Today, most people’s clinical records remain siloed at a single hospital or health network. For the most part, health apps can’t tap into these data, nor can medicine learn from them. Also, most electronic health records (EHRs) are unable to import the biometric data people are collecting from their own devices, much less interpret them.
In 2009, Kenneth Mandl, MD, MPH, and Isaac Kohane, MD, PhD, of Boston Children’s Hospital published a manifesto in The New England Journal of Medicine calling for health care information systems to have iPhone functionality. This would entail several key attributes: liquidity of data, modularity of applications, accommodation of both open-source and closed-source software through open standards, and the ability to support diverse applications.
In short, they envisioned a “plug and play” health IT platform. …
Early influenza detection and the ability to predict outbreaks are critical to public health. Reliable estimates of when influenza will peak can help drive proper timing of flu shots and prevent health systems from being blindsided by unexpected surges, as happened in the 2012-2013 flu season.
The Centers for Disease Control and Prevention collects accurate data, but with a time lag of one to two weeks. Google Flu Trends began offering real-time data in 2008, based on people’s Internet searches for flu-related terms. But it ultimately failed, at least in part because not everyone who searches “flu” is actually sick. As of last year, Google instead now sends its search data to scientists at the CDC, Columbia University and Boston Children’s Hospital.
Now, a Boston Children’s-led team demonstrates a more accurate way to pick up flu trends in near-real-time — at least a week ahead of the CDC — by harnessing data from electronic health records (EHRs). …
It’s a surprisingly simple but important question in medicine. While a body temperature of 98.6°F (37°C) is generally considered “normal,” this number doesn’t account for temperature differences between individuals — and even within individuals at various times of the day. While a common sign of infection, fever can also occur with other medical conditions, including autoimmune and autoinflammatory diseases.
“Many factors come together to set an individual’s ‘normal’ temperature, such as age, size, time of day and maybe even ancestry,” says Jared Hawkins, MMSc, PhD, the director of informatics for Boston Children’s Hospital’s Innovation & Digital Health Accelerator (IDHA) and a member of the hospital’s Computational Health Informatics Program. “We want to help create a better understanding of the normal temperature variations throughout the day, to learn to use fever as a tool to improve medical diagnosis, and to evaluate the effect of fever medications on symptoms and disease course.”
At the moment, it would appear the bacteria are winning. Antibiotic resistance is on the rise globally (in part because much of the public may not really understand how antibiotics work), threatening doctors’ ability to treat bacterial infections and potentially making surgery, chemotherapy and other medical procedures whose safety depends on antibiotic prophylaxis more risky.
Mapping antibiotic resistance — which bacteria are resistant to which drugs, and where — can help clinicians and public health officials decide how best to focus their control efforts. The challenge to date has been compiling resistance data in geographically useful ways.
“The data about antibiotic resistance are fragmented across laboratories and hospitals globally,” says Derek MacFadden, MD, a doctoral student at the Harvard T.H. Chan School of Public Health who is working with the HealthMap team in Boston Children’s Computational Health Informatics Program. “Most of the data that are available are very high level, so you can’t get an understanding of regional-level antibiotic resistance.”
This is where ResistanceOpen could come in handy. This new tool, launched by HealthMap team this week during the World Health Organization’s World Antibiotic Awareness Week, provides a window into regional and local antibiotic resistance patterns across the globe.
“The main problems with measuring patient experience by survey are the small numbers of people who respond to surveys and the lag time,” says Jared Hawkins, MMSc, PhD, of Boston Children’s Hospital’s Computational Health Informatics Program (CHIP). “It can take up to two years before survey data are released to the public. Given that social media data are close to real time, we wanted to see if we could capture this discussion and if the content is useful.”
Hawkins, with Boston Children’s chief innovation officer, John Brownstein, PhD, and their colleagues collected more than 400,000 public tweets directed at the Twitter handles of nearly 2,400 U.S. hospitals between 2012 and 2013. Using machine learning, natural language processing and manual curation, they tagged 34,735 patient experience tweets directed at 1,726 hospital-owned Twitter accounts. …
Ideally, we’re all supposed to see our doctor once a year for a checkup. It’s an opportunity to see how we’re doing from a health perspective, address any concerns or issues that we may have and catch any emerging issues before they become true problems.
But those visits are really only one-time, infrequent snapshots of health. They don’t give a full view of how we’re doing or feeling.
Now, think for a moment about how often you post something to Facebook or Twitter. Do you post anything about whether you’re feeling ill or down, or haven’t slept well? Ever share how far you ran, the route you biked or your number of steps for the day?
Every time you do, you’re creating a data point—another snapshot—about your health. Put those data points together, and what starts to emerge is a rich view of your health, much richer than one based on the records of your occasional medical visit.
“Since the  SARS outbreak, the world has seen substantial progress in transparency and rapid reporting. The extent of these advancements varies, but overall, digital disease surveillance is providing the global health community with tools supporting faster response and deeper understanding of emerging public health threats.”
We’re pretty focused on the safety of the things around us. Our drinking water gets checked for chemicals, bacteria and other things that could make us sick. Kids’ car seats are tested to make sure they’ll keep children safe in an accident.
But there’s one surprising arena where this focus on safety and testing often falls short: the medications we give our children. Not just in the United States, but globally.
There are lots of reasons why fewer drugs get tested for safety and efficacy in children than in adults. It’s time-consuming, expensive and, frankly, risky. The ethics of testing new medications in children are pretty thorny.
And, overall, the market for pediatric drugs is much, much smaller than that for drugs for adults, since children fortunately don’t get sick as often as us grown-ups.
But for some diseases like asthma and diarrheal diseases, children bear a greater burden than adults—one that’s not matched by the amount of research done on drugs for kids. …
It was after the devastating 2010 Haiti earthquake that mobile-friendly social media services like Twitter and Ushahidi came into their own as disaster management and relief tools. With the nation’s already unsteady infrastructure destroyed, these tools helped speed the deployment of people and supplies to where they were needed by giving relief workers on-the-ground intelligence about what was happening, what was needed and where in nearly real time.