Winners of the Blue Button Innovation Challenge
I think the main achievement of hackathons can be measured not by what apps are developed–reportedly, few are commercialized and maintained–but by people who find each other. The Blue Button Innovation Challenge brought together a lot of professionals who had never met before, and many formed teams that created really fun and useful apps that make you think, “Why hasn’t anyone done this yet?”
Finalists at Merck|Heritage Provider Network Innovation Challenge
Challenges and hackathons are meant to surprise you. If the winner is a known leader in the field with lists of familiar credentials festooning the team’s resumes, there was no point to starting the challenge in the first place.
Pharmaceutical company Merck and the Heritage Provider Network, the largest physician-led health network in the US, were looking for something new when they launched their challenge on diabetes and heart disease. These conditions are virtual epidemics, world-wide.
Comparative effectiveness research is key to reform
When the Affordable Care Act (ACA) was passed on a party line vote several years ago, it included a somewhat controversial provision to tax, at 2.3% starting in 2013, the sale of any medical device classified by the IRS as being taxable. The list of taxable devices includes a wide variety of products such as defibrillators, dental instruments, pacemakers, coronary stents, artificial hips, joints, and knees, surgical gloves, irradiation equipment, and advanced imaging technology. But it doesn’t stop there—patient monitoring, anesthesiology equipment, infusion pumps, and other hospital operating room digital devices are included in the IRS’s taxable device category. “Consumer” devices such as glucose monitors and potentially many upcoming “wearables” will likely also get taxed either now or soon. That’s where things get difficult for innovators and investors who want to offer next generation devices.
The medical device tax was levied partially to hinder the (over) prescription of medical devices. You and I are most familiar with devices like monitoring instruments or mobile phone sensors, but most dollars are spent on devices like stents, replacement knees, spinal fusion screws, proton beam accelerators, PET/CT scanners, etc. About $200 billion is spent on medical devices per year (about one-third the amount spent on pharmaceutical drugs). The idea behind the tax was twofold. One the one hand, Congress hoped to reduce health spending caused by the overuse of devices by taxing them. But in tandem, the influx of new patients into the health care system is expected to create more sales and revenue for device companies, allowing them to compensate for the excise the tax while bringing in more revenue for Uncle Sam.
Digital tools and data analysis to stay sharp, stay well, and overcome illness
This article was written together with Ellen M. Martin and Melinda Speckmann.
Games have been part of human culture for millennia. It is no surprise that elements of play can be powerful digital tools to grab our attention and keep us on a path to taking care of ourselves and others.
Big data is already behind brain games. The use of big data is becoming increasingly mainstream in health play applications. Once we are drawn in, game play (with big data under the hood) can help us to:
- Stay sharp,
- Stay well, and
- Overcome illness.
The 30,000-foot view and the nitty gritty details of working with electronic health data
Ever wonder what the heck “meaningful use” really means? By now, you’ve probably heard it come up in discussions of healthcare data. You might even know that it specifically pertains to electronic health records (EHRs). But what is it really about, and why should you care?
If you’ve ever had to carry a large folder of paper between specialists, or fill out the same medical history form in different offices over and over—with whatever details you happen to remember off the top of your head that day—then you already have some idea of why EHRs are a desirable thing. The idea is that EHRs will lead to better care—and better research data—through more complete and accurate record-keeping, and will eventually become part of health information exchanges (HIEs) with features like trend analysis and push-notifications. However, the mere installation of EHR software isn’t enough; we need not just cursory use but meaningful use of EHRs, and we need to ensure that the software being used meets certain standards of efficiency and security.
We must go beyond hype for incentives to provide data to researchers
The FDA order stopping 23andM3 from offering its genetic test kit strikes right into the heart of the major issue in health care reform: the tension between individual care and collective benefit. Health is not an individual matter. As I will show, we need each other. And beyond narrow regulatory questions, the 23andMe issue opens up the whole goal of information sharing and the funding of health care reform.
Esri conference highlights uses of GIS data
We’ve all seen cool maps of health data, such as these representations of diabetes prevalence by US county. But few people think about how thoroughly geospacial data is transforming public health and changing the allocation of resources at individual hospitals. I got a peek into this world at the Esri Health GIS Conference this week in Cambridge, Mass.
Impressions from Strata Rx bolster different philosophies
Everyone seems to agree that health care is the next big industry waiting to be disrupted. But who will force that change on a massive system full of conservative players? Three possibilities present themselves:
What is needed for successful reform of the health care system?
Here’s what we all know: that a data-rich health care future is coming our way. And what it will look like, in large outlines. Health care reformers have learned that no single practice will improve the system. All of the following, which were discussed at O’Reilly’s recent Strata Rx conference, must fall in place.
Archimedes advances evidence-based medicine to foster model-based medicine
This posting is by guest author Tuan Dinh, who will speak about this topic at the Strata Rx conference.
Legendary Silicon Valley investor Vinod Khosla caused quite a stir last year when he predicted at Strata Rx that “Dr. Algorithm”–artificial intelligence driven by large data sets and computational power–would replace doctors in the not-too-distant future. At that point, he said, technology will be cheaper, more accurate and objective, and will ultimately do a better job than the average human doctor at delivering routine diagnoses with standard treatments.
I not only support Khosla’s provocative prophecy, I’ll add one of my own: that Dr. Algorithm (aka Dr. A) will “come to life” in three to five years, by the time today’s first-year med school students are pulling 30-hour shifts as new interns. But what will it take to build the brain of Dr. A? And how can we teach Dr. A to account for increasingly complex medical inputs, such as laboratory tests results, genomic/genetic information, family and personal history, co-morbidities and patient preferences, so he can make optimal clinical decisions for living, breathing patients?