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ProTransport-1, a Northern California based medical transport provider has announced a software partnership with CrowdOptic, maker of mobile and wearable broadcasting solutions to deploy the CrowdOptic Google Glass broadcasting solution in its ambulances and mobile medicine units.
ProTransport-1 will use CrowdOptic’s software solution that will allow paramedics and nurses to broadcast through Google Glass a live view of complex cases from the ambulance to medical teams at the receiving hospital during transport. According to the press release, the companies aim to “improve documentation and expand medical consultative opportunities for patients en route.
“CrowdOptic’s see-what-I-see technology allows paramedics and nurses on our ambulances to broadcast the live view of complex cases to medical teams at the hospital”, said Glenn Leland, Chief Strategy Officer for ProTransport-1.
Additionally, ProTransport-1 envisions multiple opportunities to utilize CrowdOptic’s software particularly in the mobile medical setting by enabling a two-way educational forum between a patient in their home and providers. “We additionally envision a variety of dispatch, navigation, documentation and operational processes will migrate to CrowdOptic and Google Glass over time” said Glenn Leland, Chief Strategy Officer for ProTransport-1.
more at http://hitconsultant.net/2014/07/18/protransport-1-to-deploy-google-glass-in-ambulances/
Insights from our international survey can help healthcare organizations plan their next moves in the journey toward full digitization. A McKinsey & Company article.
The adoption of IT in healthcare systems has, in general, followed the same pattern as other industries. In the 1950s, when institutions began using new technology to automate highly standardized and repetitive tasks such as accounting and payroll, healthcare payors and other industry stakeholders also began using IT to process vast amounts of statistical data"
Researchers from MIT’s Laboratory for Information and Decision Systems have developed an algorithm in which distributed agents — such as robots exploring a building — collect data and analyze it independently. Pairs of agents, such as robots passing each other in the hall, then exchange analyses.
In experiments involving several different data sets, the researchers’ distributed algorithm actually outperformed a standard algorithm that works on data aggregated at a single location, as described in an arXiv paper.
Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It’s also the technique that autonomous robots typically use to build models of their environments.
That type of model-building gets complicated, however, in cases in which clusters of robots work as teams.
The robots may have gathered information that, collectively, would produce a good model but which, individually, is almost useless. If constraints on power, communication, or computation mean that the robots can’t pool their data at one location, how can they collectively build a model?
At the Uncertainty in Artificial Intelligence conference July 23 to 27, the researchers will present the new algorithm. “A single computer has a very difficult optimization problem to solve in order to learn a model from a single giant batch of data, and it can get stuck at bad solutions,” says Trevor Campbell, a graduate student in aeronautics and astronautics at MIT, who wrote the new paper with his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics. “If smaller chunks of data are first processed by individual robots and then combined, the final model is less likely to get stuck at a bad solution.”
Campbell says that the work was motivated by questions about robot collaboration. But it could also have implications for big data, since it would allow distributed servers to combine the results of their data analyses without aggregating the data at a central location.
“This procedure is completely robust to pretty much any network you can think of,” Campbell says. “It’s very much a flexible learning algorithm for decentralized networks.”
To get a sense of the problem Campbell and How solved, imagine a team of robots exploring an unfamiliar office building. If their learning algorithm is general enough, they won’t have any prior notion of what a chair is, or a table, let alone a conference room or an office. But they could determine, for instance, that some rooms contain a small number of chair-shaped objects together with roughly the same number of table-shaped objects, while other rooms contain a large number of chair-shaped objects together with a single table-shaped object.
Over time, each robot will build up its own catalogue of types of rooms and their contents. But inaccuracies are likely to creep in: One robot, for instance, might happen to encounter a conference room in which some traveler has left a suitcase and conclude that suitcases are regular features of conference rooms. Another might enter a kitchen while the coffeemaker is obscured by the open refrigerator door and leave coffeemakers off its inventory of kitchen items.
Ideally, when two robots encountered each other, they would compare their catalogues, reinforcing mutual observations and correcting omissions or overgeneralizations. The problem is that they don’t know how to match categories. Neither knows the label “kitchen” or “conference room”; they just have labels like “room 1” and “room 3,” each associated with different lists of distinguishing features. But one robot’s room 1 could be another robot’s room 3.
With Campbell and How’s algorithm, the robots try to match categories on the basis of shared list items. This is bound to lead to errors. One robot, for instance, may have inferred that sinks and pedal-operated trashcans are distinguishing features of bathrooms, another that they’re distinguishing features of kitchens. But they do their best, combining the lists that they think correspond.
When either of those robots meets another robot, it performs the same procedure, matching lists as best it can. But here’s the crucial step: It then pulls out each of the source lists independently and rematches it to the others, repeating this process until no reordering results. It does this again with every new robot it encounters, gradually building more and more accurate models.
This relatively straightforward procedure results from some pretty sophisticated mathematical analysis, which the researchers present in their paper. “The way that computer systems learn these complex models these days is that you postulate a simpler model and then use it to approximate what you would get if you were able to deal with all the crazy nuances and complexities,” Campbell says. “What our algorithm does is sort of artificially reintroduce structure, after you’ve solved that easier problem, and then use that artificial structure to combine the models properly.”
In a real application, the robots probably wouldn’t just be classifying rooms according to the objects they contain: They’d also be classifying the objects themselves, and probably their uses. But Campbell and How’s procedure generalizes to other learning problems just as well.
The example of classifying rooms according to content, moreover, is similar in structure to a classic problem in natural language processing called topic modeling, in which a computer attempts to use the relative frequency of words to classify documents according to topic. It would be wildly impractical to store all the documents on the Web in a single location, so that a traditional machine-learning algorithm could provide a consistent classification scheme for all of them. But Campbell and How’s algorithm means that scattered servers could churn away on the documents in their own corners of the Web and still produce a collective topic model.
“Distributed computing will play a critical role in the deployment of multiple autonomous agents, such as multiple autonomous land and airborne vehicles,” says Lawrence Carin, a professor of electrical and computer engineering and vice provost for research at Duke University. “The distributed variational method proposed in this paper is computationally efficient and practical. One of the keys to it is a technique for handling the breaking of symmetries manifested in Bayesian inference. The solution to this problem is very novel and is likely to be leveraged in the future by other researchers.”References:Trevor Campbell, Jonathan P. How, Approximate Decentralized Bayesian Inference, arXiv, 2014, arxiv.org/abs/1403.7471Related:Collaborative learning - for robots
Via Pierre Tran
On Indiegogo there is a campaign for a health education tool that has already reached its goal in less than a week — it’s a bear that teaches kids how to manage diabetes.
Those of us who have diagnosed a child with type 1 diabetes know how difficult of a diagnosis it is not only for the child, but for the family. Education for how to manage diabetes is a large task, but one most hospitals have great protocols for. Much of the education is aimed at the parents, with the hopes it gets reinforced to the child at home.
The company behind Jerry the Bear is Sproutel, and their hope is the toy bear will educate kids in a way that is not being done right now though positive reinforcement.
The below video shows how Jerry the Bear works:
Sproutel was hoping to raise $20,000, but has exceeded expectations by receiving almost $30,000 with more than 50 days remaining.
It would be interesting to see a study done in hospitals and outpatient settings where Jerry the Bear was compared to traditional diabetes teaching mechanisms. Either way, it’s definitely an innovative approach.
Indigogo campaignAuthor:Iltifat Husain, MD
Founder, Editor-in-Chief of iMedicalApps.com. Emergency Medicine Faculty and Director of Mobile App curriculum at Wake Forest University School of Medicine.Follow MeNo comments yet.
Via Emmanuel Capitaine
Researchers in Ireland evaluated the use of an Android smartphone app to increase patients’ activity levels, as measured by step count.
When it comes to tackling the epidemic of obesity and its associated morbidities, promoting active lifestyles is key. For many patients, setting specific achievable goals is a helpful tool in accomplishing that.
Here, researchers from the National University of Ireland and University of Aberdeen selected an app to trial among patients followed at three primary care centers to evaluate whether it could be effective in increasing activity levels. Over the roughly two month period, they found a 22% increase in basal activity levels.
A total of 90 patients using Android devices were randomized to either an intervention group which used the smartphone app or a control group. To pick the intervention app, researchers scored available pedometer apps based on three general criteria includingAutomatic feedback and trackingVisually appealing displayGoal setting functionality and feedback
Based on these criteria, they selected the Accupedo-Pro Pedometer app. All patients received up front education and counseling. After a one week run in period, patients in the intervention group were taught how to use the app. Beyond that, all patients received the same education and follow up including sharing data at the same intervals.
They found a mean difference in improvement in step count between the intervention and control groups of 2017 steps, or a 22% increase in mean step count. Other parameters followed including BMI and blood pressure did not significantly change however.
Interestingly, they found that both groups had an initial increase in step count but the control group quickly returned essentially to baseline while the intervention group continued to improve.
There are several useful takeaways from this study. First, it suggests that use of a low-cost smartphone app can help reinforce and sustain behavioral interventions. Second, it highlights the importance of “app training,” or helping patients understand how to use an app to achieve a specific goal.
As the researchers noted, 90% of Americans who own mobile phones carry their devices 24 hours a day. Here, they demonstrate how some of that time can be used to make meaningful improvements in health.Author:Satish Misra, MD
Satish is a Cardiology Fellow at the Johns Hopkins Hospital in Baltimore, Maryland. He is a founding partner and Managing Editor at iMedicalApps. He believes that mobile technology offers an opportunity to change the way health care is delivered and that iMedicalApps is a platform through which clinicians can be empowered to lead the charge.
Glynn LG, Hayes PS, Casey M, Glynn F, Alvarez-Iglesias A, Newell J, OLaighin G, Heaney D, O’Donnell M, Murphy AW. Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. Br J Gen Pract. 2014 Jul;64(624):e384-91. doi: 10.3399/bjgp14X680461.
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?
Frank X. Speidel, MD
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.
In acute situations, such as a patient’s complaint of sudden heart pain or chest tightening, wearables may allow doctors to “see” patients remotely to determine the seriousness of the discomfort, said Lydia Leavett in a Forbes article on wearables.
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.
There is also an almost entertaining naivety from some writers of the complexities of wearables. Speaking of FDA approval for a smart contact lens, one writer said it would be easy because “these devices are benign, with really small embedded sensors, so their risk is nonexistent.” Really? For decades, I have watched the simple contact lens cause conjunctivitis, keratitis and corneal ulcers. And concerns abound about low-level, electro-magnetic radiation and cataract formation in the human lens. I suspect the FDA will have significant interest in smart contact lenses.
I’ve also seen the misunderstanding that physicians are eagerly awaiting and prepared for a tsunami of clinical data from their patients.
“The vision is the doctor is sitting waiting for all this, and the doctors aren’t,” said Dr. Michael Blum, Associate Vice Chancellor of Informatics at UCSF School of Medicine to PC World. “They are running around with their hair on fire trying to do what they do right now.” I agree with Dr. Blum.
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?
Are algorithms ready to receive, store, analyze and respond to this data? The “alarm fatigue” effect is real – and well documented. In Medical Design Technology, GlobalLogic Director of Program/Account Management Jeremy Schroetter said the solution is “… careful data analysis and algorithms in order to provide physicians with the information they need…” The remedy is to apply smart business intelligence to this data flood, but a staggering undertaking to achieve. Until then, we are quenching our thirst from a fire hose.
2. What is the true cost of the data surge versus its benefits?
And who will pay for it? Consider a micro-transmitter that when attached to a medication and ingested signals a wearable device, which records the ingestion date and time and coincident heart rate of the patient. A log-in summary of this information can be provided to the patient and their physician. We can quantify the cost of obtaining this information. What is the value of the information? Is compliance or noncompliance with drug therapy not already known to the patient? Is it really information if we already know it? Is the value of this product then actually as a motivator?
The cost of wearables is more than simply the cost of the device and maintenance. We need to recognize the cost the care provider incurs in receiving, storing, analyzing and responding to the information.
Helping patients understand their health information also costs money. Providing the patient access to information about themselves empowers the patient, but without providing education with it, access to information just isn’t fair.
None of this should dismiss wearables as the “pet rock” of healthcare information technology. We stand on the verge of an artificial pancreas for diabetics. We have decades of experience with insulin infusion pumps. CGM, continuous glucose monitoring, is no longer new. Although currently “under development,” the fusion of these two technologies is both achievable and hugely clinically meaningful.
Like all healthcare information technology, wearables have huge potential – married to massive challenges.
Frank X. Speidel, MD, MBA, FACEP is Chief Medical Officer for Healthcare IT Leaders, a consultancy and HIT staff augmentation firm that matches IT talent to hospitals and health systems for EMR, ICD-10 and analytic engagements.
photo credit: IntelFreePress via cc
Wearables: A Solution Searching For Problems? by Frank SPEIDEL, MD
Via nrip, Lionel Reichardt / le Pharmageek
High School Story, the iOS and Android game from former Electronic Arts developers that previously took a stand against cyberbullying, is now aiming to raise awareness for another issue affecting teenagers: eating disorders.
Pixelberry Studios has partnered with the National Eating Disorders Association to create a new in-game storyline centered around eating disorders. The game will also offer educational resources to the estimated 20 million teenagers across the United States who are not happy with their bodies.
The new version of High School Story available today follows a character named Mia. After hearing an insensitive comment about her body type, she takes up an unhealthy diet and exercise regimen. She even edits her yearbook to make herself appear thinner.
Through High School Story's new narrative around Mia--researched by Pixelberry and NEDA--players will learn all about the causes and consequences of body image issues. The game will even allow players to reach out directly to the NEDA through the app.
Pixelberry says High School Story's previous anti-bully campaign helped 2.5 million teens learn more about cyberbullying prevention. In addition, Pixelberry adds that more than 100 players every week were directed to professional counselors through the game and that it raised over $250,000 for an anti-bullying charity.
To go deeper into what the new eating disorder content means for High School Story, we caught up with Pixelberry co-founder Oliver Miao. Our conversation is posted in full below.
GameSpot: What led you to want to tackle the issue of teenage eating disorders in High School Story?
Oliver Miao: The first inspiration came from our players, some of whom wrote to us to ask that we address the issue. Those requests really resonated with our writers, many of which also struggled with body image issues during their high school years or knew of people that had. Once we decided to tackle that issue, we were also inspired by real-life stories of teens who had done things like lobby fashion magazines to stop publishing photoshopped photos.
GS: What does your partnership with the National Eating Disorders Association let you accomplish that you couldn't before?
OM: We’re very happy that we've been able to partner with NEDA. First, they helped us make make sure our messaging around body image issues and eating disorders is accurate and helpful by providing us feedback based on the issues they've encountered. They also allow us to give our players direct support about these issues without having to leave the game. Whenever a player writes in to our in-game support system with a question about eating disorders, helpline staff from NEDA will respond. Lastly, they worked with us to create an in-game FAQ players can read to learn more about these issues.
On the other side of things, we now have over 10 million players, many of whom are teenagers. With our platform, we’re able to help an organization like NEDA reach a large number of teens through a channel that's otherwise hard to reach. In this regard, we're able to educate and support millions of teens about issues that are relevant and important to them.
GS: What kind of response have you seen from users who maybe download the game and don't necessarily know that it's tackling the kinds of social/health issues that it does?
OM: We strive to make High School Story fun first and find ways to layer in socially impactful elements afterwards. These particular quests are purposely introduced later in the game, so that when players engage these quests they’re hopefully already connected to the characters and can therefore draw more of a personal understanding to the issues these characters are facing.
So most players come to the game not because it addresses these specific issues, but because it's fun and because it speaks to their interests in general. And when they come across the quests about cyberbullying and body image, we find that most of them are really excited and happy. Not because those quests are 'socially impactful,' but because they address issues that are important to them and their friends.
GS: What kinds of data do you have that shows High School Story is actually making a difference in the way you want it to?
OM: We look at this in several ways. We have metrics that track how many players complete our special quests. We're also providing a prominent in-game link to NEDA's teen-focused site, Proud2BMe, and we'll be able to see how many teens use it. We hope to get statistics from NEDA about how many teens reach out to them after playing our game, and we also pay very close attention to our players' reviews.
The body image features are new, so we don't have any comprehensive results from them yet. But similar results from our earlier anti-cyberbullying campaign show that it's been a big success. Over 2.5 million people have played our cyberbullying-themed quest. Through the support of our players, we've already raised over $250,000 for The Cybersmile Foundation, a non-profit we partnered with for that campaign. Cybersmile also told us that after the launch of the quest, every week over 100 of our players reach out to them for help. These are often teens who are being bullied, self hurting, or even thinking about suicide. With NEDA, we are really hoping that we can have the same type of impact with teens who are facing challenges with body image or eating disorders.
GS:. Are there any specific 'success stories' you can share that have come from High School Story?
One of the first times we saw the effect High School Story could have on players was when a player wrote in telling us that she had recently moved to a school in a new country and had been struggling with fitting in. After playing our game, she realized that she liked who she was and didn't have to fit in to feel good about herself. It was a very heartwarming moment for us.
We've also had several incidences of lives actually being saved because of High School Story. The first time it happened was from a player who wrote directly to us that we were able to encourage to seek professional help. Our partner Cybersmile has also shared amazing stories with us, including a time they were able to help a High School Story player who was right on the precipice of hurting themselves.
Every time we hear one of these stories, we're amazed that the work we do really is making a difference. It's an incredible feeling.
GS:. Why did you decide to make the Mia character female instead of male?
OM: Over 60% of High School Story's players are female, and research has shown that by the time they are 17, nearly 4 out of 5 females have had body image issues. We hope that by making the Mia character female, a majority of our players can more easily identify with her and be more willing to reach out for help, if they need it.
At the same time, we recognize that these issues affect both men and women, so our writers crafted the story to appeal to both our male and female players.
GS: You've taken a stand against cyberbullying and now you're raising awareness about the dangers of eating disorders -- What other social/health issues are you looking at for future versions of High School Story?
OM: For the time being, we're focusing on body image, and are continuing to address cyberbullying as well. When we take on serious issues like these, we want to show long-term commitment to them. This gives our players time to engage with the new content and respond to it at a pace that they're comfortable with. It also allows us to build strong relationships with our nonprofit partners.
That said, we definitely plan to continue with these types of campaigns in the future. In fact, we’re about to launch a "Your Voice" feature that lets our players first share their thoughts on fun topics, like music and memes, and then later share their opinions on current events and other more serious topics. Our hope with this feature is to encourage teens to develop their voices on important issues and to discuss these issues with their friends and communities.
Via Alex Butler
he human brain may be nature’s finest computer, but artificial intelligences fed on big dataare making a convincing challenge for the crown. In the realm of healthcare, natural language processing, associative intelligence, and machine learning are revolutionizing the way physicians make decisions and diagnose complex patients, significantly improving accuracy and catching deadly issues before symptoms even present themselves.In this case study examining the impact of big data analytics on clinical decision making, Dr. Partho Sengupta, Director of Cardiac Ultrasound Research and Associate Professor of Medicine in Cardiology at the Mount Sinai Hospital, has used an associative memory engine from Saffron Technology to crunch enormous datasets for more accurate diagnoses. Using 10,000 attributes collected from 90 metrics in six different locations of the heart, all produced by a single, one-second heartbeat, the analytics technology has been able to find patterns and pinpoint disease states more quickly and accurately than even the most highly-trained physicians.Dr. Sengupta explained his ongoing work with big data analytics to HealthITAnalytics, and discussed the impact such technologies can have on cardiology patients and their outcomes.What were the underlying medical issues you were trying to solve with this study?One of the most commonly ordered diagnostic tests in cardiology is the echocardiogram. We were amazed at the amount of information that was coming in during each patient consultation, so the biggest challenge was how to make the information, which is extremely rich, easily understandable and use it in real-time in patient care scenarios. Working with Saffron, we decided that we will look into a scenario which is extremely complex which usually requires a lot of expertise, and it usually is associated with fairly complex sets of information.We decided to do a pilot test with two diseases: cardiomyopathy, which affects the heart, and pericarditis, which masquerades as if the heart is involved, but actually the heart muscle is not involved. Both diseases present with heart failure, and patients are very complex in their assessments. If you make the correct diagnosis the treatments are very disparate, very different. For pericarditis, you would do a surgery, whereas if it’s cardiomyopathy, it’s a different course. It’s medical management or a heart transplant.Misdiagnosis of these conditions is a fatal error, because if you make the wrong decision, you’re going to send a patient who’s going to be treatable by surgery to get a heart transplant and vice versa. If you open up a patient because you think they have pericarditis, and then you have to close the patient because the patient didn’t have the thickening of the membranes around the heart, that’s expensive for the hospital and puts the patient at an unnecessary risk of complications. So that’s why we use this particular technology on these diseases, because the risk of not diagnosing this disease properly is immense.How can clinical analytics supplement human intelligence to identify patterns and make diagnoses?For the study, we took a lot of the ultrasound information, which is the first step for diagnosing these patients. We took the information, which is extremely complex and started working on that using the natural intelligence platform to see if we could come up unique characterization of the disease, so that the information can be clustered for pattern recognition. You use a lot of intuitive skills to go through these datasets. I was interested in seeing how processing this data through clinical analytics can provide better decision support.The problem is that the data is scattered everywhere. It’s in the EMR, but everything is still in siloes. So either you have to make an effort to look in the EMR, then look into the e-measures, which may be existing on another system, look at the PACS system, and the himself patient is somewhere else. So, they’re all in different locations. How do we take all the information just coming from different sources and merge them together, so that we can apply it right away to the patient in real-time? That’s what we are currently focused on.Let’s say I just analyzed an echocardiogram of a patient and I track the information into am Excel file. You open that Excel file, and it will have about 30 columns and 50 to 60 rows. What we do right now is go row by row, and it’s very painful. But the analytics engine takes an entire dataset all at once, and then comes out with these rich associations. Based upon its previous learning, using its associative memory capabilities, it can tell that this dataset looks like this disease, and that dataset looks like another disease.This kind of an application can be done for any scenario. For example, diabetes can produce some very early changes in the heart muscle which the patient doesn’t even know about. He’s completely asymptomatic. You might have a signal present in this big data, but you might not be able to discover it on your own. You might not even really be looking for it, but when you process it through a complex analytics engine, you might be able to come up with some kind of signal that will show the early disease state.Diseases come in clusters, so heart disease, cancer, Alzheimer’s, they don’t come independently. They all together in one given patient, so my hope is that in future we will be able to take all the risk factors, which are common for these diseases, which are growing to epidemic proportions, and we will be able to deliver forecasting models based upon them.That’s kind of the vision. I think it would be really terrific to have a forecasting model, so then this patient has such risk factors, goes into the hospital for, let’s say a knee surgery, what are his chances he’s going to develop a heart attack when he comes out of the surgery? That’s the kind of the risk modeling we’ll be very interested to develop in the future.After using the clinical analytics engine to examine the data, what results did you find?In the initial pilot phase, when I did my own statistical algorithms, we had about 73% ability to differentiate the two diseases. But when the initial pilot run happened, we were very pleased to see that there was a discrimination of 90% between the two datasets and without any human intervention. What that means is that the highly complex analyses that were done produced a discrimination which exceeded human ability to diagnose the two conditions. Having said that, you have to be extremely cautious, but it’s very exciting that machine learning and learning intelligence platforms can reach the ability to do this differentiation, if not exceed it.Related White Papers:Webcast: Gain Deeper Insight into your EMR with Care Systems Analytics from VMwareActionable Analytics: 10 Steps to Improve Profitability and Patient ExperienceImprove Outcomes with the VMware Care Systems Analytics SolutionPredictions for Big Data in Large and Small PracticesHL7 Survival GuideBrowse all White PapersRelated Articles:NIH to boost role of genomics in research, clinical analyticsGenomics, big data can thrive through CDS, analytics tools2.5 petabytes of centralized cancer data to accelerate genomicsNew law would increase access to Medicare data for analyticsHow big pharma uses big data to develop better drugs
The physical exam is especially important to orthopaedic specialists. There are numerous eponyms and unique maneuvers that complicate the understanding of the orthopaedic exam. The American Academy of Orthopaedic Surgeons (AAOS) has attempted to clarify the intricacies of common physical exam maneuvers with a series of mobile apps. This review follows a previous review on the AAOS physical exam of the shoulder and focuses on the exam of the knee.
The application is produced by the American Academy of Orthopaedic Surgeons. The AAOS is one of the largest and most trusted medical specialty societies in the Unites States. The AAOS has a long tradition of producing quality educational content. They were one of the first specialty societies to use mobile technology as a means for member education.
The app opens with a disclaimer, credits, and then a list of exam sections on the main menu. The sections include: inspection, palpation, muscle testing, range of motion, and special tests.
Selecting any topic heading from the list takes you to more information. The Special Tests tab has several good maneuvers including pivot-shift, anterior/posterior drawer, McMurray, Ober’s test and eight other common tests. The inspection/palpation tab covers basic exam topics.
Selecting a maneuver reveals a short description of the exam maneuver with associated references. The content in these descriptions is referenced well with many up-to-date articles on the topic.
Additionally, most maneuvers have the listed specificity and sensitivity in the description section which helps in the understanding of the clinical utility of that exam.
In the upper right corner is a tab that connects to the exam video. Every exam topic has an associated video of the physical exam maneuver. The quality of the video and audio is good but short and without demonstrations of pathology.
Similarly, there are no pictures or diagrams to enhance the explanations of the exam maneuvers. The app covers most of the common exam tests and is ideal for the beginner or intermediate healthcare professional learning to perform the knee exam. There would be utility for review or consolidating the information in a single app for the more experienced clinician.
Healthcare workers that would benefit from the app
Any healthcare worker who has a role in performing orthopaedic knee exam maneuvers.
Short, to the point, explanations of exam maneuvers with references.
No images or diagrams of the exam maneuvers.
This application is easy to use, delivers well on its intended purpose, and covers several common exam maneuvers in orthopaedics. There is slightly more content in this app compared to the similar AAOS shoulder app. The app design is simple and straightforward., however, the amount of content is limited and could be enhanced with images and diagrams of techniques. Having the information consolidated into one location with this app will be beneficial to some practitioners. E.g. currently AAOS has multiple apps for multiple body parts, it would make sense for them to put their Shoulder, Knee, and Spine apps into one consolidated app.Overall ScoreUser Interface
The information is presented in a list format interface that is easy to navigate. With a few clicks, the user can navigate to topics of interest and view the associated video example.Multimedia Usage
The videos are good quality but could be enhanced with pathologic videos or positive findings. Similarly, images and diagrams would improve teaching the exam maneuvers.Price
The application is not cheap, but not expensive when taking into account the multimedia content included and the quality of content.Real World Applicability
This is a good application for quick review of common shoulder maneuvers, practical to use in practice.Device Used For Review
iPhone 5Available for DownloadiPhone
FCC: 'Telemedicine, it's coming'
Via Sam Stern
Via Alex Butler, Bart Collet
IT Web Africareports that mHealth has officially come to Zimbabwe.
Although mHealth solutions are nothing new in Africa — in fact, mHealth solutions are growing at an accelerated pace throughout the continent today – Zimbabwe has been a largely overlooked nation in recent years, relative to the immense growth documented in surrounding nations.
Zimbabweans are now gaining a service made possible by the nation’s top telecoms firm Econet Wireless.
“The Econet Health project plans to avail tips on how to manage stress, information about diseases such as diabetes as well as diet,” the report reads. “Expecting mothers are also to receive information about pregnancy.”
“People should know how to deal with stress and pregnant mothers know of what to do through their mobile phones,” Mboweni is quoted in the report.
Via Emmanuel Capitaine
Any views expressed in this article are those of the author and not of Thomson Reuters Foundation.
Jerusalem, 17 July 2014 – CARE and its partners are preparing to provide emergency mobile health teams to serve people affected by the violence in Gaza. Needs are particularly high for pregnant women and for those who can’t travel to hospitals or medical clinics. Pregnant women are travelling to hospitals in the midst of the bombing to get medical support, while other people are unable or unwilling to leave their houses for anything other than life-threatening injuries.
“We are getting reports from our partners that pregnant women are risking their lives to get to hospitals, because they feel they will be safer there than in their homes,” said Theo Alexopoulos, with CARE’s Emergency Team in Jerusalem. “But they can’t stay in the hospitals forever. Then where do they go? There is no safe place in Gaza.”
As soon as the security situation allows, CARE and it partner, Palestine Medical Relief Society (PMRS), are planning to run two mobile health teams that would visit an average of 200 patients per day, providing basic health care to people living in affected communities by the ongoing violence. The teams will include medical staff and a psychosocial worker to help traumatized families, and will focus in particular on women’s health needs, particularly pre- and post-natal care for pregnant women and new mothers with infants.
“If pregnant women can’t get the health care they need, if newborns can’t get the health care they need, there is an increased risk of medical complications, which could put the lives of the baby or the mother at risk,” said Alexopoulos.
Thursday’s ceasefire provided a brief window for people to safely get medical support, and to get food and supplies for their families. But a few hours without bombs is not enough; a permanent ceasefire and a resolution to the conflict is needed immediately, or people will continue to suffer.
The health system in Gaza is under enormous strain and is in desperate need of supplies, particularly fuel for generators, drugs, and medical supplies. Some hospitals are already reporting that they don’t have basic materials such as sutures to treat wounds of people injured.
About CARE: CARE is one of the world’s largest humanitarian aid agencies, providing assistance in nearly 70 countries. CARE has been working in Israel, West Bank and Gaza since 1948 (with a short break from 1984-1994), initially implementing programs to help immigrants after the Holocaust. Today, our programs focus on economic empowerment (including livelihoods and gender equality) in Gaza and the West Bank to assist the most vulnerable residents in meeting their basic needs. With the current fighting, CARE has temporarily suspended its programs until the security situation improves. Find out more at www.care-international.org.
Melanie Brooks (Geneva): +41 79 590 3047, email@example.com
Via Alex Butler
Cardiologists in Los Angeles have developed a gene-therapy technique that allows them to transform working heart-muscle cells into cells that regulate a pigs’ heartbeat. This procedure, described today in the Science Translational Medicine, restored normal heart rates for two weeks in pigs that usually rely on mechanical pacemakers. The experiment, researchers say, could lead to lifesaving therapies for people who suffer infections following the implantation of a mechanical pacemaker.
"We have been able for the first time to create a biological pacemaker using minimally invasive methods and to show that the new pacemaker suffices to support the demands of daily life," Eduardo Marbán, a cardiologist at the Cedars-Sinai Heart Institute and lead author of the study, told the press yesterday. The approach is practical, added Eugenio Cingolani, a cardiogeneticist also at Cedars-Sinai and a co-author of the study, because "no open-heart surgery is required to inject this gene."
In the study, researchers injected a gene called Tbx18 into the pigs’ hearts. This gene, which is also found in humans, reprogrammed a small number of heart-muscle cells into cells that emit electrical impulses and drive the beating of the heart. The area in which this change occurred — about the size of a peppercorn — doesn't normally initiate heartbeats.
"We were able to get the biological pacemaker to turn on within 48 hours," Marbán said. To get the gene to the heart, the researchers sent a modified virus into the right ventricle through a catheter. The viral vector isn’t harmful, the researchers said, because the virus they employed was engineered to be "replication deficient" — meaning that it will not reproduce and spread beyond the heart.
This year represents a turning point for wearable health trackers, out of which an obvious next one could be a gadget that delivers drugs through the skin when needed. ChronoDose now delivers nicotine for those who would like to stop smoking but the patches didn’t really seem to be working. Users can teach the gadget when it is the hardest to resist the temptation therefore it can add the next dosage in the right time.
ChronoDose is a programmable transdermal drug delivery system that’s worn as an armband. The ChronoDose will someday offer many different drugs the ability to be programmed, and administered via this transdermal device, but the buzz is all about it’s use as the world’s first programmable nicotine replacement method. ChronoDose’s use with SmartStop™ gives the device the ability to be programmed to anticipate the users cravings, and offer nicotine dosing scheduled to take effect before the urge to smoke strikes.
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Via Emmanuel Capitaine
With the unveiling of a new clinical operating system for medical devices, BlackBerry is once again making a play for mHealth.
QNX Software Systems, which was acquired by BlackBerry in 2010, has released a new operating system that's billed as being IEC 62304-compliant. With its sights set on alleviating the regulatory and financial burden for device manufacturers, the operating system supports both single- and multicore devices based on ARMv7 and Intel x86 processors. The OS also features an application programming interface to make it compatible with other QNX operating systems, officials said.
"When it comes to medical device software, the OS sets the tone: Unless it provides the architecture to enable reliable operation and a clear audit trail to substantiate claims about its dependability, the entire process of device approval can be put in jeopardy," said Grant Courville, QNX's director of product management, in a July 15 press statement. "By providing an OS that has been independently verified to comply with the IEC 62304 standard, we are helping manufacturers reduce the cost and effort of developing devices that require regulatory approval from agencies such as the FDA, MDD and MHRA."
This is far from BlackBerry's first big move into the healthcare space. In April, the telecommunications behemoth lent financial support to cloud-based health IT company NantHealth, a startup spearheaded by billionaire healthcare mogul Patrick Soon-Shiong, MD.
"We've built supercomputers that can do the genomic analysis in real-time; we've built super computers that can actually take feeds of CT scans from EMRs and feed it directly to mobile devices. All of that, regardless of where it comes from, regardless of the EMR, regardless of the device, whether it be via ventilator, or IV tube, we're agnostic to, and it speaks to this operating system," said Soon-Shiong.
JOIN For ME Engages and Educates Kids about Weight Management
We’ve all been in the same boat – you’re running on the treadmill, seemingly for ages, yet you glance at the clock and it’s only been three minutes. We want to live a healthy lifestyle, but sometimes it can be so boring. We especially want to set a good example for today’s youth, as childhood obesity rates are soaring and children are at a higher risk for developing dangerous health issues like diabetes. The question is: if we are bored with our attempt at a healthy lifestyle, why should we expect children to embrace it?
It’s true that an overwhelming majority of children today spend their time sitting in front of televisions and computers. They cling to their electronics as a drowning man would cling to a life raft. Parents become frustrated when their attempts to engage their kids in a more active lifestyle result in failure. How can you interest your child in going for a run when they are completely enthralled in a group chat on their phone about the doubtlessly dramatic occurrences of the day at school? How do you pull a kid out of a virtual world where they are saving soldiers or fighting monsters to toss around the old football? Why not incorporate the technology that kids love so much into a new, healthy lifestyle? The answer to the challenge of getting our kids to embrace physical activity is to offer them this activity in a package that will appeal to them. We’ve seen a great gamification example in Zamzee, and, very recently, LeapFrog, both in the kids’ wearables category. What
Study: Does Gaming Help Obese Children Increase Physical Activity?
Many health care groups have recognized the benefits of games in health. UnitedHealth Group recently participated in a pediatric study of the benefits of gaming in their weight-management program, JOIN for ME. JOIN for ME encourages overweight children to engage in physical activity and set realistic goals that help to reach a healthy weight. Half of the participants in JOIN for ME’s program received an XBox Kinect console and two games in order to evaluate the effects that physical gaming can have on weight loss.
The program was effective for both groups in the study, but the group using the Xbox Kinect had higher weight loss. The children enjoyed the use of the games, and did not feel as if they were being forced to exercise while they were playing. They had not been given any specific amounts of time that had to be dedicated to the games, so all of the time spent on the physically active gaming was done of their own accord. Deneen Vojta, a UnitedHealth Group executive physician, spoke highly of the study’s results:
One participant, Ravyn Hill, liked the fact that the games provided a way to exercise without being bored:
Ravyn lost almost 8 pounds during the four-month study period.
JOIN for ME is also educating children who have excessive weight and their families on healthy eating habits, choosing the right foods and portion sizes through classes at community centers and schools nationwide.
Several games have been launched in recent years to help motivate Americans to live a healthy lifestyle. Many of these exercise-based games are still geared toward adults, though, and we still struggle to implement higher levels of activity in children. Researching and using the technology that so interests youth is a very successful answer to the problem.
Via Alex Butler, ChemaCepeda
Via Sam Stern, eMedToday, E. Lacoste-Mbaye
CliniWorks today announced a strategic alliance between and Pfizer Inc.to jointly advance the parties’ respective capabilities in working with healthcare provider organizations to identify and close clinical or quality gaps to improve population health. The two companies are partnering to develop a population health management platform solution that leverages CliniWorks’ technologies in disparate data aggregation and Natural Language Processing (which interprets free text information) of de-identified healthcare data and Pfizer’s scientific, clinical and disease expertise. This platform will aim to enable large medical groups and integrated delivery system institutions to deliver near real-time and more efficient and effective quality healthcare, as well as improve patient engagement or activation, reaching the Centers for Medicare and Medicaid (CMS) Triple Aim. The development work will be partially supported by a grant received by CliniWorks and Pfizer from the BIRD Foundation ( www.birdf.com ).
Nitzan Sneh, CliniWorks CEO, said, “Pfizer’s leadership position in global healthcare and patient care complements our technology capabilities and, collectively, will bring about significant efficiencies for healthcare delivery organizations involved in the continuum of patient care.”
“This alliance builds on our existing relationship with CliniWorks and will allow us to collaborate with our key customers in innovative and impactful ways to potentially improve healthcare delivery and patient outcomes,” said Teresa Griesing, VP North America Medical Affairs, Pfizer Global Innovation Pharma Business Unit.
Pfizer Forms a Strategic Alliance with CliniWorks to Develop Population Health Management Platform by Jasmine Pennic
Via Emmanuel Capitaine
Even if big data faces much controversy and open data still has so many hurdles to go through in the healthcare industry, there is no doubt that this progress has pushed the industry straight into the information age and California's Kaiser Permanente is showing what can be done with the huge influx of data they are receiving.
Via Sébastien Letélié
What Benefits Will They Bring?Related Posts
Via Emmanuel Capitaine
Health-related apps and devices are flooding the digital market. A recent BI Intelligence analysis foundthat "health and fitness app usage has grown at nearly twice the rate of app usage overall through the first half of 2014."
Via Alex Butler, Bart Collet