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Time to focus on the national telehealth strategy Pulse+IT Magazine Last year, the Australasian Telehealth Society (ATHS) held a workshop to gather ideas for a national strategy for telehealth in Australia.
The group, EFPIA (http://www.efpia.eu), claims its move – first flagged in July – reflect member companies’ “strong support” for sharing clinical trial data to benefit patients and foster scientific discovery “in a way that maintains patient privacy,...
Electronic medical records. DNA sequencing. Big data. These technology trends are changing the way medicine is practiced today — but what’s coming next?
From artificial intelligence to natural language to processing to MEMS, here are some technologies that will change the future of healthcare.
Artificial intelligence/algorithm medicine
Predictive analytics tools that use data to help healthcare administrators identify high-risk patients and make efficient decisions are already in place in many hospitals. Now companies are developing decision support tools for clinicians that compare an individual patient’s data to large amounts of historical outcomes data.
Internet of things
This concept takes remote patient monitoring to the next level, involving multiple connected devices that can coordinate with each other through a wireless network without human intervention. Sharp, who’s in charge of clinical informatics research at Cleveland Clinic, says hospitals have just scratched the surface of this with smart infusion pumps and RFID tagging. “There’s potential for a lot of these things to talk to each other and raise alerts when something is out of whack, and potentially even detect infections,” he said.
Short for micro electro mechanical systems, MEMS involves the use of miniaturized sensors, actuators and electronics that are smaller than the thickness of a human hair. Such technology has already penetrated the research market, with speedier, more precise tools for biologists and chemists. Now companies like CardioMems and MicroCHIPS are working on commercial implantable devices that can transmit data outside of the body for clinical use. However, regulation remains a big question here.
Wearable medical devices
We’re not just talking about the fitness bands you wear around your wrist. We’re talking flexible electronics — lightweight, portable sensors that could be, for example, adhered to the skin to collect biometric data. Or swallowable (not technically wearable, but it’s the same idea) smart pills that let clinicians know when patients aren’t taking their medications. The hope is that these devices could help patients and clinicians manage chronic diseases.
Natural language processing
The medical scribe business is hot. But another way of easing the burden of collecting patient data – especially the kind that’s anecdotal – is also heating up. Some EHR vendors have embedded voice transcription technologies into their products, and more advanced products that give structure to unstructured data are on the way. Some say natural language processing could change the way we interact with healthcare data, the same way that Siri has changed the way people interact with their cellphones.
Nokia and XPRIZE are hunting for a medical tricorder, armed with $10 million as a reward, but this movement is much bigger than the contest. Sensors, mobile technology and at-home medicine meet in this concept, which calls for development of a portable screening device consumers could use to self-diagnose medical conditions a la Star Trek. Scanadu’s Scout is the most high-profile device under development, but there are dozens of teams across the world working toward this goal.
From targeted cancer drugs to molecular diagnostics, advances in genome sequencing are driving precision medicine. It’s defined by Pfizer as “an approach to discovering and developing medicines and vaccines that deliver superior outcomes for patients, by integrating clinical and molecular information to understand the basis of disease.”
Some use precision medicine synonymously with personalized medicine. Others say it’s a better term that captures the idea of personalized medicine more clearly: Not as medical care that’s tailored to an individual but rather the ability to classify individuals into smaller populations that might be more susceptible to certain diseases or respond to drugs differently. This term has been slowly gaining steam since 2011.
Time-consuming administrative tasks like medical billing, revenue cycle management and inventory management are prime targets for automating IT solutions. As more data becomes digital rather than paper-based, more opportunities open for innovation in this area to save time in hospitals and physician practices.
Last week ONC released another game-based security training module. “CyberSecure: Your Medical Practice” is aimed at providers and staff and focuses on disaster planning including data backup and recovery.
Morning Rounds: Can you get dementia from the ICU? CBS News (CBS News) CBS News chief medical correspondent Dr. Jon LaPook and CBS News contributor Dr. Holly Phillips discussed on "CBS This Morning: Saturday" the major medical stories of the week.
Building social networks for health promotion in high-poverty areas may reduce health disparities. Community involvement provides a mechanism to reach at-risk people with culturally tailored health information.
For the residents of an isolated Native American community perched on 112,870 acres of grassland and desert along the border of Nevada and Utah, healthcare will soon be available at the click of an icon.
A lightweight and field-portable device invented at UCLA that conducts kidney tests and transmits data through a smartphone attachment may significantly reduce the need for frequent office visits by people with diabetes and others with chronic kidney ailments.
The smartphone-based device was developed in the research lab of Aydogan Ozcan, a professor of electrical engineering and bioengineering at the UCLA Henry Samueli School of Engineering and Applied Science, and associate director of the California NanoSystems Institute. Weighing about one-third of a pound, the gadget can determine levels of albumin in the patient's urine and transmit the results within seconds. Albumin is a protein in blood that is a sign of danger when found in urine.
Ozcan's lab also developed the opto-mechanical phone attachment, disposable test tubes, Android app and software to transmit the data. The research was published this month by the peer-reviewed journal Lab on a Chip ("Albumin testing in urine using a smart-phone").
"Albumin testing is frequently done to assess kidney damage, especially for diabetes patients," Ozcan said. "This device provides an extremely convenient platform for chronic patients at home or in remote locations where cell phones work."
Patients at risk for diabetes, kidney disease and other ailments must regularly provide fluid samples — sometimes more than one a day — to monitor their health, which requires visits to labs or health centers.
The new device projects beams of visible light through two small fluorescent tubes attached to the device, one containing a control liquid and the other a urine sample mixed with fluorescent dyes. The smartphone camera captures the fluorescent light after it passes through an additional lens.
An Android application then processes the raw images in less than one second and the device transmits the test results to a database or health care provider. The test, which measures albumin concentration in urine, is accurate to within less than 10 micrograms per milliliter, according to the research, well within accepted clinical standards used in diagnosing conditions such as microalbuminuria, the excretion of albumin in urine.
The time it takes to conduct a test, including preparation of a sample using a small syringe to inject the urine into a fluorescent tube, is about five minutes. Ozcan estimates that the device — for which his lab also has developed an iPhone app — could be produced commercially for $50 to $100 per unit.
University of Notre Dame researchers have developed a computer-aided method that uses electronic medical records to offer the promise of rapid advances toward personalized health care, disease management and wellness.
Notre Dame computer science professor Nitesh V. Chawla and his doctoral student, Darcy A. Davis, developed the system called Collaborative Assessment and Recommendation Engine (CARE) for personalized disease risk predictions and wellbeing.
"The potential for 'personalizing' health care from a disease prevention, disease management and therapeutics perspective is increasing," Chawla said. "Health care informatics and advanced analytics, or data science, may contribute to this shift from population-based evidence for health care decision-making to the fusion of population and individual based evidence in health care. The key question is: how to leverage health population data to drive patient-centered health care?"
At the heart of CARE is a novel collaborative filtering method that captures patient similarities and produces personalized disease risk profiles for individuals. Using what is known as Big Data science, the system generates predictions focused on other diseases that are based on Big Data from similar patients.
"In its most conservative use, the CARE rankings can provide reminders for conditions that busy doctors may have overlooked," Chawla said. "Utilized to its full potential, CARE can be used to explore broader disease histories, suggest previously unconsidered concerns, and facilitate discussion about early testing and prevention, as well as wellness strategies that may ring a more familiar bell with an individual and are essentially doable.
"We believe that our work can lead to reduced re-admission rates, improved quality of care ratings and can demonstrate meaningful use, impact personal and population health, and push forward the discussion and impact on the patient-centered paradigm."
Chawla points out that the core premise of CARE is centered on patient empowerment and patient engagement.