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Survey: Patients more accepting of mobile apps than prescription drugs

Survey: Patients more accepting of mobile apps than prescription drugs | innovations for health | Scoop.it

A survey by healthcare marketing and advertising agency Digitas Health has found that 90 percent of patients would accept the offer of a mobile app, while only 66 percent of respondents would accept prescription medicine from their doctor. The results of the survey of 2,000 U.S. patients were presented at Digitas Health's Think Digital Event in London and reported by PMLiVE.

Those using mobile health technology were pretty evenly distributed across age groups, but they were highly skewed towards women, according to the article. Moreover, nearly 60 percent of the mobile health users surveyed were considering switching their treatment.

"A large number of people out there on a prescription medication are actually considering a change," Geoff McCleary, Digitas Health's group director of mobile innovations, is quoted as saying. "So there's an opportunity in this space to figure out how we can better serve patient needs and get them the treatment and information that's going to make a difference to them."

Conducted last month, the survey covered 20 disease states across the five core categories of respiratory, cardiology, CNS, gastroenterology and diabetes. A key finding from the research was that more than 60 percent of active mobile health users were diagnosed over three years ago, suggesting people with more chronic conditions seem to continue to look for more information.

"We were expecting this to be a little more recent and, looking at heavy smartphone use, we were assuming users would have only recently been diagnosed, or working with a patient," said McCleary. "But we found that we're looking at people that have had a condition for some time and are still doing research and seeking to get additional information."

In another recent U.S. survey commissioned by Royal Philips Electronics, a quarter of Americans said they trust symptom checker websites, symptom check mobile apps or home-based vital sign monitors as much as they do their doctors. In addition, about an equal proportion (26 percent) often use these resources instead of going to the doctor.

The survey was taken from a national sample of 1,003 adults, comprising of 503 men and 500 women ages 18 years and older, living in the continental United States. More than a third (35 percent) of those surveyed believe technology that allows one to monitor their own health is now the key to living a long life.

The Digitas Health survey found that around a third of those diagnosed with a particular condition are not currently being treated with a prescription medicine and that 13 percent were not on any form of medication. In addition, the survey indicates that patients and consumers are willing to spend money on a whole range of devices that help them manage their health, from monitoring devices like Nike Fuel Bands or Fit Bits to wireless scales.

"Our research has told us that they're more than willing to spend money on that and they're going to continue to grow in their usage of those things," McCleary said."So they're doing that adherence level activity already without us and we, as experts in drugs, have a great opportunity to be able to help them, by providing even more information, tools and resources to use in conjunction with their medicines."

To learn more:
- read the article

Related Articles:
Consumer-driven health plan participants more likely to use mHealth apps
Pew: Smartphone owners increasingly use their devices to get health info
Smartphone use spurs better fitness participation


Read more: Survey: Patients more accepting of mobile apps than prescription drugs - FierceMobileHealthcare http://www.fiercemobilehealthcare.com/story/survey-patients-more-accepting-mobile-app-prescription-drugs/2013-07-02#ixzz2YXwM7tzL
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Via Gilles Jourquin, dbtmobile, THE *OFFICIAL ANDREASCY*
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Indian innovators get $1million to fund high-tech health solutions

Indian innovators get $1million to fund high-tech health solutions | innovations for health | Scoop.it

The Canadian government has funded grants for ideas including a mobile phone turned into a glucose monitor for diabetes patients, and a needle-free handheld device for testing anaemia.

 

A mobile phone turned into a glucose monitor for diabetes patients, a needle-free handheld device for testing anaemia among women in village, an ultrasound probe that can be attached to a smartphone via USB and a rapid blood test to detect a heart attack. 

 

These are some of the bold ideas from Indian innovators which have won seed grants totalling $1 million (Rs 5.4 crore) in an international competition, Grand Challenges Canada, funded by the Canadian government.

 

Early detection 

Nitin Kale of NanoSniff Technologies, a start-up incubated at IT Mumbai, is developing an instrument for rapid detection of myocardial infarction among rural populace. 

 

A heart attack is usually difficult to detect early, particularly in a rural setting. 

 

"We propose to employ a microcantilever- based bio-sensing system to detect early cardiac markers like myoglobin that are released in the blood after myocardial infarction. The device can be used even by a qualified nurse at a rural primary healthcare centre," a member of the research team said.

 

"The programme seeks breakthrough and affordable innovations that could transform the way disease is treated in the developing world - innovations that may benefit health of developed world citizens as well," an official of the Grand Challenges Canada said. 

 

Other Indian grantees include Vivek Vajaratkar of Goa-based health action group Sangath, Niraj Sanghai of Sinhgad Technical Education Society, Lonavala and Kumari Smita of Battelle Science & Technology India.




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What is mHealth? Is it Mobile health or Modern health?

What is mHealth? Is it Mobile health or Modern health? | innovations for health | Scoop.it

Syllogism causes confusion among healthcare terms such as modern health, mobile health, digital health, ehealth, mhealth, telemedicine, and telehealth.

 

Clearly, Lions & Tigers are both cats, and cats are animals, but the healthcare syllogisms aren’t as straight forward. People often associate Mobile Health with the ambulance that shows up to provide care and transportation, rather than the use of mobile devices and wireless networks. They may also associate Mobile Health with the tablet device the doctor uses as she moves about, rather than a smartphone device. That’s why I drew the diagram with mHealth not entirely within Wireless Health or within Telehealth. And it’s why I added a new term to  encompass them all - Modern Health.

 

I apologize to my consumer audience if this article sometimes gets a bit technical. That’s because it was partially written to address a technical audience. You can skip the technology, go straight to the Cool mHealth Trends.

 

mHealth & Telehealth

 

Telehealth is the delivery of health-related services and information via telecommunications technologies. These services could be as simple as two health professionals discussing a case over the telephone, a video call between patient and practitioner(s), or doing robotic surgery between facilities at different ends of the globe. Telehealth is an expansion of telemedicine, because it’s not limited to clinical treatment but can also apply to prevention. Likewise,telehealth is an expansion of mHealth, because it’s not limited to cellular technologies.

 

mHealth & Wireless Health

 

Wireless health differs from mHealth in that wireless health solutions will not always be mobile and mobile health solutions will not always be wirelessly enabled. Wireless Health integrates wireless technology into traditional medicine, such as for diagnosis, monitoring and treatment of illness. Wireless technologies eliminate the cost and effort to install wires and support the ability to move about without being tethered. Wireless networks can cover very short distances such as between wearable sensors and a smartphone, entire buildings such as Wi-Fi home networks; or wider areas such as cellular networks that extend from tower to tower. These mobile broadband networks are especially useful in reaching new patients in remote areas than previously possible.

 

mHealth & eHealth

 

eHealth describes any healthcare practice supported by electronic information processing and communication, so it has broader reach than mHealth, which relates to practices using mobile  (phone or computing) technologies

 

mHealth: Mobile Health or Modern Health?

 

Many app developers view mHealth as exploiting mobile telecommunication in health care delivery. That can include mobile phones (voice & SMS text), smartphones, or a variety of other devices that include laptop computers, patient monitoring devices, MP3 players, PERS systems, and more.  The term can extend to both mobile and stationary devices, as long as they used mobile/cellular telecom technologies, but what if they don’t communicate at all?

 

What if a smartphone app uses sensors to collect health & fitness data and then stores and tracks it on the device itself without ever sending it anywhere? If the device itself is viewed as a telecom device, it might fit in the mHealthcategory, but the iPod Touch has no mobile phone connection, and even though it uses the same iPhone technology, it arguably would not fit the mHealth definition, even though it’s running the exact same code. That’s where the traditional mHealth definition breaks down, and it’s one reason that I prefer to extend mHealth to Modern Health, rather than just Mobile Health.

Modern Health encompasses innovations that collectively define the future of healthcare. They include: digital, electronic & mobile health, telehealth & telemedicine, electronic sensors & cloud-based monitoring services, video calls & telepresence, electronic medical & personal health records, big data & analytics, healthcare robotics & artificial intelligence, personalized medicine & genomics, and the wireless connections (ANT+, Bluetooth LE, ZigBee, Z-Wave, Wi-Fi, 4G, LTE), big broadband networks (fiber-optics), and regulatory & payment reforms that bind them.

 

Yes, nearly 40,000 health-related apps are available today for smartphones, and that number is up ten-fold from about 4,000 in 2010. So clearly smartphone availability and fast Internet access are driving much of the growth of modern healthcare applications, but don’t discount large mHealth opportunities on other devices and in other geographic markets.


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GE wants to use artificial intelligence to predict the future of health care innovations

GE wants to use artificial intelligence to predict the future of health care innovations | innovations for health | Scoop.it
GE Healthcare is pushing a system called Corvix for doing agent-based simulations on complex problems. In India, the technology simulated a population of 80 million people in order to determine the best places to build medical facilities.

 

Around the world, the health care system is rife with inefficiencies, and General Electric thinks it can help solve the problem using data. Only it’s not talking about bureaucrats looking at reports: GE has built an artificial intelligence system called Corvix that uses historical data to predict the future, including everything from how diseases will spread to the cities where hospitals will be needed the most.

 

It might sound futuristic, but the techniques behind Corvix have actually been around for a while. The platform uses agent-based modeling to build, essentially, a reasonable facsimile of some sort of complex system and then simulate its evolution over time. The “agents” represent the atomic units of those systems, such as individual people in the case of human populations or perhaps cells in the case of a biological simulation. They act according to a set of rules in any given situation, which is how the models are able to keep the simulations progressing.

 

However, thanks to the advent of big data, GE Healthcare Chief Economist Mitch Higashi thinks the time is right for a platform like Corvix to provide some real value to real-world decisions. There’s enough raw computing power, machine intelligence and data-modeling expertise to start doing fast, accurate simulations over very large and complicated datasets. Also, advances in user-interface design have made these types of models more consumable: GE’s Corvix uses a game-like UI “that any 10-year-old can figure out how to use in 10 minutes,” Higashi said.

 

The first live run for Corvix happened in the state of Andhra Pradesh in India, where the system simulated a population of 80 million people in order to figure out where to build hospitals and medical training centers over the coming years. The GE team used two census datasets and one health care survey in order to build what Higashi calls “a reasonable representation of 80 million people,” as well as a map of India’s existing hospital and energy grid. Health care analysts studying the problem of where to build can drag a new hospital over an area on the map and see how the situation plays out, Higashi explained.

 

The original plan, said Chaitanya Sarawate, GE’s head of health economics and reimbursement for India, was for the Public Health Foundation of India to invest $2 billion building training institutions in different cities over the next five years. Corvix suggested some possible changes in location of those institutions, including placing two institutions in the country’s most-populous state, Uttar Pradesh, instead of just one as was originally planned. The advice is part of a report from the foundation to India’s Minstry of Health, which will make the ultimate decision.

 

Developing countries such as India are great places to use this type of technology, Higashi explained, because they are doing greenfield investing in areas such as health infrastructure and a lot of good can happen if they get it right off the bat. The problem, Sarawate noted, is that they often lack detailed data that can help governments make objective comparisons — that’s the kind of stuff a company like GE, in this case, can track down and try to feed into a model that takes into account its relative importance.


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