Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English)
53.7K views | +0 today
Follow
Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English)
Your new post is loading...
Your new post is loading...
Rescooped by Celine Sportisse from Marketing & Hôpital
Scoop.it!

Patient Engagement Strategy eBook | HL7 Standards

Patient Engagement Strategy eBook | HL7 Standards | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

Leonard Kish’s first eBook titled, “Patient Engagement is a Strategy, Not a Tool. How healthcare organizations can build true patient relationships that last a lifetime.”

 

This eBook explores the following patient engagement topics:

What Is Patient Engagement?The Quest for AttentionFrom Technology to MotivationThe Rise of Contextual MedicineAligning Goals with Effective MessagingAlignment Through Social StrategyEstablish a Patient Engagement Strategy 

Author Background

Leonard Kish is a long-time contributor to HL7Standards.com who writes about patient engagement topics as they relate to healthcare technology, the government’s Meaningful Use requirements, and how proven behavior economic models should be considered by healthcare organizations and companies focused on developing patient-facing technology

 

 download the free PDF

http://www.hl7standards.com/kish-ebook/

 

 


Via Ignacio Fernández Alberti, EVELYNE PIERRON, Chanfimao
more...
No comment yet.
Rescooped by Celine Sportisse from healthcare technology
Scoop.it!

EHRs Detect Depression When Many PCPs Can’t

EHRs Detect Depression When Many PCPs Can’t | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

Depression is one of the hardest disorders to diagnose, yet it affects 14 percent of the world’s population. Researchers have found factors in EHRs may be key to predicting a diagnosis of depression.


While depression comes at a high cost to those who suffer from it, the actual price tag in the United States reaches over $44 billion annually. This takes into account, among other things, lost productivity and direct expenses. Depression is a diagnosis that is often missed by primary care physicians, despite the fact that it is the second most common chronic disorder they treat.


According to EHR Intelligence, researchers from Stanford University have worked to use EHR systems as a tool to help predict depression diagnoses. In the study, published by the Journal of the American Medical Informatics Association, researchers say valuable information already stored in the EHR can be used to predict depression up to a year in advance.


“Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment,” explain researchers. “Many depressed patients are not even diagnosed … primary care physicians, who deliver the majority of care for depression, only identify about 50 percent of true depression cases.”


The Stanford team used EHR data including demographic data, ICD-9, RxNorm, CPT codes, progress notes, and pathology, radiology, and transcription reports. From these, they used a model which factored in three criteria: the ICD-9 code, the presence of a depression disorder term in the clinical text, and the presence of an anti-depressive drug ingredient term in the clinical text.


These factors were then compared to predict a diagnosis of depression, response to treatment, and determine the severity of the condition.


more at http://www.healthitoutcomes.com/doc/ehrs-detect-depression-when-many-pcps-can-t-0001



Via nrip
more...
No comment yet.
Rescooped by Celine Sportisse from healthcare technology
Scoop.it!

Google testing contact lens that can monitor glucose levels

Google testing contact lens that can monitor glucose levels | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

 

Finger pricks and careful eating are an important part of the daily routine for most people with diabetes. While automated glucose meters are a growing option, they can still create discomfort and other inconveniences.

 

 

Google wants to go in a totally different direction with a project announced today:smart contact lenses that can detect glucose levels via the wearer’s tears and alert them when levels dip or rise.

 

 

This isn’t the first smart contact lens, and several options already exist for people interested in monitoring glaucoma. But Babak Parviz, who also leads the Google Glass team, is a smart contact pioneer and Google which is a secretive division of Google dedicated to difficult, future-looking projects, has a reputation for ably pursuing projects like this.

 

 

The lens works via a small wireless chip and glucose sensor embedded between two pieces of soft material. The current prototype puts out a reading once a second. Google is also interested in integrating an LED light, which could light up to alert the wearer of dangerous glucose levels.

 

 

The lab is now looking for parters to help bring the lens to market. It would also like to develop an app that would help wearers read and manage the data the lens takes in.

 

The lens could help people with diabetes monitor their daily health and recognize dangerous situations.

 

more at http://gigaom.com/2014/01/16/google-testing-contact-lens-that-can-monitor-glucose-levels/

 


Via nrip
more...
Beth Faulkner's curator insight, January 22, 2014 7:27 AM

Google's smart contact lens could ease the pain of diabetes monitoring.

Rescooped by Celine Sportisse from healthcare technology
Scoop.it!

Health-Care Apps That Doctors Use

Health-Care Apps That Doctors Use | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

New health-care smartphone apps for doctors and patients help with everything from diagnostics and monitoring to revealing who isn't washing their hands.

 

Mobile apps for smartphones and tablets are changing the way doctors and patients approach health care. Many are designed for the doctors themselves, ranging from handy databases about drugs and diseases to sophisticated monitors that read a person's blood pressure, glucose levels or asthma symptoms. Others are for the patients—at their doctor's recommendation—to gather diagnostic data, for example, or simply to help coordinate care, giving patients an easy way to keep track of their conditions and treatments.

 

Doctors say many of the apps are useful time savers, and have the potential to make health care more efficient by speeding diagnosis, improving patient monitoring and reducing unnecessary visits to a physician or hospital. Still, the field has a way to go, doctors add, particularly when it comes to making good use of all the patient data being generated.

 

Here are some of the apps doctors are talking about most. Some are free; others cost several hundred dollars for a year's subscription. Those that combine an app and a wireless monitor cost from $80 to $200.

 

EPOCRATES One of the oldest and most established medical apps, Epocrates gives doctors basic information about drugs, the right dosing for adults and children, and warnings about harmful interactions. It has replaced many a copy of the Physician's Desk Reference.

 

 

UPTODATE This app provides reference material doctors can consult when making treatment decisions. David Bates, an internist at Brigham and Women's Hospital in Boston, says he has used it recently to look up treatment approaches for patients who have failed to respond to existing hypertension therapies, and for information on the drug combinations needed to treat a bacterial infection called H. pylori.

 

ISABEL Every doctor needs help reaching diagnoses. Here, doctors enter symptoms, and the app lists possible diagnoses as well as medications that could cause the symptoms.

 

ALIVECOR This portable heart monitor and app—one of the programs that opened Dr. Topol's eyes—runs on a patient's smartphone to produce electrocardiograms. Patients place their fingers over the monitor's sensors, which wirelessly communicate with the phone to produce the EKG.

 

 

RESOLUTIONMD Doctors can look at X-rays and other images on a smartphone or tablet when they use this app. Some doctors say the app is handy for viewing images as soon as they're available, no matter where the doctor happens to be.

 

ISCRUB This infection-control app collects and rapidly displays data on whether hospital staff are being scrupulous about washing their hands. Most hospitals have unofficial observers of whether doctors, nurses and other staff are following hand-hygiene guidelines. Many are not.

 

BREAST CANCER DIAGNOSIS GUIDE Using this app, breast-cancer patients enter and track details of their disease and treatment, from the size of the tumor to the presence or absence of estrogen receptors.

 

CLINICAM Increasingly, doctors are using their phones to take photos of a patient's condition—such as a rash or wound—and to upload the images to the patient's electronic medical record. One problem: That could violate health-care privacy laws if the doctor leaves the photo on his or her personal phone.

 

more at the original: http://online.wsj.com/news/articles/SB10001424052702303376904579137683810827104

     


Via nrip
more...
No comment yet.
Rescooped by Celine Sportisse from Mobile Health: How Mobile Phones Support Health Care
Scoop.it!

Case study: Big data improves cardiology diagnoses by 17%

Case study: Big data improves cardiology diagnoses by 17% | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

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
Via nrip, dbtmobile
more...
No comment yet.
Rescooped by Celine Sportisse from Hacking Health
Scoop.it!

Digital health is going to need medical approval and a great UI

Digital health is going to need medical approval and a great UI | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

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? 


 Listen to the podcast at  http://soundcloud.com/gigaom-internet-of-things  Original article at http://gigaom.com/2014/06/09/digital-health-is-going-to-need-medical-approval-and-a-great-ui/ ;
Via nrip, Sébastien Letélié
more...
Vigisys's curator insight, June 15, 2014 4:22 AM

Un podcast intéressant qui évoque les freins à l'utilisation médicale des objets connectés. On y évoque le besoin de valider les usages avec des études cliniques et d'adapter les interfaces à un usage professionnel. Que du bon sens !

Rescooped by Celine Sportisse from healthcare technology
Scoop.it!

How medical augmented reality will seamlessly save your life

How medical augmented reality will seamlessly save your life | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

You walk in to the doctor’s office – nervous – thinking of the last time you received the injection of your monthly medication, remembering how painful and aggravating it was when the nurse couldn’t find your vein and poked you five different times to insert the IV.

 

This time, however, something is different. When the nurse shows up he is wearing a special pair of glasses, something you wouldn’t expect to see during such a standard procedure. The device uses advanced technology and shows the nurse a perfect highlighted image of your veins so he can insert the IV in one painless attempt.

 

Sounds like science fiction right? Wrong. This is a real device and is just one early example of how Augmented Reality (AR) technology is changing the healthcare landscape.

 

Research shows that up to 40 percent of IV sessions require multiple attempts to locate and access the vein. Augmented reality comes to the rescue in a standard procedure that still causes so much discomfort and dissatisfaction.

 

Typically, when people think of AR, they imagine glasses and screens that present new layers of content on top of real world images. This traditional model will still play a significant role in the future. However, there’s another aspect to AR that will be important, specifically in the healthcare industry and that is the ability to instantly display relevant information to people who need it most.

 

Imagine a doctor who is able to view a patient’s medical history displayed over the latest medical scan, and even over the patient himself. We are already beginning to see wearable medical devices that provide critical health information during relevant points of the day.

 

In the near future, the next time you want to bite into your hamburger, you might get a friendly reminder that your cholesterol level won’t like it.

Overcoming roadblocks with the help of the crowd

We are still facing significant barriers before we will be able to see AR’s full potential in action. Some of these barriers are practical, such as problems with Wi-Fi connectivity and battery life. Several of the barriers are conceptual, but we do see a huge shift in people’s mindsets.

Wearables will play a major role in this. For example, there have been several crowd-sourced campaigns to develop wearables that could sense your heart rate and blood oxygen levels and send you real-time notifications. In another case, there was an abdominal surgery that took place on one side of a city, and in parallel was live-streamed via glasses to a medical school class.

The right information to save lives

Medicine is one of the industries that provides tangible real-world benefits to help people live better lives, and AR can help immensely. When you’re dealing with life and death decisions, immediate access to necessary and relevant information is of the utmost importance.

 

This is where AR has the most potential to disrupt the industry – putting the information doctors and healthcare providers need in front of their eyes, when they need it. Beyond that, this same experience can be tailored for the needs of patients and everyday users unlocking the potential for a real revolution in health and in the way people think about maintaining their health.

 

more at http://thenextweb.com/dd/2014/01/01/medical-augmented-reality-will-seamlessly-save-life/#!rfRdW


Via nrip
more...
No comment yet.
Rescooped by Celine Sportisse from healthcare technology
Scoop.it!

Using Health Information Technology to Engage Patients in their Care

Using Health Information Technology to Engage Patients in their Care | Health, Digital Health, mHealth, Digital Pharma, hcsm latest trends and news (in English) | Scoop.it

Patient engagement, defined as the process of placing patients at the center and in control of their own healthcare, is becoming a chief healthcare priority

 

Concurrently, a number of national information infrastructure initiatives are targeting increased patient engagement and the design of health information systems that improve the availability of health information and integrate it in meaningful ways for patients.  So far, these technology goals have been advanced primarily through the design of personal health records (PHRs), patient portals, electronic health records (EHRs), and health information exchanges (HIEs).  However, we remain far from achieving the goal of truly engaging patients in their care.

 

Generation and exchange of health data with patients is a requirement for Stage 3 EHR meaningful use incentives. Patients are entitled to an electronically generated copy of the record of their encounters with providers. 

 

Sharing provider-generated data with patients is expected to promote patient engagement and accountability, but our own experiences suggest that the data that are being shared are currently a mixed blessing.  For example, one encounter report took the form of a 6-page document in which the vast majority of information was copied and pasted from previous encounters and in which there were several factual errors. The errors will be discussed with the provider during the next visit.

 

Certainly the report got our attention; whether empowerment will result remains an open question.  On another occasion, although the visit itself had included making decisions about future treatment, the plan was not mentioned in the document, leaving the patient to rely on her own memory and notes.

 The National eHealth Collaborative Technical Expert Panel recommends fully integrating patient-generated data (e.g., home monitoring of daily weights, blood glucose, or blood pressure readings) into the clinical workflow of healthcare providers

Although patients want this type of involvement, we have only begun to address their wishes and concerns.  In the next sections, we summarize the current status of several potential building blocks to achieving patient engagement goals and emphasize the role of the nurse informaticist as fundamental to the process.

 

more at the original : http://ojni.org/issues/?p=2848

 

 


Via nrip
more...
Brandi Carney's curator insight, January 23, 2014 6:20 PM

This site helps to encourage patients to be more aware of their health by using different pieces of technology.