audio-processing applications like speech or music recognition, coughing and snoring exhibit distinctive temporal patterns in the audio frequency spectrum, and with sufficient data an ML model can be trained to reliably recognize these patterns while simultaneously ignoring a wide variety of background noises, from a humming fan to passing cars. The model uses entirely on-device audio processing with privacy-preserving analysis, with no raw audio data sent to Google’s servers. A user can then opt to save the outputs of the processing (sound occurrences, such as the number of coughs and snore minutes) in Google Fit, in order to view personal insights and summaries of their night time wellness over time. read the entire unedited blog post at https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
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Personalized medicine is hoping to reach new heights thanks to the Cancer Moonshot, but won't get off the ground without a community-wide commitment to sharing big data. The precision medicine community has long since recognized that sharing big data, including clinical records, genomic sequencing data, community-level health indicators, and research results, will be critical to making progress against cancer, neurodegenerative diseases, inherited conditions, and expensive chronic diseases like diabetes. “Why is data sharing important? Because cancer is complex,” said Kenneth C. Anderson, MD, President-elect of the American Society of Hematology (ASH). Anderson specializes in multiple myeloma, a blood cancer with treatment options that hinge on the genetic variances of each and every patient. “We’re learning so much about cancer, and applying these insights to drug development has been incredibly fruitful,” he continued. “Now we have treatments that are specifically targeted to patients’ genetic mutations. Not only are these treatments more effective — because they correct a specific mutation — they also minimize harmful side effects that we see with traditional total-body anticancer medicines.” However, the continued development of these treatments cannot be sustained without a commitment to data sharing, he added. more at http://healthitanalytics.com/news/why-sharing-cancer-big-data-is-key-to-personalized-medicine
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The big hairy audacious goal of most every data scientist I know in healthcare is what you might call the Integrated Medical Record, or IMR, a dataset that combines detailed genetic data and rich phenotypic information, including both clinical and “real-world” (or, perhaps, “dynamic”) phenotypic data (the sort you might get from wearables).
The gold standard for clinical phenotyping are academic clinical studies (like ALLHAT and the Dallas Heart Study). These studies are typically focused on a disease category (e.g. cardiovascular), and the clinical phenotyping on these subjects – at least around the areas of scientific interest — is generally superb. The studies themselves can be enormous, are often multi-institutional, and typically create a database that’s independent of the hospital’s medical record.
Inevitably, large, prospective studies can take many years to complete. In addition, there’s generally not much real world/dynamic measurement.
The other obvious source for phenotypic data is the electronic medical record (EMR). The logic is simple: every patient has a medical record, and increasingly, especially in hospital systems, this is electronic – i.e. an EMR. EMRs (examples include Epic and Cerner) generally contain lab values, test reports, provider notes, and medication and problem lists. In theory, this should offer a broad, rich, and immediately available source of data for medical discovery.
DIY (enabled by companies such as PatientsLikeMe) represents another approach to phenotyping, and allows patients to share data with other members of the community. The obvious advantages here include the breadth and richness of data associated with what can be an unfiltered patient perspective – to say nothing of the benefit of patient empowerment. An important limitation is that the quality and consistency of the data is obviously highly dependent upon the individuals posting the information.
Pharma clinical trials would seem to represent another useful opportunity for phenotyping, given the focus on specific conditions and the rigorous attention to process and detail characteristic of pharmaceutical studies. However, pharma studies tend to be extremely focused, and companies are typically reluctant to expand protocols to pursue exploratory endpoints if there’s any chance this will diminish recruitment or adversely impact the development of the drug.
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So far the internet of things hasn’t made much headway into patient care in the medical setting, but consumers are buying wellness devices for a variety of reasons. Will the medical world embrace that data?
The intersection of healthcare and connected devices was thrown into high relief these last few weeks as both Apple and Samsung unveiled ecosystems to take consumer health data and turn it into actionable intelligence.
But this week’s guests at the Weekly podacst at GigaOm are confident that as advanced as consumer-grade consumer grade health devices get, they won’t become something doctors are hot on for years to come — if ever.
In this week’s podcast Stacey Higginbotham discusses medical connected devices and where it may meet the consumer with Rick Valencia from Qualcomm Life. Will doctor’s prescribe our apps or devices?
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A newly published Apple patent application that details ways to improve a wrist-based pedometer could represent another piece of evidence pointing to an iWatch.
The application, “Wrist Pedometer Step Detection,” came out of the U.S. Patent and Trademark Office today. This is part of the standard patent process toward issuance. It details ways to improve step detection when someone is wearing a pedometer on a wrist.
In the patent application’s implementation, the pedometer might be able to “automatically determine that the pedometer is being worn on a user’s wrist.”
Pedometers, the application points out, are often attached to a user’s trunk – on the waist or pants or shirt pocket. A commonly used algorithm to measures steps, however, doesn’t work as well when the pedometer is on a wrist, because the arm’s movement can interfere with the measurement of acceleration.
Apple’s patent application would overcome this by filtering the measured movement or inferring steps from previous measurements, leading to more accurate step counts and distance estimation. Additionally, the document notes, “users do not have to specify where the pedometer is being worn” because the software will compensate.
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The Oral-B SmartSeries 7000 can do plenty of things that normal toothbrushes can't.
The 7000 is about the same size and weight as any other fancy electric toothbrush, and is compatible with other old Oral-B brush heads.
It's handsome enough, but really: it's a toothbrush, not a fashion statement.
Anyway, as soon as the toothbrush and your phone have forged a connection over Bluetooth, firing up the 7000 will start a countdown to oral cleanliness in the companion smartphone app.
You (or your dentist, if you're the responsible type) can add and tweak those timers as desired, though the default timer will have you scrubbing different areas of your mouth for two minutes.
In case the sheer boredom of brushing your teeth for that long is too much to bear, you can also thumb through a stream of news articles or local weather reports (no, really) to help you hang in there. Turns out, just furiously mashing those bristles into your teeth isn't great either, so the timer will blink red if you're pressing too hard.
Oh, but the fun doesn't end once you're gleaming. The 7000 sends over your personal brushing data to the app, where it's turned into pretty graphs and accolades for prolonged brushing. Your dentist can specify certain areas you should focus on while brushing too, which appear during the countdown to keep you moving in all the right ways.
And if you don't have a dentist to meet regularly? The app will find and list local ones.
more at http://www.engadget.com/2014/02/25/oral-b-bluetoothbrush-smartseries-7000-hands-on/
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No one has arms long enough to wear all of the activity-tracking wristbands currently on sale or awaiting release. These devices count your steps, measure your sleep and some even monitor your heart rate.
But do you know how this information immediately applies to your lifestyle, or what you should do with it?
The services behind these trackers need to invest in immediacy by providing useful information, ideally in real time, so we can optimize our wealth of data into action.
Everyone wants to be better, but nobody has a baseline for understanding themselves.
what use is the data without knowing in real time what you, individually, can do to change it?
I’d like to know whether I need to slow down. Am I pushing myself too hard?
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2012 brought a new product category to the market and saw many acquisitions from wearable manufacturers looking to get the edge in a hotly contested space but 2013 has undoubtably been the breakout year for wearables and quantified self hitting the mainstream.
Back in June I started studying the space pretty seriously. I made a few observations and ended up co-founding my own health & fitness app that went on to amass 30,000 downloads as we tested the market (visit BodyWise).
Back when we launched BodyWise, there wasn’t much in the way of being able to track beyond your distance, steps or sleep. We brought tracking of a wide variety of health & fitness metrics to the everyday person. Since our launch I could name half a dozen serious competitors playing on our level and another half a dozen wearables that brought our features to their apps and devices.
As we develop BodyWise v2 I wanted to share with you my summary of the state of play for Wearables & the Quantified Self in 2014:
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